WorldWideScience

Sample records for ensemble study reveals

  1. Statistical ensembles and molecular dynamics studies of anisotropic solids. II

    International Nuclear Information System (INIS)

    Ray, J.R.; Rahman, A.

    1985-01-01

    We have recently discussed how the Parrinello--Rahman theory can be brought into accord with the theory of the elastic and thermodynamic behavior of anisotropic media. This involves the isoenthalpic--isotension ensemble of statistical mechanics. Nose has developed a canonical ensemble form of molecular dynamics. We combine Nose's ideas with the Parrinello--Rahman theory to obtain a canonical form of molecular dynamics appropriate to the study of anisotropic media subjected to arbitrary external stress. We employ this isothermal--isotension ensemble in a study of a fcc→ close-packed structural phase transformation in a Lennard-Jones solid subjected to uniaxial compression. Our interpretation of the Nose theory does not involve a scaling of the time variable. This latter fact leads to simplifications when studying the time dependence of quantities

  2. Dynamical predictive power of the generalized Gibbs ensemble revealed in a second quench.

    Science.gov (United States)

    Zhang, J M; Cui, F C; Hu, Jiangping

    2012-04-01

    We show that a quenched and relaxed completely integrable system is hardly distinguishable from the corresponding generalized Gibbs ensemble in a dynamical sense. To be specific, the response of the quenched and relaxed system to a second quench can be accurately reproduced by using the generalized Gibbs ensemble as a substitute. Remarkably, as demonstrated with the transverse Ising model and the hard-core bosons in one dimension, not only the steady values but even the transient, relaxation dynamics of the physical variables can be accurately reproduced by using the generalized Gibbs ensemble as a pseudoinitial state. This result is an important complement to the previously established result that a quenched and relaxed system is hardly distinguishable from the generalized Gibbs ensemble in a static sense. The relevance of the generalized Gibbs ensemble in the nonequilibrium dynamics of completely integrable systems is then greatly strengthened.

  3. A Comparative Case Study of Non-Music Major Participation in Two Contrasting Collegiate Choral Ensembles

    Science.gov (United States)

    Jones, Sara K.

    2018-01-01

    The purpose of this comparative case study was to examine the motivation for participation in traditional and non-traditional vocal ensembles by students who are not pursuing a career in music and the perceived benefits of this participation. Participants were selected from a traditional mixed choral ensemble and a student-run a cappella ensemble.…

  4. Collective Dynamics of Specific Gene Ensembles Crucial for Neutrophil Differentiation: The Existence of Genome Vehicles Revealed

    Science.gov (United States)

    Giuliani, Alessandro; Tomita, Masaru

    2010-01-01

    Cell fate decision remarkably generates specific cell differentiation path among the multiple possibilities that can arise through the complex interplay of high-dimensional genome activities. The coordinated action of thousands of genes to switch cell fate decision has indicated the existence of stable attractors guiding the process. However, origins of the intracellular mechanisms that create “cellular attractor” still remain unknown. Here, we examined the collective behavior of genome-wide expressions for neutrophil differentiation through two different stimuli, dimethyl sulfoxide (DMSO) and all-trans-retinoic acid (atRA). To overcome the difficulties of dealing with single gene expression noises, we grouped genes into ensembles and analyzed their expression dynamics in correlation space defined by Pearson correlation and mutual information. The standard deviation of correlation distributions of gene ensembles reduces when the ensemble size is increased following the inverse square root law, for both ensembles chosen randomly from whole genome and ranked according to expression variances across time. Choosing the ensemble size of 200 genes, we show the two probability distributions of correlations of randomly selected genes for atRA and DMSO responses overlapped after 48 hours, defining the neutrophil attractor. Next, tracking the ranked ensembles' trajectories, we noticed that only certain, not all, fall into the attractor in a fractal-like manner. The removal of these genome elements from the whole genomes, for both atRA and DMSO responses, destroys the attractor providing evidence for the existence of specific genome elements (named “genome vehicle”) responsible for the neutrophil attractor. Notably, within the genome vehicles, genes with low or moderate expression changes, which are often considered noisy and insignificant, are essential components for the creation of the neutrophil attractor. Further investigations along with our findings might

  5. Ensemble Methods

    Science.gov (United States)

    Re, Matteo; Valentini, Giorgio

    2012-03-01

    proposed to explain the characteristics and the successful application of ensembles to different application domains. For instance, Allwein, Schapire, and Singer interpreted the improved generalization capabilities of ensembles of learning machines in the framework of large margin classifiers [4,177], Kleinberg in the context of stochastic discrimination theory [112], and Breiman and Friedman in the light of the bias-variance analysis borrowed from classical statistics [21,70]. Empirical studies showed that both in classification and regression problems, ensembles improve on single learning machines, and moreover large experimental studies compared the effectiveness of different ensemble methods on benchmark data sets [10,11,49,188]. The interest in this research area is motivated also by the availability of very fast computers and networks of workstations at a relatively low cost that allow the implementation and the experimentation of complex ensemble methods using off-the-shelf computer platforms. However, as explained in Section 26.2 there are deeper reasons to use ensembles of learning machines, motivated by the intrinsic characteristics of the ensemble methods. The main aim of this chapter is to introduce ensemble methods and to provide an overview and a bibliography of the main areas of research, without pretending to be exhaustive or to explain the detailed characteristics of each ensemble method. The paper is organized as follows. In the next section, the main theoretical and practical reasons for combining multiple learners are introduced. Section 26.3 depicts the main taxonomies on ensemble methods proposed in the literature. In Section 26.4 and 26.5, we present an overview of the main supervised ensemble methods reported in the literature, adopting a simple taxonomy, originally proposed in Ref. [201]. Applications of ensemble methods are only marginally considered, but a specific section on some relevant applications of ensemble methods in astronomy and

  6. Atomistic structural ensemble refinement reveals non-native structure stabilizes a sub-millisecond folding intermediate of CheY

    International Nuclear Information System (INIS)

    Shi, Jade; Schwantes, Christian; Bilsel, Osman

    2017-01-01

    The dynamics of globular proteins can be described in terms of transitions between a folded native state and less-populated intermediates, or excited states, which can play critical roles in both protein folding and function. Excited states are by definition transient species, and therefore are difficult to characterize using current experimental techniques. We report an atomistic model of the excited state ensemble of a stabilized mutant of an extensively studied flavodoxin fold protein CheY. We employed a hybrid simulation and experimental approach in which an aggregate 42 milliseconds of all-atom molecular dynamics were used as an informative prior for the structure of the excited state ensemble. The resulting prior was then refined against small-angle X-ray scattering (SAXS) data employing an established method (EROS). The most striking feature of the resulting excited state ensemble was an unstructured N-terminus stabilized by non-native contacts in a conformation that is topologically simpler than the native state. We then predict incisive single molecule FRET experiments, using these results, as a means of model validation. Our study demonstrates the paradigm of uniting simulation and experiment in a statistical model to study the structure of protein excited states and rationally design validating experiments.

  7. Spirituality and Synagogue Music: A Case Study of Two Synagogue Music Ensembles

    Science.gov (United States)

    Shansky, Carol

    2012-01-01

    Participation in community music ensembles is an important and popular form of music education--with members of ensembles that perform within religious services having the opportunity of experiencing a possible extra dimension of a spiritual experience. Thus the intent of this study was to survey adult choir and band members at Temple Emeth in…

  8. SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria.

    Science.gov (United States)

    Chevrette, Marc G; Aicheler, Fabian; Kohlbacher, Oliver; Currie, Cameron R; Medema, Marnix H

    2017-10-15

    Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool. SANDPUMA is freely available at https://bitbucket.org/chevrm/sandpuma and as a docker image at https://hub.docker.com/r/chevrm/sandpuma/ under the GNU Public License 3 (GPL3). chevrette@wisc.edu or marnix.medema@wur.nl. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  9. Ensembles modeling approach to study Climate Change impacts on Wheat

    Science.gov (United States)

    Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

    2017-04-01

    Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

  10. Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

    Directory of Open Access Journals (Sweden)

    Łukasz Augustyniak

    2015-12-01

    Full Text Available We propose a novel method for counting sentiment orientation that outperforms supervised learning approaches in time and memory complexity and is not statistically significantly different from them in accuracy. Our method consists of a novel approach to generating unigram, bigram and trigram lexicons. The proposed method, called frequentiment, is based on calculating the frequency of features (words in the document and averaging their impact on the sentiment score as opposed to documents that do not contain these features. Afterwards, we use ensemble classification to improve the overall accuracy of the method. What is important is that the frequentiment-based lexicons with sentiment threshold selection outperform other popular lexicons and some supervised learners, while being 3–5 times faster than the supervised approach. We compare 37 methods (lexicons, ensembles with lexicon’s predictions as input and supervised learners applied to 10 Amazon review data sets and provide the first statistical comparison of the sentiment annotation methods that include ensemble approaches. It is one of the most comprehensive comparisons of domain sentiment analysis in the literature.

  11. Revealing the microstructure of the giant component in random graph ensembles

    Science.gov (United States)

    Tishby, Ido; Biham, Ofer; Katzav, Eytan; Kühn, Reimer

    2018-04-01

    The microstructure of the giant component of the Erdős-Rényi network and other configuration model networks is analyzed using generating function methods. While configuration model networks are uncorrelated, the giant component exhibits a degree distribution which is different from the overall degree distribution of the network and includes degree-degree correlations of all orders. We present exact analytical results for the degree distributions as well as higher-order degree-degree correlations on the giant components of configuration model networks. We show that the degree-degree correlations are essential for the integrity of the giant component, in the sense that the degree distribution alone cannot guarantee that it will consist of a single connected component. To demonstrate the importance and broad applicability of these results, we apply them to the study of the distribution of shortest path lengths on the giant component, percolation on the giant component, and spectra of sparse matrices defined on the giant component. We show that by using the degree distribution on the giant component one obtains high quality results for these properties, which can be further improved by taking the degree-degree correlations into account. This suggests that many existing methods, currently used for the analysis of the whole network, can be adapted in a straightforward fashion to yield results conditioned on the giant component.

  12. Serine Proteolytic Pathway Activation Reveals an Expanded Ensemble of Wound Response Genes in Drosophila

    Science.gov (United States)

    Patterson, Rachel A.; Juarez, Michelle T.; Hermann, Anita; Sasik, Roman; Hardiman, Gary; McGinnis, William

    2013-01-01

    After injury to the animal epidermis, a variety of genes are transcriptionally activated in nearby cells to regenerate the missing cells and facilitate barrier repair. The range and types of diffusible wound signals that are produced by damaged epidermis and function to activate repair genes during epidermal regeneration remains a subject of very active study in many animals. In Drosophila embryos, we have discovered that serine protease function is locally activated around wound sites, and is also required for localized activation of epidermal repair genes. The serine protease trypsin is sufficient to induce a striking global epidermal wound response without inflicting cell death or compromising the integrity of the epithelial barrier. We developed a trypsin wounding treatment as an amplification tool to more fully understand the changes in the Drosophila transcriptome that occur after epidermal injury. By comparing our array results with similar results on mammalian skin wounding we can see which evolutionarily conserved pathways are activated after epidermal wounding in very diverse animals. Our innovative serine protease-mediated wounding protocol allowed us to identify 8 additional genes that are activated in epidermal cells in the immediate vicinity of puncture wounds, and the functions of many of these genes suggest novel genetic pathways that may control epidermal wound repair. Additionally, our data augments the evidence that clean puncture wounding can mount a powerful innate immune transcriptional response, with different innate immune genes being activated in an interesting variety of ways. These include puncture-induced activation only in epidermal cells in the immediate vicinity of wounds, or in all epidermal cells, or specifically in the fat body, or in multiple tissues. PMID:23637905

  13. The Development of Storm Surge Ensemble Prediction System and Case Study of Typhoon Meranti in 2016

    Science.gov (United States)

    Tsai, Y. L.; Wu, T. R.; Terng, C. T.; Chu, C. H.

    2017-12-01

    Taiwan is under the threat of storm surge and associated inundation, which is located at a potentially severe storm generation zone. The use of ensemble prediction can help forecasters to know the characteristic of storm surge under the uncertainty of track and intensity. In addition, it can help the deterministic forecasting. In this study, the kernel of ensemble prediction system is based on COMCOT-SURGE (COrnell Multi-grid COupled Tsunami Model - Storm Surge). COMCOT-SURGE solves nonlinear shallow water equations in Open Ocean and coastal regions with the nested-grid scheme and adopts wet-dry-cell treatment to calculate potential inundation area. In order to consider tide-surge interaction, the global TPXO 7.1 tide model provides the tidal boundary conditions. After a series of validations and case studies, COMCOT-SURGE has become an official operating system of Central Weather Bureau (CWB) in Taiwan. In this study, the strongest typhoon in 2016, Typhoon Meranti, is chosen as a case study. We adopt twenty ensemble members from CWB WRF Ensemble Prediction System (CWB WEPS), which differs from parameters of microphysics, boundary layer, cumulus, and surface. From box-and-whisker results, maximum observed storm surges were located in the interval of the first and third quartile at more than 70 % gauge locations, e.g. Toucheng, Chengkung, and Jiangjyun. In conclusion, the ensemble prediction can effectively help forecasters to predict storm surge especially under the uncertainty of storm track and intensity

  14. Representing Color Ensembles.

    Science.gov (United States)

    Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

    2017-10-01

    Colors are rarely uniform, yet little is known about how people represent color distributions. We introduce a new method for studying color ensembles based on intertrial learning in visual search. Participants looked for an oddly colored diamond among diamonds with colors taken from either uniform or Gaussian color distributions. On test trials, the targets had various distances in feature space from the mean of the preceding distractor color distribution. Targets on test trials therefore served as probes into probabilistic representations of distractor colors. Test-trial response times revealed a striking similarity between the physical distribution of colors and their internal representations. The results demonstrate that the visual system represents color ensembles in a more detailed way than previously thought, coding not only mean and variance but, most surprisingly, the actual shape (uniform or Gaussian) of the distribution of colors in the environment.

  15. Diagnostic budget study of the internal variability in ensemble simulations of the Canadian RCM

    Energy Technology Data Exchange (ETDEWEB)

    Nikiema, Oumarou; Laprise, Rene [UQAM, Canadian Network for Regional Climate Modelling and Diagnostics, Centre ESCER, Departement des Sciences de la Terre et de l' Atmosphere, B.P. 8888, Montreal, QC (Canada)

    2011-06-15

    Due to the chaotic and nonlinear nature of the atmospheric dynamics, it is known that small differences in the initial conditions (IC) of models can grow and affect the simulation evolution. In this study, we perform a quantitative diagnostic budget calculation of the various diabatic and dynamical contributions to the time evolution and spatial distribution of internal variability (IV) in simulations with the nested Canadian Regional Climate Model. We establish prognostic budget equations of the IV for the potential temperature and the relative vorticity fields. For both of these variables, the IV equations present similar terms, notably terms relating to the transport of IV by ensemble-mean flow and to the covariance of fluctuations acting on the gradient of the ensemble-mean state. We show the skill of these equations to diagnose the IV that took place in an ensemble of 20 3-month (summer season) simulations that differed only in their IC. Our study suggests that the dominant terms responsible for the large increase of IV are either the covariance term involving the potential temperature fluctuations and diabatic heating fluctuations, or the covariance of inter-member fluctuations acting upon ensemble-mean gradients. Our results also show that, on average, the third-order terms are negligible, but they can become important when the IV is large. (orig.)

  16. Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

    Science.gov (United States)

    Fillion, Anthony; Bocquet, Marc; Gratton, Serge

    2018-04-01

    The analysis in nonlinear variational data assimilation is the solution of a non-quadratic minimization. Thus, the analysis efficiency relies on its ability to locate a global minimum of the cost function. If this minimization uses a Gauss-Newton (GN) method, it is critical for the starting point to be in the attraction basin of a global minimum. Otherwise the method may converge to a local extremum, which degrades the analysis. With chaotic models, the number of local extrema often increases with the temporal extent of the data assimilation window, making the former condition harder to satisfy. This is unfortunate because the assimilation performance also increases with this temporal extent. However, a quasi-static (QS) minimization may overcome these local extrema. It accomplishes this by gradually injecting the observations in the cost function. This method was introduced by Pires et al. (1996) in a 4D-Var context. We generalize this approach to four-dimensional strong-constraint nonlinear ensemble variational (EnVar) methods, which are based on both a nonlinear variational analysis and the propagation of dynamical error statistics via an ensemble. This forces one to consider the cost function minimizations in the broader context of cycled data assimilation algorithms. We adapt this QS approach to the iterative ensemble Kalman smoother (IEnKS), an exemplar of nonlinear deterministic four-dimensional EnVar methods. Using low-order models, we quantify the positive impact of the QS approach on the IEnKS, especially for long data assimilation windows. We also examine the computational cost of QS implementations and suggest cheaper algorithms.

  17. A Pseudoproxy-Ensemble Study of Late-Holocene Climate Field Reconstructions Using CCA

    Science.gov (United States)

    Amrhein, D. E.; Smerdon, J. E.

    2009-12-01

    Recent evaluations of late-Holocene multi-proxy reconstruction methods have used pseudoproxy experiments derived from millennial General Circulation Model (GCM) integrations. These experiments assess the performance of a reconstruction technique by comparing pseudoproxy reconstructions, which use restricted subsets of model data, against complete GCM data fields. Most previous studies have tested methodologies using different pseudoproxy noise levels, but only with single realizations for each noise classification. A more robust evaluation of performance is to create an ensemble of pseudoproxy networks with distinct sets of noise realizations and a corresponding reconstruction ensemble that can be evaluated for consistency and sensitivity to random error. This work investigates canonical correlation analysis (CCA) as a late-Holocene climate field reconstruction (CFR) technique using ensembles of pseudoproxy experiments derived from the NCAR CSM 1.4 millennial integration. Three 200-member reconstruction ensembles are computed using pseudoproxies with signal-to-noise ratios (by standard deviation) of 1, 0.5, and 0.25 and locations that approximate the spatial distribution of real-world multiproxy networks. An important component of these ensemble calculations is the independent optimization of the three CCA truncation parameters for each ensemble member. This task is accomplished using an inexpensive discrete optimization algorithm that minimizes both RMS error in the calibration interval and the number of free parameters in the reconstruction model to avoid artificial skill. Within this framework, CCA is investigated for its sensitivity to the level of noise in the pseudoproxy network and the spatial distribution of the network. Warm biases, variance losses, and validation-interval error increase with noise level and vary spatially within the reconstructed fields. Reconstruction skill, measured as grid-point correlations during the validation interval, is lowest in

  18. A Contribution to the Study of Ensemble of Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Leandro Antonio Pasa

    2015-01-01

    Full Text Available This study presents a factorial experiment to investigate the ensemble of Kohonen Self-Organizing Maps. Clusters Validity Indexes and the Mean Square Quantization Error were used as a criterion for fusing Kohonen Maps, through three different equations and four approaches. Computational simulations were performed with traditional dataset, including those with high dimensionality, not linearly separable classes, Gaussian mixtures, almost touching clusters, and unbalanced classes, from the UCI Machine Learning Repository and from Fundamental Clustering Problems Suite, with variations in map size, number of ensemble components, and the percentage of dataset bagging. The proposed method achieves a better classification than a single Kohonen Map and we applied the Wilcoxon Signed Rank Test to evidence its effectiveness.

  19. Generalized ensemble method applied to study systems with strong first order transitions

    Science.gov (United States)

    Małolepsza, E.; Kim, J.; Keyes, T.

    2015-09-01

    At strong first-order phase transitions, the entropy versus energy or, at constant pressure, enthalpy, exhibits convex behavior, and the statistical temperature curve correspondingly exhibits an S-loop or back-bending. In the canonical and isothermal-isobaric ensembles, with temperature as the control variable, the probability density functions become bimodal with peaks localized outside of the S-loop region. Inside, states are unstable, and as a result simulation of equilibrium phase coexistence becomes impossible. To overcome this problem, a method was proposed by Kim, Keyes and Straub [1], where optimally designed generalized ensemble sampling was combined with replica exchange, and denoted generalized replica exchange method (gREM). This new technique uses parametrized effective sampling weights that lead to a unimodal energy distribution, transforming unstable states into stable ones. In the present study, the gREM, originally developed as a Monte Carlo algorithm, was implemented to work with molecular dynamics in an isobaric ensemble and coded into LAMMPS, a highly optimized open source molecular simulation package. The method is illustrated in a study of the very strong solid/liquid transition in water.

  20. Data assimilation for groundwater flow modelling using Unbiased Ensemble Square Root Filter: Case study in Guantao, North China Plain

    Science.gov (United States)

    Li, N.; Kinzelbach, W.; Li, H.; Li, W.; Chen, F.; Wang, L.

    2017-12-01

    Data assimilation techniques are widely used in hydrology to improve the reliability of hydrological models and to reduce model predictive uncertainties. This provides critical information for decision makers in water resources management. This study aims to evaluate a data assimilation system for the Guantao groundwater flow model coupled with a one-dimensional soil column simulation (Hydrus 1D) using an Unbiased Ensemble Square Root Filter (UnEnSRF) originating from the Ensemble Kalman Filter (EnKF) to update parameters and states, separately or simultaneously. To simplify the coupling between unsaturated and saturated zone, a linear relationship obtained from analyzing inputs to and outputs from Hydrus 1D is applied in the data assimilation process. Unlike EnKF, the UnEnSRF updates parameter ensemble mean and ensemble perturbations separately. In order to keep the ensemble filter working well during the data assimilation, two factors are introduced in the study. One is called damping factor to dampen the update amplitude of the posterior ensemble mean to avoid nonrealistic values. The other is called inflation factor to relax the posterior ensemble perturbations close to prior to avoid filter inbreeding problems. The sensitivities of the two factors are studied and their favorable values for the Guantao model are determined. The appropriate observation error and ensemble size were also determined to facilitate the further analysis. This study demonstrated that the data assimilation of both model parameters and states gives a smaller model prediction error but with larger uncertainty while the data assimilation of only model states provides a smaller predictive uncertainty but with a larger model prediction error. Data assimilation in a groundwater flow model will improve model prediction and at the same time make the model converge to the true parameters, which provides a successful base for applications in real time modelling or real time controlling strategies

  1. NYYD Ensemble

    Index Scriptorium Estoniae

    2002-01-01

    NYYD Ensemble'i duost Traksmann - Lukk E.-S. Tüüri teosega "Symbiosis", mis on salvestatud ka hiljuti ilmunud NYYD Ensemble'i CDle. 2. märtsil Rakvere Teatri väikeses saalis ja 3. märtsil Rotermanni Soolalaos, kavas Tüür, Kaumann, Berio, Reich, Yun, Hauta-aho, Buckinx

  2. Ensembl 2004.

    Science.gov (United States)

    Birney, E; Andrews, D; Bevan, P; Caccamo, M; Cameron, G; Chen, Y; Clarke, L; Coates, G; Cox, T; Cuff, J; Curwen, V; Cutts, T; Down, T; Durbin, R; Eyras, E; Fernandez-Suarez, X M; Gane, P; Gibbins, B; Gilbert, J; Hammond, M; Hotz, H; Iyer, V; Kahari, A; Jekosch, K; Kasprzyk, A; Keefe, D; Keenan, S; Lehvaslaiho, H; McVicker, G; Melsopp, C; Meidl, P; Mongin, E; Pettett, R; Potter, S; Proctor, G; Rae, M; Searle, S; Slater, G; Smedley, D; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Storey, R; Ureta-Vidal, A; Woodwark, C; Clamp, M; Hubbard, T

    2004-01-01

    The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organize biology around the sequences of large genomes. It is a comprehensive and integrated source of annotation of large genome sequences, available via interactive website, web services or flat files. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. The facilities of the system range from sequence analysis to data storage and visualization and installations exist around the world both in companies and at academic sites. With a total of nine genome sequences available from Ensembl and more genomes to follow, recent developments have focused mainly on closer integration between genomes and external data.

  3. Sub-Ensemble Coastal Flood Forecasting: A Case Study of Hurricane Sandy

    Directory of Open Access Journals (Sweden)

    Justin A. Schulte

    2017-12-01

    Full Text Available In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster skill score. Additional sub-ensemble and composite skill scores are proposed for assessing the forecast skill of a clustered ensemble forecast. A recently proposed method for statistically increasing the number of ensemble members is used to improve sub-ensemble probabilistic estimates. Through the application of the proposed methodology to Sandy coastal flood reforecasts, it is demonstrated that statistics computed using only ensemble members belonging to a specific cluster are more representative than those computed using all ensemble members simultaneously. A cluster skill-cluster uncertainty index relationship is identified, which is the cluster analog of the documented spread-skill relationship. Two sub-ensemble skill scores are shown to be positively correlated with cluster forecast skill, suggesting that skillfully forecasting the cluster into which the observation will fall is important to overall forecast skill. The identified relationships also suggest that the number of ensemble members within in each cluster can be used as guidance for assessing the potential for forecast error. The inevitable existence of ensemble member clusters in tidally dominated total water level prediction systems suggests that clustering is a necessary post-processing step for producing representative and skillful total water level forecasts.

  4. Ensembl 2017

    OpenAIRE

    Aken, Bronwen L.; Achuthan, Premanand; Akanni, Wasiu; Amode, M. Ridwan; Bernsdorff, Friederike; Bhai, Jyothish; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Gil, Laurent; Gir?n, Carlos Garc?a; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.

    2016-01-01

    Ensembl (www.ensembl.org) is a database and genome browser for enabling research on vertebrate genomes. We import, analyse, curate and integrate a diverse collection of large-scale reference data to create a more comprehensive view of genome biology than would be possible from any individual dataset. Our extensive data resources include evidence-based gene and regulatory region annotation, genome variation and gene trees. An accompanying suite of tools, infrastructure and programmatic access ...

  5. Ensemble Sampling

    OpenAIRE

    Lu, Xiuyuan; Van Roy, Benjamin

    2017-01-01

    Thompson sampling has emerged as an effective heuristic for a broad range of online decision problems. In its basic form, the algorithm requires computing and sampling from a posterior distribution over models, which is tractable only for simple special cases. This paper develops ensemble sampling, which aims to approximate Thompson sampling while maintaining tractability even in the face of complex models such as neural networks. Ensemble sampling dramatically expands on the range of applica...

  6. An OSSE Study for Deep Argo Array using the GFDL Ensemble Coupled Data Assimilation System

    Science.gov (United States)

    Chang, You-Soon; Zhang, Shaoqing; Rosati, Anthony; Vecchi, Gabriel A.; Yang, Xiaosong

    2018-03-01

    An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, "observations" drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the "identical" twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.

  7. SASSIE: A program to study intrinsically disordered biological molecules and macromolecular ensembles using experimental scattering restraints

    Science.gov (United States)

    Curtis, Joseph E.; Raghunandan, Sindhu; Nanda, Hirsh; Krueger, Susan

    2012-02-01

    regions to vary dihedral angles, ensembles of structures can be generated. Additionally, simple two-body rigid-body rotations are supported with and without disordered regions. Generated structures can then be used to calculate small-angle scattering profiles which can then be filtered against experimentally determined data. Filtered structures can be visualized individually or as an ensemble using density plots. In the modular and expandable program framework the user can easily access our subroutines and structural coordinates can be easily obtained for study using other computational physics methods. Additional comments: The distribution file for this program is over 159 Mbytes and therefore is not delivered directly when download or Email is requested. Instead an html file giving details of how the program can be obtained is sent. Running time: Varies depending on application. Typically 10 minutes to 24 hours depending on the number of generated structures.

  8. A study of fuzzy logic ensemble system performance on face recognition problem

    Science.gov (United States)

    Polyakova, A.; Lipinskiy, L.

    2017-02-01

    Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

  9. Macroscopic Ensembles of Aligned Carbon Nanotubes in Bubble Imprints Studied by Polarized Raman Microscopy

    Directory of Open Access Journals (Sweden)

    Shota Ushiba

    2014-01-01

    Full Text Available We study the alignment of single-wall carbon nanotubes (SWCNTs in bubble imprints through polarized Raman microscopy. A hemispherical bubble containing SWCNTs is pressed against a glass substrate, resulting in an imprint of the bubble membrane with a coffee ring on the substrate. We find that macroscopic ensembles of aligned SWCNTs are obtained in the imprints, in which there are three patterns of orientations: (i azimuthal alignment on the coffee ring, (ii radial alignment at the edge of the membrane, and (iii random orientation at the center of the membrane. We also find that the alignment of SWCNTs in the imprints can be manipulated by spinning bubbles. The orientation of SWCNTs on the coffee ring is directed radially, which is orthogonal to the case of unspun bubbles. This approach enables one to align SWCNTs in large quantities and in a short time, potentially opening up a wide range of CNT-based electronic and optical applications.

  10. Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

    Science.gov (United States)

    Hu, Jianlin; Li, Xun; Huang, Lin; Ying, Qi; Zhang, Qiang; Zhao, Bin; Wang, Shuxiao; Zhang, Hongliang

    2017-11-01

    Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are -0.11 and 0.24, respectively, which are better than the MFB (-0.25 to -0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and

  11. Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

    Directory of Open Access Journals (Sweden)

    J. Hu

    2017-11-01

    Full Text Available Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ model with meteorological inputs from the Weather Research and Forecasting (WRF model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC, the Emission Inventory for China by School of Environment at Tsinghua University (SOE, the Emissions Database for Global Atmospheric Research (EDGAR, and the Regional Emission inventory in Asia version 2 (REAS2. Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB and mean fractional errors (MFEs of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16 and MFE (0.26–0.31 of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h concentrations are also improved, with mean normalized bias (MNB of 0.03 and mean normalized errors (MNE of 0.14, compared to MNB

  12. Does internal variability change in response to global warming? A large ensemble modelling study of tropical rainfall

    Science.gov (United States)

    Milinski, S.; Bader, J.; Jungclaus, J. H.; Marotzke, J.

    2017-12-01

    There is some consensus on mean state changes of rainfall under global warming; changes of the internal variability, on the other hand, are more difficult to analyse and have not been discussed as much despite their importance for understanding changes in extreme events, such as droughts or floodings. We analyse changes in the rainfall variability in the tropical Atlantic region. We use a 100-member ensemble of historical (1850-2005) model simulations with the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1) to identify changes of internal rainfall variability. To investigate the effects of global warming on the internal variability, we employ an additional ensemble of model simulations with stronger external forcing (1% CO2-increase per year, same integration length as the historical simulations) with 68 ensemble members. The focus of our study is on the oceanic Atlantic ITCZ. We find that the internal variability of rainfall over the tropical Atlantic does change due to global warming and that these changes in variability are larger than changes in the mean state in some regions. From splitting the total variance into patterns of variability, we see that the variability on the southern flank of the ITCZ becomes more dominant, i.e. explaining a larger fraction of the total variance in a warmer climate. In agreement with previous studies, we find that changes in the mean state show an increase and narrowing of the ITCZ. The large ensembles allow us to do a statistically robust differentiation between the changes in variability that can be explained by internal variability and those that can be attributed to the external forcing. Furthermore, we argue that internal variability in a transient climate is only well defined in the ensemble domain and not in the temporal domain, which requires the use of a large ensemble.

  13. The canonical ensemble redefined - 1: Formalism

    International Nuclear Information System (INIS)

    Venkataraman, R.

    1984-12-01

    For studying the thermodynamic properties of systems we propose an ensemble that lies in between the familiar canonical and microcanonical ensembles. We point out the transition from the canonical to microcanonical ensemble and prove from a comparative study that all these ensembles do not yield the same results even in the thermodynamic limit. An investigation of the coupling between two or more systems with these ensembles suggests that the state of thermodynamical equilibrium is a special case of statistical equilibrium. (author)

  14. Ensembl variation resources

    Directory of Open Access Journals (Sweden)

    Marin-Garcia Pablo

    2010-05-01

    Full Text Available Abstract Background The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics. Description The Ensembl project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the Ensembl variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within Ensembl. Conclusions Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The Ensembl variation resources are integrated into the Ensembl genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All Ensembl data is freely available at http://www.ensembl.org and from the public MySQL database server at ensembldb.ensembl.org.

  15. Photoluminescence study of as-grown vertically standing wurtzite InP nanowire ensembles.

    Science.gov (United States)

    Iqbal, Azhar; Beech, Jason P; Anttu, Nicklas; Pistol, Mats-Erik; Samuelson, Lars; Borgström, Magnus T; Yartsev, Arkady

    2013-03-22

    We demonstrate a method that enables the study of photoluminescence of as-grown nanowires on a native substrate by non-destructively suppressing the contribution of substrate photoluminescence. This is achieved by using polarized photo-excitation and photoluminescence and by making an appropriate choice of incident angle of both excitation beam and photoluminescence collection direction. Using TE-polarized excitation at a wavelength of 488 nm at an incident angle of ∼70° we suppress the InP substrate photoluminescence relative to that of the InP nanowires by about 80 times. Consequently, the photoluminescence originating from the nanowires becomes comparable to and easily distinguishable from the substrate photoluminescence. The measured photoluminescence, which peaks at photon energies of ∼1.35 eV and ∼1.49 eV, corresponds to the InP substrate with zinc-blende crystal structure and to the InP nanowires with wurtzite crystal structure, respectively. The photoluminescence quantum yield of the nanowires was found to be ∼20 times lower than that of the InP substrate. The nanowires, grown vertically in a random ensemble, neither exhibit substantial emission polarization selectivity to the axis of the nanowires nor follow excitation polarization preferences observed previously for a single nanowire.

  16. RACORO continental boundary layer cloud investigations: 1. Case study development and ensemble large-scale forcings

    Science.gov (United States)

    Vogelmann, Andrew M.; Fridlind, Ann M.; Toto, Tami; Endo, Satoshi; Lin, Wuyin; Wang, Jian; Feng, Sha; Zhang, Yunyan; Turner, David D.; Liu, Yangang; Li, Zhijin; Xie, Shaocheng; Ackerman, Andrew S.; Zhang, Minghua; Khairoutdinov, Marat

    2015-06-01

    Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60 h case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in situ measurements from the Routine AAF (Atmospheric Radiation Measurement (ARM) Aerial Facility) CLOWD (Clouds with Low Optical Water Depth) Optical Radiative Observations (RACORO) field campaign and remote sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functions for concise representation in models. Values of the aerosol hygroscopicity parameter, κ, are derived from observations to be 0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing data sets are derived from the ARM variational analysis, European Centre for Medium-Range Weather Forecasts, and a multiscale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in "trial" large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary clouds.

  17. RACORO Continental Boundary Layer Cloud Investigations: 1. Case Study Development and Ensemble Large-Scale Forcings

    Science.gov (United States)

    Vogelmann, Andrew M.; Fridlind, Ann M.; Toto, Tami; Endo, Satoshi; Lin, Wuyin; Wang, Jian; Feng, Sha; Zhang, Yunyan; Turner, David D.; Liu, Yangang; hide

    2015-01-01

    Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60 h case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in situ measurements from the Routine AAF (Atmospheric Radiation Measurement (ARM) Aerial Facility) CLOWD (Clouds with Low Optical Water Depth) Optical Radiative Observations (RACORO) field campaign and remote sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functions for concise representation in models. Values of the aerosol hygroscopicity parameter, kappa, are derived from observations to be approximately 0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing data sets are derived from the ARM variational analysis, European Centre for Medium-Range Weather Forecasts, and a multiscale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in "trial" large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary

  18. A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs

    OpenAIRE

    Bologna, Guido; Hayashi, Yoichi

    2018-01-01

    One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However, producing rules from Multilayer Perceptrons (MLPs) is an NP-hard problem. Many techniques have been introduced to generate rules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by 10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP), experime...

  19. Study of ±J Ising spin glasses via multicanonical ensemble

    International Nuclear Information System (INIS)

    Celik, T.; Berg, B.

    1993-03-01

    The authors performed numerical simulations of 2D and 3D Edwards-Anderson spin glass models by using the recently developed multicanonical ensemble. The ergodicity times increase with the lattice size approximately as V 3 . The energy, entropy and other physical quantities are easily calculable at all temperatures from a single simulation. Their finite size scalings and the zero temperature limits are also explored

  20. Finite temperature grand canonical ensemble study of the minimum electrophilicity principle.

    Science.gov (United States)

    Miranda-Quintana, Ramón Alain; Chattaraj, Pratim K; Ayers, Paul W

    2017-09-28

    We analyze the minimum electrophilicity principle of conceptual density functional theory using the framework of the finite temperature grand canonical ensemble. We provide support for this principle, both for the cases of systems evolving from a non-equilibrium to an equilibrium state and for the change from one equilibrium state to another. In doing so, we clearly delineate the cases where this principle can, or cannot, be used.

  1. A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

    Science.gov (United States)

    Azami, Hamed; Escudero, Javier

    2015-08-01

    Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

  2. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Haakon

    2016-01-08

    The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

  3. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Haakon; Chernov, Alexey; Law, Kody; Nobile, Fabio; Tempone, Raul

    2016-01-01

    The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

  4. Ensemble simulations to study the impact of land use change of Atlanta to regional climate

    Science.gov (United States)

    Liu, P.; Hu, Y.; Stone, B.; Vargo, J.; Nenes, A.; Russell, A.; Trail, M.; Tsimpidi, A.

    2012-12-01

    Studies show that urban areas may be the "first responders" to climate change (Rosenzweig et al., 2010). Of particular interest is the potential increased temperatures in urban areas, due to use of structures and surfaces that increase local heating, and how that may impact health, air quality and other environmental factors. In response, interest has grown as to how the modification of land use in urban areas, in order to mitigate the adverse effects of urbanization can serve to reduce local temperatures, and how climate is impacted more regionally. Studies have been conducted to investigate the impact of land use change on local or regional climate by dynamic downscaling using regional climate models (RCMs), the boundary conditions (BCs) and initial conditions (ICs) of which result from coarser-resolution reanalysis data or general circulation models (GCMs). However, few studies have focused on demonstrating whether the land use change in local areas significantly impacts the climate of the larger region of the domain, and the spatial scale of the impact from urban-scale changes. This work investigated the significance of the impact of land use change in the Atlanta city area on different scales, using a range of modeling resolutions, including the contiguous US (with 36km resolution), the southeastern US (with 12km resolution) and the state of Georgia (with 4km resolution). We used WRF version 3.1.1 with and ran continuous from June to August of a simulated year 2050, driven by GISS ModelE with inputs corresponding to RCP4.5. During the simulation, spectral nudging is used in the 36km resolution domain to maintain the climate patterns with scales larger than 2000km. Two-way nesting is also used in order to take into account the feedback of nesting domains across model domains. Two land use cases over the Atlanta city are chosen. For the base case, most of the urban area of Atlanta is covered with forest; while for the second, "impervious" case, all the urban

  5. Exploring diversity in ensemble classification: Applications in large area land cover mapping

    Science.gov (United States)

    Mellor, Andrew; Boukir, Samia

    2017-07-01

    Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area

  6. A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs

    Directory of Open Access Journals (Sweden)

    Guido Bologna

    2018-01-01

    Full Text Available One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However, producing rules from Multilayer Perceptrons (MLPs is an NP-hard problem. Many techniques have been introduced to generate rules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by 10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP, experiments were performed on 10 repetitions of stratified 10-fold cross-validation trials over 25 binary classification problems. The DIMLP architecture allowed us to produce rules from DIMLP ensembles, boosted shallow trees (BSTs, and Support Vector Machines (SVM. The complexity of rulesets was measured with the average number of generated rules and average number of antecedents per rule. From the 25 used classification problems, the most complex rulesets were generated from BSTs trained by “gentle boosting” and “real boosting.” Moreover, we clearly observed that the less complex the rules were, the better their fidelity was. In fact, rules generated from decision stumps trained by modest boosting were, for almost all the 25 datasets, the simplest with the highest fidelity. Finally, in terms of average predictive accuracy and average ruleset complexity, the comparison of some of our results to those reported in the literature proved to be competitive.

  7. Study of a radiation point defects ensemble in thin GaAs layers implanted by Be+ and Se+ ions

    International Nuclear Information System (INIS)

    Shcherbachev, K.D.; Bublik, V.T.; Kuripyatnik, A.V.; Yurchuk, S.Yu.

    2001-01-01

    The behaviour of a radiation point defects ensemble in SI-GaAs(100) wafers implanted by Be + (a dose of 1x10 14 at/cm 2 , an energy of 50 and 150 keV) and Se + (a dose of 5x10 14 at/cm 2 , an energy of 150, 180 and 240 keV) ions are studied by a triple-crystal diffractometry method. The strain profile and a number of residual radiation point defects are shown to be determined by defects annihilation, their sink to the surface and rechanneling during the implantation [ru

  8. Eigenfunction statistics of Wishart Brownian ensembles

    International Nuclear Information System (INIS)

    Shukla, Pragya

    2017-01-01

    We theoretically analyze the eigenfunction fluctuation measures for a Hermitian ensemble which appears as an intermediate state of the perturbation of a stationary ensemble by another stationary ensemble of Wishart (Laguerre) type. Similar to the perturbation by a Gaussian stationary ensemble, the measures undergo a diffusive dynamics in terms of the perturbation parameter but the energy-dependence of the fluctuations is different in the two cases. This may have important consequences for the eigenfunction dynamics as well as phase transition studies in many areas of complexity where Brownian ensembles appear. (paper)

  9. New type of chimera and mutual synchronization of spatiotemporal structures in two coupled ensembles of nonlocally interacting chaotic maps

    Science.gov (United States)

    Bukh, Andrei; Rybalova, Elena; Semenova, Nadezhda; Strelkova, Galina; Anishchenko, Vadim

    2017-11-01

    We study numerically the dynamics of a network made of two coupled one-dimensional ensembles of discrete-time systems. The first ensemble is represented by a ring of nonlocally coupled Henon maps and the second one by a ring of nonlocally coupled Lozi maps. We find that the network of coupled ensembles can realize all the spatio-temporal structures which are observed both in the Henon map ensemble and in the Lozi map ensemble while uncoupled. Moreover, we reveal a new type of spatiotemporal structure, a solitary state chimera, in the considered network. We also establish and describe the effect of mutual synchronization of various complex spatiotemporal patterns in the system of two coupled ensembles of Henon and Lozi maps.

  10. An ensemble study of extreme storm surge related water levels in the North Sea in a changing climate

    Directory of Open Access Journals (Sweden)

    A. Sterl

    2009-09-01

    Full Text Available The height of storm surges is extremely important for a low-lying country like The Netherlands. By law, part of the coastal defence system has to withstand a water level that on average occurs only once every 10 000 years. The question then arises whether and how climate change affects the heights of extreme storm surges. Published research points to only small changes. However, due to the limited amount of data available results are usually limited to relatively frequent extremes like the annual 99%-ile. We here report on results from a 17-member ensemble of North Sea water levels spaning the period 1950–2100. It was created by forcing a surge model of the North Sea with meteorological output from a state-of-the-art global climate model which has been driven by greenhouse gas emissions following the SRES A1b scenario. The large ensemble size enables us to calculate 10 000 year return water levels with a low statistical uncertainty. In the one model used in this study, we find no statistically significant change in the 10 000 year return values of surge heights along the Dutch during the 21st century. Also a higher sea level resulting from global warming does not impact the height of the storm surges. As a side effect of our simulations we also obtain results on the interplay between surge and tide.

  11. ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

    Directory of Open Access Journals (Sweden)

    Krasnogor Natalio

    2009-10-01

    Full Text Available Abstract Background Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

  12. The Ensemble Kalman Filter for Groundwater Plume Characterization: A Case Study.

    Science.gov (United States)

    Ross, James L; Andersen, Peter F

    2018-04-17

    The Kalman filter is an efficient data assimilation tool to refine an estimate of a state variable using measured data and the variable's correlations in space and/or time. The ensemble Kalman filter (EnKF) (Evensen, 2004, 2009) is a Kalman filter variant that employs Monte Carlo analysis to define the correlations that help to refine the updated state. While use of EnKF in hydrology is somewhat limited, it has been successfully applied in other fields of engineering (e.g. oil reservoir modeling, weather forecasting). Here, EnKF is used to refine a simulated groundwater TCE plume that underlies the Tooele Army Depot-North (TEAD-N) in Utah, based on observations of TCE in the aquifer. The resulting EnKF-based assimilated plume is simulated forward in time to predict future plume migration. The correlations that underpin EnKF updating implicitly contain information about how the plume developed over time under the influence of complex site hydrology and variable source history, as they are predicated on multiple realizations of a well-calibrated numerical groundwater flow and transport model. The EnKF methodology is compared to an ordinary kriging-based assimilation method with respect to the accurate representation of plume concentrations in order to determine the relative efficacy of EnKF for water quality data assimilation. This article is protected by copyright. All rights reserved.

  13. World Music Ensemble: Kulintang

    Science.gov (United States)

    Beegle, Amy C.

    2012-01-01

    As instrumental world music ensembles such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music ensembles just starting to gain popularity in particular parts of the United States. The kulintang ensemble, a drum and gong ensemble…

  14. Measuring social interaction in music ensembles.

    Science.gov (United States)

    Volpe, Gualtiero; D'Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano

    2016-05-05

    Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances. © 2016 The Author(s).

  15. Remote and Local Influences in Forecasting Pacific SST: a Linear Inverse Model and a Multimodel Ensemble Study

    Science.gov (United States)

    Faggiani Dias, D.; Subramanian, A. C.; Zanna, L.; Miller, A. J.

    2017-12-01

    Sea surface temperature (SST) in the Pacific sector is well known to vary on time scales from seasonal to decadal, and the ability to predict these SST fluctuations has many societal and economical benefits. Therefore, we use a suite of statistical linear inverse models (LIMs) to understand the remote and local SST variability that influences SST predictions over the North Pacific region and further improve our understanding on how the long-observed SST record can help better guide multi-model ensemble forecasts. Observed monthly SST anomalies in the Pacific sector (between 15oS and 60oN) are used to construct different regional LIMs for seasonal to decadal prediction. The forecast skills of the LIMs are compared to that from two operational forecast systems in the North American Multi-Model Ensemble (NMME) revealing that the LIM has better skill in the Northeastern Pacific than NMME models. The LIM is also found to have comparable forecast skill for SST in the Tropical Pacific with NMME models. This skill, however, is highly dependent on the initialization month, with forecasts initialized during the summer having better skill than those initialized during the winter. The forecast skill with LIM is also influenced by the verification period utilized to make the predictions, likely due to the changing character of El Niño in the 20th century. The North Pacific seems to be a source of predictability for the Tropics on seasonal to interannual time scales, while the Tropics act to worsen the skill for the forecast in the North Pacific. The data were also bandpassed into seasonal, interannual and decadal time scales to identify the relationships between time scales using the structure of the propagator matrix. For the decadal component, this coupling occurs the other way around: Tropics seem to be a source of predictability for the Extratropics, but the Extratropics don't improve the predictability for the Tropics. These results indicate the importance of temporal

  16. Layered Ensemble Architecture for Time Series Forecasting.

    Science.gov (United States)

    Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

    2016-01-01

    Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

  17. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  18. A Study of Factors that Influence First-Year Nonmusic Majors' Decisions to Participate in Music Ensembles at Small Liberal Arts Colleges in Indiana

    Science.gov (United States)

    Faber, Ardis R.

    2010-01-01

    The purpose of this study was to investigate factors that influence first-year nonmusic majors' decisions regarding participation in music ensembles at small liberal arts colleges in Indiana. A survey questionnaire was used to gather data. The data collected was analyzed to determine significant differences between the nonmusic majors who have…

  19. Ensemble forecasting of species distributions.

    Science.gov (United States)

    Araújo, Miguel B; New, Mark

    2007-01-01

    Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

  20. Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

    International Nuclear Information System (INIS)

    Shimazaki, Hideaki

    2013-01-01

    Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity

  1. An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets.

    Science.gov (United States)

    Stanescu, Ana; Caragea, Doina

    2015-01-01

    Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework.

  2. Multimodel ensembles of wheat growth

    DEFF Research Database (Denmark)

    Martre, Pierre; Wallach, Daniel; Asseng, Senthold

    2015-01-01

    , but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24...

  3. Projections of Water Stress Based on an Ensemble of Socioeconomic Growth and Climate Change Scenarios: A Case Study in Asia.

    Science.gov (United States)

    Fant, Charles; Schlosser, C Adam; Gao, Xiang; Strzepek, Kenneth; Reilly, John

    2016-01-01

    The sustainability of future water resources is of paramount importance and is affected by many factors, including population, wealth and climate. Inherent in current methods to estimate these factors in the future is the uncertainty of their prediction. In this study, we integrate a large ensemble of scenarios--internally consistent across economics, emissions, climate, and population--to develop a risk portfolio of water stress over a large portion of Asia that includes China, India, and Mainland Southeast Asia in a future with unconstrained emissions. We isolate the effects of socioeconomic growth from the effects of climate change in order to identify the primary drivers of stress on water resources. We find that water needs related to socioeconomic changes, which are currently small, are likely to increase considerably in the future, often overshadowing the effect of climate change on levels of water stress. As a result, there is a high risk of severe water stress in densely populated watersheds by 2050, compared to recent history. There is strong evidence to suggest that, in the absence of autonomous adaptation or societal response, a much larger portion of the region's population will live in water-stressed regions in the near future. Tools and studies such as these can effectively investigate large-scale system sensitivities and can be useful in engaging and informing decision makers.

  4. Advanced Atmospheric Ensemble Modeling Techniques

    Energy Technology Data Exchange (ETDEWEB)

    Buckley, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Chiswell, S. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Kurzeja, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Maze, G. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Viner, B. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Werth, D. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

    2017-09-29

    Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two release times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL’s capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.

  5. A class of energy-based ensembles in Tsallis statistics

    International Nuclear Information System (INIS)

    Chandrashekar, R; Naina Mohammed, S S

    2011-01-01

    A comprehensive investigation is carried out on the class of energy-based ensembles. The eight ensembles are divided into two main classes. In the isothermal class of ensembles the individual members are at the same temperature. A unified framework is evolved to describe the four isothermal ensembles using the currently accepted third constraint formalism. The isothermal–isobaric, grand canonical and generalized ensembles are illustrated through a study of the classical nonrelativistic and extreme relativistic ideal gas models. An exact calculation is possible only in the case of the isothermal–isobaric ensemble. The study of the ideal gas models in the grand canonical and the generalized ensembles has been carried out using a perturbative procedure with the nonextensivity parameter (1 − q) as the expansion parameter. Though all the thermodynamic quantities have been computed up to a particular order in (1 − q) the procedure can be extended up to any arbitrary order in the expansion parameter. In the adiabatic class of ensembles the individual members of the ensemble have the same value of the heat function and a unified formulation to described all four ensembles is given. The nonrelativistic and the extreme relativistic ideal gases are studied in the isoenthalpic–isobaric ensemble, the adiabatic ensemble with number fluctuations and the adiabatic ensemble with number and particle fluctuations

  6. Personal, musical and social benefits of singing in a community ensemble: Three case studies in Melbourne (Australia

    Directory of Open Access Journals (Sweden)

    Dawn Joseph

    2014-11-01

    Full Text Available Australia has a diverse, multilayered society that reflects its rich musical life. There are many community choirs formed by various cultural and linguistically diverse groups. This article is part of an ongoing project, Well-being and ageing: community, diversity and the arts (since 2008, undertaken by Deakin University and Monash University, that explores the cultural diversity within Australian society and how active music engagement fosters well-being. The singing groups selected for this discussion are the Skylarkers, the Bosnian Behar Choir, and the Coro Furlan. The Skylarkers and the Bosnian Behar Choir are mixed groups who respectively perform popular music from their generation and celebrate their culture through music. The Coro Furlan is an Italian male choir who understand themselves as custodians of their heritage. In these interpretative, qualitative case studies semi-structured interviews were undertaken and analyzed using Interpretative Phenomenological Analysis. In this approach there is an exploration of participants’ understanding of their lived experiences. The analysis of the combined data identified musical and social benefits that contribute to participants’ sense of individual well-being. Musical benefits occurred through sharing, learning and singing together. Social benefits included opportunities to build friendships, overcome isolation and gain a sense of validation. Many found that singing enhanced their health and happiness. Active music making in community choirs and music ensembles continues to be an effective way to support individuals, build community, and share culture and heritage.

  7. Multilevel ensemble Kalman filter

    KAUST Repository

    Chernov, Alexey; Hoel, Haakon; Law, Kody; Nobile, Fabio; Tempone, Raul

    2016-01-01

    This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.

  8. Entropy of network ensembles

    Science.gov (United States)

    Bianconi, Ginestra

    2009-03-01

    In this paper we generalize the concept of random networks to describe network ensembles with nontrivial features by a statistical mechanics approach. This framework is able to describe undirected and directed network ensembles as well as weighted network ensembles. These networks might have nontrivial community structure or, in the case of networks embedded in a given space, they might have a link probability with a nontrivial dependence on the distance between the nodes. These ensembles are characterized by their entropy, which evaluates the cardinality of networks in the ensemble. In particular, in this paper we define and evaluate the structural entropy, i.e., the entropy of the ensembles of undirected uncorrelated simple networks with given degree sequence. We stress the apparent paradox that scale-free degree distributions are characterized by having small structural entropy while they are so widely encountered in natural, social, and technological complex systems. We propose a solution to the paradox by proving that scale-free degree distributions are the most likely degree distribution with the corresponding value of the structural entropy. Finally, the general framework we present in this paper is able to describe microcanonical ensembles of networks as well as canonical or hidden-variable network ensembles with significant implications for the formulation of network-constructing algorithms.

  9. Multilevel ensemble Kalman filter

    KAUST Repository

    Chernov, Alexey

    2016-01-06

    This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.

  10. The Ensembl REST API: Ensembl Data for Any Language.

    Science.gov (United States)

    Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R S; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul

    2015-01-01

    We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. © The Author 2014. Published by Oxford University Press.

  11. Plasticity of the Binding Site of Renin: Optimized Selection of Protein Structures for Ensemble Docking.

    Science.gov (United States)

    Strecker, Claas; Meyer, Bernd

    2018-05-02

    Protein flexibility poses a major challenge to docking of potential ligands in that the binding site can adopt different shapes. Docking algorithms usually keep the protein rigid and only allow the ligand to be treated as flexible. However, a wrong assessment of the shape of the binding pocket can prevent a ligand from adapting a correct pose. Ensemble docking is a simple yet promising method to solve this problem: Ligands are docked into multiple structures, and the results are subsequently merged. Selection of protein structures is a significant factor for this approach. In this work we perform a comprehensive and comparative study evaluating the impact of structure selection on ensemble docking. We perform ensemble docking with several crystal structures and with structures derived from molecular dynamics simulations of renin, an attractive target for antihypertensive drugs. Here, 500 ns of MD simulations revealed binding site shapes not found in any available crystal structure. We evaluate the importance of structure selection for ensemble docking by comparing binding pose prediction, ability to rank actives above nonactives (screening utility), and scoring accuracy. As a result, for ensemble definition k-means clustering appears to be better suited than hierarchical clustering with average linkage. The best performing ensemble consists of four crystal structures and is able to reproduce the native ligand poses better than any individual crystal structure. Moreover this ensemble outperforms 88% of all individual crystal structures in terms of screening utility as well as scoring accuracy. Similarly, ensembles of MD-derived structures perform on average better than 75% of any individual crystal structure in terms of scoring accuracy at all inspected ensembles sizes.

  12. Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

    Science.gov (United States)

    Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

    2018-01-01

    Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes. PMID:29453930

  13. Musical ensembles in Ancient Mesapotamia

    NARCIS (Netherlands)

    Krispijn, T.J.H.; Dumbrill, R.; Finkel, I.

    2010-01-01

    Identification of musical instruments from ancient Mesopotamia by comparing musical ensembles attested in Sumerian and Akkadian texts with depicted ensembles. Lexicographical contributions to the Sumerian and Akkadian lexicon.

  14. High resolution isotope data and ensemble modelling reveal ecohydrological controls on catchment storage-discharge relationships and flux travel time distributions

    Science.gov (United States)

    Soulsby, C.; Kuppel, S.; Smith, A.; Tetzlaff, D.

    2017-12-01

    The dynamics of water storage in a catchment provides a fundamental insight into the interlinkages between input and output fluxes, and how these are affected by environmental change. Such dynamics also mediate, and help us understand, the fundamental difference of the rapid celerity of the rainfall-runoff (minutes to hours) response of catchments and the much slower velocity of water particles (months to decades) as they are transported through catchment systems. In this contribution we report an intensive, long-term (>10year), multi-scale isotope study in the Scottish Highlands that has sought to better understand these issues. We have integrated empirical data collection with diverse modelling approaches to quantify the dynamics and residence times of storage in different compartments of the hydrological system (vegetation canopies, soils, ground waters etc.) and their relationship between the magnitude and travel time distributions of output fluxes (stream flow, transpiration and evaporation). Use of conceptual, physically-based and probabilistic modelling approaches give broadly consistent perspectives on the storage-discharge relationships and the preferential selection of younger waters in runoff, evaporation and transpiration; while older waters predominate in groundwater. The work also highlighted the importance role vegetation plays in regulating fluxes in evaporation and transpiration and how this contributes to the differential ageing of water in mobile and bulk waters in the soil compartment. A separate case study shows how land use change can affect storage distributions in a catchment and radically change travel time distributions in output fluxes.

  15. A study on reducing update frequency of the forecast samples in the ensemble-based 4DVar data assimilation method

    Energy Technology Data Exchange (ETDEWEB)

    Shao, Aimei; Xu, Daosheng [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province; Chinese Academy of Meteorological Sciences, Beijing (China). State Key Lab. of Severe Weather; Qiu, Xiaobin [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province; Tianjin Institute of Meteorological Science (China); Qiu, Chongjian [Lanzhou Univ. (China). Key Lab. of Arid Climatic Changing and Reducing Disaster of Gansu Province

    2013-02-15

    In the ensemble-based four dimensional variational assimilation method (SVD-En4DVar), a singular value decomposition (SVD) technique is used to select the leading eigenvectors and the analysis variables are expressed as the orthogonal bases expansion of the eigenvectors. The experiments with a two-dimensional shallow-water equation model and simulated observations show that the truncation error and rejection of observed signals due to the reduced-dimensional reconstruction of the analysis variable are the major factors that damage the analysis when the ensemble size is not large enough. However, a larger-sized ensemble is daunting computational burden. Experiments with a shallow-water equation model also show that the forecast error covariances remain relatively constant over time. For that reason, we propose an approach that increases the members of the forecast ensemble while reducing the update frequency of the forecast error covariance in order to increase analysis accuracy and to reduce the computational cost. A series of experiments were conducted with the shallow-water equation model to test the efficiency of this approach. The experimental results indicate that this approach is promising. Further experiments with the WRF model show that this approach is also suitable for the real atmospheric data assimilation problem, but the update frequency of the forecast error covariances should not be too low. (orig.)

  16. Ensemble-based Kalman Filters in Strongly Nonlinear Dynamics

    Institute of Scientific and Technical Information of China (English)

    Zhaoxia PU; Joshua HACKER

    2009-01-01

    This study examines the effectiveness of ensemble Kalman filters in data assimilation with the strongly nonlinear dynamics of the Lorenz-63 model, and in particular their use in predicting the regime transition that occurs when the model jumps from one basin of attraction to the other. Four configurations of the ensemble-based Kalman filtering data assimilation techniques, including the ensemble Kalman filter, ensemble adjustment Kalman filter, ensemble square root filter and ensemble transform Kalman filter, are evaluated with their ability in predicting the regime transition (also called phase transition) and also are compared in terms of their sensitivity to both observational and sampling errors. The sensitivity of each ensemble-based filter to the size of the ensemble is also examined.

  17. An exact approach for studying cargo transport by an ensemble of molecular motors

    International Nuclear Information System (INIS)

    Materassi, Donatello; Roychowdhury, Subhrajit; Hays, Thomas; Salapaka, Murti

    2013-01-01

    Intracellular transport is crucial for many cellular processes where a large fraction of the cargo is transferred by motor-proteins over a network of microtubules. Malfunctions in the transport mechanism underlie a number of medical maladies. Existing methods for studying how motor-proteins coordinate the transfer of a shared cargo over a microtubule are either analytical or are based on Monte-Carlo simulations. Approaches that yield analytical results, while providing unique insights into transport mechanism, make simplifying assumptions, where a detailed characterization of important transport modalities is difficult to reach. On the other hand, Monte-Carlo based simulations can incorporate detailed characteristics of the transport mechanism; however, the quality of the results depend on the number and quality of simulation runs used in arriving at results. Here, for example, it is difficult to simulate and study rare-events that can trigger abnormalities in transport. In this article, a semi-analytical methodology that determines the probability distribution function of motor-protein behavior in an exact manner is developed. The method utilizes a finite-dimensional projection of the underlying infinite-dimensional Markov model, which retains the Markov property, and enables the detailed and exact determination of motor configurations, from which meaningful inferences on transport characteristics of the original model can be derived. Under this novel probabilistic approach new insights about the mechanisms of action of these proteins are found, suggesting hypothesis about their behavior and driving the design and realization of new experiments. The advantages provided in accuracy and efficiency make it possible to detect rare events in the motor protein dynamics, that could otherwise pass undetected using standard simulation methods. In this respect, the model has allowed to provide a possible explanation for possible mechanisms under which motor proteins could

  18. Conformation-related exciton localization and charge-pair formation in polythiophenes: ensemble and single-molecule study.

    Science.gov (United States)

    Sugimoto, Toshikazu; Habuchi, Satoshi; Ogino, Kenji; Vacha, Martin

    2009-09-10

    We study conformation-dependent photophysical properties of polythiophene (PT) by molecular dynamics simulations and by ensemble and single-molecule optical experiments. We use a graft copolymer consisting of a polythiophene backbone and long polystyrene branches and compare its properties with those obtained on the same polythiophene derivative without the side chains. Coarse-grain molecular dynamics simulations show that in a poor solvent, the PT without the side chains (PT-R) forms a globulelike conformation in which distances between any two conjugated segments on the chain are within the Forster radius for efficient energy transfer. In the PT with the polystyrene branches (PT-PS), the polymer main PT chain retains an extended coillike conformation, even in a poor solvent, and the calculated distances between conjugated segments favor energy transfer only between a few neighboring chromophores. The theoretical predictions are confirmed by measurements of fluorescence anisotropy and fluorescence blinking of the polymers' single chains. High anisotropy ratios and two-state blinking in PT-R are due to localization of the exciton on a single conjugated segment. These signatures of exciton localization are absent in single chains of PT-PS. Electric-field-induced quenching measured as a function of concentration of PT dispersed in an inert matrix showed that in well-isolated chains of PT-PS, the exciton dissociation is an intrachain process and that aggregation of the PT-R chains causes an increase in quenching due to the onset of interchain interactions. Measurements of the field-induced quenching on single chains indicate that in PT-R, the exciton dissociation is a slower process that takes place only after the exciton is localized on one conjugated segment.

  19. Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

    Science.gov (United States)

    Liu, Li; Xu, Yue-Ping

    2017-04-01

    Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

  20. Ensemble Data Mining Methods

    Science.gov (United States)

    Oza, Nikunj C.

    2004-01-01

    Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  1. Ensemble Data Mining Methods

    Data.gov (United States)

    National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...

  2. The classicality and quantumness of a quantum ensemble

    International Nuclear Information System (INIS)

    Zhu Xuanmin; Pang Shengshi; Wu Shengjun; Liu Quanhui

    2011-01-01

    In this Letter, we investigate the classicality and quantumness of a quantum ensemble. We define a quantity called ensemble classicality based on classical cloning strategy (ECCC) to characterize how classical a quantum ensemble is. An ensemble of commuting states has a unit ECCC, while a general ensemble can have a ECCC less than 1. We also study how quantum an ensemble is by defining a related quantity called quantumness. We find that the classicality of an ensemble is closely related to how perfectly the ensemble can be cloned, and that the quantumness of the ensemble used in a quantum key distribution (QKD) protocol is exactly the attainable lower bound of the error rate in the sifted key. - Highlights: → A quantity is defined to characterize how classical a quantum ensemble is. → The classicality of an ensemble is closely related to the cloning performance. → Another quantity is also defined to investigate how quantum an ensemble is. → This quantity gives the lower bound of the error rate in a QKD protocol.

  3. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

    DEFF Research Database (Denmark)

    Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh

    2015-01-01

    Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...... and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data...

  4. Ensemble averaged two-phase flow numerical simulation in vertical ducts for the void-studying behavior in BWRs

    International Nuclear Information System (INIS)

    Mohsen Sharifpur; Mahmoud Salehi; Ali Nouri Brojerdi; Ali Arefmanesh

    2003-01-01

    Investigation upon generation of vapor in the two-phase flow and predication of its behaviour is an important problem in nuclear industries. Here, the use of the ensemble averaging is to drive the governing equations for each phase in the bubbly two phase flow (two fluid model) and to simulate the water channel inside the four fuel rods along the vertical line. The governing equations will be simplified by having the experience on BWRs and data, which are obtained to find the distribution of void fraction, velocity and other parameters for each phase along the tube. Finally, we compare the results with the simulated results obtained from RELAP 5 Mode 2. The advantage of this work is to offer a new technique to solve the ensemble averaged two-phase flow by imposing the energy balance equation rather than to use the ordinary energy equations. (author)

  5. Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

    Science.gov (United States)

    Cohen, Charlie; Robertson, Franklin; Molod, Andrea

    2014-01-01

    The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of

  6. An operational hydrological ensemble prediction system for the city of Zurich (Switzerland: skill, case studies and scenarios

    Directory of Open Access Journals (Sweden)

    N. Addor

    2011-07-01

    Full Text Available The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS was launched operationally in 2008. This model chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH, coupled to a hydraulic model (FLORIS. The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation.

    To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2. Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that overall COSMO-LEPS-based hydrological forecasts outperforms their COSMO-7-based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts, and are used

  7. Bridging the spectral divide: a case study with PAGES2k, the CESM Last Millennium Ensemble and proxy system models

    Science.gov (United States)

    Zhu, F.; Emile-Geay, J.; Ault, T.; McKay, N.; Dee, S.

    2017-12-01

    A grand challenge for paleoclimatology is to constrain climate model behavior on timescales longer than the instrumental record. Of particular interest is the spectrum of temperature as sensed by climate proxies. The "continuum" of climate variability [Huybers & Curry, Nature 2006] is often characterized by its scaling exponent β , where the spectral density S and the frequency f satisfy the power law S ∝ f-β . Recent studies have voiced concern that climate models underestimate scaling behavior compared to proxies [Laepple & Huybers, PNAS 2014]. Part of this discrepancy is known to lie in the complex processes whereby proxies transform climate signals [Dee et al, EPSL in press], yet many questions remain open. Here we leverage a recent multiproxy compilation [PAGES 2k Consortium, Sci Data 2017] to characterize scaling behavior over the Common Era using an interpolation-free method [Kirchner & Neal, PNAS 2013]. Proxy spectra are compared to spectra derived from the CESM Last Millennium Ensemble [Otto-Bliesner et al, BAMS 2016], using: (a) a naive model where proxies are assumed linearly related to annual temperature vs (b) proxy system models [Evans et al, QSR 2013] of varying complexity. Scaling behavior varies considerably by archive: on average the strongest centennial slopes are observed for lake sediments (β =1.2), while the smallest are observed for glacier ice (β =0.24). Results confirm that the CESM Last Millennium simulation (LM) exhibits decadal-centennial scaling closer to proxy spectra than the pre-industrial control run (PI): the latter shows a "blue" spectrum (β 0), suggesting that forcings are essential to reduce the spectral divide. Yet, even with forcings, LM spectra are flatter than the proxy spectra. Subsequent work will investigate the roles of seasonal sensitivity (trees, foraminifera, alkenones), multivariate influences (corals, trees), detrending (trees) and post-depositional processes (ice cores, lake & marine sediments) on spectral

  8. Demonstrating the value of larger ensembles in forecasting physical systems

    Directory of Open Access Journals (Sweden)

    Reason L. Machete

    2016-12-01

    its relative information content (in bits using a proper skill score. Doubling the ensemble size is demonstrated to yield a non-trivial increase in the information content (forecast skill for an ensemble with well over 16 members; this result stands in forecasting a mathematical system and a physical system. Indeed, even at the largest ensemble sizes considered (128 and 256, there are lead times where the forecast information is still increasing with ensemble size. Ultimately, model error will limit the value of ever larger ensembles. No support is found, however, for limiting design studies to the sizes commonly found in seasonal and climate studies. It is suggested that ensemble size be considered more explicitly in future design studies of forecast systems on all time scales.

  9. Evaluation of stability of k-means cluster ensembles with respect to random initialization.

    Science.gov (United States)

    Kuncheva, Ludmila I; Vetrov, Dmitry P

    2006-11-01

    Many clustering algorithms, including cluster ensembles, rely on a random component. Stability of the results across different runs is considered to be an asset of the algorithm. The cluster ensembles considered here are based on k-means clusterers. Each clusterer is assigned a random target number of clusters, k and is started from a random initialization. Here, we use 10 artificial and 10 real data sets to study ensemble stability with respect to random k, and random initialization. The data sets were chosen to have a small number of clusters (two to seven) and a moderate number of data points (up to a few hundred). Pairwise stability is defined as the adjusted Rand index between pairs of clusterers in the ensemble, averaged across all pairs. Nonpairwise stability is defined as the entropy of the consensus matrix of the ensemble. An experimental comparison with the stability of the standard k-means algorithm was carried out for k from 2 to 20. The results revealed that ensembles are generally more stable, markedly so for larger k. To establish whether stability can serve as a cluster validity index, we first looked at the relationship between stability and accuracy with respect to the number of clusters, k. We found that such a relationship strongly depends on the data set, varying from almost perfect positive correlation (0.97, for the glass data) to almost perfect negative correlation (-0.93, for the crabs data). We propose a new combined stability index to be the sum of the pairwise individual and ensemble stabilities. This index was found to correlate better with the ensemble accuracy. Following the hypothesis that a point of stability of a clustering algorithm corresponds to a structure found in the data, we used the stability measures to pick the number of clusters. The combined stability index gave best results.

  10. Advances in sequential data assimilation and numerical weather forecasting: An Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic ensemble square root filters, and a test of a new time stepping scheme in an atmospheric model

    Science.gov (United States)

    Amezcua, Javier

    This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn't represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion

  11. 'Lazy' quantum ensembles

    International Nuclear Information System (INIS)

    Parfionov, George; Zapatrin, Roman

    2006-01-01

    We compare different strategies aimed to prepare an ensemble with a given density matrix ρ. Preparing the ensemble of eigenstates of ρ with appropriate probabilities can be treated as 'generous' strategy: it provides maximal accessible information about the state. Another extremity is the so-called 'Scrooge' ensemble, which is mostly stingy in sharing the information. We introduce 'lazy' ensembles which require minimal effort to prepare the density matrix by selecting pure states with respect to completely random choice. We consider two parties, Alice and Bob, playing a kind of game. Bob wishes to guess which pure state is prepared by Alice. His null hypothesis, based on the lack of any information about Alice's intention, is that Alice prepares any pure state with equal probability. Then, the average quantum state measured by Bob turns out to be ρ, and he has to make a new hypothesis about Alice's intention solely based on the information that the observed density matrix is ρ. The arising 'lazy' ensemble is shown to be the alternative hypothesis which minimizes type I error

  12. The semantic similarity ensemble

    Directory of Open Access Journals (Sweden)

    Andrea Ballatore

    2013-12-01

    Full Text Available Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach.

  13. Statistical Analysis of Protein Ensembles

    Science.gov (United States)

    Máté, Gabriell; Heermann, Dieter

    2014-04-01

    As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of ensembles of different proteins, obtained from the Ensemble Protein Database. We demonstrate that our approach correctly detects the different protein groupings.

  14. Quark ensembles with infinite correlation length

    OpenAIRE

    Molodtsov, S. V.; Zinovjev, G. M.

    2014-01-01

    By studying quark ensembles with infinite correlation length we formulate the quantum field theory model that, as we show, is exactly integrable and develops an instability of its standard vacuum ensemble (the Dirac sea). We argue such an instability is rooted in high ground state degeneracy (for 'realistic' space-time dimensions) featuring a fairly specific form of energy distribution, and with the cutoff parameter going to infinity this inherent energy distribution becomes infinitely narrow...

  15. Benchmarking Commercial Conformer Ensemble Generators.

    Science.gov (United States)

    Friedrich, Nils-Ole; de Bruyn Kops, Christina; Flachsenberg, Florian; Sommer, Kai; Rarey, Matthias; Kirchmair, Johannes

    2017-11-27

    We assess and compare the performance of eight commercial conformer ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, and OMEGA) and one leading free algorithm, the distance geometry algorithm implemented in RDKit. The comparative study is based on a new version of the Platinum Diverse Dataset, a high-quality benchmarking dataset of 2859 protein-bound ligand conformations extracted from the PDB. Differences in the performance of commercial algorithms are much smaller than those observed for free algorithms in our previous study (J. Chem. Inf. 2017, 57, 529-539). For commercial algorithms, the median minimum root-mean-square deviations measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers are between 0.46 and 0.61 Å. Commercial conformer ensemble generators are characterized by their high robustness, with at least 99% of all input molecules successfully processed and few or even no substantial geometrical errors detectable in their output conformations. The RDKit distance geometry algorithm (with minimization enabled) appears to be a good free alternative since its performance is comparable to that of the midranked commercial algorithms. Based on a statistical analysis, we elaborate on which algorithms to use and how to parametrize them for best performance in different application scenarios.

  16. Ensemble Machine Learning Methods and Applications

    CERN Document Server

    Ma, Yunqian

    2012-01-01

    It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

  17. Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

    Science.gov (United States)

    Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

    2017-04-01

    Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the

  18. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  19. Conductor gestures influence evaluations of ensemble performance.

    Science.gov (United States)

    Morrison, Steven J; Price, Harry E; Smedley, Eric M; Meals, Cory D

    2014-01-01

    Previous research has found that listener evaluations of ensemble performances vary depending on the expressivity of the conductor's gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of ensemble performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber ensemble in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the ensemble's articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the ensemble's performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall ensemble expressivity.

  20. Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

    Directory of Open Access Journals (Sweden)

    V. E. P. Lemaire

    2016-03-01

    Full Text Available Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, the computing cost of such methods requires optimizing ensemble exploration techniques. By using a training data set from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for eight regions in Europe and developed statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows selecting the members of the EuroCordex ensemble of regional climate projections that should be used in priority for future air quality projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM following the EuroCordex terminology. After having tested the validity of the statistical model in predictive mode, we can provide ranges of uncertainty attributed to the spread of the regional climate projection ensemble by the end of the century (2071–2100 for the RCP8.5. In the three regions where the statistical model of the impact of climate change on PM2.5 offers satisfactory performances, we find a climate benefit (a decrease of PM2.5 concentrations under future climate of −1.08 (±0.21, −1.03 (±0.32, −0.83 (±0.14 µg m−3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the Iberian Peninsula and the Mediterranean, the statistical model is not considered skillful enough to draw any conclusion for PM2.5. In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern Italy, the statistical model of the

  1. Supersymmetry applied to the spectrum edge of random matrix ensembles

    International Nuclear Information System (INIS)

    Andreev, A.V.; Simons, B.D.; Taniguchi, N.

    1994-01-01

    A new matrix ensemble has recently been proposed to describe the transport properties in mesoscopic quantum wires. Both analytical and numerical studies have shown that the ensemble of Laguerre or of chiral random matrices provides a good description of scattering properties in this class of systems. Until now only conventional methods of random matrix theory have been used to study statistical properties within this ensemble. We demonstrate that the supersymmetry method, already employed in the study Dyson ensembles, can be extended to treat this class of random matrix ensembles. In developing this approach we investigate both new, as well as verify known statistical measures. Although we focus on ensembles in which T-invariance is violated our approach lays the foundation for future studies of T-invariant systems. ((orig.))

  2. Landmark Study Reveals Antarctic Glacier's Long History of Retreat

    OpenAIRE

    Kuska, Dale M.

    2016-01-01

    Faculty Showcase Archive Article Approved for public release; distribution is unlimited. A major study, released in late November in the journal “Nature,” reveals the history of retreat of the massive Pine Island Glacier (PIG) in western Antarctica, widely considered one of the largest contributors to global sea-level rise.

  3. Tailored Random Graph Ensembles

    International Nuclear Information System (INIS)

    Roberts, E S; Annibale, A; Coolen, A C C

    2013-01-01

    Tailored graph ensembles are a developing bridge between biological networks and statistical mechanics. The aim is to use this concept to generate a suite of rigorous tools that can be used to quantify and compare the topology of cellular signalling networks, such as protein-protein interaction networks and gene regulation networks. We calculate exact and explicit formulae for the leading orders in the system size of the Shannon entropies of random graph ensembles constrained with degree distribution and degree-degree correlation. We also construct an ergodic detailed balance Markov chain with non-trivial acceptance probabilities which converges to a strictly uniform measure and is based on edge swaps that conserve all degrees. The acceptance probabilities can be generalized to define Markov chains that target any alternative desired measure on the space of directed or undirected graphs, in order to generate graphs with more sophisticated topological features.

  4. A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment

    Science.gov (United States)

    Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.

    2015-11-01

    This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

  5. Bioactive focus in conformational ensembles: a pluralistic approach

    Science.gov (United States)

    Habgood, Matthew

    2017-12-01

    Computational generation of conformational ensembles is key to contemporary drug design. Selecting the members of the ensemble that will approximate the conformation most likely to bind to a desired target (the bioactive conformation) is difficult, given that the potential energy usually used to generate and rank the ensemble is a notoriously poor discriminator between bioactive and non-bioactive conformations. In this study an approach to generating a focused ensemble is proposed in which each conformation is assigned multiple rankings based not just on potential energy but also on solvation energy, hydrophobic or hydrophilic interaction energy, radius of gyration, and on a statistical potential derived from Cambridge Structural Database data. The best ranked structures derived from each system are then assembled into a new ensemble that is shown to be better focused on bioactive conformations. This pluralistic approach is tested on ensembles generated by the Molecular Operating Environment's Low Mode Molecular Dynamics module, and by the Cambridge Crystallographic Data Centre's conformation generator software.

  6. The Hydrologic Ensemble Prediction Experiment (HEPEX)

    Science.gov (United States)

    Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.

    2012-12-01

    The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological ensemble prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

  7. An Efficient Ensemble Learning Method for Gene Microarray Classification

    Directory of Open Access Journals (Sweden)

    Alireza Osareh

    2013-01-01

    Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  8. Imprinting and recalling cortical ensembles.

    Science.gov (United States)

    Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael

    2016-08-12

    Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. Copyright © 2016, American Association for the Advancement of Science.

  9. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Hakon

    2016-06-14

    This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.

  10. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Hakon; Law, Kody J. H.; Tempone, Raul

    2016-01-01

    This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.

  11. A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

    Science.gov (United States)

    Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

    2016-07-01

    This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

  12. Conductor and Ensemble Performance Expressivity and State Festival Ratings

    Science.gov (United States)

    Price, Harry E.; Chang, E. Christina

    2005-01-01

    This study is the second in a series examining the relationship between conducting and ensemble performance. The purpose was to further examine the associations among conductor, ensemble performance expressivity, and festival ratings. Participants were asked to rate the expressivity of video-only conducting and parallel audio-only excerpts from a…

  13. An iterative ensemble Kalman filter for reservoir engineering applications

    NARCIS (Netherlands)

    Krymskaya, M.V.; Hanea, R.G.; Verlaan, M.

    2009-01-01

    The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the

  14. Probabilistic Determination of Native State Ensembles of Proteins

    DEFF Research Database (Denmark)

    Olsson, Simon; Vögeli, Beat Rolf; Cavalli, Andrea

    2014-01-01

    ensembles of proteins by the combination of physical force fields and experimental data through modern statistical methodology. As an example, we use NMR residual dipolar couplings to determine a native state ensemble of the extensively studied third immunoglobulin binding domain of protein G (GB3...

  15. Ensemble inequivalence: Landau theory and the ABC model

    International Nuclear Information System (INIS)

    Cohen, O; Mukamel, D

    2012-01-01

    It is well known that systems with long-range interactions may exhibit different phase diagrams when studied within two different ensembles. In many of the previously studied examples of ensemble inequivalence, the phase diagrams differ only when the transition in one of the ensembles is first order. By contrast, in a recent study of a generalized ABC model, the canonical and grand-canonical ensembles of the model were shown to differ even when they both exhibit a continuous transition. Here we show that the order of the transition where ensemble inequivalence may occur is related to the symmetry properties of the order parameter associated with the transition. This is done by analyzing the Landau expansion of a generic model with long-range interactions. The conclusions drawn from the generic analysis are demonstrated for the ABC model by explicit calculation of its Landau expansion. (paper)

  16. Diversity in random subspacing ensembles

    NARCIS (Netherlands)

    Tsymbal, A.; Pechenizkiy, M.; Cunningham, P.; Kambayashi, Y.; Mohania, M.K.; Wöß, W.

    2004-01-01

    Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. It was shown experimentally and theoretically that in order for an ensemble to be effective, it should consist of classifiers having diversity in their predictions. A number of ways are

  17. PSO-Ensemble Demo Application

    DEFF Research Database (Denmark)

    2004-01-01

    Within the framework of the PSO-Ensemble project (FU2101) a demo application has been created. The application use ECMWF ensemble forecasts. Two instances of the application are running; one for Nysted Offshore and one for the total production (except Horns Rev) in the Eltra area. The output...

  18. New concept of statistical ensembles

    International Nuclear Information System (INIS)

    Gorenstein, M.I.

    2009-01-01

    An extension of the standard concept of the statistical ensembles is suggested. Namely, the statistical ensembles with extensive quantities fluctuating according to an externally given distribution is introduced. Applications in the statistical models of multiple hadron production in high energy physics are discussed.

  19. Ensembl 2002: accommodating comparative genomics.

    Science.gov (United States)

    Clamp, M; Andrews, D; Barker, D; Bevan, P; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Hubbard, T; Kasprzyk, A; Keefe, D; Lehvaslaiho, H; Iyer, V; Melsopp, C; Mongin, E; Pettett, R; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Birney, E

    2003-01-01

    The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of human, mouse and other genome sequences, available as either an interactive web site or as flat files. Ensembl also integrates manually annotated gene structures from external sources where available. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. These range from sequence analysis to data storage and visualisation and installations exist around the world in both companies and at academic sites. With both human and mouse genome sequences available and more vertebrate sequences to follow, many of the recent developments in Ensembl have focusing on developing automatic comparative genome analysis and visualisation.

  20. Crossover ensembles of random matrices and skew-orthogonal polynomials

    International Nuclear Information System (INIS)

    Kumar, Santosh; Pandey, Akhilesh

    2011-01-01

    Highlights: → We study crossover ensembles of Jacobi family of random matrices. → We consider correlations for orthogonal-unitary and symplectic-unitary crossovers. → We use the method of skew-orthogonal polynomials and quaternion determinants. → We prove universality of spectral correlations in crossover ensembles. → We discuss applications to quantum conductance and communication theory problems. - Abstract: In a recent paper (S. Kumar, A. Pandey, Phys. Rev. E, 79, 2009, p. 026211) we considered Jacobi family (including Laguerre and Gaussian cases) of random matrix ensembles and reported exact solutions of crossover problems involving time-reversal symmetry breaking. In the present paper we give details of the work. We start with Dyson's Brownian motion description of random matrix ensembles and obtain universal hierarchic relations among the unfolded correlation functions. For arbitrary dimensions we derive the joint probability density (jpd) of eigenvalues for all transitions leading to unitary ensembles as equilibrium ensembles. We focus on the orthogonal-unitary and symplectic-unitary crossovers and give generic expressions for jpd of eigenvalues, two-point kernels and n-level correlation functions. This involves generalization of the theory of skew-orthogonal polynomials to crossover ensembles. We also consider crossovers in the circular ensembles to show the generality of our method. In the large dimensionality limit, correlations in spectra with arbitrary initial density are shown to be universal when expressed in terms of a rescaled symmetry breaking parameter. Applications of our crossover results to communication theory and quantum conductance problems are also briefly discussed.

  1. A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

    KAUST Repository

    Altaf, Muhammad

    2014-08-01

    This study evaluates and compares the performances of several variants of the popular ensembleKalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf ofMexico coastline, the authors implement and compare the standard stochastic ensembleKalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

  2. A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

    KAUST Repository

    Altaf, Muhammad; Butler, T.; Mayo, T.; Luo, X.; Dawson, C.; Heemink, A. W.; Hoteit, Ibrahim

    2014-01-01

    This study evaluates and compares the performances of several variants of the popular ensembleKalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf ofMexico coastline, the authors implement and compare the standard stochastic ensembleKalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

  3. On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

    KAUST Repository

    Luo, Xiaodong

    2010-09-19

    The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].

  4. Topological quantization of ensemble averages

    International Nuclear Information System (INIS)

    Prodan, Emil

    2009-01-01

    We define the current of a quantum observable and, under well-defined conditions, we connect its ensemble average to the index of a Fredholm operator. The present work builds on a formalism developed by Kellendonk and Schulz-Baldes (2004 J. Funct. Anal. 209 388) to study the quantization of edge currents for continuous magnetic Schroedinger operators. The generalization given here may be a useful tool to scientists looking for novel manifestations of the topological quantization. As a new application, we show that the differential conductance of atomic wires is given by the index of a certain operator. We also comment on how the formalism can be used to probe the existence of edge states

  5. Characterizing Ensembles of Superconducting Qubits

    Science.gov (United States)

    Sears, Adam; Birenbaum, Jeff; Hover, David; Rosenberg, Danna; Weber, Steven; Yoder, Jonilyn L.; Kerman, Jamie; Gustavsson, Simon; Kamal, Archana; Yan, Fei; Oliver, William

    We investigate ensembles of up to 48 superconducting qubits embedded within a superconducting cavity. Such arrays of qubits have been proposed for the experimental study of Ising Hamiltonians, and efficient methods to characterize and calibrate these types of systems are still under development. Here we leverage high qubit coherence (> 70 μs) to characterize individual devices as well as qubit-qubit interactions, utilizing the common resonator mode for a joint readout. This research was funded by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under Air Force Contract No. FA8721-05-C-0002. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the US Government.

  6. Ensemble coding remains accurate under object and spatial visual working memory load.

    Science.gov (United States)

    Epstein, Michael L; Emmanouil, Tatiana A

    2017-10-01

    A number of studies have provided evidence that the visual system statistically summarizes large amounts of information that would exceed the limitations of attention and working memory (ensemble coding). However the necessity of working memory resources for ensemble coding has not yet been tested directly. In the current study, we used a dual task design to test the effect of object and spatial visual working memory load on size averaging accuracy. In Experiment 1, we tested participants' accuracy in comparing the mean size of two sets under various levels of object visual working memory load. Although the accuracy of average size judgments depended on the difference in mean size between the two sets, we found no effect of working memory load. In Experiment 2, we tested the same average size judgment while participants were under spatial visual working memory load, again finding no effect of load on averaging accuracy. Overall our results reveal that ensemble coding can proceed unimpeded and highly accurately under both object and spatial visual working memory load, providing further evidence that ensemble coding reflects a basic perceptual process distinct from that of individual object processing.

  7. The Effects of Classical Guitar Ensembles on Student Self-Perceptions and Acquisition of Music Skills

    Science.gov (United States)

    Kramer, John R.

    2012-01-01

    Classical guitar ensembles are increasing in the United States as popular alternatives to band, choir, and orchestra. Classical guitar ensembles are offered at many middle and high schools as fine arts electives as one of the only options for classical guitarists to participate in ensembles. The purpose of this study was to explore the development…

  8. Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

    Science.gov (United States)

    Wei, Jiangfeng; Dirmeyer, Paul A.; Yang, Zong-Liang; Chen, Haishan

    2017-10-01

    Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

  9. EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data

    Energy Technology Data Exchange (ETDEWEB)

    Shu, Qingya; Guo, Hanqi; Che, Limei; Yuan, Xiaoru; Liu, Junfeng; Liang, Jie

    2016-04-19

    We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based on ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.

  10. The Hydrologic Ensemble Prediction Experiment (HEPEX)

    Science.gov (United States)

    Wood, Andy; Wetterhall, Fredrik; Ramos, Maria-Helena

    2015-04-01

    The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF), and co-sponsored by the US National Weather Service (NWS) and the European Commission (EC). The HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support. HEPEX pursues this goal through research efforts and practical implementations involving six core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. HEPEX has grown through meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. In the last decade, HEPEX has organized over a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Through these interactions and an active online blog (www.hepex.org), HEPEX has built a strong and active community of nearly 400 researchers & practitioners around the world. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

  11. Conductor gestures influence evaluations of ensemble performance

    Directory of Open Access Journals (Sweden)

    Steven eMorrison

    2014-07-01

    Full Text Available Previous research has found that listener evaluations of ensemble performances vary depending on the expressivity of the conductor’s gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of ensemble performance, articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber ensemble in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and nonmajors (N = 285 viewed sixteen 30-second performances and evaluated the quality of the ensemble’s articulation, dynamics, technique and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the ensemble’s performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall ensemble expressivity.

  12. Ensemble hydro-meteorological forecasting for early warning of floods and scheduling of hydropower production

    Science.gov (United States)

    Solvang Johansen, Stian; Steinsland, Ingelin; Engeland, Kolbjørn

    2016-04-01

    Running hydrological models with precipitation and temperature ensemble forcing to generate ensembles of streamflow is a commonly used method in operational hydrology. Evaluations of streamflow ensembles have however revealed that the ensembles are biased with respect to both mean and spread. Thus postprocessing of the ensembles is needed in order to improve the forecast skill. The aims of this study is (i) to to evaluate how postprocessing of streamflow ensembles works for Norwegian catchments within different hydrological regimes and to (ii) demonstrate how post processed streamflow ensembles are used operationally by a hydropower producer. These aims were achieved by postprocessing forecasted daily discharge for 10 lead-times for 20 catchments in Norway by using EPS forcing from ECMWF applied the semi-distributed HBV-model dividing each catchment into 10 elevation zones. Statkraft Energi uses forecasts from these catchments for scheduling hydropower production. The catchments represent different hydrological regimes. Some catchments have stable winter condition with winter low flow and a major flood event during spring or early summer caused by snow melting. Others has a more mixed snow-rain regime, often with a secondary flood season during autumn, and in the coastal areas, the stream flow is dominated by rain, and the main flood season is autumn and winter. For post processing, a Bayesian model averaging model (BMA) close to (Kleiber et al 2011) is used. The model creates a predictive PDF that is a weighted average of PDFs centered on the individual bias corrected forecasts. The weights are here equal since all ensemble members come from the same model, and thus have the same probability. For modeling streamflow, the gamma distribution is chosen as a predictive PDF. The bias correction parameters and the PDF parameters are estimated using a 30-day sliding window training period. Preliminary results show that the improvement varies between catchments depending

  13. PENGGUNAAN SKEMA KONVEKTIF MODEL CUACA WRF (BETTS MILLER JANJIC, KAIN FRITSCH DAN GRELL 3D ENSEMBLE (Studi kasus: Surabaya dan Jakarta

    Directory of Open Access Journals (Sweden)

    Roni Kurniawan

    2015-01-01

    Full Text Available Pada kajian ini dilakukan evaluasi penggunaan beberapa skema konvektif pada model WRF (Weather Research and Forecasting untuk prediksi cuaca di wilayah Indonesia. Terdapat tiga skema konvektif yang akan dievaluasi yaitu; skema konvektif cumulus BMJ (Betts Miller Janjic, KF (Kain Fritsch, dan GD (Grell 3D ensemble. Data yang digunakan untuk evaluasi adalah data curah hujan per 3 jam dan data angin per 12 jam (level ketinggian; permukaan, 850, 500, 250 mb dari hasil pengolahan model WRF dan observasi selama periode bulan Agustus 2011 dan Februari 2012 di stasiun Juanda-Surabaya dan Cengkareng-Jakarta. Hasil verifikasi dari tiga skema konvektif pada model WRF terhadap data observasi menunjukkan bahwa untuk prakiraan curah hujan, penggunaan skema konvektif BMJ lebih baik dari skema KF dan GD, dan untuk prakiraan arah dan kecepatan angin skema BMJ dan GD relatif lebih baik dari skema KF. Berdasarkan analisis hasil verifikasi yang diperoleh, pemilihan skema konvektif cumulus BMJ cenderung lebih baik dari skema konvektif KF dan GD untuk di aplikasikan pada model WRF.   In this study, the use of some convective schemes on the model WRF (Weather Research and Forecasting for weather prediction in Indonesian region has been evaluated. There are two models evaluated; BMJ cumulus convective scheme (Betts Miller Janjic, KF (Kain Fritsch, and GD (Grell 3D ensemble. The data used in the evaluation are the 3 hourly rainfall data, and the 12 hourly wind data (level height; surface, 850, 500, 250mb from the WRF models and observation processing during August 2011 and February 2012 period at the Juanda-Surabaya and Cengkareng-Jakarta stations. The results of the verification of the three convective schemes in WRF models against observation data indicate that for precipitation forecasts, the application of the BMJ convective scheme is better than the KF and GD schemes, and for direction and wind speed forecast, BMJ and GD schemes is relatively better than the KF

  14. Contact planarization of ensemble nanowires

    Science.gov (United States)

    Chia, A. C. E.; LaPierre, R. R.

    2011-06-01

    The viability of four organic polymers (S1808, SC200, SU8 and Cyclotene) as filling materials to achieve planarization of ensemble nanowire arrays is reported. Analysis of the porosity, surface roughness and thermal stability of each filling material was performed. Sonication was used as an effective method to remove the tops of the nanowires (NWs) to achieve complete planarization. Ensemble nanowire devices were fully fabricated and I-V measurements confirmed that Cyclotene effectively planarizes the NWs while still serving the role as an insulating layer between the top and bottom contacts. These processes and analysis can be easily implemented into future characterization and fabrication of ensemble NWs for optoelectronic device applications.

  15. Reveal genes functionally associated with ACADS by a network study.

    Science.gov (United States)

    Chen, Yulong; Su, Zhiguang

    2015-09-15

    Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. Credit scoring using ensemble of various classifiers on reduced feature set

    Directory of Open Access Journals (Sweden)

    Dahiya Shashi

    2015-01-01

    Full Text Available Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model.

  17. On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

    KAUST Repository

    Luo, Xiaodong; Hoteit, Ibrahim; Moroz, Irene M.

    2010-01-01

    However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].

  18. Ensemble manifold regularization.

    Science.gov (United States)

    Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng

    2012-06-01

    We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.

  19. The Ensembl genome database project.

    Science.gov (United States)

    Hubbard, T; Barker, D; Birney, E; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Huminiecki, L; Kasprzyk, A; Lehvaslaiho, H; Lijnzaad, P; Melsopp, C; Mongin, E; Pettett, R; Pocock, M; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Clamp, M

    2002-01-01

    The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.

  20. Decadal climate predictions improved by ocean ensemble dispersion filtering

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-06-01

    ensemble average, improves a prediction system. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Our study shows that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure applying the average during the model run, called ensemble dispersion filter, results in more accurate results than the standard prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution.

  1. Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.

    2016-01-01

    the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new...... approach, called d-ECC, is applied to wind forecasts from the high resolution ensemble system COSMO-DE-EPS run operationally at the German weather service. Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and in a product...

  2. Lipid Dynamics Studied by Calculation of 31P Solid-State NMR Spectra Using Ensembles from Molecular Dynamics Simulations

    DEFF Research Database (Denmark)

    Hansen, Sara Krogh; Vestergaard, Mikkel; Thøgersen, Lea

    2014-01-01

    , for example, order parameters. Therefore, valuable insight into the dynamics of biomolecules may be achieved by the present method. We have applied this method to study the dynamics of lipid bilayers containing the antimicrobial peptide alamethicin, and we show that the calculated 31P spectra obtained...

  3. Ensemble hydrological forecast efficiency evolution over various issue dates and lead-time: case study for the Cheboksary reservoir (Volga River)

    Science.gov (United States)

    Gelfan, Alexander; Moreido, Vsevolod

    2017-04-01

    Ensemble hydrological forecasting allows for describing uncertainty caused by variability of meteorological conditions in the river basin for the forecast lead-time. At the same time, in snowmelt-dependent river basins another significant source of uncertainty relates to variability of initial conditions of the basin (snow water equivalent, soil moisture content, etc.) prior to forecast issue. Accurate long-term hydrological forecast is most crucial for large water management systems, such as the Cheboksary reservoir (the catchment area is 374 000 sq.km) located in the Middle Volga river in Russia. Accurate forecasts of water inflow volume, maximum discharge and other flow characteristics are of great value for this basin, especially before the beginning of the spring freshet season that lasts here from April to June. The semi-distributed hydrological model ECOMAG was used to develop long-term ensemble forecast of daily water inflow into the Cheboksary reservoir. To describe variability of the meteorological conditions and construct ensemble of possible weather scenarios for the lead-time of the forecast, two approaches were applied. The first one utilizes 50 weather scenarios observed in the previous years (similar to the ensemble streamflow prediction (ESP) procedure), the second one uses 1000 synthetic scenarios simulated by a stochastic weather generator. We investigated the evolution of forecast uncertainty reduction, expressed as forecast efficiency, over various consequent forecast issue dates and lead time. We analyzed the Nash-Sutcliffe efficiency of inflow hindcasts for the period 1982 to 2016 starting from 1st of March with 15 days frequency for lead-time of 1 to 6 months. This resulted in the forecast efficiency matrix with issue dates versus lead-time that allows for predictability identification of the basin. The matrix was constructed separately for observed and synthetic weather ensembles.

  4. An Integrated Ensemble-Based Operational Framework to Predict Urban Flooding: A Case Study of Hurricane Sandy in the Passaic and Hackensack River Basins

    Science.gov (United States)

    Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.

    2016-12-01

    Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus

  5. A WRF/Chem sensitivity study using ensemble modelling for a high ozone episode in Slovenia and the Northern Adriatic area

    Science.gov (United States)

    Žabkar, Rahela; Koračin, Darko; Rakovec, Jože

    2013-10-01

    A high ozone (O3) concentrations episode during a heat wave event in the Northeastern Mediterranean was investigated using the WRF/Chem model. To understand the major model uncertainties and errors as well as the impacts of model inputs on the model accuracy, an ensemble modelling experiment was conducted. The 51-member ensemble was designed by varying model physics parameterization options (PBL schemes with different surface layer and land-surface modules, and radiation schemes); chemical initial and boundary conditions; anthropogenic and biogenic emission inputs; and model domain setup and resolution. The main impacts of the geographical and emission characteristics of three distinct regions (suburban Mediterranean, continental urban, and continental rural) on the model accuracy and O3 predictions were investigated. In spite of the large ensemble set size, the model generally failed to simulate the extremes; however, as expected from probabilistic forecasting the ensemble spread improved results with respect to extremes compared to the reference run. Noticeable model nighttime overestimations at the Mediterranean and some urban and rural sites can be explained by too strong simulated winds, which reduce the impact of dry deposition and O3 titration in the near surface layers during the nighttime. Another possible explanation could be inaccuracies in the chemical mechanisms, which are suggested also by model insensitivity to variations in the nitrogen oxides (NOx) and volatile organic compounds (VOC) emissions. Major impact factors for underestimations of the daytime O3 maxima at the Mediterranean and some rural sites include overestimation of the PBL depths, a lack of information on forest fires, too strong surface winds, and also possible inaccuracies in biogenic emissions. This numerical experiment with the ensemble runs also provided guidance on an optimum model setup and input data.

  6. Selecting a climate model subset to optimise key ensemble properties

    Directory of Open Access Journals (Sweden)

    N. Herger

    2018-02-01

    Full Text Available End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

  7. Selecting a climate model subset to optimise key ensemble properties

    Science.gov (United States)

    Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.

    2018-02-01

    End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

  8. Modeling task-specific neuronal ensembles improves decoding of grasp

    Science.gov (United States)

    Smith, Ryan J.; Soares, Alcimar B.; Rouse, Adam G.; Schieber, Marc H.; Thakor, Nitish V.

    2018-06-01

    Objective. Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed. Approach. In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed. Main results. Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p  <  0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units. Significance. These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more

  9. Developing of Thai Classical Music Ensemble in Rattanakosin Period

    OpenAIRE

    Pansak Vandee

    2013-01-01

    The research titled “Developing of Thai Classical Music Ensemble in Rattanakosin Period" aimed 1) to study the history of Thai Classical Music Ensemble in Rattanakosin Period and 2) to analyze changing in each period of Rattanakosin Era. This is the historical and documentary research. The data was collected by in-depth interview those musicians, and academic music experts and field study. The focus group discussion was conducted to analyze and conclude the findings. The research found that t...

  10. Visualization and classification of physiological failure modes in ensemble hemorrhage simulation

    Science.gov (United States)

    Zhang, Song; Pruett, William Andrew; Hester, Robert

    2015-01-01

    In an emergency situation such as hemorrhage, doctors need to predict which patients need immediate treatment and care. This task is difficult because of the diverse response to hemorrhage in human population. Ensemble physiological simulations provide a means to sample a diverse range of subjects and may have a better chance of containing the correct solution. However, to reveal the patterns and trends from the ensemble simulation is a challenging task. We have developed a visualization framework for ensemble physiological simulations. The visualization helps users identify trends among ensemble members, classify ensemble member into subpopulations for analysis, and provide prediction to future events by matching a new patient's data to existing ensembles. We demonstrated the effectiveness of the visualization on simulated physiological data. The lessons learned here can be applied to clinically-collected physiological data in the future.

  11. Improving Climate Projections Using "Intelligent" Ensembles

    Science.gov (United States)

    Baker, Noel C.; Taylor, Patrick C.

    2015-01-01

    Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and

  12. Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China

    Science.gov (United States)

    Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping

    2017-11-01

    Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For

  13. Quantum ensembles of quantum classifiers.

    Science.gov (United States)

    Schuld, Maria; Petruccione, Francesco

    2018-02-09

    Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

  14. Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

    Energy Technology Data Exchange (ETDEWEB)

    Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

    2016-10-01

    The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

  15. The PH domain of phosphoinositide-dependent kinase-1 exhibits a novel, phospho-regulated monomer-dimer equilibrium with important implications for kinase domain activation: single-molecule and ensemble studies.

    Science.gov (United States)

    Ziemba, Brian P; Pilling, Carissa; Calleja, Véronique; Larijani, Banafshé; Falke, Joseph J

    2013-07-16

    Phosphoinositide-dependent kinase-1 (PDK1) is an essential master kinase recruited to the plasma membrane by the binding of its C-terminal PH domain to the signaling lipid phosphatidylinositol-3,4,5-trisphosphate (PIP3). Membrane binding leads to PDK1 phospho-activation, but despite the central role of PDK1 in signaling and cancer biology, this activation mechanism remains poorly understood. PDK1 has been shown to exist as a dimer in cells, and one crystal structure of its isolated PH domain exhibits a putative dimer interface. It has been proposed that phosphorylation of PH domain residue T513 (or the phospho-mimetic T513E mutation) may regulate a novel PH domain dimer-monomer equilibrium, thereby converting an inactive PDK1 dimer to an active monomer. However, the oligomeric states of the PH domain on the membrane have not yet been determined, nor whether a negative charge at position 513 is sufficient to regulate its oligomeric state. This study investigates the binding of purified wild-type (WT) and T513E PDK1 PH domains to lipid bilayers containing the PIP3 target lipid, using both single-molecule and ensemble measurements. Single-molecule analysis of the brightness of the fluorescent PH domain shows that the PIP3-bound WT PH domain on membranes is predominantly dimeric while the PIP3-bound T513E PH domain is monomeric, demonstrating that negative charge at the T513 position is sufficient to dissociate the PH domain dimer and is thus likely to play a central role in PDK1 monomerization and activation. Single-molecule analysis of two-dimensional (2D) diffusion of PH domain-PIP3 complexes reveals that the dimeric WT PH domain diffuses at the same rate as a single lipid molecule, indicating that only one of its two PIP3 binding sites is occupied and there is little penetration of the protein into the bilayer as observed for other PH domains. The 2D diffusion of T513E PH domain is slower, suggesting the negative charge disrupts local structure in a way that allows

  16. Statistical ensembles for money and debt

    Science.gov (United States)

    Viaggiu, Stefano; Lionetto, Andrea; Bargigli, Leonardo; Longo, Michele

    2012-10-01

    We build a statistical ensemble representation of two economic models describing respectively, in simplified terms, a payment system and a credit market. To this purpose we adopt the Boltzmann-Gibbs distribution where the role of the Hamiltonian is taken by the total money supply (i.e. including money created from debt) of a set of interacting economic agents. As a result, we can read the main thermodynamic quantities in terms of monetary ones. In particular, we define for the credit market model a work term which is related to the impact of monetary policy on credit creation. Furthermore, with our formalism we recover and extend some results concerning the temperature of an economic system, previously presented in the literature by considering only the monetary base as a conserved quantity. Finally, we study the statistical ensemble for the Pareto distribution.

  17. ABCD of Beta Ensembles and Topological Strings

    CERN Document Server

    Krefl, Daniel

    2012-01-01

    We study beta-ensembles with Bn, Cn, and Dn eigenvalue measure and their relation with refined topological strings. Our results generalize the familiar connections between local topological strings and matrix models leading to An measure, and illustrate that all those classical eigenvalue ensembles, and their topological string counterparts, are related one to another via various deformations and specializations, quantum shifts and discrete quotients. We review the solution of the Gaussian models via Macdonald identities, and interpret them as conifold theories. The interpolation between the various models is plainly apparent in this case. For general polynomial potential, we calculate the partition function in the multi-cut phase in a perturbative fashion, beyond tree-level in the large-N limit. The relation to refined topological string orientifolds on the corresponding local geometry is discussed along the way.

  18. On-line Learning of Unlearnable True Teacher through Mobile Ensemble Teachers

    Science.gov (United States)

    Hirama, Takeshi; Hukushima, Koji

    2008-09-01

    The on-line learning of a hierarchical learning model is studied by a method based on statistical mechanics. In our model, a student of a simple perceptron learns from not a true teacher directly, but ensemble teachers who learn from a true teacher with a perceptron learning rule. Since the true teacher and ensemble teachers are expressed as nonmonotonic and simple perceptrons, respectively, the ensemble teachers go around the unlearnable true teacher with the distance between them fixed in an asymptotic steady state. The generalization performance of the student is shown to exceed that of the ensemble teachers in a transient state, as was shown in similar ensemble-teachers models. Furthermore, it is found that moving the ensemble teachers even in the steady state, in contrast to the fixed ensemble teachers, is efficient for the performance of the student.

  19. The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

    Directory of Open Access Journals (Sweden)

    Gulshan Kumar

    2012-01-01

    Full Text Available In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI- based techniques play prominent role in development of ensemble for intrusion detection (ID and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1 architecture & approach followed; (2 different methods utilized in different phases of ensemble learning; (3 other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs.

  20. Isobars of an ideal Bose gas within the grand canonical ensemble

    International Nuclear Information System (INIS)

    Jeon, Imtak; Park, Jeong-Hyuck; Kim, Sang-Woo

    2011-01-01

    We investigate the isobar of an ideal Bose gas confined in a cubic box within the grand canonical ensemble for a large yet finite number of particles, N. After solving the equation of the spinodal curve, we derive precise formulas for the supercooling and the superheating temperatures that reveal an N -1/3 or N -1/4 power correction to the known Bose-Einstein condensation temperature in the thermodynamic limit. Numerical computations confirm the accuracy of our analytical approximation, and further show that the isobar zigzags on the temperature-volume plane if N≥14 393. In particular, for the Avogadro's number of particles, the volume expands discretely about 10 5 times. Our results quantitatively agree with a previous study on the canonical ensemble within 0.1% error.

  1. Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons.

    Science.gov (United States)

    Hoseini, Mahmood S; Wessel, Ralf

    2016-01-01

    Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits. Copyright © 2016 the American Physiological Society.

  2. Ensemble method for dengue prediction.

    Science.gov (United States)

    Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan

    2018-01-01

    In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

  3. Ensemble method for dengue prediction.

    Directory of Open Access Journals (Sweden)

    Anna L Buczak

    Full Text Available In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico during four dengue seasons: 1 peak height (i.e., maximum weekly number of cases during a transmission season; 2 peak week (i.e., week in which the maximum weekly number of cases occurred; and 3 total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date.Our approach used ensemble models created by combining three disparate types of component models: 1 two-dimensional Method of Analogues models incorporating both dengue and climate data; 2 additive seasonal Holt-Winters models with and without wavelet smoothing; and 3 simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations.Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week.The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

  4. Developing an Ensemble Prediction System based on COSMO-DE

    Science.gov (United States)

    Theis, S.; Gebhardt, C.; Buchhold, M.; Ben Bouallègue, Z.; Ohl, R.; Paulat, M.; Peralta, C.

    2010-09-01

    The numerical weather prediction model COSMO-DE is a configuration of the COSMO model with a horizontal grid size of 2.8 km. It has been running operationally at DWD since 2007, it covers the area of Germany and produces forecasts with a lead time of 0-21 hours. The model COSMO-DE is convection-permitting, which means that it does without a parametrisation of deep convection and simulates deep convection explicitly. One aim is an improved forecast of convective heavy rain events. Convection-permitting models are in operational use at several weather services, but currently not in ensemble mode. It is expected that an ensemble system could reveal the advantages of a convection-permitting model even better. The probabilistic approach is necessary, because the explicit simulation of convective processes for more than a few hours cannot be viewed as a deterministic forecast anymore. This is due to the chaotic behaviour and short life cycle of the processes which are simulated explicitly now. In the framework of the project COSMO-DE-EPS, DWD is developing and implementing an ensemble prediction system (EPS) for the model COSMO-DE. The project COSMO-DE-EPS comprises the generation of ensemble members, as well as the verification and visualization of the ensemble forecasts and also statistical postprocessing. A pre-operational mode of the EPS with 20 ensemble members is foreseen to start in 2010. Operational use is envisaged to start in 2012, after an upgrade to 40 members and inclusion of statistical postprocessing. The presentation introduces the project COSMO-DE-EPS and describes the design of the ensemble as it is planned for the pre-operational mode. In particular, the currently implemented method for the generation of ensemble members will be explained and discussed. The method includes variations of initial conditions, lateral boundary conditions, and model physics. At present, pragmatic methods are applied which resemble the basic ideas of a multi-model approach

  5. Mathematics Teachers' Views of Accountability Testing Revealed through Lesson Study

    Science.gov (United States)

    Yarema, Connie H.

    2010-01-01

    The practice of lesson study, a professional development model originating in Japan, aligns well with recommendations from research for teacher professional development. Lesson study is also an inductive research method that uncovers student thinking and, in parallel, grants teacher-educators the opportunity to study teachers' thinking about…

  6. Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models

    Directory of Open Access Journals (Sweden)

    H. Wan

    2014-09-01

    Full Text Available This paper explores the feasibility of an experimentation strategy for investigating sensitivities in fast components of atmospheric general circulation models. The basic idea is to replace the traditional serial-in-time long-term climate integrations by representative ensembles of shorter simulations. The key advantage of the proposed method lies in its efficiency: since fewer days of simulation are needed, the computational cost is less, and because individual realizations are independent and can be integrated simultaneously, the new dimension of parallelism can dramatically reduce the turnaround time in benchmark tests, sensitivities studies, and model tuning exercises. The strategy is not appropriate for exploring sensitivity of all model features, but it is very effective in many situations. Two examples are presented using the Community Atmosphere Model, version 5. In the first example, the method is used to characterize sensitivities of the simulated clouds to time-step length. Results show that 3-day ensembles of 20 to 50 members are sufficient to reproduce the main signals revealed by traditional 5-year simulations. A nudging technique is applied to an additional set of simulations to help understand the contribution of physics–dynamics interaction to the detected time-step sensitivity. In the second example, multiple empirical parameters related to cloud microphysics and aerosol life cycle are perturbed simultaneously in order to find out which parameters have the largest impact on the simulated global mean top-of-atmosphere radiation balance. It turns out that 12-member ensembles of 10-day simulations are able to reveal the same sensitivities as seen in 4-year simulations performed in a previous study. In both cases, the ensemble method reduces the total computational time by a factor of about 15, and the turnaround time by a factor of several hundred. The efficiency of the method makes it particularly useful for the development of

  7. Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

    Science.gov (United States)

    Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

    2017-12-01

    A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

  8. Ensemble Clustering using Semidefinite Programming with Applications.

    Science.gov (United States)

    Singh, Vikas; Mukherjee, Lopamudra; Peng, Jiming; Xu, Jinhui

    2010-05-01

    In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0-1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.

  9. Study Reveals Brain Biology behind Self-Control

    Science.gov (United States)

    Sparks, Sarah D.

    2011-01-01

    A new neuroscience twist on a classic psychology study offers some clues to what makes one student able to buckle down for hours of homework before a test while his classmates party. The study published in the September 2011 edition of "Proceedings of the National Academy of Science," suggests environmental cues may "hijack" the brain's mechanisms…

  10. Impact of ensemble learning in the assessment of skeletal maturity.

    Science.gov (United States)

    Cunha, Pedro; Moura, Daniel C; Guevara López, Miguel Angel; Guerra, Conceição; Pinto, Daniela; Ramos, Isabel

    2014-09-01

    The assessment of the bone age, or skeletal maturity, is an important task in pediatrics that measures the degree of maturation of children's bones. Nowadays, there is no standard clinical procedure for assessing bone age and the most widely used approaches are the Greulich and Pyle and the Tanner and Whitehouse methods. Computer methods have been proposed to automatize the process; however, there is a lack of exploration about how to combine the features of the different parts of the hand, and how to take advantage of ensemble techniques for this purpose. This paper presents a study where the use of ensemble techniques for improving bone age assessment is evaluated. A new computer method was developed that extracts descriptors for each joint of each finger, which are then combined using different ensemble schemes for obtaining a final bone age value. Three popular ensemble schemes are explored in this study: bagging, stacking and voting. Best results were achieved by bagging with a rule-based regression (M5P), scoring a mean absolute error of 10.16 months. Results show that ensemble techniques improve the prediction performance of most of the evaluated regression algorithms, always achieving best or comparable to best results. Therefore, the success of the ensemble methods allow us to conclude that their use may improve computer-based bone age assessment, offering a scalable option for utilizing multiple regions of interest and combining their output.

  11. An automated approach to network features of protein structure ensembles

    Science.gov (United States)

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

    2013-01-01

    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896

  12. A trade-off study revealing nested timescales of constraint

    NARCIS (Netherlands)

    Wijnants, M.L.; Cox, R.F.A; Hasselman, F.; Bosman, A. M. T.; Van Orden, G.

    2012-01-01

    This study investigates human performance in a cyclic Fitts task at three different scales of observation, either in the presence (difficult condition) or in the absence (easy condition) of a speed–accuracy trade-off. At the fastest scale, the harmonicity of the back and forth movements, which

  13. Teaching Strategies for Specialized Ensembles.

    Science.gov (United States)

    Teaching Music, 1999

    1999-01-01

    Provides a strategy, from the book "Strategies for Teaching Specialized Ensembles," that addresses Standard 9A of the National Standards for Music Education. Explains that students will identify and describe the musical and historical characteristics of the classical era in music they perform and in audio examples. (CMK)

  14. Spectral Diagonal Ensemble Kalman Filters

    Czech Academy of Sciences Publication Activity Database

    Kasanický, Ivan; Mandel, Jan; Vejmelka, Martin

    2015-01-01

    Roč. 22, č. 4 (2015), s. 485-497 ISSN 1023-5809 R&D Projects: GA ČR GA13-34856S Grant - others:NSF(US) DMS-1216481 Institutional support: RVO:67985807 Keywords : data assimilation * ensemble Kalman filter * spectral representation Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.321, year: 2015

  15. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    Marquardt algorithm by varying conditions such as inputs, hidden neurons, initialization, training sets and random Gaussian noise injection to ... Several such ensembles formed the population which was evolved to generate the fittest ensemble.

  16. Global Ensemble Forecast System (GEFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  17. Localization of atomic ensembles via superfluorescence

    International Nuclear Information System (INIS)

    Macovei, Mihai; Evers, Joerg; Keitel, Christoph H.; Zubairy, M. Suhail

    2007-01-01

    The subwavelength localization of an ensemble of atoms concentrated to a small volume in space is investigated. The localization relies on the interaction of the ensemble with a standing wave laser field. The light scattered in the interaction of the standing wave field and the atom ensemble depends on the position of the ensemble relative to the standing wave nodes. This relation can be described by a fluorescence intensity profile, which depends on the standing wave field parameters and the ensemble properties and which is modified due to collective effects in the ensemble of nearby particles. We demonstrate that the intensity profile can be tailored to suit different localization setups. Finally, we apply these results to two localization schemes. First, we show how to localize an ensemble fixed at a certain position in the standing wave field. Second, we discuss localization of an ensemble passing through the standing wave field

  18. SVM and SVM Ensembles in Breast Cancer Prediction.

    Science.gov (United States)

    Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

    2017-01-01

    Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

  19. Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

    Directory of Open Access Journals (Sweden)

    Jinyin Ye

    2016-01-01

    Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

  20. Three-model ensemble wind prediction in southern Italy

    Science.gov (United States)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

    2016-03-01

    Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  1. SVM and SVM Ensembles in Breast Cancer Prediction.

    Directory of Open Access Journals (Sweden)

    Min-Wei Huang

    Full Text Available Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

  2. In vitro study revealed different size behavior of different nanoparticles

    International Nuclear Information System (INIS)

    Schaudien, Dirk; Knebel, Jan; Creutzenberg, Otto

    2012-01-01

    Toxicity of nanoparticles is depending not only on the size of the primary particles but on the size of their agglomerates. Therefore, further studies are needed to examine the behavior of nanoparticles after they have gotten in contact with cells. The presented study investigated the change of size of different commercially available nanoparticles after applying them to different cell lines such as A549, Calu-3, 16HBE14o and LK004 representative for the different parts of the human lung. The different nanoparticles exhibited differences in behavior of size. TiO 2 P25 showed a tendency to increase, whereas TiO 2 T805 and Printex ® 90 remained more or less at the same size. In contrast, ZnO < 50 nm particles showed a significant decrease of size.

  3. Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

    Science.gov (United States)

    Wallach, Daniel; Mearns, Linda O.; Ruane, Alexander C.; Roetter, Reimund P.; Asseng, Senthold

    2016-01-01

    Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

  4. Despite some concerns, impact study reveals support for nuke

    International Nuclear Information System (INIS)

    Anon.

    1978-01-01

    A survey of 37 Hartsville, Tennessee community leaders indicates the community supports construction of a nuclear generating plant and does not see any major environmental impacts. The leaders do anticipate growth-related disruptions during the construction phase. The ERDA-sponsored social impact study is being conducted by the Oak Ridge National Laboratory to serve as a guide in planning future energy projects and for comparison with a coal-fired project under development in Georgia. Statistics from the survey are summarized. General observations note that the leaders tend to overestimate opposition to the nuclear plant and that local concerns focus on housing and traffic problems. The Tennessee Valley Authority is supplying funds for a mitigation plan, which is generally well received by the community

  5. Squeezing of Collective Excitations in Spin Ensembles

    DEFF Research Database (Denmark)

    Kraglund Andersen, Christian; Mølmer, Klaus

    2012-01-01

    We analyse the possibility to create two-mode spin squeezed states of two separate spin ensembles by inverting the spins in one ensemble and allowing spin exchange between the ensembles via a near resonant cavity field. We investigate the dynamics of the system using a combination of numerical an...

  6. Nationwide Genomic Study in Denmark Reveals Remarkable Population Homogeneity.

    Science.gov (United States)

    Athanasiadis, Georgios; Cheng, Jade Y; Vilhjálmsson, Bjarni J; Jørgensen, Frank G; Als, Thomas D; Le Hellard, Stephanie; Espeseth, Thomas; Sullivan, Patrick F; Hultman, Christina M; Kjærgaard, Peter C; Schierup, Mikkel H; Mailund, Thomas

    2016-10-01

    Denmark has played a substantial role in the history of Northern Europe. Through a nationwide scientific outreach initiative, we collected genetic and anthropometrical data from ∼800 high school students and used them to elucidate the genetic makeup of the Danish population, as well as to assess polygenic predictions of phenotypic traits in adolescents. We observed remarkable homogeneity across different geographic regions, although we could still detect weak signals of genetic structure reflecting the history of the country. Denmark presented genomic affinity with primarily neighboring countries with overall resemblance of decreasing weight from Britain, Sweden, Norway, Germany, and France. A Polish admixture signal was detected in Zealand and Funen, and our date estimates coincided with historical evidence of Wend settlements in the south of Denmark. We also observed considerably diverse demographic histories among Scandinavian countries, with Denmark having the smallest current effective population size compared to Norway and Sweden. Finally, we found that polygenic prediction of self-reported adolescent height in the population was remarkably accurate (R 2 = 0.639 ± 0.015). The high homogeneity of the Danish population could render population structure a lesser concern for the upcoming large-scale gene-mapping studies in the country. Copyright © 2016 by the Genetics Society of America.

  7. Performance Analysis of Local Ensemble Kalman Filter

    Science.gov (United States)

    Tong, Xin T.

    2018-03-01

    Ensemble Kalman filter (EnKF) is an important data assimilation method for high-dimensional geophysical systems. Efficient implementation of EnKF in practice often involves the localization technique, which updates each component using only information within a local radius. This paper rigorously analyzes the local EnKF (LEnKF) for linear systems and shows that the filter error can be dominated by the ensemble covariance, as long as (1) the sample size exceeds the logarithmic of state dimension and a constant that depends only on the local radius; (2) the forecast covariance matrix admits a stable localized structure. In particular, this indicates that with small system and observation noises, the filter error will be accurate in long time even if the initialization is not. The analysis also reveals an intrinsic inconsistency caused by the localization technique, and a stable localized structure is necessary to control this inconsistency. While this structure is usually taken for granted for the operation of LEnKF, it can also be rigorously proved for linear systems with sparse local observations and weak local interactions. These theoretical results are also validated by numerical implementation of LEnKF on a simple stochastic turbulence in two dynamical regimes.

  8. A mechanistic understanding of ageing revealed by studying the young.

    Science.gov (United States)

    Crespi, Erica J

    2012-03-01

    A main focus within biomedical research is to understand how adverse environmental conditions experienced during early development affects lifelong health (Barker 1992). Within this context, extensive research in rodent models and humans has shown that intrauterine growth retardation (IUGR) caused by nutrient restriction during early development is often followed by post-natal 'catch-up' growth when access to food resources improves. However, this accelerated growth rate seems to come at a cost, as metabolic and endocrine processes that are programmed during this time cause later-life onset of diseases such as obesity, insulin resistance and cardiovascular disease (reviewed in Crespi & Denver 2005). In this issue Molecular Ecology, Geiger et al. (2012) asked what are the costs of catch-up growth in nutrient-restricted king penguin chicks (Fig. 1) by measuring lengths of telomeres, the protective DNA sequences at the end of chromosomes, before and after catch-up growth, as the amount and rate of telomere sequence loss over time has been associated with reduced lifespan in both model and nonmodel organisms (see reviews of Costantini et al. 2010; Haussmann & Marchetto 2010). Geiger et al. (2011) found that chicks entering the post-winter growth season at a smaller size exhibited increased growth rates (i.e. catch-up growth) at the cost of increased oxidative stress and reduced telomere lengths compared with the chicks entering the growth period at a larger size. Furthermore, chicks that did not survive had drastically shorter telomere lengths and reduced antioxidant capacities at the beginning of the growth period than all other chicks, thereby directly associating telomere length to mortality. These results suggest that while catch-up growth allows smaller chicks to head off into the world on equal footing with chicks that hatched at a larger size, it likely comes at the cost of a shortened lifespan. Thus, this study provides a mechanism that supports the

  9. An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors

    Directory of Open Access Journals (Sweden)

    Runtao Yang

    2015-09-01

    Full Text Available Antifreeze proteins (AFPs play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study proposes an AFP identification system called AFP-Ensemble. In this system, random forest classifiers are trained by different training subsets and then aggregated into a consensus classifier by majority voting. The resulting predictor yields a sensitivity of 0.892, a specificity of 0.940, an accuracy of 0.938 and a balanced accuracy of 0.916 on an independent dataset, which are far better than the results obtained by previous methods. These results reveal that AFP-Ensemble is an effective and promising predictor for large-scale determination of AFPs. The detailed feature analysis in this study may give useful insights into the molecular mechanisms of AFP-ice interactions and provide guidance for the related experimental validation. A web server has been designed to implement the proposed method.

  10. Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms

    Science.gov (United States)

    Janjic, Tijana; Mclaughlin, Dennis; Cohn, Stephen E.; Verlaan, Martin

    2014-01-01

    This paper considers the incorporation of constraints to enforce physically based conservation laws in the ensemble Kalman filter. In particular, constraints are used to ensure that the ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. In certain situations filtering algorithms such as the ensemble Kalman filter (EnKF) and ensemble transform Kalman filter (ETKF) yield updated ensembles that conserve mass but are negative, even though the actual states must be nonnegative. In such situations if negative values are set to zero, or a log transform is introduced, the total mass will not be conserved. In this study, mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate non-negativity constraints. Simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. In two examples, an update that includes a non-negativity constraint is able to properly describe the transport of a sharp feature (e.g., a triangle or cone). A number of implementation questions still need to be addressed, particularly the need to develop a computationally efficient quadratic programming update for large ensemble.

  11. Shallow cumuli ensemble statistics for development of a stochastic parameterization

    Science.gov (United States)

    Sakradzija, Mirjana; Seifert, Axel; Heus, Thijs

    2014-05-01

    According to a conventional deterministic approach to the parameterization of moist convection in numerical atmospheric models, a given large scale forcing produces an unique response from the unresolved convective processes. This representation leaves out the small-scale variability of convection, as it is known from the empirical studies of deep and shallow convective cloud ensembles, there is a whole distribution of sub-grid states corresponding to the given large scale forcing. Moreover, this distribution gets broader with the increasing model resolution. This behavior is also consistent with our theoretical understanding of a coarse-grained nonlinear system. We propose an approach to represent the variability of the unresolved shallow-convective states, including the dependence of the sub-grid states distribution spread and shape on the model horizontal resolution. Starting from the Gibbs canonical ensemble theory, Craig and Cohen (2006) developed a theory for the fluctuations in a deep convective ensemble. The micro-states of a deep convective cloud ensemble are characterized by the cloud-base mass flux, which, according to the theory, is exponentially distributed (Boltzmann distribution). Following their work, we study the shallow cumulus ensemble statistics and the distribution of the cloud-base mass flux. We employ a Large-Eddy Simulation model (LES) and a cloud tracking algorithm, followed by a conditional sampling of clouds at the cloud base level, to retrieve the information about the individual cloud life cycles and the cloud ensemble as a whole. In the case of shallow cumulus cloud ensemble, the distribution of micro-states is a generalized exponential distribution. Based on the empirical and theoretical findings, a stochastic model has been developed to simulate the shallow convective cloud ensemble and to test the convective ensemble theory. Stochastic model simulates a compound random process, with the number of convective elements drawn from a

  12. Sensitivity of CAM-Chem/DART MOPITT CO Assimilation Performance to the Choice of Ensemble System Configuration: A Case Study for Fires in the Amazon

    Science.gov (United States)

    Arellano, A. F., Jr.; Tang, W.

    2017-12-01

    Assimilating observational data of chemical constituents into a modeling system is a powerful approach in assessing changes in atmospheric composition and estimating associated emissions. However, the results of such chemical data assimilation (DA) experiments are largely subject to various key factors such as: a) a priori information, b) error specification and representation, and c) structural biases in the modeling system. Here we investigate the sensitivity of an ensemble-based data assimilation state and emission estimates to these key factors. We focus on investigating the assimilation performance of the Community Earth System Model (CESM)/CAM-Chem with the Data Assimilation Research Testbed (DART) in representing biomass burning plumes in the Amazonia during the 2008 fire season. We conduct the following ensemble DA MOPITT CO experiments: 1) use of monthly-average NCAR's FINN surface fire emissionss, 2) use of daily FINN surface fire emissions, 3) use of daily FINN emissions with climatological injection heights, and 4) use of perturbed FINN emission parameters to represent not only the uncertainties in combustion activity but also in combustion efficiency. We show key diagnostics of assimilation performance for these experiments and verify with available ground-based and aircraft-based measurements.

  13. Nonequilibrium statistical mechanics ensemble method

    CERN Document Server

    Eu, Byung Chan

    1998-01-01

    In this monograph, nonequilibrium statistical mechanics is developed by means of ensemble methods on the basis of the Boltzmann equation, the generic Boltzmann equations for classical and quantum dilute gases, and a generalised Boltzmann equation for dense simple fluids The theories are developed in forms parallel with the equilibrium Gibbs ensemble theory in a way fully consistent with the laws of thermodynamics The generalised hydrodynamics equations are the integral part of the theory and describe the evolution of macroscopic processes in accordance with the laws of thermodynamics of systems far removed from equilibrium Audience This book will be of interest to researchers in the fields of statistical mechanics, condensed matter physics, gas dynamics, fluid dynamics, rheology, irreversible thermodynamics and nonequilibrium phenomena

  14. Ensemble methods for handwritten digit recognition

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Liisberg, Christian; Salamon, P.

    1992-01-01

    Neural network ensembles are applied to handwritten digit recognition. The individual networks of the ensemble are combinations of sparse look-up tables (LUTs) with random receptive fields. It is shown that the consensus of a group of networks outperforms the best individual of the ensemble....... It is further shown that it is possible to estimate the ensemble performance as well as the learning curve on a medium-size database. In addition the authors present preliminary analysis of experiments on a large database and show that state-of-the-art performance can be obtained using the ensemble approach...... by optimizing the receptive fields. It is concluded that it is possible to improve performance significantly by introducing moderate-size ensembles; in particular, a 20-25% improvement has been found. The ensemble random LUTs, when trained on a medium-size database, reach a performance (without rejects) of 94...

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

    Science.gov (United States)

    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.

  16. Regional interdependency of precipitation indices across Denmark in two ensembles of high-resolution RCMs

    DEFF Research Database (Denmark)

    Sunyer Pinya, Maria Antonia; Madsen, Henrik; Rosbjerg, Dan

    2013-01-01

    all these methods is that the climate models are independent. This study addresses the validity of this assumption for two ensembles of regional climate models (RCMs) from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project based on the land cells covering Denmark....... Daily precipitation indices from an ensemble of RCMs driven by the 40-yrECMWFRe-Analysis (ERA-40) and an ensemble of the same RCMs driven by different general circulation models (GCMs) are analyzed. Two different methods are used to estimate the amount of independent information in the ensembles....... These are based on different statistical properties of a measure of climate model error. Additionally, a hierarchical cluster analysis is carried out. Regardless of the method used, the effective number of RCMs is smaller than the total number of RCMs. The estimated effective number of RCMs varies depending...

  17. Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices

    Directory of Open Access Journals (Sweden)

    F. Fundel

    2013-01-01

    Full Text Available Streamflow droughts, characterized by low runoff as consequence of a drought event, affect numerous aspects of life. Economic sectors that are impacted by low streamflow are, e.g., power production, agriculture, tourism, water quality management and shipping. Those sectors could potentially benefit from forecasts of streamflow drought events, even of short events on the monthly time scales or below. Numerical hydrometeorological models have increasingly been used to forecast low streamflow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the evaluation of low streamflow and of the derived indices as duration, severity and magnitude, characterizing streamflow droughts up to a lead time of one month.

    The ECMWF VarEPS 5-member ensemble reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification reveals that, compared to probabilistic peak-flow forecasts, which show skill up to a lead time of two weeks, forecasts of streamflow droughts are skilful over the entire forecast range of one month. For forecasts at the lower end of the runoff regime, the quality of the initial state seems to be crucial to achieve a good forecast quality in the longer range. It is shown that the states used in this study to initialize forecasts satisfy this requirement. The produced forecasts of streamflow drought indices, derived from the ensemble forecasts, could be beneficially included in a decision-making process. This is valid for probabilistic forecasts of streamflow drought events falling below a daily varying threshold, based on a quantile derived from a runoff climatology. Although the forecasts have a tendency to overpredict streamflow droughts, it is shown that the relative economic value of the ensemble forecasts reaches up to 60%, in case a forecast user is able to take preventive

  18. Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices

    Science.gov (United States)

    Fundel, F.; Jörg-Hess, S.; Zappa, M.

    2013-01-01

    Streamflow droughts, characterized by low runoff as consequence of a drought event, affect numerous aspects of life. Economic sectors that are impacted by low streamflow are, e.g., power production, agriculture, tourism, water quality management and shipping. Those sectors could potentially benefit from forecasts of streamflow drought events, even of short events on the monthly time scales or below. Numerical hydrometeorological models have increasingly been used to forecast low streamflow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the evaluation of low streamflow and of the derived indices as duration, severity and magnitude, characterizing streamflow droughts up to a lead time of one month. The ECMWF VarEPS 5-member ensemble reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification reveals that, compared to probabilistic peak-flow forecasts, which show skill up to a lead time of two weeks, forecasts of streamflow droughts are skilful over the entire forecast range of one month. For forecasts at the lower end of the runoff regime, the quality of the initial state seems to be crucial to achieve a good forecast quality in the longer range. It is shown that the states used in this study to initialize forecasts satisfy this requirement. The produced forecasts of streamflow drought indices, derived from the ensemble forecasts, could be beneficially included in a decision-making process. This is valid for probabilistic forecasts of streamflow drought events falling below a daily varying threshold, based on a quantile derived from a runoff climatology. Although the forecasts have a tendency to overpredict streamflow droughts, it is shown that the relative economic value of the ensemble forecasts reaches up to 60%, in case a forecast user is able to take preventive action based on the forecast.

  19. Low-wave number analysis of observations and ensemble forecasts to develop metrics for the selection of most realistic members to study multi-scale interactions between the environment and the convective organization of hurricanes: Focus on Rapid Intensification

    Science.gov (United States)

    Hristova-Veleva, S. M.; Chen, H.; Gopalakrishnan, S.; Haddad, Z. S.

    2017-12-01

    Tropical cyclones (TCs) are the product of complex multi-scale processes and interactions. The role of the environment has long been recognized. However, recent research has shown that convective-scale processes in the hurricane core might also play a crucial role in determining TCs intensity and size. Several studies have linked Rapid Intensification to the characteristics of the convective clouds (shallow versus deep), their organization (isolated versus wide-spread) and their location with respect to dynamical controls (the vertical shear, the radius of maximum wind). Yet a third set of controls signifies the interaction between the storm-scale and large-scale processes. Our goal is to use observations and models to advance the still-lacking understanding of these processes. Recently, hurricane models have improved significantly. However, deterministic forecasts have limitations due to the uncertainty in the representation of the physical processes and initial conditions. A crucial step forward is the use of high-resolution ensembles. We adopt the following approach: i) generate a high resolution ensemble forecast using HWRF; ii) produce synthetic data (e.g. brightness temperature) from the model fields for direct comparison to satellite observations; iii) develop metrics to allow us to sub-select the realistic members of the ensemble, based on objective measures of the similarity between observed and forecasted structures; iv) for these most-realistic members, determine the skill in forecasting TCs to provide"guidance on guidance"; v) use the members with the best predictive skill to untangle the complex multi-scale interactions. We will report on the first three goals of our research, using forecasts and observations of hurricane Edouard (2014), focusing on RI. We will focus on describing the metrics for the selection of the most appropriate ensemble members, based on applying low-wave number analysis (WNA - Hristova-Veleva et al., 2016) to the observed and

  20. Universal critical wrapping probabilities in the canonical ensemble

    Directory of Open Access Journals (Sweden)

    Hao Hu

    2015-09-01

    Full Text Available Universal dimensionless quantities, such as Binder ratios and wrapping probabilities, play an important role in the study of critical phenomena. We study the finite-size scaling behavior of the wrapping probability for the Potts model in the random-cluster representation, under the constraint that the total number of occupied bonds is fixed, so that the canonical ensemble applies. We derive that, in the limit L→∞, the critical values of the wrapping probability are different from those of the unconstrained model, i.e. the model in the grand-canonical ensemble, but still universal, for systems with 2yt−d>0 where yt=1/ν is the thermal renormalization exponent and d is the spatial dimension. Similar modifications apply to other dimensionless quantities, such as Binder ratios. For systems with 2yt−d≤0, these quantities share same critical universal values in the two ensembles. It is also derived that new finite-size corrections are induced. These findings apply more generally to systems in the canonical ensemble, e.g. the dilute Potts model with a fixed total number of vacancies. Finally, we formulate an efficient cluster-type algorithm for the canonical ensemble, and confirm these predictions by extensive simulations.

  1. A New Method for Determining Structure Ensemble: Application to a RNA Binding Di-Domain Protein.

    Science.gov (United States)

    Liu, Wei; Zhang, Jingfeng; Fan, Jing-Song; Tria, Giancarlo; Grüber, Gerhard; Yang, Daiwen

    2016-05-10

    Structure ensemble determination is the basis of understanding the structure-function relationship of a multidomain protein with weak domain-domain interactions. Paramagnetic relaxation enhancement has been proven a powerful tool in the study of structure ensembles, but there exist a number of challenges such as spin-label flexibility, domain dynamics, and overfitting. Here we propose a new (to our knowledge) method to describe structure ensembles using a minimal number of conformers. In this method, individual domains are considered rigid; the position of each spin-label conformer and the structure of each protein conformer are defined by three and six orthogonal parameters, respectively. First, the spin-label ensemble is determined by optimizing the positions and populations of spin-label conformers against intradomain paramagnetic relaxation enhancements with a genetic algorithm. Subsequently, the protein structure ensemble is optimized using a more efficient genetic algorithm-based approach and an overfitting indicator, both of which were established in this work. The method was validated using a reference ensemble with a set of conformers whose populations and structures are known. This method was also applied to study the structure ensemble of the tandem di-domain of a poly (U) binding protein. The determined ensemble was supported by small-angle x-ray scattering and nuclear magnetic resonance relaxation data. The ensemble obtained suggests an induced fit mechanism for recognition of target RNA by the protein. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  2. Pauci ex tanto numero: reducing redundancy in multi-model ensembles

    Science.gov (United States)

    Solazzo, E.; Riccio, A.; Kioutsioukis, I.; Galmarini, S.

    2013-02-01

    We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date no attempts in this direction are documented within the air quality (AQ) community, although the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared biases among models will determine a biased ensemble, making therefore essential the errors of the ensemble members to be independent so that bias can cancel out. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated) we discourage selecting the members of the ensemble simply on the basis of scores, that is, independence and skills need to be considered disjointly.

  3. Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

    Science.gov (United States)

    Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

    2009-04-01

    In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

  4. Curve Boxplot: Generalization of Boxplot for Ensembles of Curves.

    Science.gov (United States)

    Mirzargar, Mahsa; Whitaker, Ross T; Kirby, Robert M

    2014-12-01

    In simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics.

  5. Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex

    Science.gov (United States)

    Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F.; Schnitzer, Mark J.; Anderson, David J.

    2017-10-01

    All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a ‘hard-wired’ system.

  6. Risk assessments of regional climate change over Europe: generation of probabilistic ensemble and uncertainty assessment for EURO-CODEX

    Science.gov (United States)

    Yuan, J.; Kopp, R. E.

    2017-12-01

    Quantitative risk analysis of regional climate change is crucial for risk management and impact assessment of climate change. Two major challenges to assessing the risks of climate change are: CMIP5 model runs, which drive EURO-CODEX downscaling runs, do not cover the full range of uncertainty of future projections; Climate models may underestimate the probability of tail risks (i.e. extreme events). To overcome the difficulties, this study offers a viable avenue, where a set of probabilistic climate ensemble is generated using the Surrogate/Model Mixed Ensemble (SMME) method. The probabilistic ensembles for temperature and precipitation are used to assess the range of uncertainty covered by five bias-corrected simulations from the high-resolution (0.11º) EURO-CODEX database, which are selected by the PESETA (The Projection of Economic impacts of climate change in Sectors of the European Union based on bottom-up Analysis) III project. Results show that the distribution of SMME ensemble is notably wider than both distribution of raw ensemble of GCMs and the spread of the five EURO-CORDEX in RCP8.5. Tail risks are well presented by the SMME ensemble. Both SMME ensemble and EURO-CORDEX projections are aggregated to administrative level, and are integrated into impact functions of PESETA III to assess climate risks in Europe. To further evaluate the uncertainties introduced by the downscaling process, we compare the 5 runs from EURO-CORDEX with runs from the corresponding GCMs. Time series of regional mean, spatial patterns, and climate indices are examined for the future climate (2080-2099) deviating from the present climate (1981-2010). The downscaling processes do not appear to be trend-preserving, e.g. the increase in regional mean temperature from EURO-CORDEX is slower than that from the corresponding GCM. The spatial pattern comparison reveals that the differences between each pair of GCM and EURO-CORDEX are small in winter. In summer, the temperatures of EURO

  7. The "Accafellows:" Exploring the Music Making and Culture of a Collegiate a Cappella Ensemble

    Science.gov (United States)

    Paparo, Stephen A.

    2013-01-01

    Despite the growth in number and popularity of collegiate a cappella ensembles in the USA over the past 20 years, few researchers have studied these self-governed, student-run, popular music ensembles. This ethnographic case study examined the music making and culture of the "Accafellows", an all-male a cappella group at a mid-western…

  8. Modulation of the disordered conformational ensembles of the p53 transactivation domain by cancer-associated mutations.

    Directory of Open Access Journals (Sweden)

    Debabani Ganguly

    2015-04-01

    Full Text Available Intrinsically disordered proteins (IDPs are frequently associated with human diseases such as cancers, and about one-fourth of disease-associated missense mutations have been mapped into predicted disordered regions. Understanding how these mutations affect the structure-function relationship of IDPs is a formidable task that requires detailed characterization of the disordered conformational ensembles. Implicit solvent coupled with enhanced sampling has been proposed to provide a balance between accuracy and efficiency necessary for systematic and comparative assessments of the effects of mutations as well as post-translational modifications on IDP structure and interaction. Here, we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain (TAD of tumor suppressor p53 and several cancer-associated mutants in implicit solvent. The simulations are critically assessed by quantitative comparisons with several types of experimental data that provide structural information on both secondary and tertiary levels. The results show that the calculated ensembles reproduce local structural features of wild-type p53-TAD and the effects of K24N mutation quantitatively. On the tertiary level, the simulated ensembles are overly compact, even though they appear to recapitulate the overall features of transient long-range contacts qualitatively. A key finding is that, while p53-TAD and its cancer mutants sample a similar set of conformational states, cancer mutants could introduce both local and long-range structural modulations to potentially perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD. The current study clearly demonstrates the promise of atomistic simulations for detailed characterization of IDP

  9. Statistical ensembles in quantum mechanics

    International Nuclear Information System (INIS)

    Blokhintsev, D.

    1976-01-01

    The interpretation of quantum mechanics presented in this paper is based on the concept of quantum ensembles. This concept differs essentially from the canonical one by that the interference of the observer into the state of a microscopic system is of no greater importance than in any other field of physics. Owing to this fact, the laws established by quantum mechanics are not of less objective character than the laws governing classical statistical mechanics. The paradoxical nature of some statements of quantum mechanics which result from the interpretation of the wave functions as the observer's notebook greatly stimulated the development of the idea presented. (Auth.)

  10. Wind Power Prediction using Ensembles

    DEFF Research Database (Denmark)

    Giebel, Gregor; Badger, Jake; Landberg, Lars

    2005-01-01

    offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...

  11. Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

    International Nuclear Information System (INIS)

    Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo

    2011-01-01

    The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

  12. Optical properties of indium phosphide nanowire ensembles at various temperatures

    Energy Technology Data Exchange (ETDEWEB)

    Lohn, Andrew J; Onishi, Takehiro; Kobayashi, Nobuhiko P [Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064 (United States); Nanostructured Energy Conversion Technology and Research (NECTAR), Advanced Studies Laboratories, University of California Santa Cruz-NASA Ames Research Center, Moffett Field, CA 94035 (United States)

    2010-09-03

    Ensembles that contain two types (zincblende and wurtzite) of indium phosphide nanowires grown on non-single crystalline surfaces were studied by micro-photoluminescence and micro-Raman spectroscopy at various low temperatures. The obtained spectra are discussed with the emphasis on the effects of differing lattice types, geometries, and crystallographic orientations present within an ensemble of nanowires grown on non-single crystalline surfaces. In the photoluminescence spectra, a typical Varshni dependence of band gap energy on temperature was observed for emissions from zincblende nanowires and in the high temperature regime energy transfer from excitonic transitions and band-edge transitions was identified. In contrast, the photoluminescence emissions associated with wurtzite nanowires were rather insensitive to temperature. Raman spectra were collected simultaneously from zincblende and wurtzite nanowires coexisting in an ensemble. Raman peaks of the wurtzite nanowires are interpreted as those related to the zincblende nanowires by a folding of the phonon dispersion.

  13. Optical properties of indium phosphide nanowire ensembles at various temperatures

    International Nuclear Information System (INIS)

    Lohn, Andrew J; Onishi, Takehiro; Kobayashi, Nobuhiko P

    2010-01-01

    Ensembles that contain two types (zincblende and wurtzite) of indium phosphide nanowires grown on non-single crystalline surfaces were studied by micro-photoluminescence and micro-Raman spectroscopy at various low temperatures. The obtained spectra are discussed with the emphasis on the effects of differing lattice types, geometries, and crystallographic orientations present within an ensemble of nanowires grown on non-single crystalline surfaces. In the photoluminescence spectra, a typical Varshni dependence of band gap energy on temperature was observed for emissions from zincblende nanowires and in the high temperature regime energy transfer from excitonic transitions and band-edge transitions was identified. In contrast, the photoluminescence emissions associated with wurtzite nanowires were rather insensitive to temperature. Raman spectra were collected simultaneously from zincblende and wurtzite nanowires coexisting in an ensemble. Raman peaks of the wurtzite nanowires are interpreted as those related to the zincblende nanowires by a folding of the phonon dispersion.

  14. EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms

    KAUST Repository

    Rapakoulia, Trisevgeni; Theofilatos, Konstantinos A.; Kleftogiannis, Dimitrios A.; Likothanasis, Spiridon D.; Tsakalidis, Athanasios K.; Mavroudi, Seferina P.

    2014-01-01

    do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a twostep algorithm, which in its first step applies a novel

  15. Urban runoff forecasting with ensemble weather predictions

    DEFF Research Database (Denmark)

    Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca

    This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice.......This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice....

  16. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...... of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset...

  17. Breaking of ensembles of linear and nonlinear oscillators

    International Nuclear Information System (INIS)

    Buts, V.A.

    2016-01-01

    Some results concerning the study of the dynamics of ensembles of linear and nonlinear oscillators are stated. It is shown that, in general, a stable ensemble of linear oscillator has a limited number of oscillators. This number has been defined for some simple models. It is shown that the features of the dynamics of linear oscillators can be used for conversion of the low-frequency energy oscillations into high frequency oscillations. The dynamics of coupled nonlinear oscillators in most cases is chaotic. For such a case, it is shown that the statistical characteristics (moments) of chaotic motion can significantly reduce potential barriers that keep the particles in the capture region

  18. Joys of Community Ensemble Playing: The Case of the Happy Roll Elastic Ensemble in Taiwan

    Science.gov (United States)

    Hsieh, Yuan-Mei; Kao, Kai-Chi

    2012-01-01

    The Happy Roll Elastic Ensemble (HREE) is a community music ensemble supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of ensemble playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…

  19. The Effect of Ensemble Performance Quality on the Evaluation of Conducting Expressivity

    Science.gov (United States)

    Silvey, Brian A.

    2011-01-01

    This study was designed to examine whether the presence of excellent or poor ensemble performances would influence the ratings assigned by ensemble members to conductors who demonstrated highly expressive conducting. Two conductors were videotaped conducting one of two excerpts from an arrangement of Frank Ticheli's "Loch Lomond." These videos…

  20. Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

    KAUST Repository

    Liu, Bo

    2015-11-11

    We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.

  1. Pauci ex tanto numero: reduce redundancy in multi-model ensembles

    Science.gov (United States)

    Solazzo, E.; Riccio, A.; Kioutsioukis, I.; Galmarini, S.

    2013-08-01

    We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date, no attempts in this direction have been documented within the air quality (AQ) community despite the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared, dependant biases among models do not cancel out but will instead determine a biased ensemble. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated), we discourage selecting the members of the ensemble simply on the basis of scores; that is, independence and skills need to be considered disjointly.

  2. Reliability of multi-model and structurally different single-model ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Yokohata, Tokuta [National Institute for Environmental Studies, Center for Global Environmental Research, Tsukuba, Ibaraki (Japan); Annan, James D.; Hargreaves, Julia C. [Japan Agency for Marine-Earth Science and Technology, Research Institute for Global Change, Yokohama, Kanagawa (Japan); Collins, Matthew [University of Exeter, College of Engineering, Mathematics and Physical Sciences, Exeter (United Kingdom); Jackson, Charles S.; Tobis, Michael [The University of Texas at Austin, Institute of Geophysics, 10100 Burnet Rd., ROC-196, Mail Code R2200, Austin, TX (United States); Webb, Mark J. [Met Office Hadley Centre, Exeter (United Kingdom)

    2012-08-15

    The performance of several state-of-the-art climate model ensembles, including two multi-model ensembles (MMEs) and four structurally different (perturbed parameter) single model ensembles (SMEs), are investigated for the first time using the rank histogram approach. In this method, the reliability of a model ensemble is evaluated from the point of view of whether the observations can be regarded as being sampled from the ensemble. Our analysis reveals that, in the MMEs, the climate variables we investigated are broadly reliable on the global scale, with a tendency towards overdispersion. On the other hand, in the SMEs, the reliability differs depending on the ensemble and variable field considered. In general, the mean state and historical trend of surface air temperature, and mean state of precipitation are reliable in the SMEs. However, variables such as sea level pressure or top-of-atmosphere clear-sky shortwave radiation do not cover a sufficiently wide range in some. It is not possible to assess whether this is a fundamental feature of SMEs generated with particular model, or a consequence of the algorithm used to select and perturb the values of the parameters. As under-dispersion is a potentially more serious issue when using ensembles to make projections, we recommend the application of rank histograms to assess reliability when designing and running perturbed physics SMEs. (orig.)

  3. Popular Music and the Instrumental Ensemble.

    Science.gov (United States)

    Boespflug, George

    1999-01-01

    Discusses popular music, the role of the musical performer as a creator, and the styles of jazz and popular music. Describes the pop ensemble at the college level, focusing on improvisation, rehearsals, recording, and performance. Argues that pop ensembles be used in junior and senior high school. (CMK)

  4. Ensemble methods for seasonal limited area forecasts

    DEFF Research Database (Denmark)

    Arritt, Raymond W.; Anderson, Christopher J.; Takle, Eugene S.

    2004-01-01

    The ensemble prediction methods used for seasonal limited area forecasts were examined by comparing methods for generating ensemble simulations of seasonal precipitation. The summer 1993 model over the north-central US was used as a test case. The four methods examined included the lagged-average...

  5. Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF)

    Science.gov (United States)

    Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.

    2017-12-01

    Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.

  6. Reproducing multi-model ensemble average with Ensemble-averaged Reconstructed Forcings (ERF) in regional climate modeling

    Science.gov (United States)

    Erfanian, A.; Fomenko, L.; Wang, G.

    2016-12-01

    Multi-model ensemble (MME) average is considered the most reliable for simulating both present-day and future climates. It has been a primary reference for making conclusions in major coordinated studies i.e. IPCC Assessment Reports and CORDEX. The biases of individual models cancel out each other in MME average, enabling the ensemble mean to outperform individual members in simulating the mean climate. This enhancement however comes with tremendous computational cost, which is especially inhibiting for regional climate modeling as model uncertainties can originate from both RCMs and the driving GCMs. Here we propose the Ensemble-based Reconstructed Forcings (ERF) approach to regional climate modeling that achieves a similar level of bias reduction at a fraction of cost compared with the conventional MME approach. The new method constructs a single set of initial and boundary conditions (IBCs) by averaging the IBCs of multiple GCMs, and drives the RCM with this ensemble average of IBCs to conduct a single run. Using a regional climate model (RegCM4.3.4-CLM4.5), we tested the method over West Africa for multiple combination of (up to six) GCMs. Our results indicate that the performance of the ERF method is comparable to that of the MME average in simulating the mean climate. The bias reduction seen in ERF simulations is achieved by using more realistic IBCs in solving the system of equations underlying the RCM physics and dynamics. This endows the new method with a theoretical advantage in addition to reducing computational cost. The ERF output is an unaltered solution of the RCM as opposed to a climate state that might not be physically plausible due to the averaging of multiple solutions with the conventional MME approach. The ERF approach should be considered for use in major international efforts such as CORDEX. Key words: Multi-model ensemble, ensemble analysis, ERF, regional climate modeling

  7. Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling

    Institute of Scientific and Technical Information of China (English)

    Yong Mao; Xiao-Bo Zhou; Dao-Ying Pi; You-Xian Sun

    2005-01-01

    In this study, we present a constructive algorithm for training cooperative support vector machine ensembles (CSVMEs). CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.

  8. Microcanonical ensemble and algebra of conserved generators for generalized quantum dynamics

    International Nuclear Information System (INIS)

    Adler, S.L.; Horwitz, L.P.

    1996-01-01

    It has recently been shown, by application of statistical mechanical methods to determine the canonical ensemble governing the equilibrium distribution of operator initial values, that complex quantum field theory can emerge as a statistical approximation to an underlying generalized quantum dynamics. This result was obtained by an argument based on a Ward identity analogous to the equipartition theorem of classical statistical mechanics. We construct here a microcanonical ensemble which forms the basis of this canonical ensemble. This construction enables us to define the microcanonical entropy and free energy of the field configuration of the equilibrium distribution and to study the stability of the canonical ensemble. We also study the algebraic structure of the conserved generators from which the microcanonical and canonical ensembles are constructed, and the flows they induce on the phase space. copyright 1996 American Institute of Physics

  9. Music Ensemble Participation: Personality Traits and Music Experience

    Science.gov (United States)

    Torrance, Tracy A.; Bugos, Jennifer A.

    2017-01-01

    The purpose of this study was two-fold: (1) to examine the relationship between personality type and ensemble choice and (2) to examine the differences in personality across age and music experience in young adults. Participants (N = 137; 68 instrumentalists, 69 vocalists) completed a demographic survey and the Big Five Personality Inventory.…

  10. Random walk loop soups and conformal loop ensembles

    NARCIS (Netherlands)

    van de Brug, T.; Camia, F.; Lis, M.

    2016-01-01

    The random walk loop soup is a Poissonian ensemble of lattice loops; it has been extensively studied because of its connections to the discrete Gaussian free field, but was originally introduced by Lawler and Trujillo Ferreras as a discrete version of the Brownian loop soup of Lawler and Werner, a

  11. Impact of hybrid GSI analysis using ETR ensembles

    Indian Academy of Sciences (India)

    Impact of hybrid GSI analysis using ETR ensembles. V S Prasad∗ and C J .... In this study, impact of hybrid ..... of water between vapour, clouds and ice (Damrath et al. 2000). ... flooding – June 2013; Weather and Climate Extremes 4. 22–34.

  12. The National Solo and Ensemble Contest 1929-1937

    Science.gov (United States)

    Meyers, Brian D.

    2012-01-01

    This study is the first investigation of the nine-year history of the National Solo and Ensemble Contests, held in the United States in conjunction with the National School Band and Orchestra Contests of the late 1920s and early to mid-1930s. Primary sources used include letters from those involved with the planning of the contests, meeting…

  13. Nuclear multifragmentation within the framework of different statistical ensembles

    International Nuclear Information System (INIS)

    Aguiar, C.E.; Donangelo, R.; Souza, S.R.

    2006-01-01

    The sensitivity of the statistical multifragmentation model to the underlying statistical assumptions is investigated. We concentrate on its microcanonical, canonical, and isobaric formulations. As far as average values are concerned, our results reveal that all the ensembles make very similar predictions, as long as the relevant macroscopic variables (such as temperature, excitation energy, and breakup volume) are the same in all statistical ensembles. It also turns out that the multiplicity dependence of the breakup volume in the microcanonical version of the model mimics a system at (approximately) constant pressure, at least in the plateau region of the caloric curve. However, in contrast to average values, our results suggest that the distributions of physical observables are quite sensitive to the statistical assumptions. This finding may help in deciding which hypothesis corresponds to the best picture for the freeze-out stage

  14. Spin storage in quantum dot ensembles and single quantum dots

    International Nuclear Information System (INIS)

    Heiss, Dominik

    2009-01-01

    This thesis deals with the investigation of spin relaxation of electrons and holes in small ensembles of self-assembled quantum dots using optical techniques. Furthermore, a method to detect the spin orientation in a single quantum dot was developed in the framework of this thesis. A spin storage device was used to optically generate oriented electron spins in small frequency selected quantum dot ensembles using circularly polarized optical excitation. The spin orientation can be determined by the polarization of the time delayed electroluminescence signal generated by the device after a continuously variable storage time. The degree of spin polarized initialization was found to be limited to 0.6 at high magnetic fields, where anisotropic effects are compensated. The spin relaxation was directly measured as a function of magnetic field, lattice temperature and s-shell transition energy of the quantum dot by varying the spin storage time up to 30 ms. Very long spin lifetimes are obtained with a lower limit of T 1 =20 ms at B=4 T and T=1 K. A strong magnetic field dependence T 1 ∝B -5 has been observed for low temperatures of T=1 K which weakens as the temperature is increased. In addition, the temperature dependence has been determined with T 1 ∝T -1 . The characteristic dependencies on magnetic field and temperature lead to the identification of the spin relaxation mechanism, which is governed by spin-orbit coupling and mediated by single phonon scattering. This finding is qualitatively supported by the energy dependent measurements. The investigations were extended to a modified device design that enabled studying the spin relaxation dynamics of heavy holes in self-assembled quantum dots. The measurements show a polarization memory effect for holes with up to 0.1 degree of polarization. Furthermore, investigations of the time dynamics of the hole spin relaxation reveal surprisingly long lifetimes T 1 h in the microsecond range, therefore, comparable with

  15. Spin storage in quantum dot ensembles and single quantum dots

    Energy Technology Data Exchange (ETDEWEB)

    Heiss, Dominik

    2009-10-15

    This thesis deals with the investigation of spin relaxation of electrons and holes in small ensembles of self-assembled quantum dots using optical techniques. Furthermore, a method to detect the spin orientation in a single quantum dot was developed in the framework of this thesis. A spin storage device was used to optically generate oriented electron spins in small frequency selected quantum dot ensembles using circularly polarized optical excitation. The spin orientation can be determined by the polarization of the time delayed electroluminescence signal generated by the device after a continuously variable storage time. The degree of spin polarized initialization was found to be limited to 0.6 at high magnetic fields, where anisotropic effects are compensated. The spin relaxation was directly measured as a function of magnetic field, lattice temperature and s-shell transition energy of the quantum dot by varying the spin storage time up to 30 ms. Very long spin lifetimes are obtained with a lower limit of T{sub 1}=20 ms at B=4 T and T=1 K. A strong magnetic field dependence T{sub 1}{proportional_to}B{sup -5} has been observed for low temperatures of T=1 K which weakens as the temperature is increased. In addition, the temperature dependence has been determined with T{sub 1}{proportional_to}T{sup -1}. The characteristic dependencies on magnetic field and temperature lead to the identification of the spin relaxation mechanism, which is governed by spin-orbit coupling and mediated by single phonon scattering. This finding is qualitatively supported by the energy dependent measurements. The investigations were extended to a modified device design that enabled studying the spin relaxation dynamics of heavy holes in self-assembled quantum dots. The measurements show a polarization memory effect for holes with up to 0.1 degree of polarization. Furthermore, investigations of the time dynamics of the hole spin relaxation reveal surprisingly long lifetimes T{sub 1}{sup h

  16. Tweet-based Target Market Classification Using Ensemble Method

    Directory of Open Access Journals (Sweden)

    Muhammad Adi Khairul Anshary

    2016-09-01

    Full Text Available Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract features. Classification models were constructed to manipulate the training data using two ensemble methods (bagging and boosting. To investigate the effectiveness of the ensemble methods, this study used the CART (classification and regression tree algorithm for comparison. Three categories of consumer goods (computers, mobile phones and cameras and three categories of sentiments (positive, negative and neutral were classified towards three target-market categories. Machine learning was performed using Weka 3.6.9. The results of the test data showed that the bagging method improved the accuracy of CART with 1.9% (to 85.20%. On the other hand, for sentiment classification, the ensemble methods were not successful in increasing the accuracy of CART. The results of this study may be taken into consideration by companies who approach their customers through social media, especially Twitter.

  17. Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

    Science.gov (United States)

    Vogel, Peter; Knippertz, Peter; Fink, Andreas H.; Schlueter, Andreas; Gneiting, Tilmann

    2018-04-01

    Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, we analyze the performance of nine operational global ensemble prediction systems (EPSs) relative to climatology-based forecasts for 1 to 5-day accumulated precipitation based on the monsoon seasons 2007-2014 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, we apply state-of-the-art statistical postprocessing methods in form of Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), and verify against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated, unreliable, and underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. Differences between raw ensemble and climatological forecasts are large, and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles, but - somewhat disappointingly - typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007-2014, but overall have little added value compared to climatology. We suspect that the parametrization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

  18. 2 × 2 random matrix ensembles with reduced symmetry: from Hermitian to PT -symmetric matrices

    International Nuclear Information System (INIS)

    Gong Jiangbin; Wang Qinghai

    2012-01-01

    A possibly fruitful extension of conventional random matrix ensembles is proposed by imposing symmetry constraints on conventional Hermitian matrices or parity–time (PT)-symmetric matrices. To illustrate the main idea, we first study 2 × 2 complex Hermitian matrix ensembles with O(2)-invariant constraints, yielding novel level-spacing statistics such as singular distributions, the half-Gaussian distribution, distributions interpolating between the GOE (Gaussian orthogonal ensemble) distribution and half-Gaussian distributions, as well as the gapped-GOE distribution. Such a symmetry-reduction strategy is then used to explore 2 × 2 PT-symmetric matrix ensembles with real eigenvalues. In particular, PT-symmetric random matrix ensembles with U(2) invariance can be constructed, with the conventional complex Hermitian random matrix ensemble being a special case. In two examples of PT-symmetric random matrix ensembles, the level-spacing distributions are found to be the standard GUE (Gaussian unitary ensemble) statistics or the ‘truncated-GUE’ statistics. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Quantum physics with non-Hermitian operators’. (paper)

  19. Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure

    Directory of Open Access Journals (Sweden)

    Xiaodong Zeng

    2014-01-01

    Full Text Available A weighted accuracy and diversity (WAD method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases.

  20. An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting

    Directory of Open Access Journals (Sweden)

    F. Anctil

    2009-11-01

    Full Text Available Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap.

    In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS, especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

  1. Optical properties of single semiconductor nanowires and nanowire ensembles. Probing surface physics by photoluminescence spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Pfueller, Carsten

    2011-06-27

    This thesis presents a detailed investigation of the optical properties of semiconductor nanowires (NWs) in general and single GaN NWs and GaN NW ensembles in particular by photoluminescence (PL) spectroscopy. NWs are often considered as potential building blocks for future nanometer-scaled devices. This vision is based on several attractive features that are generally ascribed to NWs. For instance, they are expected to grow virtually free of strain and defects even on substrates with a large structural mismatch. In the first part of the thesis, some of these expectations are examined using semiconductor NWs of different materials. On the basis of the temperature-dependent PL of Au- and selfassisted GaAs/(Al,Ga)As core-shell NWs, the influence of foreign catalyst particles on the optical properties of NWs is investigated. For the Au-assisted NWs, we find a thermally activated, nonradiative recombination channel, possibly related to Auatoms incorporated from the catalyst. These results indicate the limited suitability of catalyst-assisted NWs for optoelectronic applications. The effect of the substrate choice is studied by comparing the PL of ZnO NWs grown on Si, Al{sub 2}O{sub 3}, and ZnO substrates. Their virtually identical optical characteristics indicate that the synthesis of NWs may indeed overcome the constraints that limit the heteroepitaxial deposition of thin films. The major part of this thesis discusses the optical properties of GaN NWs grown on Si substrates. The investigation of the PL of single GaN NWs and GaN NW ensembles reveals the significance of their large surface-to-volume ratio. Differences in the recombination behavior of GaNNW ensembles and GaN layers are observed. First, the large surface-to-volume ratio is discussed to be responsible for the different recombination mechanisms apparent in NWs. Second, certain optical features are only found in the PL of GaN NWs, but not in that of GaN layers. An unexpected broadening of the donor

  2. A Simple Ensemble Simulation Technique for Assessment of Future Variations in Specific High-Impact Weather Events

    Science.gov (United States)

    Taniguchi, Kenji

    2018-04-01

    To investigate future variations in high-impact weather events, numerous samples are required. For the detailed assessment in a specific region, a high spatial resolution is also required. A simple ensemble simulation technique is proposed in this paper. In the proposed technique, new ensemble members were generated from one basic state vector and two perturbation vectors, which were obtained by lagged average forecasting simulations. Sensitivity experiments with different numbers of ensemble members, different simulation lengths, and different perturbation magnitudes were performed. Experimental application to a global warming study was also implemented for a typhoon event. Ensemble-mean results and ensemble spreads of total precipitation, atmospheric conditions showed similar characteristics across the sensitivity experiments. The frequencies of the maximum total and hourly precipitation also showed similar distributions. These results indicate the robustness of the proposed technique. On the other hand, considerable ensemble spread was found in each ensemble experiment. In addition, the results of the application to a global warming study showed possible variations in the future. These results indicate that the proposed technique is useful for investigating various meteorological phenomena and the impacts of global warming. The results of the ensemble simulations also enable the stochastic evaluation of differences in high-impact weather events. In addition, the impacts of a spectral nudging technique were also examined. The tracks of a typhoon were quite different between cases with and without spectral nudging; however, the ranges of the tracks among ensemble members were comparable. It indicates that spectral nudging does not necessarily suppress ensemble spread.

  3. Nonlocal inhomogeneous broadening in plasmonic nanoparticle ensembles

    DEFF Research Database (Denmark)

    Tserkezis, Christos; Maack, Johan Rosenkrantz; Liu, Z.

    Nonclassical effects are increasingly more relevant in plasmonics as modern nanofabrication techniques rapidly approach the extreme nanoscale limits, for which departing from classical electrodynamics becomes important. One of the largest-scale necessary corrections towards this direction...... is to abandon the local response approximation (LRA) and take the nonlocal response of the metal into account, typically through the simple hydrodynamic Drude model (HDM), which predicts a sizedependent deviation of plasmon modes from the quasistatic (QS) limit. While this behaviour has been explored for simple...... metallic nanoparticles (NPs) or NP dimers, the possibility of inhomogeneous resonance broadening due to size variation in a large NP collection and the resulting spectral overlap of modes (as depicted in Fig. 1), has been so far overlooked. Here we study theoretically the effect of nonlocality on ensemble...

  4. Dynamical Engineering of Interactions in Qudit Ensembles

    Science.gov (United States)

    Choi, Soonwon; Yao, Norman Y.; Lukin, Mikhail D.

    2017-11-01

    We propose and analyze a method to engineer effective interactions in an ensemble of d -level systems (qudits) driven by global control fields. In particular, we present (i) a necessary and sufficient condition under which a given interaction can be decoupled, (ii) the existence of a universal sequence that decouples any (cancelable) interaction, and (iii) an efficient algorithm to engineer a target Hamiltonian from an initial Hamiltonian (if possible). We illustrate the potential of this method with two examples. Specifically, we present a 6-pulse sequence that decouples effective spin-1 dipolar interactions and demonstrate that a spin-1 Ising chain can be engineered to study transitions among three distinct symmetry protected topological phases. Our work enables new approaches for the realization of both many-body quantum memories and programmable analog quantum simulators using existing experimental platforms.

  5. Precision bounds for gradient magnetometry with atomic ensembles

    Science.gov (United States)

    Apellaniz, Iagoba; Urizar-Lanz, Iñigo; Zimborás, Zoltán; Hyllus, Philipp; Tóth, Géza

    2018-05-01

    We study gradient magnetometry with an ensemble of atoms with arbitrary spin. We calculate precision bounds for estimating the gradient of the magnetic field based on the quantum Fisher information. For quantum states that are invariant under homogeneous magnetic fields, we need to measure a single observable to estimate the gradient. On the other hand, for states that are sensitive to homogeneous fields, a simultaneous measurement is needed, as the homogeneous field must also be estimated. We prove that for the cases studied in this paper, such a measurement is feasible. We present a method to calculate precision bounds for gradient estimation with a chain of atoms or with two spatially separated atomic ensembles. We also consider a single atomic ensemble with an arbitrary density profile, where the atoms cannot be addressed individually, and which is a very relevant case for experiments. Our model can take into account even correlations between particle positions. While in most of the discussion we consider an ensemble of localized particles that are classical with respect to their spatial degree of freedom, we also discuss the case of gradient metrology with a single Bose-Einstein condensate.

  6. Multicomponent ensemble models to forecast induced seismicity

    Science.gov (United States)

    Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.

    2018-01-01

    In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels

  7. Multimodel hydrological ensemble forecasts for the Baskatong catchment in Canada using the TIGGE database.

    Science.gov (United States)

    Tito Arandia Martinez, Fabian

    2014-05-01

    combined to form a grand ensemble. Results show that the hydrological forecasts derived from the grand ensemble perform better than the pseudo ensemble forecasts actually used operationally at Hydro-Québec. References: [1] M. Verbunt, A. Walser, J. Gurtz et al., "Probabilistic flood forecasting with a limited-area ensemble prediction system: Selected case studies," Journal of Hydrometeorology, vol. 8, no. 4, pp. 897-909, Aug, 2007. [2] N. Evora, Valorisation des prévisions météorologiques d'ensemble, Institu de recherceh d'Hydro-Québec 2005. [3] V. Fortin, Le modèle météo-apport HSAMI: historique, théorie et application, Institut de recherche d'Hydro-Québec, 2000.

  8. A genetic ensemble approach for gene-gene interaction identification

    Directory of Open Access Journals (Sweden)

    Ho Joshua WK

    2010-10-01

    Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of

  9. Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

    Directory of Open Access Journals (Sweden)

    Federico Divina

    2018-04-01

    Full Text Available The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.

  10. Exploiting ensemble learning for automatic cataract detection and grading.

    Science.gov (United States)

    Yang, Ji-Jiang; Li, Jianqiang; Shen, Ruifang; Zeng, Yang; He, Jian; Bi, Jing; Li, Yong; Zhang, Qinyan; Peng, Lihui; Wang, Qing

    2016-02-01

    Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  11. Reliability of windstorm predictions in the ECMWF ensemble prediction system

    Science.gov (United States)

    Becker, Nico; Ulbrich, Uwe

    2016-04-01

    Windstorms caused by extratropical cyclones are one of the most dangerous natural hazards in the European region. Therefore, reliable predictions of such storm events are needed. Case studies have shown that ensemble prediction systems (EPS) are able to provide useful information about windstorms between two and five days prior to the event. In this work, ensemble predictions with the European Centre for Medium-Range Weather Forecasts (ECMWF) EPS are evaluated in a four year period. Within the 50 ensemble members, which are initialized every 12 hours and are run for 10 days, windstorms are identified and tracked in time and space. By using a clustering approach, different predictions of the same storm are identified in the different ensemble members and compared to reanalysis data. The occurrence probability of the predicted storms is estimated by fitting a bivariate normal distribution to the storm track positions. Our results show, for example, that predicted storm clusters with occurrence probabilities of more than 50% have a matching observed storm in 80% of all cases at a lead time of two days. The predicted occurrence probabilities are reliable up to 3 days lead time. At longer lead times the occurrence probabilities are overestimated by the EPS.

  12. Short-term ensemble radar rainfall forecasts for hydrological applications

    Science.gov (United States)

    Codo de Oliveira, M.; Rico-Ramirez, M. A.

    2016-12-01

    Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

  13. Improving wave forecasting by integrating ensemble modelling and machine learning

    Science.gov (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.

    2017-12-01

    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  14. MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

    Science.gov (United States)

    Chen, Lei; Kamel, Mohamed S.

    2016-01-01

    In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

  15. Statistical Analysis of the First Passage Path Ensemble of Jump Processes

    Science.gov (United States)

    von Kleist, Max; Schütte, Christof; Zhang, Wei

    2018-02-01

    The transition mechanism of jump processes between two different subsets in state space reveals important dynamical information of the processes and therefore has attracted considerable attention in the past years. In this paper, we study the first passage path ensemble of both discrete-time and continuous-time jump processes on a finite state space. The main approach is to divide each first passage path into nonreactive and reactive segments and to study them separately. The analysis can be applied to jump processes which are non-ergodic, as well as continuous-time jump processes where the waiting time distributions are non-exponential. In the particular case that the jump processes are both Markovian and ergodic, our analysis elucidates the relations between the study of the first passage paths and the study of the transition paths in transition path theory. We provide algorithms to numerically compute statistics of the first passage path ensemble. The computational complexity of these algorithms scales with the complexity of solving a linear system, for which efficient methods are available. Several examples demonstrate the wide applicability of the derived results across research areas.

  16. Creating ensembles of decision trees through sampling

    Science.gov (United States)

    Kamath, Chandrika; Cantu-Paz, Erick

    2005-08-30

    A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.

  17. Derivation of Mayer Series from Canonical Ensemble

    International Nuclear Information System (INIS)

    Wang Xian-Zhi

    2016-01-01

    Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula. (paper)

  18. Derivation of Mayer Series from Canonical Ensemble

    Science.gov (United States)

    Wang, Xian-Zhi

    2016-02-01

    Mayer derived the Mayer series from both the canonical ensemble and the grand canonical ensemble by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical ensemble by use of this recursion formula.

  19. Ensemble stacking mitigates biases in inference of synaptic connectivity.

    Science.gov (United States)

    Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

    2018-01-01

    A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

  20. Ensemble models of neutrophil trafficking in severe sepsis.

    Directory of Open Access Journals (Sweden)

    Sang Ok Song

    Full Text Available A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection. This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues. In order to better understand the pathophysiologic mechanisms involved, we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP (cecal ligation and puncture-induced sepsis in rats. This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response. Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling. Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets. Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung, consequently contributing to tissue damage and mortality. Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis. Furthermore, the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats. Simulations identified a sub-population (about 18% of the treated population that benefited from blood purification. Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils, contributing to improved outcome. The model ensemble presented herein provides a platform for generating and testing hypotheses in silico, as well as motivating further experimental

  1. The Reduced Rank of Ensemble Kalman Filter to Estimate the Temperature of Non Isothermal Continue Stirred Tank Reactor

    OpenAIRE

    Erna Apriliani; Dieky Adzkiya; Arief Baihaqi

    2011-01-01

    Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability and reduce the computational time. In this paper we study the efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ense...

  2. Short ensembles: An Efficient Method for Discerning Climate-relevant Sensitivities in Atmospheric General Circulation Models

    Energy Technology Data Exchange (ETDEWEB)

    Wan, Hui; Rasch, Philip J.; Zhang, Kai; Qian, Yun; Yan, Huiping; Zhao, Chun

    2014-09-08

    This paper explores the feasibility of an experimentation strategy for investigating sensitivities in fast components of atmospheric general circulation models. The basic idea is to replace the traditional serial-in-time long-term climate integrations by representative ensembles of shorter simulations. The key advantage of the proposed method lies in its efficiency: since fewer days of simulation are needed, the computational cost is less, and because individual realizations are independent and can be integrated simultaneously, the new dimension of parallelism can dramatically reduce the turnaround time in benchmark tests, sensitivities studies, and model tuning exercises. The strategy is not appropriate for exploring sensitivity of all model features, but it is very effective in many situations. Two examples are presented using the Community Atmosphere Model version 5. The first example demonstrates that the method is capable of characterizing the model cloud and precipitation sensitivity to time step length. A nudging technique is also applied to an additional set of simulations to help understand the contribution of physics-dynamics interaction to the detected time step sensitivity. In the second example, multiple empirical parameters related to cloud microphysics and aerosol lifecycle are perturbed simultaneously in order to explore which parameters have the largest impact on the simulated global mean top-of-atmosphere radiation balance. Results show that in both examples, short ensembles are able to correctly reproduce the main signals of model sensitivities revealed by traditional long-term climate simulations for fast processes in the climate system. The efficiency of the ensemble method makes it particularly useful for the development of high-resolution, costly and complex climate models.

  3. Ensemble Weight Enumerators for Protograph LDPC Codes

    Science.gov (United States)

    Divsalar, Dariush

    2006-01-01

    Recently LDPC codes with projected graph, or protograph structures have been proposed. In this paper, finite length ensemble weight enumerators for LDPC codes with protograph structures are obtained. Asymptotic results are derived as the block size goes to infinity. In particular we are interested in obtaining ensemble average weight enumerators for protograph LDPC codes which have minimum distance that grows linearly with block size. As with irregular ensembles, linear minimum distance property is sensitive to the proportion of degree-2 variable nodes. In this paper the derived results on ensemble weight enumerators show that linear minimum distance condition on degree distribution of unstructured irregular LDPC codes is a sufficient but not a necessary condition for protograph LDPC codes.

  4. Ensemble Kalman filtering with residual nudging

    KAUST Repository

    Luo, X.; Hoteit, Ibrahim

    2012-01-01

    Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work

  5. AUC-Maximizing Ensembles through Metalearning.

    Science.gov (United States)

    LeDell, Erin; van der Laan, Mark J; Petersen, Maya

    2016-05-01

    Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree.

  6. Multivariate localization methods for ensemble Kalman filtering

    KAUST Repository

    Roh, S.; Jun, M.; Szunyogh, I.; Genton, Marc G.

    2015-01-01

    the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function

  7. Polarized ensembles of random pure states

    International Nuclear Information System (INIS)

    Cunden, Fabio Deelan; Facchi, Paolo; Florio, Giuseppe

    2013-01-01

    A new family of polarized ensembles of random pure states is presented. These ensembles are obtained by linear superposition of two random pure states with suitable distributions, and are quite manageable. We will use the obtained results for two purposes: on the one hand we will be able to derive an efficient strategy for sampling states from isopurity manifolds. On the other, we will characterize the deviation of a pure quantum state from separability under the influence of noise. (paper)

  8. Polarized ensembles of random pure states

    Science.gov (United States)

    Deelan Cunden, Fabio; Facchi, Paolo; Florio, Giuseppe

    2013-08-01

    A new family of polarized ensembles of random pure states is presented. These ensembles are obtained by linear superposition of two random pure states with suitable distributions, and are quite manageable. We will use the obtained results for two purposes: on the one hand we will be able to derive an efficient strategy for sampling states from isopurity manifolds. On the other, we will characterize the deviation of a pure quantum state from separability under the influence of noise.

  9. Orbital magnetism in ensembles of ballistic billiards

    International Nuclear Information System (INIS)

    Ullmo, D.; Richter, K.; Jalabert, R.A.

    1993-01-01

    The magnetic response of ensembles of small two-dimensional structures at finite temperatures is calculated. Using semiclassical methods and numerical calculation it is demonstrated that only short classical trajectories are relevant. The magnetic susceptibility is enhanced in regular systems, where these trajectories appear in families. For ensembles of squares large paramagnetic susceptibility is obtained, in good agreement with recent measurements in the ballistic regime. (authors). 20 refs., 2 figs

  10. Multivariate localization methods for ensemble Kalman filtering

    OpenAIRE

    S. Roh; M. Jun; I. Szunyogh; M. G. Genton

    2015-01-01

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of ...

  11. EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms

    KAUST Repository

    Rapakoulia, Trisevgeni

    2014-04-26

    Motivation: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem ofmissing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a twostep algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. © The Author 2014.

  12. Ensemble stacking mitigates biases in inference of synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Brendan Chambers

    2018-03-01

    Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures

  13. Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique

    Directory of Open Access Journals (Sweden)

    Jan D. Keller

    2008-12-01

    Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.

  14. A revealed-preference study of behavioural impacts of real-time traffic information

    NARCIS (Netherlands)

    Knockaert, J.S.A.; Tseng, Y.; Verhoef, E.T.

    2013-01-01

    In the present study, we investigate the impact of real-time traffic information on traveller behaviour by using repeated day-to-day revealed-preference (RP) observations from a reward experiment. We estimate a trip scheduling model of morning peak behaviour that allows us to determine the impact of

  15. Rotationally invariant family of Levy-like random matrix ensembles

    International Nuclear Information System (INIS)

    Choi, Jinmyung; Muttalib, K A

    2009-01-01

    We introduce a family of rotationally invariant random matrix ensembles characterized by a parameter λ. While λ = 1 corresponds to well-known critical ensembles, we show that λ ≠ 1 describes 'Levy-like' ensembles, characterized by power-law eigenvalue densities. For λ > 1 the density is bounded, as in Gaussian ensembles, but λ < 1 describes ensembles characterized by densities with long tails. In particular, the model allows us to evaluate, in terms of a novel family of orthogonal polynomials, the eigenvalue correlations for Levy-like ensembles. These correlations differ qualitatively from those in either the Gaussian or the critical ensembles. (fast track communication)

  16. A variational ensemble scheme for noisy image data assimilation

    Science.gov (United States)

    Yang, Yin; Robinson, Cordelia; Heitz, Dominique; Mémin, Etienne

    2014-05-01

    Data assimilation techniques aim at recovering a system state variables trajectory denoted as X, along time from partially observed noisy measurements of the system denoted as Y. These procedures, which couple dynamics and noisy measurements of the system, fulfill indeed a twofold objective. On one hand, they provide a denoising - or reconstruction - procedure of the data through a given model framework and on the other hand, they provide estimation procedures for unknown parameters of the dynamics. A standard variational data assimilation problem can be formulated as the minimization of the following objective function with respect to the initial discrepancy, η, from the background initial guess: δ« J(η(x)) = 1∥Xb (x) - X (t ,x)∥2 + 1 tf∥H(X (t,x ))- Y (t,x)∥2dt. 2 0 0 B 2 t0 R (1) where the observation operator H links the state variable and the measurements. The cost function can be interpreted as the log likelihood function associated to the a posteriori distribution of the state given the past history of measurements and the background. In this work, we aim at studying ensemble based optimal control strategies for data assimilation. Such formulation nicely combines the ingredients of ensemble Kalman filters and variational data assimilation (4DVar). It is also formulated as the minimization of the objective function (1), but similarly to ensemble filter, it introduces in its objective function an empirical ensemble-based background-error covariance defined as: B ≡ )(Xb - )T>. (2) Thus, it works in an off-line smoothing mode rather than on the fly like sequential filters. Such resulting ensemble variational data assimilation technique corresponds to a relatively new family of methods [1,2,3]. It presents two main advantages: first, it does not require anymore to construct the adjoint of the dynamics tangent linear operator, which is a considerable advantage with respect to the method's implementation, and second, it enables the handling of a flow

  17. Analogies between random matrix ensembles and the one-component plasma in two-dimensions

    Directory of Open Access Journals (Sweden)

    Peter J. Forrester

    2016-03-01

    Full Text Available The eigenvalue PDF for some well known classes of non-Hermitian random matrices — the complex Ginibre ensemble for example — can be interpreted as the Boltzmann factor for one-component plasma systems in two-dimensional domains. We address this theme in a systematic fashion, identifying the plasma system for the Ginibre ensemble of non-Hermitian Gaussian random matrices G, the spherical ensemble of the product of an inverse Ginibre matrix and a Ginibre matrix G1−1G2, and the ensemble formed by truncating unitary matrices, as well as for products of such matrices. We do this when each has either real, complex or real quaternion elements. One consequence of this analogy is that the leading form of the eigenvalue density follows as a corollary. Another is that the eigenvalue correlations must obey sum rules known to characterise the plasma system, and this leads us to an exhibit of an integral identity satisfied by the two-particle correlation for real quaternion matrices in the neighbourhood of the real axis. Further random matrix ensembles investigated from this viewpoint are self dual non-Hermitian matrices, in which a previous study has related to the one-component plasma system in a disk at inverse temperature β=4, and the ensemble formed by the single row and column of quaternion elements from a member of the circular symplectic ensemble.

  18. Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting

    Directory of Open Access Journals (Sweden)

    Jianzhong Zhou

    2018-05-01

    Full Text Available This paper introduces three artificial neural network (ANN architectures for monthly streamflow forecasting: a radial basis function network, an extreme learning machine, and the Elman network. Three ensemble techniques, a simple average ensemble, a weighted average ensemble, and an ANN-based ensemble, were used to combine the outputs of the individual ANN models. The objective was to highlight the performance of the general regression neural network-based ensemble technique (GNE through an improvement of monthly streamflow forecasting accuracy. Before the construction of an ANN model, data preanalysis techniques, such as empirical wavelet transform (EWT, were exploited to eliminate the oscillations of the streamflow series. Additionally, a theory of chaos phase space reconstruction was used to select the most relevant and important input variables for forecasting. The proposed GNE ensemble model has been applied for the mean monthly streamflow observation data from the Wudongde hydrological station in the Jinsha River Basin, China. Comparisons and analysis of this study have demonstrated that the denoised streamflow time series was less disordered and unsystematic than was suggested by the original time series according to chaos theory. Thus, EWT can be adopted as an effective data preanalysis technique for the prediction of monthly streamflow. Concurrently, the GNE performed better when compared with other ensemble techniques.

  19. Dispersion of aerosol particles in the free atmosphere using ensemble forecasts

    Directory of Open Access Journals (Sweden)

    T. Haszpra

    2013-10-01

    Full Text Available The dispersion of aerosol particle pollutants is studied using 50 members of an ensemble forecast in the example of a hypothetical free atmospheric emission above Fukushima over a period of 2.5 days. Considerable differences are found among the dispersion predictions of the different ensemble members, as well as between the ensemble mean and the deterministic result at the end of the observation period. The variance is found to decrease with the particle size. The geographical area where a threshold concentration is exceeded in at least one ensemble member expands to a 5–10 times larger region than the area from the deterministic forecast, both for air column "concentration" and in the "deposition" field. We demonstrate that the root-mean-square distance of any particle from its own clones in the ensemble members can reach values on the order of one thousand kilometers. Even the centers of mass of the particle cloud of the ensemble members deviate considerably from that obtained by the deterministic forecast. All these indicate that an investigation of the dispersion of aerosol particles in the spirit of ensemble forecast contains useful hints for the improvement of risk assessment.

  20. Benefits of an ultra large and multiresolution ensemble for estimating available wind power

    Science.gov (United States)

    Berndt, Jonas; Hoppe, Charlotte; Elbern, Hendrik

    2016-04-01

    In this study we investigate the benefits of an ultra large ensemble with up to 1000 members including multiple nesting with a target horizontal resolution of 1 km. The ensemble shall be used as a basis to detect events of extreme errors in wind power forecasting. Forecast value is the wind vector at wind turbine hub height (~ 100 m) in the short range (1 to 24 hour). Current wind power forecast systems rest already on NWP ensemble models. However, only calibrated ensembles from meteorological institutions serve as input so far, with limited spatial resolution (˜10 - 80 km) and member number (˜ 50). Perturbations related to the specific merits of wind power production are yet missing. Thus, single extreme error events which are not detected by such ensemble power forecasts occur infrequently. The numerical forecast model used in this study is the Weather Research and Forecasting Model (WRF). Model uncertainties are represented by stochastic parametrization of sub-grid processes via stochastically perturbed parametrization tendencies and in conjunction via the complementary stochastic kinetic-energy backscatter scheme already provided by WRF. We perform continuous ensemble updates by comparing each ensemble member with available observations using a sequential importance resampling filter to improve the model accuracy while maintaining ensemble spread. Additionally, we use different ensemble systems from global models (ECMWF and GFS) as input and boundary conditions to capture different synoptic conditions. Critical weather situations which are connected to extreme error events are located and corresponding perturbation techniques are applied. The demanding computational effort is overcome by utilising the supercomputer JUQUEEN at the Forschungszentrum Juelich.

  1. Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

    Science.gov (United States)

    Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

    2016-08-01

    The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon 'Talas' of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.

  2. Synchronization dynamics in a small pacemaker neuronal ensemble via a robust adaptive controller

    International Nuclear Information System (INIS)

    Cornejo-Pérez, O.; Solis-Perales, G.C.; Arenas-Prado, J.A.

    2012-01-01

    The synchronization dynamics of a pacemaker neuronal ensemble under the action of a control command is studied herein. The ensemble corresponds to the pyloric central pattern generator of the stomatogastric ganglion of lobster. The desired dynamics is provided by means of an external master neuron and it is induced via a nonlinear controller. Such a controller is composed of a linearizing-like controller and a high gain observer; the controller is able to counteract uncertainties and external perturbations in the controlled system. Numerical simulations of the robust synchronization dynamics of the master neuron and the pacemaker neuronal ensemble are displayed.

  3. Control of inhomogeneous atomic ensembles of hyperfine qudits

    DEFF Research Database (Denmark)

    Mischuck, Brian Edward; Merkel, Seth T.; Deutsch, Ivan H.

    2012-01-01

    We study the ability to control d-dimensional quantum systems (qudits) encoded in the hyperfine spin of alkali-metal atoms through the application of radio- and microwave-frequency magnetic fields in the presence of inhomogeneities in amplitude and detuning. Such a capability is essential...... to the design of robust pulses that mitigate the effects of experimental uncertainty and also for application to tomographic addressing of particular members of an extended ensemble. We study the problem of preparing an arbitrary state in the Hilbert space from an initial fiducial state. We prove...... that inhomogeneous control of qudit ensembles is possible based on a semianalytic protocol that synthesizes the target through a sequence of alternating rf and microwave-driven SU(2) rotations in overlapping irreducible subspaces. Several examples of robust control are studied, and the semianalytic protocol...

  4. Kinetics and dynamics of near-resonant vibrational energy transfer in gas ensembles of atmospheric interest

    Science.gov (United States)

    McCaffery, Anthony J.

    2018-03-01

    This study of near-resonant, vibration-vibration (V-V) gas-phase energy transfer in diatomic molecules uses the theoretical/computational method, of Marsh & McCaffery (Marsh & McCaffery 2002 J. Chem. Phys. 117, 503 (doi:10.1063/1.1489998)) The method uses the angular momentum (AM) theoretical formalism to compute quantum-state populations within the component molecules of large, non-equilibrium, gas mixtures as the component species proceed to equilibration. Computed quantum-state populations are displayed in a number of formats that reveal the detailed mechanism of the near-resonant V-V process. Further, the evolution of quantum-state populations, for each species present, may be followed as the number of collision cycles increases, displaying the kinetics of evolution for each quantum state of the ensemble's molecules. These features are illustrated for ensembles containing vibrationally excited N2 in H2, O2 and N2 initially in their ground states. This article is part of the theme issue `Modern theoretical chemistry'.

  5. Extracting information from an ensemble of GCMs to reliably assess future global runoff change

    NARCIS (Netherlands)

    Sperna Weiland, F.C.; Beek, L.P.H. van; Weerts, A.H.; Bierkens, M.F.P.

    2011-01-01

    Future runoff projections derived from different global climate models (GCMs) show large differences. Therefore, within this study the, information from multiple GCMs has been combined to better assess hydrological changes. For projections of precipitation and temperature the Reliability ensemble

  6. Transcriptome classification reveals molecular subtypes in psoriasis

    Directory of Open Access Journals (Sweden)

    Ainali Chrysanthi

    2012-09-01

    Full Text Available Abstract Background Psoriasis is an immune-mediated disease characterised by chronically elevated pro-inflammatory cytokine levels, leading to aberrant keratinocyte proliferation and differentiation. Although certain clinical phenotypes, such as plaque psoriasis, are well defined, it is currently unclear whether there are molecular subtypes that might impact on prognosis or treatment outcomes. Results We present a pipeline for patient stratification through a comprehensive analysis of gene expression in paired lesional and non-lesional psoriatic tissue samples, compared with controls, to establish differences in RNA expression patterns across all tissue types. Ensembles of decision tree predictors were employed to cluster psoriatic samples on the basis of gene expression patterns and reveal gene expression signatures that best discriminate molecular disease subtypes. This multi-stage procedure was applied to several published psoriasis studies and a comparison of gene expression patterns across datasets was performed. Conclusion Overall, classification of psoriasis gene expression patterns revealed distinct molecular sub-groups within the clinical phenotype of plaque psoriasis. Enrichment for TGFb and ErbB signaling pathways, noted in one of the two psoriasis subgroups, suggested that this group may be more amenable to therapies targeting these pathways. Our study highlights the potential biological relevance of using ensemble decision tree predictors to determine molecular disease subtypes, in what may initially appear to be a homogenous clinical group. The R code used in this paper is available upon request.

  7. A Simple Approach to Account for Climate Model Interdependence in Multi-Model Ensembles

    Science.gov (United States)

    Herger, N.; Abramowitz, G.; Angelil, O. M.; Knutti, R.; Sanderson, B.

    2016-12-01

    Multi-model ensembles are an indispensable tool for future climate projection and its uncertainty quantification. Ensembles containing multiple climate models generally have increased skill, consistency and reliability. Due to the lack of agreed-on alternatives, most scientists use the equally-weighted multi-model mean as they subscribe to model democracy ("one model, one vote").Different research groups are known to share sections of code, parameterizations in their model, literature, or even whole model components. Therefore, individual model runs do not represent truly independent estimates. Ignoring this dependence structure might lead to a false model consensus, wrong estimation of uncertainty and effective number of independent models.Here, we present a way to partially address this problem by selecting a subset of CMIP5 model runs so that its climatological mean minimizes the RMSE compared to a given observation product. Due to the cancelling out of errors, regional biases in the ensemble mean are reduced significantly.Using a model-as-truth experiment we demonstrate that those regional biases persist into the future and we are not fitting noise, thus providing improved observationally-constrained projections of the 21st century. The optimally selected ensemble shows significantly higher global mean surface temperature projections than the original ensemble, where all the model runs are considered. Moreover, the spread is decreased well beyond that expected from the decreased ensemble size.Several previous studies have recommended an ensemble selection approach based on performance ranking of the model runs. Here, we show that this approach can perform even worse than randomly selecting ensemble members and can thus be harmful. We suggest that accounting for interdependence in the ensemble selection process is a necessary step for robust projections for use in impact assessments, adaptation and mitigation of climate change.

  8. Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR Soil Moisture Retrieval Errors

    Directory of Open Access Journals (Sweden)

    Ju Hyoung Lee

    2016-04-01

    Full Text Available Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE, while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

  9. System size effects on the mechanical response of cohesive-frictional granular ensembles

    Directory of Open Access Journals (Sweden)

    Singh Saurabh

    2017-01-01

    Full Text Available Shear resistance in granular ensembles is a result of interparticle interaction and friction. However, even the presence of small amounts of cohesion between the particles changes the landscape of the mechanical response considerably. Very often such cohesive frictional (c-ϕ granular ensembles are encountered in nature as well as while handling and storage of granular materials in the pharmaceutical, construction and mining industries. Modeling of these c-ϕ materials, especially in engineering applications have relied on the oft-made assumption of a “continua” and have utilized the popular tenets of continuum plasticity theory. We present an experimental investigation on the fundamental mechanics of c-ϕ materials specifically; we investigate if there exists a system size effect and any additional length scales beyond the continuum length scale on their mechanical response. For this purpose, we conduct a series of 1-D compression (UC tests on cylindrical specimens reconstituted in the laboratory with a range of model particle–binder combinations such as sandcement, sand-epoxy, and glass ballotini-epoxy mixtures. Specimens are reconstituted to various diameters ranging from 10 mm to 150 mm (with an aspect ratio of 2 to a predefined packing fraction. In addition to the effect of the type of binder (cement, epoxy and system size, the mean particle size is also varied from 0.5 to 2.5 mm. The peak strength of these materials is significant as it signals the initiation of the cohesive-bond breaking and onset of mobilization of the inter particle frictional resistance. For these model systems, the peak strength is a strong function of the system size of the ensemble as well as the mean particle size. This intriguing observation is counter to the traditional notion of a continuum plastic typical granular ensemble. Microstructure studies in a computed-tomograph have revealed the existence of a web patterned ‘entangled-chain’ like structure

  10. System size effects on the mechanical response of cohesive-frictional granular ensembles

    Science.gov (United States)

    Singh, Saurabh; Kandasami, Ramesh Kannan; Mahendran, Rupesh Kumar; Murthy, Tejas

    2017-06-01

    Shear resistance in granular ensembles is a result of interparticle interaction and friction. However, even the presence of small amounts of cohesion between the particles changes the landscape of the mechanical response considerably. Very often such cohesive frictional (c-ϕ) granular ensembles are encountered in nature as well as while handling and storage of granular materials in the pharmaceutical, construction and mining industries. Modeling of these c-ϕ materials, especially in engineering applications have relied on the oft-made assumption of a "continua" and have utilized the popular tenets of continuum plasticity theory. We present an experimental investigation on the fundamental mechanics of c-ϕ materials specifically; we investigate if there exists a system size effect and any additional length scales beyond the continuum length scale on their mechanical response. For this purpose, we conduct a series of 1-D compression (UC) tests on cylindrical specimens reconstituted in the laboratory with a range of model particle-binder combinations such as sandcement, sand-epoxy, and glass ballotini-epoxy mixtures. Specimens are reconstituted to various diameters ranging from 10 mm to 150 mm (with an aspect ratio of 2) to a predefined packing fraction. In addition to the effect of the type of binder (cement, epoxy) and system size, the mean particle size is also varied from 0.5 to 2.5 mm. The peak strength of these materials is significant as it signals the initiation of the cohesive-bond breaking and onset of mobilization of the inter particle frictional resistance. For these model systems, the peak strength is a strong function of the system size of the ensemble as well as the mean particle size. This intriguing observation is counter to the traditional notion of a continuum plastic typical granular ensemble. Microstructure studies in a computed-tomograph have revealed the existence of a web patterned `entangled-chain' like structure, we argue that this ushers

  11. Weighted ensemble transform Kalman filter for image assimilation

    Directory of Open Access Journals (Sweden)

    Sebastien Beyou

    2013-01-01

    Full Text Available This study proposes an extension of the Weighted Ensemble Kalman filter (WEnKF proposed by Papadakis et al. (2010 for the assimilation of image observations. The main focus of this study is on a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF, incorporating directly as a measurement model a non-linear image reconstruction criterion. This technique has been compared to the original WEnKF on numerical and real world data of 2-D turbulence observed through the transport of a passive scalar. In particular, it has been applied for the reconstruction of oceanic surface current vorticity fields from sea surface temperature (SST satellite data. This latter technique enables a consistent recovery along time of oceanic surface currents and vorticity maps in presence of large missing data areas and strong noise.

  12. Enhancing optical nonreciprocity by an atomic ensemble in two coupled cavities

    Science.gov (United States)

    Song, L. N.; Wang, Z. H.; Li, Yong

    2018-05-01

    We study the optical nonreciprocal propagation in an optical molecule of two coupled cavities with one of them interacting with a two-level atomic ensemble. The effect of increasing the number of atoms on the optical isolation ratio of the system is studied. We demonstrate that the significant nonlinearity supplied by the coupling of the atomic ensemble with the cavity leads to the realization of greatly-enhanced optical nonreciprocity compared with the case of single atom.

  13. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    KAUST Repository

    Toye, Habib

    2017-05-26

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  14. New technologies for examining the role of neuronal ensembles in drug addiction and fear.

    Science.gov (United States)

    Cruz, Fabio C; Koya, Eisuke; Guez-Barber, Danielle H; Bossert, Jennifer M; Lupica, Carl R; Shaham, Yavin; Hope, Bruce T

    2013-11-01

    Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. In addition, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches--Daun02 inactivation, FACS sorting of activated neurons and Fos-GFP transgenic rats--that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools--Fos-tTA transgenic mice and inactivation of CREB-overexpressing neurons--that have been used to study the role of neuronal ensembles in conditioned fear.

  15. New technologies for examining neuronal ensembles in drug addiction and fear

    Science.gov (United States)

    Cruz, Fabio C.; Koya, Eisuke; Guez-Barber, Danielle H.; Bossert, Jennifer M.; Lupica, Carl R.; Shaham, Yavin; Hope, Bruce T.

    2015-01-01

    Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. Additionally, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches—Daun02 inactivation, FACS sorting of activated neurons and c-fos-GFP transgenic rats — that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools — c-fos-tTA mice and inactivation of CREB-overexpressing neurons — that have been used to study the role of neuronal ensembles in conditioned fear. PMID:24088811

  16. Dynamics of multi-frequency oscillator ensembles with resonant coupling

    Science.gov (United States)

    Lück, S.; Pikovsky, A.

    2011-07-01

    We study dynamics of populations of resonantly coupled oscillators having different frequencies. Starting from the coupled van der Pol equations we derive the Kuramoto-type phase model for the situation, where the natural frequencies of two interacting subpopulations are in relation 2:1. Depending on the parameter of coupling, ensembles can demonstrate fully synchronous clusters, partial synchrony (only one subpopulation synchronizes), or asynchrony in both subpopulations. Theoretical description of the dynamics based on the Watanabe-Strogatz approach is developed.

  17. Using synchronization in multi-model ensembles to improve prediction

    Science.gov (United States)

    Hiemstra, P.; Selten, F.

    2012-04-01

    In recent decades, many climate models have been developed to understand and predict the behavior of the Earth's climate system. Although these models are all based on the same basic physical principles, they still show different behavior. This is for example caused by the choice of how to parametrize sub-grid scale processes. One method to combine these imperfect models, is to run a multi-model ensemble. The models are given identical initial conditions and are integrated forward in time. A multi-model estimate can for example be a weighted mean of the ensemble members. We propose to go a step further, and try to obtain synchronization between the imperfect models by connecting the multi-model ensemble, and exchanging information. The combined multi-model ensemble is also known as a supermodel. The supermodel has learned from observations how to optimally exchange information between the ensemble members. In this study we focused on the density and formulation of the onnections within the supermodel. The main question was whether we could obtain syn-chronization between two climate models when connecting only a subset of their state spaces. Limiting the connected subspace has two advantages: 1) it limits the transfer of data (bytes) between the ensemble, which can be a limiting factor in large scale climate models, and 2) learning the optimal connection strategy from observations is easier. To answer the research question, we connected two identical quasi-geostrohic (QG) atmospheric models to each other, where the model have different initial conditions. The QG model is a qualitatively realistic simulation of the winter flow on the Northern hemisphere, has three layers and uses a spectral imple-mentation. We connected the models in the original spherical harmonical state space, and in linear combinations of these spherical harmonics, i.e. Empirical Orthogonal Functions (EOFs). We show that when connecting through spherical harmonics, we only need to connect 28% of

  18. Assessment of Mediterranean cyclones in the multi-ensemble EC-Earth

    Science.gov (United States)

    Gil, Victoria; Liberato, Margarida L. R.; Trigo, Isabel F.; Trigo, Ricardo M.

    2015-04-01

    The geographical location and characteristics of the Mediterranean basin make this a particularly active region in terms of cyclone forming and re-development (Trigo et al., 2002). The area is affected by moving depressions, most originated over the North Atlantic, which may later be forced by the orography surrounding the Mediterranean Sea and enhanced by the local source of moisture and heat fluxes over the Sea itself. The present work analyses the response of Mediterranean cyclones to climate change by means of 7 ensemble members of EC-EARTH model from CMIP5 (Fifth Coupled Model Intercomparison Project). We restrict the analysis to a relatively small subset (7 members) of the total number of ensemble members available in order to take into account only the members present in the three selected experiments for robust detection of extra-tropical cyclones in the Mediterranean (Trigo, 2006). We have applied the standard procedure by comparing a common 25-year period of the historical (1980-2004), present day simulations, and the future climate simulations (2074-2098) forced by RCP4.5 and RCP8.5 scenarios. The study area corresponds to the window between 10°W-42°E and 27°N-48°N. The analysis is performed with a focus in spatial distribution density and main characteristics of the overall cyclones for winter (DJF) and summer (JJA) seasons. Despite the discrepancies in cyclone numbers when compared with the ERA Interim common period (reducing to only 72% in DJF and 78% in JJA), the ensemble average matches relatively well the main spatial patterns of areas. Results indicate that the ensemble average is characterized by a small decrease in winter (-3%) and a notable increase in summer (+10%) in total number of cyclones and that the individual ensemble members reveal small spread. Such tendency is particularly pronounced under the high RCP8.5 emission scenario being more moderated under the RCP4.5 scenario. Additionally, an assessment of changes in the annual cycle

  19. The limit shape problem for ensembles of Young diagrams

    CERN Document Server

    Hora, Akihito

    2016-01-01

    This book treats ensembles of Young diagrams originating from group-theoretical contexts and investigates what statistical properties are observed there in a large-scale limit. The focus is mainly on analyzing the interesting phenomenon that specific curves appear in the appropriate scaling limit for the profiles of Young diagrams. This problem is regarded as an important origin of recent vital studies on harmonic analysis of huge symmetry structures. As mathematics, an asymptotic theory of representations is developed of the symmetric groups of degree n as n goes to infinity. The framework of rigorous limit theorems (especially the law of large numbers) in probability theory is employed as well as combinatorial analysis of group characters of symmetric groups and applications of Voiculescu's free probability. The central destination here is a clear description of the asymptotic behavior of rescaled profiles of Young diagrams in the Plancherel ensemble from both static and dynamic points of view.

  20. Generation of macroscopic singlet states in atomic ensembles

    Science.gov (United States)

    Tóth, Géza; Mitchell, Morgan W.

    2010-05-01

    We study squeezing of the spin uncertainties by quantum non-demolition (QND) measurement in non-polarized spin ensembles. Unlike the case of polarized ensembles, the QND measurements can be performed with negligible back-action, which allows, in principle, perfect spin squeezing as quantified by Tóth et al (2007 Phys. Rev. Lett. 99 250405). The generated spin states approach many-body singlet states and contain a macroscopic number of entangled particles even when individual spin is large. We introduce the Gaussian treatment of unpolarized spin states and use it to estimate the achievable spin squeezing for realistic experimental parameters. Our proposal might have applications for magnetometry with a high spatial resolution or quantum memories storing information in decoherence free subspaces.

  1. Security Enrichment in Intrusion Detection System Using Classifier Ensemble

    Directory of Open Access Journals (Sweden)

    Uma R. Salunkhe

    2017-01-01

    Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

  2. Generalized ensemble theory with non-extensive statistics

    Science.gov (United States)

    Shen, Ke-Ming; Zhang, Ben-Wei; Wang, En-Ke

    2017-12-01

    The non-extensive canonical ensemble theory is reconsidered with the method of Lagrange multipliers by maximizing Tsallis entropy, with the constraint that the normalized term of Tsallis' q -average of physical quantities, the sum ∑ pjq, is independent of the probability pi for Tsallis parameter q. The self-referential problem in the deduced probability and thermal quantities in non-extensive statistics is thus avoided, and thermodynamical relationships are obtained in a consistent and natural way. We also extend the study to the non-extensive grand canonical ensemble theory and obtain the q-deformed Bose-Einstein distribution as well as the q-deformed Fermi-Dirac distribution. The theory is further applied to the generalized Planck law to demonstrate the distinct behaviors of the various generalized q-distribution functions discussed in literature.

  3. Detection of eardrum abnormalities using ensemble deep learning approaches

    Science.gov (United States)

    Senaras, Caglar; Moberly, Aaron C.; Teknos, Theodoros; Essig, Garth; Elmaraghy, Charles; Taj-Schaal, Nazhat; Yua, Lianbo; Gurcan, Metin N.

    2018-02-01

    In this study, we proposed an approach to report the condition of the eardrum as "normal" or "abnormal" by ensembling two different deep learning architectures. In the first network (Network 1), we applied transfer learning to the Inception V3 network by using 409 labeled samples. As a second network (Network 2), we designed a convolutional neural network to take advantage of auto-encoders by using additional 673 unlabeled eardrum samples. The individual classification accuracies of the Network 1 and Network 2 were calculated as 84.4%(+/- 12.1%) and 82.6% (+/- 11.3%), respectively. Only 32% of the errors of the two networks were the same, making it possible to combine two approaches to achieve better classification accuracy. The proposed ensemble method allows us to achieve robust classification because it has high accuracy (84.4%) with the lowest standard deviation (+/- 10.3%).

  4. Ensemble streamflow assimilation with the National Water Model.

    Science.gov (United States)

    Rafieeinasab, A.; McCreight, J. L.; Noh, S.; Seo, D. J.; Gochis, D.

    2017-12-01

    Through case studies of flooding across the US, we compare the performance of the National Water Model (NWM) data assimilation (DA) scheme to that of a newly implemented ensemble Kalman filter approach. The NOAA National Water Model (NWM) is an operational implementation of the community WRF-Hydro modeling system. As of August 2016, the NWM forecasts of distributed hydrologic states and fluxes (including soil moisture, snowpack, ET, and ponded water) over the contiguous United States have been publicly disseminated by the National Center for Environmental Prediction (NCEP) . It also provides streamflow forecasts at more than 2.7 million river reaches up to 30 days in advance. The NWM employs a nudging scheme to assimilate more than 6,000 USGS streamflow observations and provide initial conditions for its forecasts. A problem with nudging is how the forecasts relax quickly to open-loop bias in the forecast. This has been partially addressed by an experimental bias correction approach which was found to have issues with phase errors during flooding events. In this work, we present an ensemble streamflow data assimilation approach combining new channel-only capabilities of the NWM and HydroDART (a coupling of the offline WRF-Hydro model and NCAR's Data Assimilation Research Testbed; DART). Our approach focuses on the single model state of discharge and incorporates error distributions on channel-influxes (overland and groundwater) in the assimilation via an ensemble Kalman filter (EnKF). In order to avoid filter degeneracy associated with a limited number of ensemble at large scale, DART's covariance inflation (Anderson, 2009) and localization capabilities are implemented and evaluated. The current NWM data assimilation scheme is compared to preliminary results from the EnKF application for several flooding case studies across the US.

  5. Ensemble forecasting of potential habitat for three invasive fishes

    Science.gov (United States)

    Poulos, Helen M.; Chernoff, Barry; Fuller, Pam L.; Butman, David

    2012-01-01

    Aquatic invasive species pose major ecological and economic threats to aquatic ecosystems worldwide via displacement, predation, or hybridization with native species and the alteration of aquatic habitats and hydrologic cycles. Modeling the habitat suitability of alien aquatic species through spatially explicit mapping is an increasingly important risk assessment tool. Habitat modeling also facilitates identification of key environmental variables influencing invasive species distributions. We compared four modeling methods to predict the potential continental United States distributions of northern snakehead Channa argus (Cantor, 1842), round goby Neogobius melanostomus (Pallas, 1814), and silver carp Hypophthalmichthys molitrix (Valenciennes, 1844) using maximum entropy (Maxent), the genetic algorithm for rule set production (GARP), DOMAIN, and support vector machines (SVM). We used inventory records from the USGS Nonindigenous Aquatic Species Database and a geographic information system of 20 climatic and environmental variables to generate individual and ensemble distribution maps for each species. The ensemble maps from our study performed as well as or better than all of the individual models except Maxent. The ensemble and Maxent models produced significantly higher accuracy individual maps than GARP, one-class SVMs, or DOMAIN. The key environmental predictor variables in the individual models were consistent with the tolerances of each species. Results from this study provide insights into which locations and environmental conditions may promote the future spread of invasive fish in the US.

  6. Understanding ensemble protein folding at atomic detail

    International Nuclear Information System (INIS)

    Wallin, Stefan; Shakhnovich, Eugene I

    2008-01-01

    Although far from routine, simulating the folding of specific short protein chains on the computer, at a detailed atomic level, is starting to become a reality. This remarkable progress, which has been made over the last decade or so, allows a fundamental aspect of the protein folding process to be addressed, namely its statistical nature. In order to make quantitative comparisons with experimental kinetic data a complete ensemble view of folding must be achieved, with key observables averaged over the large number of microscopically different folding trajectories available to a protein chain. Here we review recent advances in atomic-level protein folding simulations and the new insight provided by them into the protein folding process. An important element in understanding ensemble folding kinetics are methods for analyzing many separate folding trajectories, and we discuss techniques developed to condense the large amount of information contained in an ensemble of trajectories into a manageable picture of the folding process. (topical review)

  7. Lattice gauge theory in the microcanonical ensemble

    International Nuclear Information System (INIS)

    Callaway, D.J.E.; Rahman, A.

    1983-01-01

    The microcanonical-ensemble formulation of lattice gauge theory proposed recently is examined in detail. Expectation values in this new ensemble are determined by solving a large set of coupled ordinary differential equations, after the fashion of a molecular dynamics simulation. Following a brief review of the microcanonical ensemble, calculations are performed for the gauge groups U(1), SU(2), and SU(3). The results are compared and contrasted with standard methods of computation. Several advantages of the new formalism are noted. For example, no random numbers are required to update the system. Also, this update is performed in a simultaneous fashion. Thus the microcanonical method presumably adapts well to parallel processing techniques, especially when the p action is highly nonlocal (such as when fermions are included)

  8. Ensemble Network Architecture for Deep Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Xi-liang Chen

    2018-01-01

    Full Text Available The popular deep Q learning algorithm is known to be instability because of the Q-value’s shake and overestimation action values under certain conditions. These issues tend to adversely affect their performance. In this paper, we develop the ensemble network architecture for deep reinforcement learning which is based on value function approximation. The temporal ensemble stabilizes the training process by reducing the variance of target approximation error and the ensemble of target values reduces the overestimate and makes better performance by estimating more accurate Q-value. Our results show that this architecture leads to statistically significant better value evaluation and more stable and better performance on several classical control tasks at OpenAI Gym environment.

  9. Embedded random matrix ensembles in quantum physics

    CERN Document Server

    Kota, V K B

    2014-01-01

    Although used with increasing frequency in many branches of physics, random matrix ensembles are not always sufficiently specific to account for important features of the physical system at hand. One refinement which retains the basic stochastic approach but allows for such features consists in the use of embedded ensembles.  The present text is an exhaustive introduction to and survey of this important field. Starting with an easy-to-read introduction to general random matrix theory, the text then develops the necessary concepts from the beginning, accompanying the reader to the frontiers of present-day research. With some notable exceptions, to date these ensembles have primarily been applied in nuclear spectroscopy. A characteristic example is the use of a random two-body interaction in the framework of the nuclear shell model. Yet, topics in atomic physics, mesoscopic physics, quantum information science and statistical mechanics of isolated finite quantum systems can also be addressed using these ensemb...

  10. Classroom Environment as Related to Contest Ratings among High School Performing Ensembles.

    Science.gov (United States)

    Hamann, Donald L.; And Others

    1990-01-01

    Examines influence of classroom environments, measured by the Classroom Environment Scale, Form R (CESR), on vocal and instrumental ensembles' musical achievement at festival contests. Using random sample, reveals subjects with higher scores on CESR scales of involvement, affiliation, teacher support, and organization received better contest…

  11. Quantitative proteomic study of Aspergillus Fumigatus secretome revealed deamidation of secretory enzymes.

    Science.gov (United States)

    Adav, Sunil S; Ravindran, Anita; Sze, Siu Kwan

    2015-04-24

    Aspergillus sp. plays an essential role in lignocellulosic biomass recycling and is also exploited as cell factories for the production of industrial enzymes. This study profiled the secretome of Aspergillus fumigatus when grown with cellulose, xylan and starch by high throughput quantitative proteomics using isobaric tags for relative and absolute quantification (iTRAQ). Post translational modifications (PTMs) of proteins play a critical role in protein functions. However, our understanding of the PTMs in secretory proteins is limited. Here, we present the identification of PTMs such as deamidation of secreted proteins of A. fumigatus. This study quantified diverse groups of extracellular secreted enzymes and their functional classification revealed cellulases and glycoside hydrolases (32.9%), amylases (0.9%), hemicellulases (16.2%), lignin degrading enzymes (8.1%), peptidases and proteases (11.7%), chitinases, lipases and phosphatases (7.6%), and proteins with unknown function (22.5%). The comparison of quantitative iTRAQ results revealed that cellulose and xylan stimulates expression of specific cellulases and hemicellulases, and their abundance level as a function of substrate. In-depth data analysis revealed deamidation as a major PTM of key cellulose hydrolyzing enzymes like endoglucanases, cellobiohydrolases and glucosidases. Hemicellulose degrading endo-1,4-beta-xylanase, monosidases, xylosidases, lignin degrading laccase, isoamyl alcohol oxidase and oxidoreductases were also found to be deamidated. The filamentous fungi play an essential role in lignocellulosic biomass recycling and fungal strains belonging to Aspergillus were also exploited as cell factories for the production of organic acids, pharmaceuticals, and industrially important enzymes. In this study, extracellular proteins secreted by thermophilic A. fumigatus when grown with cellulose, xylan and starch were profiled using isobaric tags for relative and absolute quantification (iTRAQ) by

  12. Ensemble Kalman methods for inverse problems

    International Nuclear Information System (INIS)

    Iglesias, Marco A; Law, Kody J H; Stuart, Andrew M

    2013-01-01

    The ensemble Kalman filter (EnKF) was introduced by Evensen in 1994 (Evensen 1994 J. Geophys. Res. 99 10143–62) as a novel method for data assimilation: state estimation for noisily observed time-dependent problems. Since that time it has had enormous impact in many application domains because of its robustness and ease of implementation, and numerical evidence of its accuracy. In this paper we propose the application of an iterative ensemble Kalman method for the solution of a wide class of inverse problems. In this context we show that the estimate of the unknown function that we obtain with the ensemble Kalman method lies in a subspace A spanned by the initial ensemble. Hence the resulting error may be bounded above by the error found from the best approximation in this subspace. We provide numerical experiments which compare the error incurred by the ensemble Kalman method for inverse problems with the error of the best approximation in A, and with variants on traditional least-squares approaches, restricted to the subspace A. In so doing we demonstrate that the ensemble Kalman method for inverse problems provides a derivative-free optimization method with comparable accuracy to that achieved by traditional least-squares approaches. Furthermore, we also demonstrate that the accuracy is of the same order of magnitude as that achieved by the best approximation. Three examples are used to demonstrate these assertions: inversion of a compact linear operator; inversion of piezometric head to determine hydraulic conductivity in a Darcy model of groundwater flow; and inversion of Eulerian velocity measurements at positive times to determine the initial condition in an incompressible fluid. (paper)

  13. Single-particle model of a strongly driven, dense, nanoscale quantum ensemble

    Science.gov (United States)

    DiLoreto, C. S.; Rangan, C.

    2018-01-01

    We study the effects of interatomic interactions on the quantum dynamics of a dense, nanoscale, atomic ensemble driven by a strong electromagnetic field. We use a self-consistent, mean-field technique based on the pseudospectral time-domain method and a full, three-directional basis to solve the coupled Maxwell-Liouville equations. We find that interatomic interactions generate a decoherence in the state of an ensemble on a much faster time scale than the excited-state lifetime of individual atoms. We present a single-particle model of the driven, dense ensemble by incorporating interactions into a dephasing rate. This single-particle model reproduces the essential physics of the full simulation and is an efficient way of rapidly estimating the collective dynamics of a dense ensemble.

  14. A target recognition method for maritime surveillance radars based on hybrid ensemble selection

    Science.gov (United States)

    Fan, Xueman; Hu, Shengliang; He, Jingbo

    2017-11-01

    In order to improve the generalisation ability of the maritime surveillance radar, a novel ensemble selection technique, termed Optimisation and Dynamic Selection (ODS), is proposed. During the optimisation phase, the non-dominated sorting genetic algorithm II for multi-objective optimisation is used to find the Pareto front, i.e. a set of ensembles of classifiers representing different tradeoffs between the classification error and diversity. During the dynamic selection phase, the meta-learning method is used to predict whether a candidate ensemble is competent enough to classify a query instance based on three different aspects, namely, feature space, decision space and the extent of consensus. The classification performance and time complexity of ODS are compared against nine other ensemble methods using a self-built full polarimetric high resolution range profile data-set. The experimental results clearly show the effectiveness of ODS. In addition, the influence of the selection of diversity measures is studied concurrently.

  15. Interpolation of property-values between electron numbers is inconsistent with ensemble averaging

    Energy Technology Data Exchange (ETDEWEB)

    Miranda-Quintana, Ramón Alain [Laboratory of Computational and Theoretical Chemistry, Faculty of Chemistry, University of Havana, Havana (Cuba); Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1 (Canada); Ayers, Paul W. [Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1 (Canada)

    2016-06-28

    In this work we explore the physical foundations of models that study the variation of the ground state energy with respect to the number of electrons (E vs. N models), in terms of general grand-canonical (GC) ensemble formulations. In particular, we focus on E vs. N models that interpolate the energy between states with integer number of electrons. We show that if the interpolation of the energy corresponds to a GC ensemble, it is not differentiable. Conversely, if the interpolation is smooth, then it cannot be formulated as any GC ensemble. This proves that interpolation of electronic properties between integer electron numbers is inconsistent with any form of ensemble averaging. This emphasizes the role of derivative discontinuities and the critical role of a subsystem’s surroundings in determining its properties.

  16. MVL spatiotemporal analysis for model intercomparison in EPS: application to the DEMETER multi-model ensemble

    Science.gov (United States)

    Fernández, J.; Primo, C.; Cofiño, A. S.; Gutiérrez, J. M.; Rodríguez, M. A.

    2009-08-01

    In a recent paper, Gutiérrez et al. (Nonlinear Process Geophys 15(1):109-114, 2008) introduced a new characterization of spatiotemporal error growth—the so called mean-variance logarithmic (MVL) diagram—and applied it to study ensemble prediction systems (EPS); in particular, they analyzed single-model ensembles obtained by perturbing the initial conditions. In the present work, the MVL diagram is applied to multi-model ensembles analyzing also the effect of model formulation differences. To this aim, the MVL diagram is systematically applied to the multi-model ensemble produced in the EU-funded DEMETER project. It is shown that the shared building blocks (atmospheric and ocean components) impose similar dynamics among different models and, thus, contribute to poorly sampling the model formulation uncertainty. This dynamical similarity should be taken into account, at least as a pre-screening process, before applying any objective weighting method.

  17. Changes in Appetitive Associative Strength Modulates Nucleus Accumbens, But Not Orbitofrontal Cortex Neuronal Ensemble Excitability.

    Science.gov (United States)

    Ziminski, Joseph J; Hessler, Sabine; Margetts-Smith, Gabriella; Sieburg, Meike C; Crombag, Hans S; Koya, Eisuke

    2017-03-22

    occur specifically on neuronal ensembles that encode appetitive associations. Here, we reveal that sucrose cue exposure recruited a more excitable ensemble in the nucleus accumbens, but not orbitofrontal cortex, compared with their surrounding neurons. This excitability difference was not observed when the cue's salience was diminished after extinction learning. These novel data provide evidence that the intrinsic excitability of appetitive memory-encoding ensembles is regulated differentially across brain areas and adapts dynamically to changes in associative strength. Copyright © 2017 the authors 0270-6474/17/373160-11$15.00/0.

  18. Cluster ensembles, quantization and the dilogarithm

    DEFF Research Database (Denmark)

    Fock, Vladimir; Goncharov, Alexander B.

    2009-01-01

    A cluster ensemble is a pair of positive spaces (i.e. varieties equipped with positive atlases), coming with an action of a symmetry group . The space is closely related to the spectrum of a cluster algebra [ 12 ]. The two spaces are related by a morphism . The space is equipped with a closed -form......, possibly degenerate, and the space has a Poisson structure. The map is compatible with these structures. The dilogarithm together with its motivic and quantum avatars plays a central role in the cluster ensemble structure. We define a non-commutative -deformation of the -space. When is a root of unity...

  19. Ensemble computing for the petroleum industry

    International Nuclear Information System (INIS)

    Annaratone, M.; Dossa, D.

    1995-01-01

    Computer downsizing is one of the most often used buzzwords in today's competitive business, and the petroleum industry is at the forefront of this revolution. Ensemble computing provides the key for computer downsizing with its first incarnation, i.e., workstation farms. This paper concerns the importance of increasing the productivity cycle and not just the execution time of a job. The authors introduce the concept of ensemble computing and workstation farms. The they discuss how different computing paradigms can be addressed by workstation farms

  20. Evolutionary Meta-Analysis of Association Studies Reveals Ancient Constraints Affecting Disease Marker Discovery

    Science.gov (United States)

    Dudley, Joel T.; Chen, Rong; Sanderford, Maxwell; Butte, Atul J.; Kumar, Sudhir

    2012-01-01

    Genome-wide disease association studies contrast genetic variation between disease cohorts and healthy populations to discover single nucleotide polymorphisms (SNPs) and other genetic markers revealing underlying genetic architectures of human diseases. Despite scores of efforts over the past decade, many reproducible genetic variants that explain substantial proportions of the heritable risk of common human diseases remain undiscovered. We have conducted a multispecies genomic analysis of 5,831 putative human risk variants for more than 230 disease phenotypes reported in 2,021 studies. We find that the current approaches show a propensity for discovering disease-associated SNPs (dSNPs) at conserved genomic positions because the effect size (odds ratio) and allelic P value of genetic association of an SNP relates strongly to the evolutionary conservation of their genomic position. We propose a new measure for ranking SNPs that integrates evolutionary conservation scores and the P value (E-rank). Using published data from a large case-control study, we demonstrate that E-rank method prioritizes SNPs with a greater likelihood of bona fide and reproducible genetic disease associations, many of which may explain greater proportions of genetic variance. Therefore, long-term evolutionary histories of genomic positions offer key practical utility in reassessing data from existing disease association studies, and in the design and analysis of future studies aimed at revealing the genetic basis of common human diseases. PMID:22389448

  1. Disease-associated mutations that alter the RNA structural ensemble.

    Directory of Open Access Journals (Sweden)

    Matthew Halvorsen

    2010-08-01

    Full Text Available Genome-wide association studies (GWAS often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs from the Human Gene Mutation Database (HGMD that map to the untranslated regions (UTRs of a gene. Rather than using minimum free energy approaches (e.g. mFold, we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, beta-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD, and Hypertension, we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5' UTRs of FTL and RB1 SNP-induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a "RiboSNitch," that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble.

  2. Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

    Science.gov (United States)

    Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.

    2014-12-01

    Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.

  3. Geometric integrator for simulations in the canonical ensemble

    Energy Technology Data Exchange (ETDEWEB)

    Tapias, Diego, E-mail: diego.tapias@nucleares.unam.mx [Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad Universitaria, Ciudad de México 04510 (Mexico); Sanders, David P., E-mail: dpsanders@ciencias.unam.mx [Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad Universitaria, Ciudad de México 04510 (Mexico); Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Bravetti, Alessandro, E-mail: alessandro.bravetti@iimas.unam.mx [Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad Universitaria, Ciudad de México 04510 (Mexico)

    2016-08-28

    We introduce a geometric integrator for molecular dynamics simulations of physical systems in the canonical ensemble that preserves the invariant distribution in equations arising from the density dynamics algorithm, with any possible type of thermostat. Our integrator thus constitutes a unified framework that allows the study and comparison of different thermostats and of their influence on the equilibrium and non-equilibrium (thermo-)dynamic properties of a system. To show the validity and the generality of the integrator, we implement it with a second-order, time-reversible method and apply it to the simulation of a Lennard-Jones system with three different thermostats, obtaining good conservation of the geometrical properties and recovering the expected thermodynamic results. Moreover, to show the advantage of our geometric integrator over a non-geometric one, we compare the results with those obtained by using the non-geometric Gear integrator, which is frequently used to perform simulations in the canonical ensemble. The non-geometric integrator induces a drift in the invariant quantity, while our integrator has no such drift, thus ensuring that the system is effectively sampling the correct ensemble.

  4. Effective theory of the D = 3 center vortex ensemble

    Science.gov (United States)

    Oxman, L. E.; Reinhardt, H.

    2018-03-01

    By means of lattice calculations, center vortices have been established as the infrared dominant gauge field configurations of Yang-Mills theory. In this work, we investigate an ensemble of center vortices in D = 3 Euclidean space-time dimension where they form closed flux loops. To account for the properties of center vortices detected on the lattice, they are equipped with tension, stiffness and a repulsive contact interaction. The ensemble of oriented center vortices is then mapped onto an effective theory of a complex scalar field with a U(1) symmetry. For a positive tension, small vortex loops are favoured and the Wilson loop displays a perimeter law while for a negative tension, large loops dominate the ensemble. In this case the U(1) symmetry of the effective scalar field theory is spontaneously broken and the Wilson loop shows an area law. To account for the large quantum fluctuations of the corresponding Goldstone modes, we use a lattice representation, which results in an XY model with frustration, for which we also study the Villain approximation.

  5. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  6. Village Building Identification Based on Ensemble Convolutional Neural Networks

    Science.gov (United States)

    Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

    2017-01-01

    In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

  7. Girsanov reweighting for path ensembles and Markov state models

    Science.gov (United States)

    Donati, L.; Hartmann, C.; Keller, B. G.

    2017-06-01

    The sensitivity of molecular dynamics on changes in the potential energy function plays an important role in understanding the dynamics and function of complex molecules. We present a method to obtain path ensemble averages of a perturbed dynamics from a set of paths generated by a reference dynamics. It is based on the concept of path probability measure and the Girsanov theorem, a result from stochastic analysis to estimate a change of measure of a path ensemble. Since Markov state models (MSMs) of the molecular dynamics can be formulated as a combined phase-space and path ensemble average, the method can be extended to reweight MSMs by combining it with a reweighting of the Boltzmann distribution. We demonstrate how to efficiently implement the Girsanov reweighting in a molecular dynamics simulation program by calculating parts of the reweighting factor "on the fly" during the simulation, and we benchmark the method on test systems ranging from a two-dimensional diffusion process and an artificial many-body system to alanine dipeptide and valine dipeptide in implicit and explicit water. The method can be used to study the sensitivity of molecular dynamics on external perturbations as well as to reweight trajectories generated by enhanced sampling schemes to the original dynamics.

  8. Thermodynamics and kinetics of a molecular motor ensemble.

    Science.gov (United States)

    Baker, J E; Thomas, D D

    2000-10-01

    If, contrary to conventional models of muscle, it is assumed that molecular forces equilibrate among rather than within molecular motors, an equation of state and an expression for energy output can be obtained for a near-equilibrium, coworking ensemble of molecular motors. These equations predict clear, testable relationships between motor structure, motor biochemistry, and ensemble motor function, and we discuss these relationships in the context of various experimental studies. In this model, net work by molecular motors is performed with the relaxation of a near-equilibrium intermediate step in a motor-catalyzed reaction. The free energy available for work is localized to this step, and the rate at which this free energy is transferred to work is accelerated by the free energy of a motor-catalyzed reaction. This thermodynamic model implicitly deals with a motile cell system as a dynamic network (not a rigid lattice) of molecular motors within which the mechanochemistry of one motor influences and is influenced by the mechanochemistry of other motors in the ensemble.

  9. Geometric integrator for simulations in the canonical ensemble

    International Nuclear Information System (INIS)

    Tapias, Diego; Sanders, David P.; Bravetti, Alessandro

    2016-01-01

    We introduce a geometric integrator for molecular dynamics simulations of physical systems in the canonical ensemble that preserves the invariant distribution in equations arising from the density dynamics algorithm, with any possible type of thermostat. Our integrator thus constitutes a unified framework that allows the study and comparison of different thermostats and of their influence on the equilibrium and non-equilibrium (thermo-)dynamic properties of a system. To show the validity and the generality of the integrator, we implement it with a second-order, time-reversible method and apply it to the simulation of a Lennard-Jones system with three different thermostats, obtaining good conservation of the geometrical properties and recovering the expected thermodynamic results. Moreover, to show the advantage of our geometric integrator over a non-geometric one, we compare the results with those obtained by using the non-geometric Gear integrator, which is frequently used to perform simulations in the canonical ensemble. The non-geometric integrator induces a drift in the invariant quantity, while our integrator has no such drift, thus ensuring that the system is effectively sampling the correct ensemble.

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

    Directory of Open Access Journals (Sweden)

    Nam-phuong Nguyen

    2016-11-01

    Full Text Available Abstract Background 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. Results 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. Conclusion 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 .

  11. Using simulation to interpret experimental data in terms of protein conformational ensembles.

    Science.gov (United States)

    Allison, Jane R

    2017-04-01

    In their biological environment, proteins are dynamic molecules, necessitating an ensemble structural description. Molecular dynamics simulations and solution-state experiments provide complimentary information in the form of atomically detailed coordinates and averaged or distributions of structural properties or related quantities. Recently, increases in the temporal and spatial scale of conformational sampling and comparison of the more diverse conformational ensembles thus generated have revealed the importance of sampling rare events. Excitingly, new methods based on maximum entropy and Bayesian inference are promising to provide a statistically sound mechanism for combining experimental data with molecular dynamics simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Mechanisms of appearance of amplitude and phase chimera states in ensembles of nonlocally coupled chaotic systems

    Science.gov (United States)

    Bogomolov, Sergey A.; Slepnev, Andrei V.; Strelkova, Galina I.; Schöll, Eckehard; Anishchenko, Vadim S.

    2017-02-01

    We explore the bifurcation transition from coherence to incoherence in ensembles of nonlocally coupled chaotic systems. It is firstly shown that two types of chimera states, namely, amplitude and phase, can be found in a network of coupled logistic maps, while only amplitude chimera states can be observed in a ring of continuous-time chaotic systems. We reveal a bifurcation mechanism by analyzing the evolution of space-time profiles and the coupling function with varying coupling coefficient and formulate the necessary and sufficient conditions for realizing the chimera states in the ensembles.

  13. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  14. Coherent Rabi Dynamics of a Superradiant Spin Ensemble in a Microwave Cavity

    Science.gov (United States)

    Rose, B. C.; Tyryshkin, A. M.; Riemann, H.; Abrosimov, N. V.; Becker, P.; Pohl, H.-J.; Thewalt, M. L. W.; Itoh, K. M.; Lyon, S. A.

    2017-07-01

    We achieve the strong-coupling regime between an ensemble of phosphorus donor spins in a highly enriched 28Si crystal and a 3D dielectric resonator. Spins are polarized beyond Boltzmann equilibrium using spin-selective optical excitation of the no-phonon bound exciton transition resulting in N =3.6 ×1 013 unpaired spins in the ensemble. We observe a normal mode splitting of the spin-ensemble-cavity polariton resonances of 2 g √{N }=580 kHz (where each spin is coupled with strength g ) in a cavity with a quality factor of 75 000 (γ ≪κ ≈60 kHz , where γ and κ are the spin dephasing and cavity loss rates, respectively). The spin ensemble has a long dephasing time (T2*=9 μ s ) providing a wide window for viewing the dynamics of the coupled spin-ensemble-cavity system. The free-induction decay shows up to a dozen collapses and revivals revealing a coherent exchange of excitations between the superradiant state of the spin ensemble and the cavity at the rate g √{N }. The ensemble is found to evolve as a single large pseudospin according to the Tavis-Cummings model due to minimal inhomogeneous broadening and uniform spin-cavity coupling. We demonstrate independent control of the total spin and the initial Z projection of the psuedospin using optical excitation and microwave manipulation, respectively. We vary the microwave excitation power to rotate the pseudospin on the Bloch sphere and observe a long delay in the onset of the superradiant emission as the pseudospin approaches full inversion. This delay is accompanied by an abrupt π -phase shift in the peusdospin microwave emission. The scaling of this delay with the initial angle and the sudden phase shift are explained by the Tavis-Cummings model.

  15. A Single-column Model Ensemble Approach Applied to the TWP-ICE Experiment

    Science.gov (United States)

    Davies, L.; Jakob, C.; Cheung, K.; DelGenio, A.; Hill, A.; Hume, T.; Keane, R. J.; Komori, T.; Larson, V. E.; Lin, Y.; hide

    2013-01-01

    Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

  16. Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.

    Science.gov (United States)

    Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

    2014-01-01

    The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

  17. Comparative Visualization of Vector Field Ensembles Based on Longest Common Subsequence

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Richen; Guo, Hanqi; Zhang, Jiang; Yuan, Xiaoru

    2016-04-19

    We propose a longest common subsequence (LCS) based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble pathlines passing through, the LCS distance defines the similarity among vector field ensembles by counting the number of sharing domain data blocks. Compared to the traditional methods (e.g. point-wise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outlier, data missing, and sampling rate of pathline timestep. Taking the advantages of smaller and reusable intermediate output, visualization based on the proposed LCS approach revealing temporal trends in the data at low storage cost, and avoiding tracing pathlines repeatedly. Finally, we evaluate our method on both synthetic data and simulation data, which demonstrate the robustness of the proposed approach.

  18. Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter

    Science.gov (United States)

    Wang, S.; Huang, G. H.; Baetz, B. W.; Cai, X. M.; Ancell, B. C.; Fan, Y. R.

    2017-11-01

    The ensemble Kalman filter (EnKF) is recognized as a powerful data assimilation technique that generates an ensemble of model variables through stochastic perturbations of forcing data and observations. However, relatively little guidance exists with regard to the proper specification of the magnitude of the perturbation and the ensemble size, posing a significant challenge in optimally implementing the EnKF. This paper presents a robust data assimilation system (RDAS), in which a multi-factorial design of the EnKF experiments is first proposed for hydrologic ensemble predictions. A multi-way analysis of variance is then used to examine potential interactions among factors affecting the EnKF experiments, achieving optimality of the RDAS with maximized performance of hydrologic predictions. The RDAS is applied to the Xiangxi River watershed which is the most representative watershed in China's Three Gorges Reservoir region to demonstrate its validity and applicability. Results reveal that the pairwise interaction between perturbed precipitation and streamflow observations has the most significant impact on the performance of the EnKF system, and their interactions vary dynamically across different settings of the ensemble size and the evapotranspiration perturbation. In addition, the interactions among experimental factors vary greatly in magnitude and direction depending on different statistical metrics for model evaluation including the Nash-Sutcliffe efficiency and the Box-Cox transformed root-mean-square error. It is thus necessary to test various evaluation metrics in order to enhance the robustness of hydrologic prediction systems.

  19. A Matrix-Free Posterior Ensemble Kalman Filter Implementation Based on a Modified Cholesky Decomposition

    Directory of Open Access Journals (Sweden)

    Elias D. Nino-Ruiz

    2017-07-01

    Full Text Available In this paper, a matrix-free posterior ensemble Kalman filter implementation based on a modified Cholesky decomposition is proposed. The method works as follows: the precision matrix of the background error distribution is estimated based on a modified Cholesky decomposition. The resulting estimator can be expressed in terms of Cholesky factors which can be updated based on a series of rank-one matrices in order to approximate the precision matrix of the analysis distribution. By using this matrix, the posterior ensemble can be built by either sampling from the posterior distribution or using synthetic observations. Furthermore, the computational effort of the proposed method is linear with regard to the model dimension and the number of observed components from the model domain. Experimental tests are performed making use of the Lorenz-96 model. The results reveal that, the accuracy of the proposed implementation in terms of root-mean-square-error is similar, and in some cases better, to that of a well-known ensemble Kalman filter (EnKF implementation: the local ensemble transform Kalman filter. In addition, the results are comparable to those obtained by the EnKF with large ensemble sizes.

  20. A method for ensemble wildland fire simulation

    Science.gov (United States)

    Mark A. Finney; Isaac C. Grenfell; Charles W. McHugh; Robert C. Seli; Diane Trethewey; Richard D. Stratton; Stuart Brittain

    2011-01-01

    An ensemble simulation system that accounts for uncertainty in long-range weather conditions and two-dimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis...

  1. The Phantasmagoria of Competition in School Ensembles

    Science.gov (United States)

    Abramo, Joseph Michael

    2017-01-01

    Participation in competition festivals--where students and ensembles compete against each other for high scores and accolades--is a widespread practice in North American formal music education. In this article, I use Marx's theories of labor, value, and phantasmagoria to suggest a capitalist logic that structures these competitions. Marx's…

  2. Ensembl Genomes 2016: more genomes, more complexity.

    Science.gov (United States)

    Kersey, Paul Julian; Allen, James E; Armean, Irina; Boddu, Sanjay; Bolt, Bruce J; Carvalho-Silva, Denise; Christensen, Mikkel; Davis, Paul; Falin, Lee J; Grabmueller, Christoph; Humphrey, Jay; Kerhornou, Arnaud; Khobova, Julia; Aranganathan, Naveen K; Langridge, Nicholas; Lowy, Ernesto; McDowall, Mark D; Maheswari, Uma; Nuhn, Michael; Ong, Chuang Kee; Overduin, Bert; Paulini, Michael; Pedro, Helder; Perry, Emily; Spudich, Giulietta; Tapanari, Electra; Walts, Brandon; Williams, Gareth; Tello-Ruiz, Marcela; Stein, Joshua; Wei, Sharon; Ware, Doreen; Bolser, Daniel M; Howe, Kevin L; Kulesha, Eugene; Lawson, Daniel; Maslen, Gareth; Staines, Daniel M

    2016-01-04

    Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of programmatic and interactive interfaces to a rich range of data including reference sequence, gene models, transcriptional data, genetic variation and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments. These include the development of new analyses and views to represent polyploid genomes (of which bread wheat is the primary exemplar); and the continued up-scaling of the resource, which now includes over 23 000 bacterial genomes, 400 fungal genomes and 100 protist genomes, in addition to 55 genomes from invertebrate metazoa and 39 genomes from plants. This dramatic increase in the number of included genomes is one part of a broader effort to automate the integration of archival data (genome sequence, but also associated RNA sequence data and variant calls) within the context of reference genomes and make it available through the Ensembl user interfaces. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  3. NYYD Ensemble ja Riho Sibul / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-

    2001-01-01

    Gavin Bryarsi teos "Jesus' Blood Never Failed Me Yet" NYYD Ensemble'i ja Riho Sibula esituses 27. detsembril Pauluse kirikus Tartus ja 28. detsembril Rootsi- Mihkli kirikus Tallinnas. Kaastegevad Tartu Ülikooli Kammerkoor (Tartus) ja kammerkoor Voces Musicales (Tallinnas). Kunstiline juht Olari Elts

  4. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    NJD

    Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.

  5. Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

    Science.gov (United States)

    Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

    2017-04-01

    Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums

  6. Both Isochronous and Non-Isochronous Metrical Subdivision Afford Precise and Stable Ensemble Entrainment: A Corpus Study of Malian Jembe Drumming.

    Science.gov (United States)

    Polak, Rainer; London, Justin; Jacoby, Nori

    2016-01-01

    Most approaches to musical rhythm, whether in music theory, music psychology, or musical neuroscience, presume that musical rhythms are based on isochronous (temporally equidistant) beats and/or beat subdivisions. However, rhythms that are based on non-isochronous, or unequal patterns of time are prominent in the music of Southeast Europe, the Near East and Southern Asia, and in the music of Africa and the African diaspora. The present study examines one such style found in contemporary Malian jembe percussion music. A corpus of 15 representative performances of three different pieces ("Manjanin," "Maraka," and "Woloso") containing ~43,000 data points was analyzed. Manjanin and Woloso are characterized by non-isochronous beat subdivisions (a short IOI followed by two longer IOIs), while Maraka subdivisions are quasi-isochronous. Analyses of onsets and asynchronies show no significant differences in timing precision and coordination between the isochronously timed Maraka vs. the non-isochronously timed Woloso performances, though both pieces were slightly less variable than non-isochronous Manjanin. Thus, the precision and stability of rhythm and entrainment in human music does not necessarily depend on metric isochrony, consistent with the hypothesis that isochrony is not a biologically-based constraint on human rhythmic behavior. Rather, it may represent a historically popular option within a variety of culturally contingent options for metric organization.

  7. A Theoretical Analysis of Why Hybrid Ensembles Work

    Directory of Open Access Journals (Sweden)

    Kuo-Wei Hsu

    2017-01-01

    Full Text Available Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

  8. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-11-01

    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  9. Global Ensemble Forecast System (GEFS) [2.5 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  10. Identification of Protein Pupylation Sites Using Bi-Profile Bayes Feature Extraction and Ensemble Learning

    Directory of Open Access Journals (Sweden)

    Xiaowei Zhao

    2013-01-01

    Full Text Available Pupylation, one of the most important posttranslational modifications of proteins, typically takes place when prokaryotic ubiquitin-like protein (Pup is attached to specific lysine residues on a target protein. Identification of pupylation substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of pupylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, a new bioinformatics tool named EnsemblePup was developed that used an ensemble of support vector machine classifiers to predict pupylation sites. The highlight of EnsemblePup was to utilize the Bi-profile Bayes feature extraction as the encoding scheme. The performance of EnsemblePup was measured with a sensitivity of 79.49%, a specificity of 82.35%, an accuracy of 85.43%, and a Matthews correlation coefficient of 0.617 using the 5-fold cross validation on the training dataset. When compared with other existing methods on a benchmark dataset, the EnsemblePup provided better predictive performance, with a sensitivity of 80.00%, a specificity of 83.33%, an accuracy of 82.00%, and a Matthews correlation coefficient of 0.629. The experimental results suggested that EnsemblePup presented here might be useful to identify and annotate potential pupylation sites in proteins of interest. A web server for predicting pupylation sites was developed.

  11. The thermal insulation difference of clothing ensembles on the dry and perspiration manikins

    International Nuclear Information System (INIS)

    Xiaohong, Zhou; Chunqin, Zheng; Yingming, Qiang; Holmér, Ingvar; Gao, Chuansi; Kuklane, Kalev

    2010-01-01

    There are about a hundred manikin users around the world. Some of them use the manikin such as 'Walter' and 'Tore' to evaluate the comfort of clothing ensembles according to their thermal insulation and moisture resistance. A 'Walter' manikin is made of water and waterproof breathable fabric 'skin', which simulates the characteristics of human perspiration. So evaporation, condensation or sorption and desorption are always accompanied by heat transfer. A 'Tore' manikin only has dry heat exchange by conduction, radiation and convection from the manikin through clothing ensembles to environments. It is an ideal apparatus to measure the thermal insulation of the clothing ensemble and allows evaluation of thermal comfort. This paper compares thermal insulation measured with dry 'Tore' and sweating 'Walter' manikins. Clothing ensembles consisted of permeable and impermeable clothes. The results showed that the clothes covering the 'Walter' manikin absorbed the moisture evaporated from the manikin. When the moisture transferred through the permeable clothing ensembles, heat of condensation could be neglected. But it was observed that heavy condensation occurred if impermeable clothes were tested on the 'Walter' manikin. This resulted in a thermal insulation difference of clothing ensembles on the dry and perspiration manikins. The thermal insulation obtained from the 'Walter' manikin has to be modified when heavy condensation occurs. The modified equation is obtained in this study

  12. Using ensemble forecasting for wind power

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Landberg, L.; Badger, J. [Risoe National Lab., Roskilde (Denmark); Sattler, K.

    2003-07-01

    Short-term prediction of wind power has a long tradition in Denmark. It is an essential tool for the operators to keep the grid from becoming unstable in a region like Jutland, where more than 27% of the electricity consumption comes from wind power. This means that the minimum load is already lower than the maximum production from wind energy alone. Danish utilities have therefore used short-term prediction of wind energy since the mid-90ies. However, the accuracy is still far from being sufficient in the eyes of the utilities (used to have load forecasts accurate to within 5% on a one-week horizon). The Ensemble project tries to alleviate the dependency of the forecast quality on one model by using multiple models, and also will investigate the possibilities of using the model spread of multiple models or of dedicated ensemble runs for a prediction of the uncertainty of the forecast. Usually, short-term forecasting works (especially for the horizon beyond 6 hours) by gathering input from a Numerical Weather Prediction (NWP) model. This input data is used together with online data in statistical models (this is the case eg in Zephyr/WPPT) to yield the output of the wind farms or of a whole region for the next 48 hours (only limited by the NWP model horizon). For the accuracy of the final production forecast, the accuracy of the NWP prediction is paramount. While many efforts are underway to increase the accuracy of the NWP forecasts themselves (which ultimately are limited by the amount of computing power available, the lack of a tight observational network on the Atlantic and limited physics modelling), another approach is to use ensembles of different models or different model runs. This can be either an ensemble of different models output for the same area, using different data assimilation schemes and different model physics, or a dedicated ensemble run by a large institution, where the same model is run with slight variations in initial conditions and

  13. CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

    Science.gov (United States)

    Zhang, Li; Ai, Haixin; Chen, Wen; Yin, Zimo; Hu, Huan; Zhu, Junfeng; Zhao, Jian; Zhao, Qi; Liu, Hongsheng

    2017-05-18

    Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).

  14. The Reduced Rank of Ensemble Kalman Filter to Estimate the Temperature of Non Isothermal Continue Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    Erna Apriliani

    2011-01-01

    Full Text Available Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability and reduce the computational time. In this paper we study the efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ensemble estimation by using the singular value decomposition (the SVD, and then we reduced the rank of the diagonal matrix of those singular values. We make a simulation by using Matlab program. We took some the number of ensemble such as 100, 200 and 500. We compared the computational time and the accuracy between the square root ensemble Kalman filter and the ensemble Kalman filter. The reduced rank ensemble Kalman filter can’t be applied in this problem because the dimension of state variable is too less.

  15. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    KAUST Repository

    Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim

    2017-01-01

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation

  16. Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter

    KAUST Repository

    Luo, Xiaodong; Hoteit, Ibrahim

    2011-01-01

    A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used

  17. Quantum canonical ensemble: A projection operator approach

    Science.gov (United States)

    Magnus, Wim; Lemmens, Lucien; Brosens, Fons

    2017-09-01

    Knowing the exact number of particles N, and taking this knowledge into account, the quantum canonical ensemble imposes a constraint on the occupation number operators. The constraint particularly hampers the systematic calculation of the partition function and any relevant thermodynamic expectation value for arbitrary but fixed N. On the other hand, fixing only the average number of particles, one may remove the above constraint and simply factorize the traces in Fock space into traces over single-particle states. As is well known, that would be the strategy of the grand-canonical ensemble which, however, comes with an additional Lagrange multiplier to impose the average number of particles. The appearance of this multiplier can be avoided by invoking a projection operator that enables a constraint-free computation of the partition function and its derived quantities in the canonical ensemble, at the price of an angular or contour integration. Introduced in the recent past to handle various issues related to particle-number projected statistics, the projection operator approach proves beneficial to a wide variety of problems in condensed matter physics for which the canonical ensemble offers a natural and appropriate environment. In this light, we present a systematic treatment of the canonical ensemble that embeds the projection operator into the formalism of second quantization while explicitly fixing N, the very number of particles rather than the average. Being applicable to both bosonic and fermionic systems in arbitrary dimensions, transparent integral representations are provided for the partition function ZN and the Helmholtz free energy FN as well as for two- and four-point correlation functions. The chemical potential is not a Lagrange multiplier regulating the average particle number but can be extracted from FN+1 -FN, as illustrated for a two-dimensional fermion gas.

  18. Stochastic Approaches Within a High Resolution Rapid Refresh Ensemble

    Science.gov (United States)

    Jankov, I.

    2017-12-01

    It is well known that global and regional numerical weather prediction (NWP) ensemble systems are under-dispersive, producing unreliable and overconfident ensemble forecasts. Typical approaches to alleviate this problem include the use of multiple dynamic cores, multiple physics suite configurations, or a combination of the two. While these approaches may produce desirable results, they have practical and theoretical deficiencies and are more difficult and costly to maintain. An active area of research that promotes a more unified and sustainable system is the use of stochastic physics. Stochastic approaches include Stochastic Parameter Perturbations (SPP), Stochastic Kinetic Energy Backscatter (SKEB), and Stochastic Perturbation of Physics Tendencies (SPPT). The focus of this study is to assess model performance within a convection-permitting ensemble at 3-km grid spacing across the Contiguous United States (CONUS) using a variety of stochastic approaches. A single physics suite configuration based on the operational High-Resolution Rapid Refresh (HRRR) model was utilized and ensemble members produced by employing stochastic methods. Parameter perturbations (using SPP) for select fields were employed in the Rapid Update Cycle (RUC) land surface model (LSM) and Mellor-Yamada-Nakanishi-Niino (MYNN) Planetary Boundary Layer (PBL) schemes. Within MYNN, SPP was applied to sub-grid cloud fraction, mixing length, roughness length, mass fluxes and Prandtl number. In the RUC LSM, SPP was applied to hydraulic conductivity and tested perturbing soil moisture at initial time. First iterative testing was conducted to assess the initial performance of several configuration settings (e.g. variety of spatial and temporal de-correlation lengths). Upon selection of the most promising candidate configurations using SPP, a 10-day time period was run and more robust statistics were gathered. SKEB and SPPT were included in additional retrospective tests to assess the impact of using

  19. An ensemble model of QSAR tools for regulatory risk assessment.

    Science.gov (United States)

    Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

    2016-01-01

    .63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

  20. Exploring and Listening to Chinese Classical Ensembles in General Music

    Science.gov (United States)

    Zhang, Wenzhuo

    2017-01-01

    Music diversity is valued in theory, but the extent to which it is efficiently presented in music class remains limited. Within this article, I aim to bridge this gap by introducing four genres of Chinese classical ensembles--Qin and Xiao duets, Jiang Nan bamboo and silk ensembles, Cantonese ensembles, and contemporary Chinese orchestras--into the…

  1. Critical Listening in the Ensemble Rehearsal: A Community of Learners

    Science.gov (United States)

    Bell, Cindy L.

    2018-01-01

    This article explores a strategy for engaging ensemble members in critical listening analysis of performances and presents opportunities for improving ensemble sound through rigorous dialogue, reflection, and attentive rehearsing. Critical listening asks ensemble members to draw on individual playing experience and knowledge to describe what they…

  2. The hippocampal CA2 ensemble is sensitive to contextual change.

    Science.gov (United States)

    Wintzer, Marie E; Boehringer, Roman; Polygalov, Denis; McHugh, Thomas J

    2014-02-19

    Contextual learning involves associating cues with an environment and relating them to past experience. Previous data indicate functional specialization within the hippocampal circuit: the dentate gyrus (DG) is crucial for discriminating similar contexts, whereas CA3 is required for associative encoding and recall. Here, we used Arc/H1a catFISH imaging to address the contribution of the largely overlooked CA2 region to contextual learning by comparing ensemble codes across CA3, CA2, and CA1 in mice exposed to familiar, altered, and novel contexts. Further, to manipulate the quality of information arriving in CA2 we used two hippocampal mutant mouse lines, CA3-NR1 KOs and DG-NR1 KOs, that result in hippocampal CA3 neuronal activity that is uncoupled from the animal's sensory environment. Our data reveal largely coherent responses across the CA axis in control mice in purely novel or familiar contexts; however, in the mutant mice subject to these protocols the CA2 response becomes uncoupled from CA1 and CA3. Moreover, we show in wild-type mice that the CA2 ensemble is more sensitive than CA1 and CA3 to small changes in overall context. Our data suggest that CA2 may be tuned to remap in response to any conflict between stored and current experience.

  3. Ensemble composition and activity levels of insectivorous bats in response to management intensification in coffee agroforestry systems.

    Science.gov (United States)

    Williams-Guillén, Kimberly; Perfecto, Ivette

    2011-01-26

    Shade coffee plantations have received attention for their role in biodiversity conservation. Bats are among the most diverse mammalian taxa in these systems; however, previous studies of bats in coffee plantations have focused on the largely herbivorous leaf-nosed bats (Phyllostomidae). In contrast, we have virtually no information on how ensembles of aerial insectivorous bats--nearly half the Neotropical bat species--change in response to habitat modification. To evaluate the effects of agroecosystem management on insectivorous bats, we studied their diversity and activity in southern Chiapas, Mexico, a landscape dominated by coffee agroforestry. We used acoustic monitoring and live captures to characterize the insectivorous bat ensemble in forest fragments and coffee plantations differing in the structural and taxonomic complexity of shade trees. We captured bats of 12 non-phyllostomid species; acoustic monitoring revealed the presence of at least 12 more species of aerial insectivores. Richness of forest bats was the same across all land-use types; in contrast, species richness of open-space bats increased in low shade, intensively managed coffee plantations. Conversely, only forest bats demonstrated significant differences in ensemble structure (as measured by similarity indices) across land-use types. Both overall activity and feeding activity of forest bats declined significantly with increasing management intensity, while the overall activity, but not feeding activity, of open-space bats increased. We conclude that diverse shade coffee plantations in our study area serve as valuable foraging and commuting habitat for aerial insectivorous bats, and several species also commute through or forage in low shade coffee monocultures.

  4. Ensemble composition and activity levels of insectivorous bats in response to management intensification in coffee agroforestry systems.

    Directory of Open Access Journals (Sweden)

    Kimberly Williams-Guillén

    Full Text Available Shade coffee plantations have received attention for their role in biodiversity conservation. Bats are among the most diverse mammalian taxa in these systems; however, previous studies of bats in coffee plantations have focused on the largely herbivorous leaf-nosed bats (Phyllostomidae. In contrast, we have virtually no information on how ensembles of aerial insectivorous bats--nearly half the Neotropical bat species--change in response to habitat modification. To evaluate the effects of agroecosystem management on insectivorous bats, we studied their diversity and activity in southern Chiapas, Mexico, a landscape dominated by coffee agroforestry. We used acoustic monitoring and live captures to characterize the insectivorous bat ensemble in forest fragments and coffee plantations differing in the structural and taxonomic complexity of shade trees. We captured bats of 12 non-phyllostomid species; acoustic monitoring revealed the presence of at least 12 more species of aerial insectivores. Richness of forest bats was the same across all land-use types; in contrast, species richness of open-space bats increased in low shade, intensively managed coffee plantations. Conversely, only forest bats demonstrated significant differences in ensemble structure (as measured by similarity indices across land-use types. Both overall activity and feeding activity of forest bats declined significantly with increasing management intensity, while the overall activity, but not feeding activity, of open-space bats increased. We conclude that diverse shade coffee plantations in our study area serve as valuable foraging and commuting habitat for aerial insectivorous bats, and several species also commute through or forage in low shade coffee monocultures.

  5. Kinetics of particle ensembles with variable charges

    International Nuclear Information System (INIS)

    Ivlev, A. V.; Zhdanov, S.; Klumov, B.; Morfill, G.; Tsytovich, V. N.; Angelis, U. de

    2005-01-01

    One of the remarkable features distinguishing complex (dusty) plasmas from usual plasmas is that charges on the grains are not constant, but fluctuate in time around some equilibrium value which, in then, is some function of spatial coordinates. Generally, ensembles of particles with variable charges are non-Hamiltonian systems where the mutual collisions do not conserve energy. Therefore, the use of thermodynamic potentials to describe such systems is not really valid. An appropriate way to investigate their evolution is to employ the kinetic approach. We studied (both analytical and numerically) two cases: (a) inhomogeneous charge-it depends on the particle coordinate but does not change in time, and (b)fluctuating charge-it changes in time around the equilibrium value, which is constant in space. For both cases we used the Fokker-Planck approach to derive the collision integral which describes the momentum and energy transfer in mutual particle collisions as well as in the collisions with neutrals. We obtained that the mean particle energy grows in time when the neutral friction is below a certain threshold (as shown in Fig. 1). In case (a) the energy changes as ∞(t c r-t)''2, in case (b) it scales as ∞(t c r-t)''-1, exhibiting the explosion-like growth with t c r a critical time scale. The obtained solutions can be of significant importance for laboratory dusty plasmas as well as for space plasma environments, where inhomogeneous charge distributions are often present. For instance, the instability can cause dust heating in low-pressure complex plasma experiments, it can be responsible for the melting of plasma crystals, it might operate in protoplanetary disks and effect the kinetics of the planet formation, etc. (Author)

  6. Talaromyces marneffei Genomic, Transcriptomic, Proteomic and Metabolomic Studies Reveal Mechanisms for Environmental Adaptations and Virulence

    Directory of Open Access Journals (Sweden)

    Susanna K. P. Lau

    2017-06-01

    Full Text Available Talaromyces marneffei is a thermally dimorphic fungus causing systemic infections in patients positive for HIV or other immunocompromised statuses. Analysis of its ~28.9 Mb draft genome and additional transcriptomic, proteomic and metabolomic studies revealed mechanisms for environmental adaptations and virulence. Meiotic genes and genes for pheromone receptors, enzymes which process pheromones, and proteins involved in pheromone response pathway are present, indicating its possibility as a heterothallic fungus. Among the 14 Mp1p homologs, only Mp1p is a virulence factor binding a variety of host proteins, fatty acids and lipids. There are 23 polyketide synthase genes, one for melanin and two for mitorubrinic acid/mitorubrinol biosynthesis, which are virulence factors. Another polyketide synthase is for biogenesis of the diffusible red pigment, which consists of amino acid conjugates of monascorubin and rubropunctatin. Novel microRNA-like RNAs (milRNAs and processing proteins are present. The dicer protein, dcl-2, is required for biogenesis of two milRNAs, PM-milR-M1 and PM-milR-M2, which are more highly expressed in hyphal cells. Comparative transcriptomics showed that tandem repeat-containing genes were overexpressed in yeast phase, generating protein polymorphism among cells, evading host’s immunity. Comparative proteomics between yeast and hyphal cells revealed that glyceraldehyde-3-phosphate dehydrogenase, up-regulated in hyphal cells, is an adhesion factor for conidial attachment.

  7. Influences of Appalachian orography on heavy rainfall and rainfall variability associated with the passage of hurricane Isabel by ensemble simulations

    Science.gov (United States)

    Oldaker, Guy; Liu, Liping; Lin, Yuh-Lang

    2017-12-01

    This study focuses on the heavy rainfall event associated with hurricane Isabel's (2003) passage over the Appalachian mountains of the eastern United States. Specifically, an ensemble consisting of two groups of simulations using the Weather Research and Forecasting model (WRF), with and without topography, is performed to investigate the orographic influences on heavy rainfall and rainfall variability. In general, the simulated ensemble mean with full terrain is able to reproduce the key observed 24-h rainfall amount and distribution, while the flat-terrain mean lacks in this respect. In fact, 30-h rainfall amounts are reduced by 75% with the removal of topography. Rainfall variability is also significantly increased with the presence of orography. Further analysis shows that the complex interaction between the hurricane and terrain along with contributions from varied microphysics, cumulus parametrization, and planetary boundary layer schemes have a pronounced effect on rainfall and rainfall variability. This study follows closely with a previous study, but for a different TC case of Isabel (2003). It is an important sensitivity test for a different TC in a very different environment. This study reveals that the rainfall variability behaves similarly, even with different settings of the environment.

  8. Revealed Preference Methods for Studying Bicycle Route Choice—A Systematic Review

    Directory of Open Access Journals (Sweden)

    Ray Pritchard

    2018-03-01

    Full Text Available One fundamental aspect of promoting utilitarian bicycle use involves making modifications to the built environment to improve the safety, efficiency and enjoyability of cycling. Revealed preference data on bicycle route choice can assist greatly in understanding the actual behaviour of a highly heterogeneous group of users, which in turn assists the prioritisation of infrastructure or other built environment initiatives. This systematic review seeks to compare the relative strengths and weaknesses of the empirical approaches for evaluating whole journey route choices of bicyclists. Two electronic databases were systematically searched for a selection of keywords pertaining to bicycle and route choice. In total seven families of methods are identified: GPS devices, smartphone applications, crowdsourcing, participant-recalled routes, accompanied journeys, egocentric cameras and virtual reality. The study illustrates a trade-off in the quality of data obtainable and the average number of participants. Future additional methods could include dockless bikeshare, multiple camera solutions using computer vision and immersive bicycle simulator environments.

  9. A passionate way of being: A qualitative study revealing the passion spiral

    Directory of Open Access Journals (Sweden)

    Susanna M. Halonen

    2014-06-01

    Full Text Available Being engaged in an activity one is passionate about has been tied to feeling life is worth living for. Existing research in passion has explored this phenomenon purely using quantitative research methodology, and by tying an individual’s passion to a specific activity. In this study, passion was explored in semi-structured interviews with 12 participants. The qualitative grounded theory analysis revealed a passionate way of being, with passion being located in the individual rather than in a specific activity. A new phenomenon to positive psychology, a passionate way of being is about having a purpose, creating positive impact, and pursuing variety. These key elements, amongst others, created a reinforcing, self-sustaining spiral, which offered a route to hedonic and eudaimonic happiness, generally serving to enhance life (though it could also detract from life if it became overpowering.

  10. Microspectroscopic Study of Liposome-to-cell Interaction Revealed by Förster Resonance Energy Transfer.

    Science.gov (United States)

    Yefimova, Svetlana L; Kurilchenko, Irina Yu; Tkacheva, Tatyana N; Kavok, Nataliya S; Todor, Igor N; Lukianova, Nataliya Yu; Chekhun, Vasyl F; Malyukin, Yuriy V

    2014-03-01

    We report the Förster resonance energy transfer (FRET)-labeling of liposomal vesicles as an effective approach to study in dynamics the interaction of liposomes with living cells of different types (rat hepatocytes, rat bone marrow, mouse fibroblast-like cells and human breast cancer cells) and cell organelles (hepatocyte nuclei). The in vitro experiments were performed using fluorescent microspectroscopic technique. Two fluorescent dyes (DiO as the energy donor and DiI as an acceptor) were preloaded in lipid bilayers of phosphatidylcholine liposomes that ensures the necessary distance between the dyes for effective FRET. The change in time of the donor and acceptor relative fluorescence intensities was used to visualize and trace the liposome-to-cell interaction. We show that FRET-labeling of liposome vesicles allows one to reveal the differences in efficiency and dynamics of these interactions, which are associated with composition, fluidity, and metabolic activity of cell plasma membranes.

  11. Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

    Science.gov (United States)

    Kobayashi, Kenichiro; Otsuka, Shigenori; Apip; Saito, Kazuo

    2016-08-01

    This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall-runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall-runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s-1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.

  12. Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars.

    Science.gov (United States)

    Lin, Hong; Rao, Jun; Shi, Jianxin; Hu, Chaoyang; Cheng, Fang; Wilson, Zoe A; Zhang, Dabing; Quan, Sheng

    2014-09-01

    Soybean [Glycine max (L.) Merr.] is one of the world's major crops, and soybean seeds are a rich and important resource for proteins and oils. While "omics" studies, such as genomics, transcriptomics, and proteomics, have been widely applied in soybean molecular research, fewer metabolomic studies have been conducted for large-scale detection of low molecular weight metabolites, especially in soybean seeds. In this study, we investigated the seed metabolomes of 29 common soybean cultivars through combined gas chromatography-mass spectrometry and ultra-performance liquid chromatography-tandem mass spectrometry. One hundred sixty-nine named metabolites were identified and subsequently used to construct a metabolic network of mature soybean seed. Among the 169 detected metabolites, 104 were found to be significantly variable in their levels across tested cultivars. Metabolite markers that could be used to distinguish genetically related soybean cultivars were also identified, and metabolite-metabolite correlation analysis revealed some significant associations within the same or among different metabolite groups. Findings from this work may potentially provide the basis for further studies on both soybean seed metabolism and metabolic engineering to improve soybean seed quality and yield. © 2014 Institute of Botany, Chinese Academy of Sciences.

  13. Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars

    Institute of Scientific and Technical Information of China (English)

    Hong Lin; Jun Rao; Jianxin Shi; Chaoyang Hu; Fang Cheng; Zoe AWilson; Dabing Zhang; Sheng Quan

    2014-01-01

    Soybean [Glycine max (L.) Merr.] is one of the world’s major crops, and soybean seeds are a rich and important resource for proteins and oils. While “omics”studies, such as genomics, transcriptomics, and proteomics, have been widely applied in soybean molecular research, fewer metabolomic studies have been conducted for large-scale detection of low molecular weight metabolites, especial y in soybean seeds. In this study, we investigated the seed metabolomes of 29 common soybean cultivars through combined gas chromatography-mass spectrometry and ultra-performance liquid chromatography-tandem mass spectrometry. One hundred sixty-nine named metabolites were identified and subsequently used to construct a metabolic network of mature soybean seed. Among the 169 detected metabolites, 104 were found to be significantly variable in their levels across tested cultivars. Metabolite markers that could be used to distinguish genetical y related soybean cultivars were also identified, and metabolite-metabolite correlation analysis revealed some significant associations within the same or among different metabolite groups. Findings from this work may potentially provide the basis for further studies on both soybean seed metabolism and metabolic engineering to improve soybean seed quality and yield.

  14. Surface phenomena revealed by in situ imaging: studies from adhesion, wear and cutting

    Science.gov (United States)

    Viswanathan, Koushik; Mahato, Anirban; Yeung, Ho; Chandrasekar, Srinivasan

    2017-03-01

    Surface deformation and flow phenomena are ubiquitous in mechanical processes. In this work we present an in situ imaging framework for studying a range of surface mechanical phenomena at high spatial resolution and across a range of time scales. The in situ framework is capable of resolving deformation and flow fields quantitatively in terms of surface displacements, velocities, strains and strain rates. Three case studies are presented demonstrating the power of this framework for studying surface deformation. In the first, the origin of stick-slip motion in adhesive polymer interfaces is investigated, revealing a intimate link between stick-slip and surface wave propagation. Second, the role of flow in mediating formation of surface defects and wear particles in metals is analyzed using a prototypical sliding process. It is shown that conventional post-mortem observation and inference can lead to erroneous conclusions with regard to formation of surface cracks and wear particles. The in situ framework is shown to unambiguously capture delamination wear in sliding. Third, material flow and surface deformation in a typical cutting process is analyzed. It is shown that a long-standing problem in the cutting of annealed metals is resolved by the imaging, with other benefits such as estimation of energy dissipation and power from the flow fields. In closure, guidelines are provided for profitably exploiting in situ observations to study large-strain deformation, flow and friction phenomena at surfaces that display a variety of time-scales.

  15. Rainfall downscaling of weekly ensemble forecasts using self-organising maps

    Directory of Open Access Journals (Sweden)

    Masamichi Ohba

    2016-03-01

    Full Text Available This study presents an application of self-organising maps (SOMs to downscaling medium-range ensemble forecasts and probabilistic prediction of local precipitation in Japan. SOM was applied to analyse and connect the relationship between atmospheric patterns over Japan and local high-resolution precipitation data. Multiple SOM was simultaneously employed on four variables derived from the JRA-55 reanalysis over the area of study (south-western Japan, and a two-dimensional lattice of weather patterns (WPs was obtained. Weekly ensemble forecasts can be downscaled to local precipitation using the obtained multiple SOM. The downscaled precipitation is derived by the five SOM lattices based on the WPs of the global model ensemble forecasts for a particular day in 2009–2011. Because this method effectively handles the stochastic uncertainties from the large number of ensemble members, a probabilistic local precipitation is easily and quickly obtained from the ensemble forecasts. This downscaling of ensemble forecasts provides results better than those from a 20-km global spectral model (i.e. capturing the relatively detailed precipitation distribution over the region. To capture the effect of the detailed pattern differences in each SOM node, a statistical model is additionally concreted for each SOM node. The predictability skill of the ensemble forecasts is significantly improved under the neural network-statistics hybrid-downscaling technique, which then brings a much better skill score than the traditional method. It is expected that the results of this study will provide better guidance to the user community and contribute to the future development of dam-management models.

  16. Design and experimentation of an empirical multistructure framework for accurate, sharp and reliable hydrological ensembles

    Science.gov (United States)

    Seiller, G.; Anctil, F.; Roy, R.

    2017-09-01

    This paper outlines the design and experimentation of an Empirical Multistructure Framework (EMF) for lumped conceptual hydrological modeling. This concept is inspired from modular frameworks, empirical model development, and multimodel applications, and encompasses the overproduce and select paradigm. The EMF concept aims to reduce subjectivity in conceptual hydrological modeling practice and includes model selection in the optimisation steps, reducing initial assumptions on the prior perception of the dominant rainfall-runoff transformation processes. EMF generates thousands of new modeling options from, for now, twelve parent models that share their functional components and parameters. Optimisation resorts to ensemble calibration, ranking and selection of individual child time series based on optimal bias and reliability trade-offs, as well as accuracy and sharpness improvement of the ensemble. Results on 37 snow-dominated Canadian catchments and 20 climatically-diversified American catchments reveal the excellent potential of the EMF in generating new individual model alternatives, with high respective performance values, that may be pooled efficiently into ensembles of seven to sixty constitutive members, with low bias and high accuracy, sharpness, and reliability. A group of 1446 new models is highlighted to offer good potential on other catchments or applications, based on their individual and collective interests. An analysis of the preferred functional components reveals the importance of the production and total flow elements. Overall, results from this research confirm the added value of ensemble and flexible approaches for hydrological applications, especially in uncertain contexts, and open up new modeling possibilities.

  17. Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

    Directory of Open Access Journals (Sweden)

    I. Kioutsioukis

    2016-12-01

    Full Text Available Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism. Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3, nitrogen dioxide (NO2 and particulate matter (PM10. Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII. The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint

  18. Revealed social preference for potable groundwater: An Eastern Iowa case study

    Science.gov (United States)

    Raunikar, R. P.; Bernknopf, R. L.; Forney, W.; Mishra, S.

    2011-12-01

    The spatially explicit land use and land cover information provided by Landsat moderate-resolution land imagery (MRLI) is needed to more efficiently balance the production of goods and services over landscapes. For example, economic trade-offs are needed to provide both clean groundwater resources and other non-environmental goods and services produced by activities that affect the vadose zone and thus contribute to contamination of groundwater. These trade-off choices are made by numerous economic agents and are constrained by many social institutions including governmental regulations at many levels, contractual obligations and traditions. In effect, on a social level, society acts as if it values groundwater by foregoing other goods to protect these resources. The result of the protection afforded to groundwater resources is observable by measuring contamination in well samples. This observed level of groundwater contamination risk is the revealed preference of society as a whole for clean groundwater. We observed the risk of groundwater contamination in a sampling of well data from our study area (35 counties of Eastern Iowa.) We used a proportional hazard model to quantify the nitrate contamination survival implied by the panel of 19,873 well data, where remaining below a 10 mg/ml maximum contamination level (MCL) is defined as survival. We tested the data for evidence that the levels of protection provided to these resources is correlated with aquifer and vadose zone characteristics and geographic location and whether it changed over time and with economic and other conditions. We demonstrate the use of a nitrate conditioned hazard function for projecting the survival of wells based on nitrate exposure information over the 1940 to 2010 time period. We discuss results of simulations of the survival process that demonstrate the economic significance of this approach. We find that aquifer survival has been significantly improving over time. The principle of

  19. Quark ensembles with the infinite correlation length

    Science.gov (United States)

    Zinov'ev, G. M.; Molodtsov, S. V.

    2015-01-01

    A number of exactly integrable (quark) models of quantum field theory with the infinite correlation length have been considered. It has been shown that the standard vacuum quark ensemble—Dirac sea (in the case of the space-time dimension higher than three)—is unstable because of the strong degeneracy of a state, which is due to the character of the energy distribution. When the momentum cutoff parameter tends to infinity, the distribution becomes infinitely narrow, leading to large (unlimited) fluctuations. Various vacuum ensembles—Dirac sea, neutral ensemble, color superconductor, and BCS state—have been compared. In the case of the color interaction between quarks, the BCS state has been certainly chosen as the ground state of the quark ensemble.

  20. Quark ensembles with the infinite correlation length

    International Nuclear Information System (INIS)

    Zinov’ev, G. M.; Molodtsov, S. V.

    2015-01-01

    A number of exactly integrable (quark) models of quantum field theory with the infinite correlation length have been considered. It has been shown that the standard vacuum quark ensemble—Dirac sea (in the case of the space-time dimension higher than three)—is unstable because of the strong degeneracy of a state, which is due to the character of the energy distribution. When the momentum cutoff parameter tends to infinity, the distribution becomes infinitely narrow, leading to large (unlimited) fluctuations. Various vacuum ensembles—Dirac sea, neutral ensemble, color superconductor, and BCS state—have been compared. In the case of the color interaction between quarks, the BCS state has been certainly chosen as the ground state of the quark ensemble

  1. Quark ensembles with the infinite correlation length

    Energy Technology Data Exchange (ETDEWEB)

    Zinov’ev, G. M. [National Academy of Sciences of Ukraine, Bogoliubov Institute for Theoretical Physics (Ukraine); Molodtsov, S. V., E-mail: molodtsov@itep.ru [Joint Institute for Nuclear Research (Russian Federation)

    2015-01-15

    A number of exactly integrable (quark) models of quantum field theory with the infinite correlation length have been considered. It has been shown that the standard vacuum quark ensemble—Dirac sea (in the case of the space-time dimension higher than three)—is unstable because of the strong degeneracy of a state, which is due to the character of the energy distribution. When the momentum cutoff parameter tends to infinity, the distribution becomes infinitely narrow, leading to large (unlimited) fluctuations. Various vacuum ensembles—Dirac sea, neutral ensemble, color superconductor, and BCS state—have been compared. In the case of the color interaction between quarks, the BCS state has been certainly chosen as the ground state of the quark ensemble.

  2. Various multistage ensembles for prediction of heating energy consumption

    Directory of Open Access Journals (Sweden)

    Radisa Jovanovic

    2015-04-01

    Full Text Available Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.

  3. Can decadal climate predictions be improved by ocean ensemble dispersion filtering?

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-12-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years inadvance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-termweather forecasts represent an initial value problem and long-term climate projections represent a boundarycondition problem, the decadal climate prediction falls in-between these two time scales. The ocean memorydue to its heat capacity holds big potential skill on the decadal scale. In recent years, more precise initializationtechniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions.Ensembles are another important aspect. Applying slightly perturbed predictions results in an ensemble. Insteadof using and evaluating one prediction, but the whole ensemble or its ensemble average, improves a predictionsystem. However, climate models in general start losing the initialized signal and its predictive skill from oneforecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improvedby a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. Wefound that this procedure, called ensemble dispersion filter, results in more accurate results than the standarddecadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions showan increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with largerensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from oceanensemble dispersion filtering toward the ensemble mean. This study is part of MiKlip (fona-miklip.de) - a major project on decadal climate prediction in Germany.We focus on the Max-Planck-Institute Earth System Model using the low-resolution version (MPI-ESM-LR) andMiKlip's basic initialization strategy as in 2017 published decadal climate forecast: http

  4. Products of random matrices from fixed trace and induced Ginibre ensembles

    Science.gov (United States)

    Akemann, Gernot; Cikovic, Milan

    2018-05-01

    We investigate the microcanonical version of the complex induced Ginibre ensemble, by introducing a fixed trace constraint for its second moment. Like for the canonical Ginibre ensemble, its complex eigenvalues can be interpreted as a two-dimensional Coulomb gas, which are now subject to a constraint and a modified, collective confining potential. Despite the lack of determinantal structure in this fixed trace ensemble, we compute all its density correlation functions at finite matrix size and compare to a fixed trace ensemble of normal matrices, representing a different Coulomb gas. Our main tool of investigation is the Laplace transform, that maps back the fixed trace to the induced Ginibre ensemble. Products of random matrices have been used to study the Lyapunov and stability exponents for chaotic dynamical systems, where the latter are based on the complex eigenvalues of the product matrix. Because little is known about the universality of the eigenvalue distribution of such product matrices, we then study the product of m induced Ginibre matrices with a fixed trace constraint—which are clearly non-Gaussian—and M  ‑  m such Ginibre matrices without constraint. Using an m-fold inverse Laplace transform, we obtain a concise result for the spectral density of such a mixed product matrix at finite matrix size, for arbitrary fixed m and M. Very recently local and global universality was proven by the authors and their coworker for a more general, single elliptic fixed trace ensemble in the bulk of the spectrum. Here, we argue that the spectral density of mixed products is in the same universality class as the product of M independent induced Ginibre ensembles.

  5. Multi-objective optimization for generating a weighted multi-model ensemble

    Science.gov (United States)

    Lee, H.

    2017-12-01

    Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

  6. Assessment of Surface Air Temperature over China Using Multi-criterion Model Ensemble Framework

    Science.gov (United States)

    Li, J.; Zhu, Q.; Su, L.; He, X.; Zhang, X.

    2017-12-01

    The General Circulation Models (GCMs) are designed to simulate the present climate and project future trends. It has been noticed that the performances of GCMs are not always in agreement with each other over different regions. Model ensemble techniques have been developed to post-process the GCMs' outputs and improve their prediction reliabilities. To evaluate the performances of GCMs, root-mean-square error, correlation coefficient, and uncertainty are commonly used statistical measures. However, the simultaneous achievements of these satisfactory statistics cannot be guaranteed when using many model ensemble techniques. Meanwhile, uncertainties and future scenarios are critical for Water-Energy management and operation. In this study, a new multi-model ensemble framework was proposed. It uses a state-of-art evolutionary multi-objective optimization algorithm, termed Multi-Objective Complex Evolution Global Optimization with Principle Component Analysis and Crowding Distance (MOSPD), to derive optimal GCM ensembles and demonstrate the trade-offs among various solutions. Such trade-off information was further analyzed with a robust Pareto front with respect to different statistical measures. A case study was conducted to optimize the surface air temperature (SAT) ensemble solutions over seven geographical regions of China for the historical period (1900-2005) and future projection (2006-2100). The results showed that the ensemble solutions derived with MOSPD algorithm are superior over the simple model average and any single model output during the historical simulation period. For the future prediction, the proposed ensemble framework identified that the largest SAT change would occur in the South Central China under RCP 2.6 scenario, North Eastern China under RCP 4.5 scenario, and North Western China under RCP 8.5 scenario, while the smallest SAT change would occur in the Inner Mongolia under RCP 2.6 scenario, South Central China under RCP 4.5 scenario, and

  7. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

    Science.gov (United States)

    Sørensen, Lauge; Nielsen, Mads

    2018-05-15

    The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. Online Learning of Commission Avoidant Portfolio Ensembles

    OpenAIRE

    Uziel, Guy; El-Yaniv, Ran

    2016-01-01

    We present a novel online ensemble learning strategy for portfolio selection. The new strategy controls and exploits any set of commission-oblivious portfolio selection algorithms. The strategy handles transaction costs using a novel commission avoidance mechanism. We prove a logarithmic regret bound for our strategy with respect to optimal mixtures of the base algorithms. Numerical examples validate the viability of our method and show significant improvement over the state-of-the-art.

  9. Modeling Coordination Problems in a Music Ensemble

    DEFF Research Database (Denmark)

    Frimodt-Møller, Søren R.

    2008-01-01

    This paper considers in general terms, how musicians are able to coordinate through rational choices in a situation of (temporary) doubt in an ensemble performance. A fictitious example involving a 5-bar development in an unknown piece of music is analyzed in terms of epistemic logic, more...... to coordinate. Such coordination can be described in terms of Michael Bacharach's theory of variable frames as an aid to solve game theoretic coordination problems....

  10. Microcanonical ensemble formulation of lattice gauge theory

    International Nuclear Information System (INIS)

    Callaway, D.J.E.; Rahman, A.

    1982-01-01

    A new formulation of lattice gauge theory without explicit path integrals or sums is obtained by using the microcanonical ensemble of statistical mechanics. Expectation values in the new formalism are calculated by solving a large set of coupled, nonlinear, ordinary differential equations. The average plaquette for compact electrodynamics calculated in this fashion agrees with standard Monte Carlo results. Possible advantages of the microcanonical method in applications to fermionic systems are discussed

  11. Ensemble forecasts of road surface temperatures

    Czech Academy of Sciences Publication Activity Database

    Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr, jr.; Pešice, Petr; Škuthan, M.

    2017-01-01

    Roč. 187, 1 May (2017), s. 33-41 ISSN 0169-8095 R&D Projects: GA ČR GA13-34856S; GA TA ČR(CZ) TA01031509 Institutional support: RVO:68378289 Keywords : ensemble prediction * road surface temperature * road weather forecast Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 3.778, year: 2016 http://www.sciencedirect.com/science/article/pii/S0169809516307311

  12. Pre- and post-processing of hydro-meteorological ensembles for the Norwegian flood forecasting system in 145 basins.

    Science.gov (United States)

    Jahr Hegdahl, Trine; Steinsland, Ingelin; Merete Tallaksen, Lena; Engeland, Kolbjørn

    2016-04-01

    Probabilistic flood forecasting has an added value for decision making. The Norwegian flood forecasting service is based on a flood forecasting model that run for 145 basins. Covering all of Norway the basins differ in both size and hydrological regime. Currently the flood forecasting is based on deterministic meteorological forecasts, and an auto-regressive procedure is used to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The hydrological ensembles are based on forcing a hydrological model with meteorological ensemble forecasts of precipitation and temperature. However, the ensembles of precipitation are often biased and the spread is too small, especially for the shortest lead times, i.e. they are not calibrated. These properties will, to some extent, propagate to hydrological ensembles, that most likely will be uncalibrated as well. Pre- and post-processing methods are commonly used to obtain calibrated meteorological and hydrological ensembles respectively. Quantitative studies showing the effect of the combined processing of the meteorological (pre-processing) and the hydrological (post-processing) ensembles are however few. The aim of this study is to evaluate the influence of pre- and post-processing on the skill of streamflow predictions, and we will especially investigate if the forecasting skill depends on lead-time, basin size and hydrological regime. This aim is achieved by applying the 51 medium-range ensemble forecast of precipitation and temperature provided by the European Center of Medium-Range Weather Forecast (ECMWF). These ensembles are used as input to the operational Norwegian flood forecasting model, both raw and pre-processed. Precipitation ensembles are calibrated using a zero-adjusted gamma distribution. Temperature ensembles are calibrated using a Gaussian distribution and altitude corrected by a constant gradient

  13. SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.

    Science.gov (United States)

    Liu, Jing; Zhao, Songzheng; Wang, Gang

    2018-01-01

    With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., drug indication and beneficial effect) could hold between the drug and adverse event mentions, making ADE relation extraction - distinguishing ADE relationship from other relation types - necessary. However, conducting ADE relation extraction in social media environment is not a trivial task because of the expertise-dependent, time-consuming and costly annotation process, and the feature space's high-dimensionality attributed to intrinsic characteristics of social media data. This study aims to develop a framework for ADE relation extraction using patient-generated content in social media with better performance than that delivered by previous efforts. To achieve the objective, a general semi-supervised ensemble learning framework, SSEL-ADE, was developed. The framework exploited various lexical, semantic, and syntactic features, and integrated ensemble learning and semi-supervised learning. A series of experiments were conducted to verify the effectiveness of the proposed framework. Empirical results demonstrate the effectiveness of each component of SSEL-ADE and reveal that our proposed framework outperforms most of existing ADE relation extraction methods The SSEL-ADE can facilitate enhanced ADE relation extraction performance, thereby providing more reliable support for pharmacovigilance. Moreover, the proposed semi-supervised ensemble methods have the potential of being applied to effectively deal with other social media-based problems. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

    Directory of Open Access Journals (Sweden)

    Amir Givati

    2017-01-01

    Full Text Available This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October–April 1982–2010. The six National Multimodel Ensemble (NMME models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME. The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.

  15. Multi-wheat-model ensemble responses to interannual climatic variability

    DEFF Research Database (Denmark)

    Ruane, A C; Hudson, N I; Asseng, S

    2016-01-01

    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and ......-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.......We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we...... evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal...

  16. Spam comments prediction using stacking with ensemble learning

    Science.gov (United States)

    Mehmood, Arif; On, Byung-Won; Lee, Ingyu; Ashraf, Imran; Choi, Gyu Sang

    2018-01-01

    Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.

  17. Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

    Science.gov (United States)

    Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

    2016-01-01

    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

  18. Microcanonical ensemble extensive thermodynamics of Tsallis statistics

    International Nuclear Information System (INIS)

    Parvan, A.S.

    2005-01-01

    The microscopic foundation of the generalized equilibrium statistical mechanics based on the Tsallis entropy is given by using the Gibbs idea of statistical ensembles of the classical and quantum mechanics.The equilibrium distribution functions are derived by the thermodynamic method based upon the use of the fundamental equation of thermodynamics and the statistical definition of the functions of the state of the system. It is shown that if the entropic index ξ = 1/q - 1 in the microcanonical ensemble is an extensive variable of the state of the system, then in the thermodynamic limit z bar = 1/(q - 1)N = const the principle of additivity and the zero law of thermodynamics are satisfied. In particular, the Tsallis entropy of the system is extensive and the temperature is intensive. Thus, the Tsallis statistics completely satisfies all the postulates of the equilibrium thermodynamics. Moreover, evaluation of the thermodynamic identities in the microcanonical ensemble is provided by the Euler theorem. The principle of additivity and the Euler theorem are explicitly proved by using the illustration of the classical microcanonical ideal gas in the thermodynamic limit

  19. Modeling polydispersive ensembles of diamond nanoparticles

    International Nuclear Information System (INIS)

    Barnard, Amanda S

    2013-01-01

    While significant progress has been made toward production of monodispersed samples of a variety of nanoparticles, in cases such as diamond nanoparticles (nanodiamonds) a significant degree of polydispersivity persists, so scaling-up of laboratory applications to industrial levels has its challenges. In many cases, however, monodispersivity is not essential for reliable application, provided that the inevitable uncertainties are just as predictable as the functional properties. As computational methods of materials design are becoming more widespread, there is a growing need for robust methods for modeling ensembles of nanoparticles, that capture the structural complexity characteristic of real specimens. In this paper we present a simple statistical approach to modeling of ensembles of nanoparticles, and apply it to nanodiamond, based on sets of individual simulations that have been carefully selected to describe specific structural sources that are responsible for scattering of fundamental properties, and that are typically difficult to eliminate experimentally. For the purposes of demonstration we show how scattering in the Fermi energy and the electronic band gap are related to different structural variations (sources), and how these results can be combined strategically to yield statistically significant predictions of the properties of an entire ensemble of nanodiamonds, rather than merely one individual ‘model’ particle or a non-representative sub-set. (paper)

  20. Multivariate localization methods for ensemble Kalman filtering

    KAUST Repository

    Roh, S.

    2015-12-03

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  1. Decimated Input Ensembles for Improved Generalization

    Science.gov (United States)

    Tumer, Kagan; Oza, Nikunj C.; Norvig, Peter (Technical Monitor)

    1999-01-01

    Recently, many researchers have demonstrated that using classifier ensembles (e.g., averaging the outputs of multiple classifiers before reaching a classification decision) leads to improved performance for many difficult generalization problems. However, in many domains there are serious impediments to such "turnkey" classification accuracy improvements. Most notable among these is the deleterious effect of highly correlated classifiers on the ensemble performance. One particular solution to this problem is generating "new" training sets by sampling the original one. However, with finite number of patterns, this causes a reduction in the training patterns each classifier sees, often resulting in considerably worsened generalization performance (particularly for high dimensional data domains) for each individual classifier. Generally, this drop in the accuracy of the individual classifier performance more than offsets any potential gains due to combining, unless diversity among classifiers is actively promoted. In this work, we introduce a method that: (1) reduces the correlation among the classifiers; (2) reduces the dimensionality of the data, thus lessening the impact of the 'curse of dimensionality'; and (3) improves the classification performance of the ensemble.

  2. Multivariate localization methods for ensemble Kalman filtering

    KAUST Repository

    Roh, S.

    2015-05-08

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  3. Multivariate localization methods for ensemble Kalman filtering

    Science.gov (United States)

    Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.

    2015-12-01

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  4. Multivariate localization methods for ensemble Kalman filtering

    KAUST Repository

    Roh, S.; Jun, M.; Szunyogh, I.; Genton, Marc G.

    2015-01-01

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  5. Microcanonical ensemble extensive thermodynamics of Tsallis statistics

    International Nuclear Information System (INIS)

    Parvan, A.S.

    2006-01-01

    The microscopic foundation of the generalized equilibrium statistical mechanics based on the Tsallis entropy is given by using the Gibbs idea of statistical ensembles of the classical and quantum mechanics. The equilibrium distribution functions are derived by the thermodynamic method based upon the use of the fundamental equation of thermodynamics and the statistical definition of the functions of the state of the system. It is shown that if the entropic index ξ=1/(q-1) in the microcanonical ensemble is an extensive variable of the state of the system, then in the thermodynamic limit z-bar =1/(q-1)N=const the principle of additivity and the zero law of thermodynamics are satisfied. In particular, the Tsallis entropy of the system is extensive and the temperature is intensive. Thus, the Tsallis statistics completely satisfies all the postulates of the equilibrium thermodynamics. Moreover, evaluation of the thermodynamic identities in the microcanonical ensemble is provided by the Euler theorem. The principle of additivity and the Euler theorem are explicitly proved by using the illustration of the classical microcanonical ideal gas in the thermodynamic limit

  6. On combining revealed and stated preferences to forecast customer behaviour: three case studies

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); P.C. Verhoef (Peter)

    2002-01-01

    textabstractMany companies collect stated preference data (SP) like intentions and satisfaction as well as revealed preference data (RP) like actual purchasing behavior. It seems relevant to examine the predictive usefulness of this information for future revealed preferences, that is, customer

  7. Conditions for equivalence of statistical ensembles in nuclear multifragmentation

    International Nuclear Information System (INIS)

    Mallik, Swagata; Chaudhuri, Gargi

    2012-01-01

    Statistical models based on canonical and grand canonical ensembles are extensively used to study intermediate energy heavy-ion collisions. The underlying physical assumption behind canonical and grand canonical models is fundamentally different, and in principle agree only in the thermodynamical limit when the number of particles become infinite. Nevertheless, we show that these models are equivalent in the sense that they predict similar results if certain conditions are met even for finite nuclei. In particular, the results converge when nuclear multifragmentation leads to the formation of predominantly nucleons and low mass clusters. The conditions under which the equivalence holds are amenable to present day experiments.

  8. Decoding ensemble activity from neurophysiological recordings in the temporal cortex.

    Science.gov (United States)

    Kreiman, Gabriel

    2011-01-01

    We study subjects with pharmacologically intractable epilepsy who undergo semi-chronic implantation of electrodes for clinical purposes. We record physiological activity from tens to more than one hundred electrodes implanted in different parts of neocortex. These recordings provide higher spatial and temporal resolution than non-invasive measures of human brain activity. Here we discuss our efforts to develop hardware and algorithms to interact with the human brain by decoding ensemble activity in single trials. We focus our discussion on decoding visual information during a variety of visual object recognition tasks but the same technologies and algorithms can also be directly applied to other cognitive phenomena.

  9. Ensemble learned vaccination uptake prediction using web search queries

    DEFF Research Database (Denmark)

    Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre

    2016-01-01

    We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official...... vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the ?first study to predict vaccination uptake...

  10. Fast image reconstruction for Compton camera using stochastic origin ensemble approach.

    Science.gov (United States)

    Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

    2011-01-01

    Compton camera has been proposed as a potential imaging tool in astronomy, industry, homeland security, and medical diagnostics. Due to the inherent geometrical complexity of Compton camera data, image reconstruction of distributed sources can be ineffective and/or time-consuming when using standard techniques such as filtered backprojection or maximum likelihood-expectation maximization (ML-EM). In this article, the authors demonstrate a fast reconstruction of Compton camera data using a novel stochastic origin ensembles (SOE) approach based on Markov chains. During image reconstruction, the origins of the measured events are randomly assigned to locations on conical surfaces, which are the Compton camera analogs of lines-of-responses in PET. Therefore, the image is defined as an ensemble of origin locations of all possible event origins. During the course of reconstruction, the origins of events are stochastically moved and the acceptance of the new event origin is determined by the predefined acceptance probability, which is proportional to the change in event density. For example, if the event density at the new location is higher than in the previous location, the new position is always accepted. After several iterations, the reconstructed distribution of origins converges to a quasistationary state which can be voxelized and displayed. Comparison with the list-mode ML-EM reveals that the postfiltered SOE algorithm has similar performance in terms of image quality while clearly outperforming ML-EM in relation to reconstruction time. In this study, the authors have implemented and tested a new image reconstruction algorithm for the Compton camera based on the stochastic origin ensembles with Markov chains. The algorithm uses list-mode data, is parallelizable, and can be used for any Compton camera geometry. SOE algorithm clearly outperforms list-mode ML-EM for simple Compton camera geometry in terms of reconstruction time. The difference in computational time

  11. Fish gut-liver immunity during homeostasis or inflammation revealed by integrative transcriptome and proteome studies

    Science.gov (United States)

    Wu, Nan; Song, Yu-Long; Wang, Bei; Zhang, Xiang-Yang; Zhang, Xu-Jie; Wang, Ya-Li; Cheng, Ying-Yin; Chen, Dan-Dan; Xia, Xiao-Qin; Lu, Yi-Shan; Zhang, Yong-An

    2016-11-01

    The gut-associated lymphoid tissue, connected with liver via bile and blood, constructs a local immune environment of both defense and tolerance. The gut-liver immunity has been well-studied in mammals, yet in fish remains largely unknown, even though enteritis as well as liver and gallbladder syndrome emerged as a limitation in aquaculture. In this study, we performed integrative bioinformatic analysis for both transcriptomic (gut and liver) and proteomic (intestinal mucus and bile) data, in both healthy and infected tilapias. We found more categories of immune transcripts in gut than liver, as well as more adaptive immune in gut meanwhile more innate in liver. Interestingly reduced differential immune transcripts between gut and liver upon inflammation were also revealed. In addition, more immune proteins in bile than intestinal mucus were identified. And bile probably providing immune effectors to intestinal mucus upon inflammation was deduced. Specifically, many key immune transcripts in gut or liver as well as key immune proteins in mucus or bile were demonstrated. Accordingly, we proposed a hypothesized profile of fish gut-liver immunity, during either homeostasis or inflammation. Current data suggested that fish gut and liver may collaborate immunologically while keep homeostasis using own strategies, including potential unique mechanisms.

  12. A second life for old data: Global patterns in pollution ecology revealed from published observational studies

    Energy Technology Data Exchange (ETDEWEB)

    Kozlov, Mikhail V., E-mail: mikoz@utu.fi [Section of Ecology, University of Turku, 20014 Turku (Finland); Zvereva, Elena L. [Section of Ecology, University of Turku, 20014 Turku (Finland)

    2011-05-15

    A synthesis of research on the responses of terrestrial biota (1095 effect sizes) to industrial pollution (206 point emission sources) was conducted to reveal regional and global patterns from small-scale observational studies. A meta-analysis, in combination with other statistical methods, showed that the effects of pollution depend on characteristics of the specific polluter (type, amount of emission, duration of impact on biota), the affected organism (trophic group, life history), the level at which the response was measured (organism, population, community), and the environment (biome, climate). In spite of high heterogeneity in responses, we have detected several general patterns. We suggest that the development of evolutionary adaptations to pollution is a common phenomenon and that the harmful effects of pollution on terrestrial ecosystems are likely to increase as the climate warms. We argue that community- and ecosystem-level responses to pollution should be explored directly, rather than deduced from organism-level studies. - Research synthesis demonstrated that the harmful effects of pollution on terrestrial ecosystems are likely to increase as the climate warms.

  13. Study of morphological variation of northern Neotropical Ariidae reveals conservatism despite macrohabitat transitions.

    Science.gov (United States)

    Stange, Madlen; Aguirre-Fernández, Gabriel; Salzburger, Walter; Sánchez-Villagra, Marcelo R

    2018-03-27

    Morphological convergence triggered by trophic adaptations is a common pattern in adaptive radiations. The study of shape variation in an evolutionary context is usually restricted to well-studied fish models. We take advantage of the recently revised systematics of New World Ariidae and investigate skull shape evolution in six genera of northern Neotropical Ariidae. They constitute a lineage that diversified in the marine habitat but repeatedly adapted to freshwater habitats. 3D geometric morphometrics was applied for the first time in catfish skulls and phylogenetically informed statistical analyses were performed to test for the impact of habitat on skull diversification after habitat transition in this lineage. We found that skull shape is conserved throughout phylogeny. A morphospace analysis revealed that freshwater and marine species occupy extreme ends of the first principal component axis and that they exhibit similar Procrustes variances. Yet freshwater species occupy the smallest shape space compared to marine and brackish species (based on partial disparity), and marine and freshwater species have the largest Procrustes distance to each other. We observed a single case of shape convergence as derived from 'C-metrics', which cannot be explained by the occupation of the same habitat. Although Ariidae occupy such a broad spectrum of different habitats from sea to freshwater, the morphospace analysis and analyses of shape and co-variation with habitat in a phylogenetic context shows that conservatism dominates skull shape evolution among ariid genera.

  14. A second life for old data: Global patterns in pollution ecology revealed from published observational studies

    International Nuclear Information System (INIS)

    Kozlov, Mikhail V.; Zvereva, Elena L.

    2011-01-01

    A synthesis of research on the responses of terrestrial biota (1095 effect sizes) to industrial pollution (206 point emission sources) was conducted to reveal regional and global patterns from small-scale observational studies. A meta-analysis, in combination with other statistical methods, showed that the effects of pollution depend on characteristics of the specific polluter (type, amount of emission, duration of impact on biota), the affected organism (trophic group, life history), the level at which the response was measured (organism, population, community), and the environment (biome, climate). In spite of high heterogeneity in responses, we have detected several general patterns. We suggest that the development of evolutionary adaptations to pollution is a common phenomenon and that the harmful effects of pollution on terrestrial ecosystems are likely to increase as the climate warms. We argue that community- and ecosystem-level responses to pollution should be explored directly, rather than deduced from organism-level studies. - Research synthesis demonstrated that the harmful effects of pollution on terrestrial ecosystems are likely to increase as the climate warms.

  15. River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method

    Directory of Open Access Journals (Sweden)

    H. Sanikhani

    2016-02-01

    Full Text Available Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1 methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE methodology for an application to runoff prediction, (2 methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3 methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system. Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models

  16. Limited-area short-range ensemble predictions targeted for heavy rain in Europe

    Directory of Open Access Journals (Sweden)

    K. Sattler

    2005-01-01

    Full Text Available Inherent uncertainties in short-range quantitative precipitation forecasts (QPF from the high-resolution, limited-area numerical weather prediction model DMI-HIRLAM (LAM are addressed using two different approaches to creating a small ensemble of LAM simulations, with focus on prediction of extreme rainfall events over European river basins. The first ensemble type is designed to represent uncertainty in the atmospheric state of the initial condition and at the lateral LAM boundaries. The global ensemble prediction system (EPS from ECMWF serves as host model to the LAM and provides the state perturbations, from which a small set of significant members is selected. The significance is estimated on the basis of accumulated precipitation over a target area of interest, which contains the river basin(s under consideration. The selected members provide the initial and boundary data for the ensemble integration in the LAM. A second ensemble approach tries to address a portion of the model-inherent uncertainty responsible for errors in the forecasted precipitation field by utilising different parameterisation schemes for condensation and convection in the LAM. Three periods around historical heavy rain events that caused or contributed to disastrous river flooding in Europe are used to study the performance of the LAM ensemble designs. The three cases exhibit different dynamic and synoptic characteristics and provide an indication of the ensemble qualities in different weather situations. Precipitation analyses from the Deutsche Wetterdienst (DWD are used as the verifying reference and a comparison of daily rainfall amounts is referred to the respective river basins of the historical cases.

  17. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Directory of Open Access Journals (Sweden)

    Matthew B Biggs

    2017-03-01

    Full Text Available Genome-scale metabolic network reconstructions (GENREs are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA. We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  18. Genome-Wide Association Study Reveals Natural Variations Contributing to Drought Resistance in Crops

    Directory of Open Access Journals (Sweden)

    Hongwei Wang

    2017-06-01

    Full Text Available Crops are often cultivated in regions where they will face environmental adversities; resulting in substantial yield loss which can ultimately lead to food and societal problems. Thus, significant efforts have been made to breed stress tolerant cultivars in an attempt to minimize these problems and to produce more stability with respect to crop yields across broad geographies. Since stress tolerance is a complex and multi-genic trait, advancements with classical breeding approaches have been challenging. On the other hand, molecular breeding, which is based on transgenics, marker-assisted selection and genome editing technologies; holds great promise to enable farmers to better cope with these challenges. However, identification of the key genetic components underlying the trait is critical and will serve as the foundation for future crop genetic improvement. Recently, genome-wide association studies have made significant contributions to facilitate the discovery of natural variation contributing to stress tolerance in crops. From these studies, the identified loci can serve as targets for genomic selection or editing to enable the molecular design of new cultivars. Here, we summarize research progress on this issue and focus on the genetic basis of drought tolerance as revealed by genome-wide association studies and quantitative trait loci mapping. Although many favorable loci have been identified, elucidation of their molecular mechanisms contributing to increased stress tolerance still remains a challenge. Thus, continuous efforts are still required to functionally dissect this complex trait through comprehensive approaches, such as system biological studies. It is expected that proper application of the acquired knowledge will enable the development of stress tolerant cultivars; allowing agricultural production to become more sustainable under dynamic environmental conditions.

  19. On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Webb, M.J.; Senior, C.A.; Sexton, D.M.H.; Ingram, W.J.; Williams, K.D.; Ringer, M.A. [Hadley Centre for Climate Prediction and Research, Met Office, Exeter (United Kingdom); McAvaney, B.J.; Colman, R. [Bureau of Meteorology Research Centre (BMRC), Melbourne (Australia); Soden, B.J. [University of Miami, Rosenstiel School for Marine and Atmospheric Science, Miami, FL (United States); Gudgel, R.; Knutson, T. [Geophysical Fluid Dynamics Laboratory (GFDL), Princeton, NJ (United States); Emori, S.; Ogura, T. [National Institute for Environmental Studies (NIES), Tsukuba (Japan); Tsushima, Y. [Japan Agency for Marine-Earth Science and Technology, Frontier Research Center for Global Change (FRCGC), Kanagawa (Japan); Andronova, N. [University of Michigan, Department of Atmospheric, Oceanic and Space Sciences, Ann Arbor, MI (United States); Li, B. [University of Illinois at Urbana-Champaign (UIUC), Department of Atmospheric Sciences, Urbana, IL (United States); Musat, I.; Bony, S. [Institut Pierre Simon Laplace (IPSL), Paris (France); Taylor, K.E. [Program for Climate Model Diagnosis and Intercomparison (PCMDI), Livermore, CA (United States)

    2006-07-15

    Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO{sub 2} doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level

  20. Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia

    Science.gov (United States)

    Yang, Quan‐Jun; Zhao, Jiang‐Rong; Hao, Juan; Li, Bin; Huo, Yan; Han, Yong‐Long; Wan, Li‐Li; Li, Jie; Huang, Jinlu; Lu, Jin

    2017-01-01

    Abstract Background Cachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance‐based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model. Methods Eighty‐four cancer cachexia patients, 33 pre‐cachectic patients, 105 weight‐stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model. Results Forty‐six cancer cachexia patients, 22 pre‐cachectic patients, 68 weight‐stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre‐cachectic patients, 37 weight‐stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age‐matched and sex‐matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = −400.53 – 481.88

  1. Time delayed Ensemble Nudging Method

    Science.gov (United States)

    An, Zhe; Abarbanel, Henry

    Optimal nudging method based on time delayed embedding theory has shows potentials on analyzing and data assimilation in previous literatures. To extend the application and promote the practical implementation, new nudging assimilation method based on the time delayed embedding space is presented and the connection with other standard assimilation methods are studied. Results shows the incorporating information from the time series of data can reduce the sufficient observation needed to preserve the quality of numerical prediction, making it a potential alternative in the field of data assimilation of large geophysical models.

  2. Biochemistry and structural studies of kynurenine 3-monooxygenase reveal allosteric inhibition by Ro 61-8048.

    Science.gov (United States)

    Gao, Jingjing; Yao, Licheng; Xia, Tingting; Liao, Xuebin; Zhu, Deyu; Xiang, Ye

    2018-04-01

    The human kynurenine 3-monooxygenase (hKMO) is a potential therapeutic target for neurodegenerative and neurologic disorders. Inhibition of KMO by Ro 61-8048, a potent, selective, and the most widely used inhibitor of KMO, was shown effective in various models of neurodegenerative or neurologic disorders. However, the molecular basis of hKMO inhibition by Ro 61-8048 is not clearly understood. Here, we report biochemistry studies on hKMO and crystal structures of an hKMO homolog, pfKMO from Pseudomonas fluorescens, in complex with the substrate l-kynurenine and Ro 61-8048. We found that the C-terminal ∼110 aa are essential for the enzymatic activity of hKMO and the homologous C-terminal region of pfKMO folds into a distinct, all-α-helical domain, which associates with the N-terminal catalytic domain to form a unique tunnel in proximity to the substrate-binding pocket. The tunnel binds the Ro 61-8048 molecule, which fills most of the tunnel, and Ro 61-8048 is hydrogen bonded with several completely conserved residues, including an essential catalytic residue. Modification of Ro 61-8048 and biochemical studies of the modified Ro 61-8048 derivatives suggested that Ro 61-8048 inhibits the enzyme in an allosteric manner by affecting the conformation of the essential catalytic residue and by blocking entry of the substrate or product release. The unique binding sites distinguish Ro 61-8048 as a noncompetitive and highly selective inhibitor from other competitive inhibitors, which should facilitate further optimization of Ro 61-8048 and the development of new inhibitory drugs to hKMO.-Gao, J., Yao, L., Xia, T., Liao, X., Zhu, D., Xiang, Y. Biochemistry and structural studies of kynurenine 3-monooxygenase reveal allosteric inhibition by Ro 61-8048.

  3. Quantifying polypeptide conformational space: sensitivity to conformation and ensemble definition.

    Science.gov (United States)

    Sullivan, David C; Lim, Carmay

    2006-08-24

    Quantifying the density of conformations over phase space (the conformational distribution) is needed to model important macromolecular processes such as protein folding. In this work, we quantify the conformational distribution for a simple polypeptide (N-mer polyalanine) using the cumulative distribution function (CDF), which gives the probability that two randomly selected conformations are separated by less than a "conformational" distance and whose inverse gives conformation counts as a function of conformational radius. An important finding is that the conformation counts obtained by the CDF inverse depend critically on the assignment of a conformation's distance span and the ensemble (e.g., unfolded state model): varying ensemble and conformation definition (1 --> 2 A) varies the CDF-based conformation counts for Ala(50) from 10(11) to 10(69). In particular, relatively short molecular dynamics (MD) relaxation of Ala(50)'s random-walk ensemble reduces the number of conformers from 10(55) to 10(14) (using a 1 A root-mean-square-deviation radius conformation definition) pointing to potential disconnections in comparing the results from simplified models of unfolded proteins with those from all-atom MD simulations. Explicit waters are found to roughen the landscape considerably. Under some common conformation definitions, the results herein provide (i) an upper limit to the number of accessible conformations that compose unfolded states of proteins, (ii) the optimal clustering radius/conformation radius for counting conformations for a given energy and solvent model, (iii) a means of comparing various studies, and (iv) an assessment of the applicability of random search in protein folding.

  4. Cartilaginous Metabolomic Study Reveals Potential Mechanisms of Osteophyte Formation in Osteoarthritis.

    Science.gov (United States)

    Xu, Zhongwei; Chen, Tingmei; Luo, Jiao; Ding, Shijia; Gao, Sichuan; Zhang, Jian

    2017-04-07

    Osteophyte is one of the inevitable consequences of progressive osteoarthritis with the main characteristics of cartilage degeneration and endochondral ossification. The pathogenesis of osteophyte formation is not fully understood to date. In this work, metabolomic approaches were employed to explore potential mechanisms of osteophyte formation by detecting metabolic variations between extracts of osteophyte cartilage tissues (n = 32) and uninvolved control cartilage tissues (n = 34), based on the platform of ultraperformance liquid chromatography tandem quadrupole time-of-flight mass spectrometry, as well as the use of multivariate statistic analysis and univariate statistic analysis. The osteophyte group was significantly separated from the control group by the orthogonal partial least-squares discriminant analysis models, indicating that metabolic state of osteophyte cartilage had been changed. In total, 28 metabolic variations further validated by mass spectrum (MS) match, tandom mass spectrum (MS/MS) match, and standards match mainly included amino acids, sulfonic acids, glycerophospholipids, and fatty acyls. These metabolites were related to some specific physiological or pathological processes (collagen dissolution, boundary layers destroyed, self-restoration triggered, etc.) which might be associated with the procedure of osteophyte formation. Pathway analysis showed phenylalanine metabolism (PI = 0.168, p = 0.004) was highly correlative to this degenerative process. Our findings provided a direction for targeted metabolomic study and an insight into further reveal the molecular mechanisms of ostophyte formation.

  5. Structural Studies Reveal the Functional Modularity of the Scc2-Scc4 Cohesin Loader

    Directory of Open Access Journals (Sweden)

    William C.H. Chao

    2015-08-01

    Full Text Available The remarkable accuracy of eukaryotic cell division is partly maintained by the cohesin complex acting as a molecular glue to prevent premature sister chromatid separation. The loading of cohesin onto chromosomes is catalyzed by the Scc2-Scc4 loader complex. Here, we report the crystal structure of Scc4 bound to the N terminus of Scc2 and show that Scc4 is a tetratricopeptide repeat (TPR superhelix. The Scc2 N terminus adopts an extended conformation and is entrapped by the core of the Scc4 superhelix. Electron microscopy (EM analysis reveals that the Scc2-Scc4 loader complex comprises three domains: a head, body, and hook. Deletion studies unambiguously assign the Scc2N-Scc4 as the globular head domain, whereas in vitro cohesin loading assays show that the central body and the hook domains are sufficient to catalyze cohesin loading onto circular DNA, but not chromatinized DNA in vivo, suggesting a possible role for Scc4 as a chromatin adaptor.

  6. Multicohort Genomewide Association Study Reveals a New Signal of Protection Against HIV-1 Acquisition

    Science.gov (United States)

    Limou, Sophie; Delaneau, Olivier; van Manen, Daniëlle; An, Ping; Sezgin, Efe; Le Clerc, Sigrid; Coulonges, Cédric; Troyer, Jennifer L.; Veldink, Jan H.; van den Berg, Leonard H.; Spadoni, Jean-Louis; Taing, Lieng; Labib, Taoufik; Montes, Matthieu; Delfraissy, Jean-François; Schachter, François; O’Brien, Stephen J.; Buchbinder, Susan; van Natta, Mark L.; Jabs, Douglas A.; Froguel, Philippe; Schuitemaker, Hanneke; Winkler, Cheryl A.

    2012-01-01

    Background. To date, only mutations in CCR5 have been shown to confer resistance to human immunodeficiency virus type 1 (HIV-1) infection, and these explain only a small fraction of the observed variability in HIV susceptibility. Methods. We performed a meta-analysis between 2 independent European genomewide association studies, each comparing HIV-1 seropositive cases with normal population controls known to be HIV uninfected, to identify single-nucleotide polymorphisms (SNPs) associated with the HIV-1 acquisition phenotype. SNPs exhibiting P < 10−5 in this first stage underwent second-stage analysis in 2 independent US cohorts of European descent. Results. After the first stage, a single highly significant association was revealed for the chromosome 8 rs6996198 with HIV-1 acquisition and was replicated in both second-stage cohorts. Across the 4 groups, the rs6996198-T allele was consistently associated with a significant reduced risk of HIV-1 infection, and the global meta-analysis reached genomewide significance: Pcombined = 7.76 × 10−8. Conclusions. We provide strong evidence of association for a common variant with HIV-1 acquisition in populations of European ancestry. This protective signal against HIV-1 infection is the first identified outside the CCR5 nexus. First clues point to a potential functional role for a nearby candidate gene, CYP7B1, but this locus warrants further investigation. PMID:22362864

  7. A genome-wide study reveals rare CNVs exclusive to extreme phenotypes of Alzheimer disease.

    Science.gov (United States)

    Rovelet-Lecrux, Anne; Legallic, Solenn; Wallon, David; Flaman, Jean-Michel; Martinaud, Olivier; Bombois, Stéphanie; Rollin-Sillaire, Adeline; Michon, Agnès; Le Ber, Isabelle; Pariente, Jérémie; Puel, Michèle; Paquet, Claire; Croisile, Bernard; Thomas-Antérion, Catherine; Vercelletto, Martine; Lévy, Richard; Frébourg, Thierry; Hannequin, Didier; Campion, Dominique

    2012-06-01

    Studying rare extreme forms of Alzheimer disease (AD) may prove to be a useful strategy in identifying new genes involved in monogenic determinism of AD. Amyloid precursor protein (APP), PSEN1, and PSEN2 mutations account for only 85% of autosomal dominant early-onset AD (ADEOAD) families. We hypothesised that rare copy number variants (CNVs) could be involved in ADEOAD families without mutations in known genes, as well as in rare sporadic young-onset AD cases. Using high-resolution array comparative genomic hybridisation, we assessed the presence of rare CNVs in 21 unrelated ADEOAD cases, having no alteration on known genes, and 12 sporadic AD cases, with an age of onset younger than 55 years. The analysis revealed the presence of 7 singleton CNVs (4 in ADEOAD and 3 in sporadic cases) absent in 1078 controls and 912 late-onset AD cases. Strikingly, 4 out of 7 rearrangements target genes (KLK6, SLC30A3, MEOX2, and FPR2) encoding proteins that are tightly related to amyloid-β peptide metabolism or signalling. Although these variants are individually rare and restricted to particular subgroups of patients, these findings support the causal role, in human pathology, of a set of genes coding for molecules suspected for a long time to modify Aβ metabolism or signalling, and for which animal or cellular models have already been developed.

  8. DNA barcoding and morphological studies reveal two new species of waxcap mushrooms (Hygrophoraceae in Britain

    Directory of Open Access Journals (Sweden)

    Antony Ainsworth

    2013-09-01

    Full Text Available Rigorous diagnostics and documentation of fungal species are fundamental to their conservation. During the course of a species-level study of UK waxcap (Hygrophoraceae diversity, two previously unrecognized species were discovered. We describe Gliophorus europerplexus sp. nov. and G. reginae sp. nov., respectively orange–brown and purple–pink waxcap mushrooms, from nutrient-poor grasslands in Britain. Both share some morphological features with specimens assigned to Gliophorus (=Hygrocybe psittacinus. However, analysis of sequences of the nuclear ITS DNA barcode region from these and related taxa confirms the phylogenetic distinctness of these lineages. Furthermore, we demonstrated that the holotype of Hygrophorus perplexus, a North American species morphologically resembling G. europerplexus, is phylogenetically divergent from all our collections. It is likely that further collections of G. europerplexus will be revealed by sequencing European material currently filed under G. perplexus and its synonyms. However, two such collections in the Kew fungarium yielded sequences that clustered together but were divergent from those of G. europerplexus, G. perplexus and G. psittacinus and may represent a further novel taxon. By contrast, G. reginae is morphologically distinct and can usually be recognized in the field by its purplish viscid pileus and relatively stout, flexuose, pale stipe. It is named to commemorate the diamond jubilee of Her Majesty Queen Elizabeth II in 2012 and the 60th anniversary of her coronation in 2013.

  9. Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: A TBSS study

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yaou, E-mail: asiaeurope80@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Duan, Yunyun, E-mail: xiaoyun81.love@163.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); He, Yong, E-mail: yong.h.he@gmail.com [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China); Yu, Chunshui, E-mail: csyuster@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Wang, Jun, E-mail: jun_wang@bnu.edu.cn [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China); Huang, Jing, E-mail: sainthj@126.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Ye, Jing, E-mail: jingye.2007@yahoo.com.cn [Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Parizel, Paul M., E-mail: paul.parizel@ua.ac.be [Department of Radiology, Antwerp University Hospital and University of Antwerp, Wilrijkstraat 10, 2650 Edegem, 8 Belgium (Belgium); Li, Kuncheng, E-mail: kunchengli55@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Shu, Ni, E-mail: nshu55@gmail.com [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China)

    2012-10-15

    Objective: To investigate whole brain white matter changes in multiple sclerosis (MS) by multiple diffusion indices, we examined patients with diffusion tensor imaging and utilized tract-based spatial statistics (TBSS) method to analyze the data. Methods: Forty-one relapsing-remitting multiple sclerosis (RRMS) patients and 41 age- and gender-matched normal controls were included in this study. Diffusion weighted images were acquired by employing a single-shot echo planar imaging sequence on a 1.5 T MR scanner. Voxel-wise analyses of multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed with TBSS. Results: The MS patients had significantly decreased FA (9.11%), increased MD (8.26%), AD (3.48%) and RD (13.17%) in their white matter skeletons compared with the controls. Through TBSS analyses, we found abnormal diffusion changes in widespread white matter regions in MS patients. Specifically, decreased FA, increased MD and increased RD were involved in whole-brain white matter, while several regions exhibited increased AD. Furthermore, white matter regions with significant correlations between the diffusion metrics and the clinical variables (the EDSS scores, disease durations and white matter lesion loads) in MS patients were identified. Conclusion: Widespread white matter abnormalities were observed in MS patients revealed by multiple diffusion metrics. The diffusion changes and correlations with clinical variables were mainly attributed to increased RD, implying the predominant role of RD in reflecting the subtle pathological changes in MS.

  10. Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: A TBSS study

    International Nuclear Information System (INIS)

    Liu, Yaou; Duan, Yunyun; He, Yong; Yu, Chunshui; Wang, Jun; Huang, Jing; Ye, Jing; Parizel, Paul M.; Li, Kuncheng; Shu, Ni

    2012-01-01

    Objective: To investigate whole brain white matter changes in multiple sclerosis (MS) by multiple diffusion indices, we examined patients with diffusion tensor imaging and utilized tract-based spatial statistics (TBSS) method to analyze the data. Methods: Forty-one relapsing-remitting multiple sclerosis (RRMS) patients and 41 age- and gender-matched normal controls were included in this study. Diffusion weighted images were acquired by employing a single-shot echo planar imaging sequence on a 1.5 T MR scanner. Voxel-wise analyses of multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed with TBSS. Results: The MS patients had significantly decreased FA (9.11%), increased MD (8.26%), AD (3.48%) and RD (13.17%) in their white matter skeletons compared with the controls. Through TBSS analyses, we found abnormal diffusion changes in widespread white matter regions in MS patients. Specifically, decreased FA, increased MD and increased RD were involved in whole-brain white matter, while several regions exhibited increased AD. Furthermore, white matter regions with significant correlations between the diffusion metrics and the clinical variables (the EDSS scores, disease durations and white matter lesion loads) in MS patients were identified. Conclusion: Widespread white matter abnormalities were observed in MS patients revealed by multiple diffusion metrics. The diffusion changes and correlations with clinical variables were mainly attributed to increased RD, implying the predominant role of RD in reflecting the subtle pathological changes in MS

  11. Ensemble Atmospheric Properties of Small Planets around M Dwarfs

    Science.gov (United States)

    Guo, Xueying; Ballard, Sarah; Dragomir, Diana

    2018-01-01

    With the growing number of planets discovered by the Kepler mission and ground-base surveys, people start to try to understand the atmospheric features of those uncovered new worlds. While it has been found that hot Jupiters exhibit diverse atmosphere composition with both clear and cloudy/hazy atmosphere possible, similar studies on ensembles of smaller planets (Earth analogs) have been held up due to the faintness of most of their host stars. In this work, a sample of 20 Earth analogs of similar periods around M dwarfs with existing Kepler transit information and Spitzer observations is composed, complemented with previously studies GJ1214b and GJ1132b, as well as the recently announced 7 small planets in the TRAPPIST-1 system. We evaluate their transit depths with uncertainties on the Spitzer 4.5 micron band using the “pixel-level decorrelation” method, and together with their well analyzed Kepler data and Hubble data, we put constraints on their atmosphere haze slopes and cloud levels. Aside from improving the understanding of ensemble properties of small planets, this study will also provide clues of potential targets for detailed atmospheric studies using the upcoming James Webb Telescope.

  12. Monthly ENSO Forecast Skill and Lagged Ensemble Size

    Science.gov (United States)

    Trenary, L.; DelSole, T.; Tippett, M. K.; Pegion, K.

    2018-04-01

    The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real-time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real-time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8-10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.

  13. Developing an approach to effectively use super ensemble experiments for the projection of hydrological extremes under climate change

    Science.gov (United States)

    Watanabe, S.; Kim, H.; Utsumi, N.

    2017-12-01

    This study aims to develop a new approach which projects hydrology under climate change using super ensemble experiments. The use of multiple ensemble is essential for the estimation of extreme, which is a major issue in the impact assessment of climate change. Hence, the super ensemble experiments are recently conducted by some research programs. While it is necessary to use multiple ensemble, the multiple calculations of hydrological simulation for each output of ensemble simulations needs considerable calculation costs. To effectively use the super ensemble experiments, we adopt a strategy to use runoff projected by climate models directly. The general approach of hydrological projection is to conduct hydrological model simulations which include land-surface and river routing process using atmospheric boundary conditions projected by climate models as inputs. This study, on the other hand, simulates only river routing model using runoff projected by climate models. In general, the climate model output is systematically biased so that a preprocessing which corrects such bias is necessary for impact assessments. Various bias correction methods have been proposed, but, to the best of our knowledge, no method has proposed for variables other than surface meteorology. Here, we newly propose a method for utilizing the projected future runoff directly. The developed method estimates and corrects the bias based on the pseudo-observation which is a result of retrospective offline simulation. We show an application of this approach to the super ensemble experiments conducted under the program of Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI). More than 400 ensemble experiments from multiple climate models are available. The results of the validation using historical simulations by HAPPI indicates that the output of this approach can effectively reproduce retrospective runoff variability. Likewise, the bias of runoff from super ensemble climate

  14. Benchmarking ensemble streamflow prediction skill in the UK

    Science.gov (United States)

    Harrigan, Shaun; Prudhomme, Christel; Parry, Simon; Smith, Katie; Tanguy, Maliko

    2018-03-01

    ; correlation between catchment base flow index (BFI) and ESP skill was very strong (Spearman's rank correlation coefficient = 0.90 at 1-month lead time). This was in contrast to the more highly responsive catchments in the north and west which were generally not skilful at seasonal lead times. Overall, this work provides scientific justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK. This study, furthermore, creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.

  15. Analyzing the impact of changing size and composition of a crop model ensemble

    Science.gov (United States)

    Rodríguez, Alfredo

    2017-04-01

    The use of an ensemble of crop growth simulation models is a practice recently adopted in order to quantify aspects of uncertainties in model simulations. Yet, while the climate modelling community has extensively investigated the properties of model ensembles and their implications, this has hardly been investigated for crop model ensembles (Wallach et al., 2016). In their ensemble of 27 wheat models, Martre et al. (2015) found that the accuracy of the multi-model ensemble-average only increases up to an ensemble size of ca. 10, but does not improve when including more models in the analysis. However, even when this number of members is reached, questions about the impact of the addition or removal of a member to/from the ensemble arise. When selecting ensemble members, identifying members with poor performance or giving implausible results can make a large difference on the outcome. The objective of this study is to set up a methodology that defines indicators to show the effects of changing the ensemble composition and size on simulation results, when a selection procedure of ensemble members is applied. Ensemble mean or median, and variance are measures used to depict ensemble results among other indicators. We are utilizing simulations from an ensemble of wheat models that have been used to construct impact response surfaces (Pirttioja et al., 2015) (IRSs). These show the response of an impact variable (e.g., crop yield) to systematic changes in two explanatory variables (e.g., precipitation and temperature). Using these, we compare different sub-ensembles in terms of the mean, median and spread, and also by comparing IRSs. The methodology developed here allows comparing an ensemble before and after applying any procedure that changes the ensemble composition and size by measuring the impact of this decision on the ensemble central tendency measures. The methodology could also be further developed to compare the effect of changing ensemble composition and size

  16. Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

    Science.gov (United States)

    Li, Qi; Shi, Kaiyang; Gao, Ning; Li, Jian; Bai, Ou

    2018-03-27

    P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue. This study aimed to develop a method for acquiring more training data based on a collected small training set. A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.

  17. Response of ENSO amplitude to global warming in CESM large ensemble: uncertainty due to internal variability

    Science.gov (United States)

    Zheng, Xiao-Tong; Hui, Chang; Yeh, Sang-Wook

    2018-06-01

    El Niño-Southern Oscillation (ENSO) is the dominant mode of variability in the coupled ocean-atmospheric system. Future projections of ENSO change under global warming are highly uncertain among models. In this study, the effect of internal variability on ENSO amplitude change in future climate projections is investigated based on a 40-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project. A large uncertainty is identified among ensemble members due to internal variability. The inter-member diversity is associated with a zonal dipole pattern of sea surface temperature (SST) change in the mean along the equator, which is similar to the second empirical orthogonal function (EOF) mode of tropical Pacific decadal variability (TPDV) in the unforced control simulation. The uncertainty in CESM-LE is comparable in magnitude to that among models of the Coupled Model Intercomparison Project phase 5 (CMIP5), suggesting the contribution of internal variability to the intermodel uncertainty in ENSO amplitude change. However, the causations between changes in ENSO amplitude and the mean state are distinct between CESM-LE and CMIP5 ensemble. The CESM-LE results indicate that a large ensemble of 15 members is needed to separate the relative contributions to ENSO amplitude change over the twenty-first century between forced response and internal variability.

  18. The role of model dynamics in ensemble Kalman filter performance for chaotic systems

    Science.gov (United States)

    Ng, G.-H.C.; McLaughlin, D.; Entekhabi, D.; Ahanin, A.

    2011-01-01

    The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or 'diverging', when applied to large chaotic systems such as atmospheric and ocean models. Past studies have demonstrated the adverse impact of sampling error during the filter's update step. We examine how system dynamics affect EnKF performance, and whether the absence of certain dynamic features in the ensemble may lead to divergence. The EnKF is applied to a simple chaotic model, and ensembles are checked against singular vectors of the tangent linear model, corresponding to short-term growth and Lyapunov vectors, corresponding to long-term growth. Results show that the ensemble strongly aligns itself with the subspace spanned by unstable Lyapunov vectors. Furthermore, the filter avoids divergence only if the full linearized long-term unstable subspace is spanned. However, short-term dynamics also become important as non-linearity in the system increases. Non-linear movement prevents errors in the long-term stable subspace from decaying indefinitely. If these errors then undergo linear intermittent growth, a small ensemble may fail to properly represent all important modes, causing filter divergence. A combination of long and short-term growth dynamics are thus critical to EnKF performance. These findings can help in developing practical robust filters based on model dynamics. ?? 2011 The Authors Tellus A ?? 2011 John Wiley & Sons A/S.

  19. Wang-Landau Reaction Ensemble Method: Simulation of Weak Polyelectrolytes and General Acid-Base Reactions.

    Science.gov (United States)

    Landsgesell, Jonas; Holm, Christian; Smiatek, Jens

    2017-02-14

    We present a novel method for the study of weak polyelectrolytes and general acid-base reactions in molecular dynamics and Monte Carlo simulations. The approach combines the advantages of the reaction ensemble and the Wang-Landau sampling method. Deprotonation and protonation reactions are simulated explicitly with the help of the reaction ensemble method, while the accurate sampling of the corresponding phase space is achieved by the Wang-Landau approach. The combination of both techniques provides a sufficient statistical accuracy such that meaningful estimates for the density of states and the partition sum can be obtained. With regard to these estimates, several thermodynamic observables like the heat capacity or reaction free energies can be calculated. We demonstrate that the computation times for the calculation of titration curves with a high statistical accuracy can be significantly decreased when compared to the original reaction ensemble method. The applicability of our approach is validated by the study of weak polyelectrolytes and their thermodynamic properties.

  20. Stabilizing canonical-ensemble calculations in the auxiliary-field Monte Carlo method

    Science.gov (United States)

    Gilbreth, C. N.; Alhassid, Y.

    2015-03-01

    Quantum Monte Carlo methods are powerful techniques for studying strongly interacting Fermi systems. However, implementing these methods on computers with finite-precision arithmetic requires careful attention to numerical stability. In the auxiliary-field Monte Carlo (AFMC) method, low-temperature or large-model-space calculations require numerically stabilized matrix multiplication. When adapting methods used in the grand-canonical ensemble to the canonical ensemble of fixed particle number, the numerical stabilization increases the number of required floating-point operations for computing observables by a factor of the size of the single-particle model space, and thus can greatly limit the systems that can be studied. We describe an improved method for stabilizing canonical-ensemble calculations in AFMC that exhibits better scaling, and present numerical tests that demonstrate the accuracy and improved performance of the method.

  1. Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook

    Directory of Open Access Journals (Sweden)

    M. Sofiev

    2017-10-01

    Full Text Available The paper presents the first modelling experiment of the European-scale olive pollen dispersion, analyses the quality of the predictions, and outlines the research needs. A 6-model strong ensemble of Copernicus Atmospheric Monitoring Service (CAMS was run throughout the olive season of 2014, computing the olive pollen distribution. The simulations have been compared with observations in eight countries, which are members of the European Aeroallergen Network (EAN. Analysis was performed for individual models, the ensemble mean and median, and for a dynamically optimised combination of the ensemble members obtained via fusion of the model predictions with observations. The models, generally reproducing the olive season of 2014, showed noticeable deviations from both observations and each other. In particular, the season was reported to start too early by 8 days, but for some models the error mounted to almost 2 weeks. For the end of the season, the disagreement between the models and the observations varied from a nearly perfect match up to 2 weeks too late. A series of sensitivity studies carried out to understand the origin of the disagreements revealed the crucial role of ambient temperature and consistency of its representation by the meteorological models and heat-sum-based phenological model. In particular, a simple correction to the heat-sum threshold eliminated the shift of the start of the season but its validity in other years remains to be checked. The short-term features of the concentration time series were reproduced better, suggesting that the precipitation events and cold/warm spells, as well as the large-scale transport, were represented rather well. Ensemble averaging led to more robust results. The best skill scores were obtained with data fusion, which used the previous days' observations to identify the optimal weighting coefficients of the individual model forecasts. Such combinations were tested for the forecasting

  2. Three-dimensional theory for interaction between atomic ensembles and free-space light

    International Nuclear Information System (INIS)

    Duan, L.-M.; Cirac, J.I.; Zoller, P.

    2002-01-01

    Atomic ensembles have shown to be a promising candidate for implementations of quantum information processing by many recently discovered schemes. All these schemes are based on the interaction between optical beams and atomic ensembles. For description of these interactions, one assumed either a cavity-QED model or a one-dimensional light propagation model, which is still inadequate for a full prediction and understanding of most of the current experimental efforts that are actually taken in the three-dimensional free space. Here, we propose a perturbative theory to describe the three-dimensional effects in interaction between atomic ensembles and free-space light with a level configuration important for several applications. The calculations reveal some significant effects that were not known before from the other approaches, such as the inherent mode-mismatching noise and the optimal mode-matching conditions. The three-dimensional theory confirms the collective enhancement of the signal-to-noise ratio which is believed to be one of the main advantages of the ensemble-based quantum information processing schemes, however, it also shows that this enhancement needs to be understood in a more subtle way with an appropriate mode-matching method

  3. Impact of community-acquired paediatric rotavirus gastroenteritis on family life: data from the REVEAL study.

    Science.gov (United States)

    Van der Wielen, Marie; Giaquinto, Carlo; Gothefors, Leif; Huelsse, Christel; Huet, Frédéric; Littmann, Martina; Maxwell, Melanie; Talayero, José M P; Todd, Peter; Vila, Miguel T; Cantarutti, Luigi; Van Damme, Pierre

    2010-03-15

    Rotavirus is the leading cause of acute gastroenteritis (AGE) and the most frequent cause of severe diarrhoea in children aged less than 5 years. Although the epidemiology of rotavirus gastroenteritis (RVGE) is well documented, there are few data on the impact of RVGE on the families of affected children. Data associated with the burden of RVGE, including number of working days lost, levels of parental stress, the need for alternative childcare arrangements and additional nappies used, were extracted from questionnaires completed by parents of children participating in a prospective, multicentre, observational study (Rotavirus gastroenteritis Epidemiology and Viral types in Europe Accounting for Losses in public health and society, REVEAL), conducted during 2004-2005 in selected areas of Belgium, France, Germany, Italy, Spain, Sweden, and the United Kingdom to estimate the incidence of RVGE in children aged less than 5 years seeking medical care as a result of AGE. 1102 children with RVGE were included in the present analysis. The proportion of RVGE cases that required at least one parent or other person to be absent from work was 39%-91% in the hospital setting, 44%-64% in the emergency department, and 20%-64% in primary care. Self-reported levels of parental stress were generally high (mean stress levels, > or = 5 on a 10-point visual analogue scale). Additional childcare arrangements were required in up to 21% of RVGE episodes. The mean number of nappies used per day during RVGE episodes was approximately double that used when the child was not ill. Paediatric RVGE cases cause disruption to families and parental stress. The burden of RVGE on children and their families could be substantially reduced by routine rotavirus vaccination of infants.

  4. Impact of community-acquired paediatric rotavirus gastroenteritis on family life: data from the REVEAL study

    Directory of Open Access Journals (Sweden)

    Talayero José MP

    2010-03-01

    Full Text Available Abstract Background Rotavirus is the leading cause of acute gastroenteritis (AGE and the most frequent cause of severe diarrhoea in children aged less than 5 years. Although the epidemiology of rotavirus gastroenteritis (RVGE is well documented, there are few data on the impact of RVGE on the families of affected children. Methods Data associated with the burden of RVGE, including number of working days lost, levels of parental stress, the need for alternative childcare arrangements and additional nappies used, were extracted from questionnaires completed by parents of children participating in a prospective, multicentre, observational study (Rotavirus gastroenteritis Epidemiology and Viral types in Europe Accounting for Losses in public health and society, REVEAL, conducted during 2004-2005 in selected areas of Belgium, France, Germany, Italy, Spain, Sweden, and the United Kingdom to estimate the incidence of RVGE in children aged less than 5 years seeking medical care as a result of AGE. Results 1102 children with RVGE were included in the present analysis. The proportion of RVGE cases that required at least one parent or other person to be absent from work was 39%-91% in the hospital setting, 44%-64% in the emergency department, and 20%-64% in primary care. Self-reported levels of parental stress were generally high (mean stress levels, ≥ 5 on a 10-point visual analogue scale. Additional childcare arrangements were required in up to 21% of RVGE episodes. The mean number of nappies used per day during RVGE episodes was approximately double that used when the child was not ill. Conclusions Paediatric RVGE cases cause disruption to families and parental stress. The burden of RVGE on children and their families could be substantially reduced by routine rotavirus vaccination of infants.

  5. Comparative study of human mitochondrial proteome reveals extensive protein subcellular relocalization after gene duplications

    Directory of Open Access Journals (Sweden)

    Huang Yong

    2009-11-01

    Full Text Available Abstract Background Gene and genome duplication is the principle creative force in evolution. Recently, protein subcellular relocalization, or neolocalization was proposed as one of the mechanisms responsible for the retention of duplicated genes. This hypothesis received support from the analysis of yeast genomes, but has not been tested thoroughly on animal genomes. In order to evaluate the importance of subcellular relocalizations for retention of duplicated genes in animal genomes, we systematically analyzed nuclear encoded mitochondrial proteins in the human genome by reconstructing phylogenies of mitochondrial multigene families. Results The 456 human mitochondrial proteins selected for this study were clustered into 305 gene families including 92 multigene families. Among the multigene families, 59 (64% consisted of both mitochondrial and cytosolic (non-mitochondrial proteins (mt-cy families while the remaining 33 (36% were composed of mitochondrial proteins (mt-mt families. Phylogenetic analyses of mt-cy families revealed three different scenarios of their neolocalization following gene duplication: 1 relocalization from mitochondria to cytosol, 2 from cytosol to mitochondria and 3 multiple subcellular relocalizations. The neolocalizations were most commonly enabled by the gain or loss of N-terminal mitochondrial targeting signals. The majority of detected subcellular relocalization events occurred early in animal evolution, preceding the evolution of tetrapods. Mt-mt protein families showed a somewhat different pattern, where gene duplication occurred more evenly in time. However, for both types of protein families, most duplication events appear to roughly coincide with two rounds of genome duplications early in vertebrate evolution. Finally, we evaluated the effects of inaccurate and incomplete annotation of mitochondrial proteins and found that our conclusion of the importance of subcellular relocalization after gene duplication on

  6. Force estimation from ensembles of Golgi tendon organs

    Science.gov (United States)

    Mileusnic, M. P.; Loeb, G. E.

    2009-06-01

    Golgi tendon organs (GTOs) located in the skeletal muscles provide the central nervous system with information about muscle tension. The ensemble firing of all GTO receptors in the muscle has been hypothesized to represent a reliable measure of the whole muscle force but the precision and accuracy of that information are largely unknown because it is impossible to record activity simultaneously from all GTOs in a muscle. In this study, we combined a new mathematical model of force sampling and transduction in individual GTOs with various models of motor unit (MU) organization and recruitment simulating various normal, pathological and neural prosthetic conditions. Our study suggests that in the intact muscle the ensemble GTO activity accurately encodes force information according to a nonlinear, monotonic relationship that has its steepest slope for low force levels and tends to saturate at the highest force levels. The relationship between the aggregate GTO activity and whole muscle tension under some pathological conditions is similar to one seen in the intact muscle during rapidly modulated, phasic excitation of the motor pool (typical for many natural movements) but quite different when the muscle is activated slowly or held at a given force level. Substantial deviations were also observed during simulated functional electrical stimulation.

  7. On characteristic polynomials for a generalized chiral random matrix ensemble with a source

    Science.gov (United States)

    Fyodorov, Yan V.; Grela, Jacek; Strahov, Eugene

    2018-04-01

    We evaluate averages involving characteristic polynomials, inverse characteristic polynomials and ratios of characteristic polynomials for a N× N random matrix taken from a L-deformed chiral Gaussian Unitary Ensemble with an external source Ω. Relation to a recently studied statistics of bi-orthogonal eigenvectors in the complex Ginibre ensemble, see Fyodorov (2017 arXiv:1710.04699), is briefly discussed as a motivation to study asymptotics of these objects in the case of external source proportional to the identity matrix. In particular, for an associated complex bulk/chiral edge scaling regime we retrieve the kernel related to Bessel/Macdonald functions.

  8. Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

    Directory of Open Access Journals (Sweden)

    Seungpil Jung

    2018-02-01

    Full Text Available This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF and ensemble smoother (ES as representative frameworks, discusses their pros and cons, and investigates recent progress to overcome their drawbacks. The typical weaknesses of ensemble-based methods are non-Gaussian parameters, improper prior ensembles and finite population size. Three categorized approaches, to mitigate these limitations, are reviewed with recent accomplishments; improvement of Kalman gains, add-on of transformation functions, and independent evaluation of observed data. The data assimilation in heterogeneous reservoirs, applying the improved ensemble methods, is discussed on predicting unknown dynamic data in reservoir characterization.

  9. Grand Canonical Ensembles in General Relativity

    International Nuclear Information System (INIS)

    Klein, David; Yang, Wei-Shih

    2012-01-01

    We develop a formalism for general relativistic, grand canonical ensembles in space-times with timelike Killing fields. Using that, we derive ideal gas laws, and show how they depend on the geometry of the particular space-times. A systematic method for calculating Newtonian limits is given for a class of these space-times, which is illustrated for Kerr space-time. In addition, we prove uniqueness of the infinite volume Gibbs measure, and absence of phase transitions for a class of interaction potentials in anti-de Sitter space.

  10. A Lagrangian formalism for nonequilibrium ensembles

    International Nuclear Information System (INIS)

    Sobouti, Y.

    1989-08-01

    It is suggested to formulate a nonequilibrium ensemble theory by maximizing a time-integrated entropy constrained by Liouville's equation. This leads to distribution functions of the form f = Z -1 exp(-g/kT), where g(p,q,t) is a solution of Liouville's equation. A further requirement that the entropy should be an additivie functional of the integrals of Liouville's equation, limits the choice of g to linear superpositions of the nonlinearly independent integrals of motion. Time-dependent and time-independent integrals may participate in this superposition. (author). 14 refs

  11. Extension of the GHJW theorem for operator ensembles

    International Nuclear Information System (INIS)

    Choi, Jeong Woon; Hong, Dowon; Chang, Ku-Young; Chi, Dong Pyo; Lee, Soojoon

    2011-01-01

    The Gisin-Hughston-Jozsa-Wootters theorem plays an important role in analyzing various theories about quantum information, quantum communication, and quantum cryptography. It means that any purifications on the extended system which yield indistinguishable state ensembles on their subsystem should have a specific local unitary relation. In this Letter, we show that the local relation is also established even when the indistinguishability of state ensembles is extended to that of operator ensembles.

  12. Convergence of the Square Root Ensemble Kalman Filter in the Large Ensemble Limit

    Czech Academy of Sciences Publication Activity Database

    Kwiatkowski, E.; Mandel, Jan

    2015-01-01

    Roč. 3, č. 1 (2015), s. 1-17 ISSN 2166-2525 R&D Projects: GA ČR GA13-34856S Institutional support: RVO:67985807 Keywords : data assimilation * Lp laws of large numbers * Hilbert space * ensemble Kalman filter Subject RIV: IN - Informatics, Computer Science

  13. New technique for ensemble dressing combining Multimodel SuperEnsemble and precipitation PDF

    Science.gov (United States)

    Cane, D.; Milelli, M.

    2009-09-01

    The Multimodel SuperEnsemble technique (Krishnamurti et al., Science 285, 1548-1550, 1999) is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other ensemble analysis techniques by the use of an adequate weighting of the input forecast models to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure (Cane and Milelli, Meteorologische Zeitschrift, 15, 2, 2006), the Multimodel SuperEnsemble gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts applied on a wide spectrum of results over Piemonte very dense non-GTS weather station network. We will focus particularly on an accurate statistical method for bias correction and on the ensemble dressing in agreement with the observed precipitation forecast-conditioned PDF. Acknowledgement: this work is supported by the Italian Civil Defence Department.

  14. Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    . The obtained ensemble forecasts of wind power are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which are recursively estimated in order to maximize the overall skill of obtained...

  15. One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

    Directory of Open Access Journals (Sweden)

    Jin Xiao

    2014-01-01

    Full Text Available Scientific customer value segmentation (CVS is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

  16. THE TOPOLOGICAL AND DYNAMIC CHARACTERISTICS OF NEURONIC ENSEMBLES IN THE BRAIN AS PERCOLATING FRACTAL SETS

    Directory of Open Access Journals (Sweden)

    Sergey L’vovich Molchatsky

    2017-10-01

    Full Text Available The objective of the research was to determine of neuronic ensembles in the brain. The research was based that neuronic ensembles of a brain are considered as the percolating clusters. In the basic part of the study the main concern was determination of the following parameters: fractal dimension on a passing threshold df; for geodetic lines on a fractal dθ and for trajectories of particles in a turbulence field dw. In the same part of a research the index of a compendency (θ of neuronic ensembles of animals and the human brain is defined. As well as it was supposed has a negative value θ 1. Numerical calculations with use of results of computer analysis frontal section images of a hypothalamus of a brain of animals and human are shown, that the considered objects can be ranked to the special class of fractal objects. Such class of objects is called asymptotically arcwise connected.

  17. An Ensemble of Neural Networks for Online Electron Filtering at the ATLAS Experiment.

    CERN Document Server

    Da Fonseca Pinto, Joao Victor; The ATLAS collaboration

    2018-01-01

    In 2017 the ATLAS experiment implemented an ensemble of neural networks (NeuralRinger algorithm) dedicated to improving the performance of filtering events containing electrons in the high-input rate online environment of the Large Hadron Collider at CERN, Geneva. The ensemble employs a concept of calorimetry rings. The training procedure and final structure of the ensemble are used to minimize fluctuations from detector response, according to the particle energy and position of incidence. A detailed study was carried out to assess profile distortions in crucial offline quantities through the usage of statistical tests and residual analysis. These details and the online performance of this algorithm during the 2017 data-taking will be presented.

  18. Biological ensemble modeling to evaluate potential futures of living marine resources

    DEFF Research Database (Denmark)

    Gårdmark, Anna; Lindegren, Martin; Neuenfeldt, Stefan

    2013-01-01

    ) as an example. The core of the approach is to expose an ensemble of models with different ecological assumptions to climate forcing, using multiple realizations of each climate scenario. We simulated the long-term response of cod to future fishing and climate change in seven ecological models ranging from...... model assumptions from the statistical uncertainty of future climate, and (3) identified results common for the whole model ensemble. Species interactions greatly influenced the simulated response of cod to fishing and climate, as well as the degree to which the statistical uncertainty of climate...... in all models, intense fishing prevented recovery, and climate change further decreased the cod population. Our study demonstrates how the biological ensemble modeling approach makes it possible to evaluate the relative importance of different sources of uncertainty in future species responses, as well...

  19. Charge transfer excitations from exact and approximate ensemble Kohn-Sham theory

    Science.gov (United States)

    Gould, Tim; Kronik, Leeor; Pittalis, Stefano

    2018-05-01

    By studying the lowest excitations of an exactly solvable one-dimensional soft-Coulomb molecular model, we show that components of Kohn-Sham ensembles can be used to describe charge transfer processes. Furthermore, we compute the approximate excitation energies obtained by using the exact ensemble densities in the recently formulated ensemble Hartree-exchange theory [T. Gould and S. Pittalis, Phys. Rev. Lett. 119, 243001 (2017)]. Remarkably, our results show that triplet excitations are accurately reproduced across a dissociation curve in all cases tested, even in systems where ground state energies are poor due to strong static correlations. Singlet excitations exhibit larger deviations from exact results but are still reproduced semi-quantitatively.

  20. Problems of a Statistical Ensemble Theory for Systems Far from Equilibrium

    Science.gov (United States)

    Ebeling, Werner

    The development of a general statistical physics of nonequilibrium systems was one of the main unfinished tasks of statistical physics of the 20th century. The aim of this work is the study of a special class of nonequilibrium systems where the formulation of an ensemble theory of some generality is possible. These are the so-called canonical-dissipative systems, where the driving terms are determined by invariants of motion. We construct canonical-dissipative systems which are ergodic on certain surfaces on the phase plane. These systems may be described by a non-equilibrium microcanocical ensemble, corresponding to an equal distribution on the target surface. Next we construct and solve Fokker-Planck equations; this leads to a kind of canonical-dissipative ensemble. In the last part we discuss the thoretical problem how to define bifurcations in the framework of nonequilibrium statistics and several possible applications.

  1. Opening the Big Black Box: European study reveals visitors' impressions of science laboratories

    CERN Multimedia

    2004-01-01

    "On 29 - 30 March the findings of 'Inside the Big Black Box'- a Europe-wide science and society project - will be revealed during a two-day seminar hosted by CERN*. The principle aim of Inside the Big Black Box (IN3B) is to determine whether a working scientific laboratory can capture the curiosity of the general public through visits" (1 page)

  2. Choice experiments versus revealed choice models : a before-after study of consumer spatial shopping behavior

    NARCIS (Netherlands)

    Timmermans, H.J.P.; Borgers, A.W.J.; Waerden, van der P.J.H.J.

    1992-01-01

    The purpose of this article is to compare a set of multinomial logit models derived from revealed choice data and a decompositional choice model derived from experimental data in terms of predictive success in the context of consumer spatial shopping behavior. Data on consumer shopping choice

  3. Ovis: A framework for visual analysis of ocean forecast ensembles

    KAUST Repository

    Hollt, Thomas; Magdy, Ahmed; Zhan, Peng; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D.; Hadwiger, Markus

    2014-01-01

    We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea. © 1995-2012 IEEE.

  4. Statistical hadronization and hadronic micro-canonical ensemble II

    International Nuclear Information System (INIS)

    Becattini, F.; Ferroni, L.

    2004-01-01

    We present a Monte Carlo calculation of the micro-canonical ensemble of the ideal hadron-resonance gas including all known states up to a mass of about 1.8 GeV and full quantum statistics. The micro-canonical average multiplicities of the various hadron species are found to converge to the canonical ones for moderately low values of the total energy, around 8 GeV, thus bearing out previous analyses of hadronic multiplicities in the canonical ensemble. The main numerical computing method is an importance sampling Monte Carlo algorithm using the product of Poisson distributions to generate multi-hadronic channels. It is shown that the use of this multi-Poisson distribution allows for an efficient and fast computation of averages, which can be further improved in the limit of very large clusters. We have also studied the fitness of a previously proposed computing method, based on the Metropolis Monte Carlo algorithm, for event generation in the statistical hadronization model. We find that the use of the multi-Poisson distribution as proposal matrix dramatically improves the computation performance. However, due to the correlation of subsequent samples, this method proves to be generally less robust and effective than the importance sampling method. (orig.)

  5. Rainfall estimation with TFR model using Ensemble Kalman filter

    Science.gov (United States)

    Asyiqotur Rohmah, Nabila; Apriliani, Erna

    2018-03-01

    Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.

  6. Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles.

    Science.gov (United States)

    Höllt, Thomas; Magdy, Ahmed; Zhan, Peng; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D; Hadwiger, Markus

    2014-08-01

    We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.

  7. Ovis: A framework for visual analysis of ocean forecast ensembles

    KAUST Repository

    Hollt, Thomas

    2014-08-01

    We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea. © 1995-2012 IEEE.

  8. Encoding of Spatial Attention by Primate Prefrontal Cortex Neuronal Ensembles

    Science.gov (United States)

    Treue, Stefan

    2018-01-01

    Abstract Single neurons in the primate lateral prefrontal cortex (LPFC) encode information about the allocation of visual attention and the features of visual stimuli. However, how this compares to the performance of neuronal ensembles at encoding the same information is poorly understood. Here, we recorded the responses of neuronal ensembles in the LPFC of two macaque monkeys while they performed a task that required attending to one of two moving random dot patterns positioned in different hemifields and ignoring the other pattern. We found single units selective for the location of the attended stimulus as well as for its motion direction. To determine the coding of both variables in the population of recorded units, we used a linear classifier and progressively built neuronal ensembles by iteratively adding units according to their individual performance (best single units), or by iteratively adding units based on their contribution to the ensemble performance (best ensemble). For both methods, ensembles of relatively small sizes (n decoding performance relative to individual single units. However, the decoder reached similar performance using fewer neurons with the best ensemble building method compared with the best single units method. Our results indicate that neuronal ensembles within the LPFC encode more information about the attended spatial and nonspatial features of visual stimuli than individual neurons. They further suggest that efficient coding of attention can be achieved by relatively small neuronal ensembles characterized by a certain relationship between signal and noise correlation structures. PMID:29568798

  9. Bayesian ensemble refinement by replica simulations and reweighting

    Science.gov (United States)

    Hummer, Gerhard; Köfinger, Jürgen

    2015-12-01

    We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

  10. Design ensemble machine learning model for breast cancer diagnosis.

    Science.gov (United States)

    Hsieh, Sheau-Ling; Hsieh, Sung-Huai; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei

    2012-10-01

    In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

  11. Ensemble atmospheric dispersion calculations for decision support systems

    International Nuclear Information System (INIS)

    Borysiewicz, M.; Potempski, S.; Galkowski, A.; Zelazny, R.

    2003-01-01

    This document describes two approaches to long-range atmospheric dispersion of pollutants based on the ensemble concept. In the first part of the report some experiences related to the exercises undertaken under the ENSEMBLE project of the European Union are presented. The second part is devoted to the implementation of mesoscale numerical prediction models RAMS and atmospheric dispersion model HYPACT on Beowulf cluster and theirs usage for ensemble forecasting and long range atmospheric ensemble dispersion calculations based on available meteorological data from NCEO, NOAA (USA). (author)

  12. Quantitative ligand and receptor binding studies reveal the mechanism of interleukin-36 (IL-36) pathway activation.

    Science.gov (United States)

    Zhou, Li; Todorovic, Viktor; Kakavas, Steve; Sielaff, Bernhard; Medina, Limary; Wang, Leyu; Sadhukhan, Ramkrishna; Stockmann, Henning; Richardson, Paul L; DiGiammarino, Enrico; Sun, Chaohong; Scott, Victoria

    2018-01-12

    IL-36 cytokines signal through the IL-36 receptor (IL-36R) and a shared subunit, IL-1RAcP (IL-1 receptor accessory protein). The activation mechanism for the IL-36 pathway is proposed to be similar to that of IL-1 in that an IL-36R agonist (IL-36α, IL-36β, or IL-36γ) forms a binary complex with IL-36R, which then recruits IL-1RAcP. Recent studies have shown that IL-36R interacts with IL-1RAcP even in the absence of an agonist. To elucidate the IL-36 activation mechanism, we considered all possible binding events for IL-36 ligands/receptors and examined these events in direct binding assays. Our results indicated that the agonists bind the IL-36R extracellular domain with micromolar affinity but do not detectably bind IL-1RAcP. Using surface plasmon resonance (SPR), we found that IL-1RAcP also does not bind IL-36R when no agonist is present. In the presence of IL-36α, however, IL-1RAcP bound IL-36R strongly. These results suggested that the main pathway to the IL-36R·IL-36α·IL-1RAcP ternary complex is through the IL-36R·IL-36α binary complex, which recruits IL-1RAcP. We could not measure the binding affinity of IL-36R to IL-1RAcP directly, so we engineered a fragment crystallizable-linked construct to induce IL-36R·IL-1RAcP heterodimerization and predicted the binding affinity during a complete thermodynamic cycle to be 74 μm The SPR analysis also indicated that the IL-36R antagonist IL-36Ra binds IL-36R with higher affinity and a much slower off rate than the IL-36R agonists, shedding light on IL-36 pathway inhibition. Our results reveal the landscape of IL-36 ligand and receptor interactions, improving our understanding of IL-36 pathway activation and inhibition. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

  13. DroidEnsemble: Detecting Android Malicious Applications with Ensemble of String and Structural Static Features

    KAUST Repository

    Wang, Wei

    2018-05-11

    Android platform has dominated the Operating System of mobile devices. However, the dramatic increase of Android malicious applications (malapps) has caused serious software failures to Android system and posed a great threat to users. The effective detection of Android malapps has thus become an emerging yet crucial issue. Characterizing the behaviors of Android applications (apps) is essential to detecting malapps. Most existing work on detecting Android malapps was mainly based on string static features such as permissions and API usage extracted from apps. There also exists work on the detection of Android malapps with structural features, such as Control Flow Graph (CFG) and Data Flow Graph (DFG). As Android malapps have become increasingly polymorphic and sophisticated, using only one type of static features may result in false negatives. In this work, we propose DroidEnsemble that takes advantages of both string features and structural features to systematically and comprehensively characterize the static behaviors of Android apps and thus build a more accurate detection model for the detection of Android malapps. We extract each app’s string features, including permissions, hardware features, filter intents, restricted API calls, used permissions, code patterns, as well as structural features like function call graph. We then use three machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Random Forest (RF), to evaluate the performance of these two types of features and of their ensemble. In the experiments, We evaluate our methods and models with 1386 benign apps and 1296 malapps. Extensive experimental results demonstrate the effectiveness of DroidEnsemble. It achieves the detection accuracy as 95.8% with only string features and as 90.68% with only structural features. DroidEnsemble reaches the detection accuracy as 98.4% with the ensemble of both types of features, reducing 9 false positives and 12 false

  14. Tailored graph ensembles as proxies or null models for real networks I: tools for quantifying structure

    International Nuclear Information System (INIS)

    Annibale, A; Coolen, A C C; Fernandes, L P; Fraternali, F; Kleinjung, J

    2009-01-01

    We study the tailoring of structured random graph ensembles to real networks, with the objective of generating precise and practical mathematical tools for quantifying and comparing network topologies macroscopically, beyond the level of degree statistics. Our family of ensembles can produce graphs with any prescribed degree distribution and any degree-degree correlation function; its control parameters can be calculated fully analytically, and as a result we can calculate (asymptotically) formulae for entropies and complexities and for information-theoretic distances between networks, expressed directly and explicitly in terms of their measured degree distribution and degree correlations.

  15. Estimation of the uncertainty of a climate model using an ensemble simulation

    Science.gov (United States)

    Barth, A.; Mathiot, P.; Goosse, H.

    2012-04-01

    The atmospheric forcings play an important role in the study of the ocean and sea-ice dynamics of the Southern Ocean. Error in the atmospheric forcings will inevitably result in uncertain model results. The sensitivity of the model results to errors in the atmospheric forcings are studied with ensemble simulations using multivariate perturbations of the atmospheric forcing fields. The numerical ocean model used is the NEMO-LIM in a global configuration with an horizontal resolution of 2°. NCEP reanalyses are used to provide air temperature and wind data to force the ocean model over the last 50 years. A climatological mean is used to prescribe relative humidity, cloud cover and precipitation. In a first step, the model results is compared with OSTIA SST and OSI SAF sea ice concentration of the southern hemisphere. The seasonal behavior of the RMS difference and bias in SST and ice concentration is highlighted as well as the regions with relatively high RMS errors and biases such as the Antarctic Circumpolar Current and near the ice-edge. Ensemble simulations are performed to statistically characterize the model error due to uncertainties in the atmospheric forcings. Such information is a crucial element for future data assimilation experiments. Ensemble simulations are performed with perturbed air temperature and wind forcings. A Fourier decomposition of the NCEP wind vectors and air temperature for 2007 is used to generate ensemble perturbations. The perturbations are scaled such that the resulting ensemble spread matches approximately the RMS differences between the satellite SST and sea ice concentration. The ensemble spread and covariance are analyzed for the minimum and maximum sea ice extent. It is shown that errors in the atmospheric forcings can extend to several hundred meters in depth near the Antarctic Circumpolar Current.

  16. Cluster Ensemble-Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoru Wang

    2013-07-01

    Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

  17. Nanobiosensing with Arrays and Ensembles of Nanoelectrodes

    Directory of Open Access Journals (Sweden)

    Najmeh Karimian

    2016-12-01

    Full Text Available Since the first reports dating back to the mid-1990s, ensembles and arrays of nanoelectrodes (NEEs and NEAs, respectively have gained an important role as advanced electroanalytical tools thank to their unique characteristics which include, among others, dramatically improved signal/noise ratios, enhanced mass transport and suitability for extreme miniaturization. From the year 2000 onward, these properties have been exploited to develop electrochemical biosensors in which the surfaces of NEEs/NEAs have been functionalized with biorecognition layers using immobilization modes able to take the maximum advantage from the special morphology and composite nature of their surface. This paper presents an updated overview of this field. It consists of two parts. In the first, we discuss nanofabrication methods and the principles of functioning of NEEs/NEAs, focusing, in particular, on those features which are important for the development of highly sensitive and miniaturized biosensors. In the second part, we review literature references dealing the bioanalytical and biosensing applications of sensors based on biofunctionalized arrays/ensembles of nanoelectrodes, focusing our attention on the most recent advances, published in the last five years. The goal of this review is both to furnish fundamental knowledge to researchers starting their activity in this field and provide critical information on recent achievements which can stimulate new ideas for future developments to experienced scientists.

  18. Ensemble Kalman filtering with residual nudging

    KAUST Repository

    Luo, X.

    2012-10-03

    Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.

  19. Deterministic Mean-Field Ensemble Kalman Filtering

    KAUST Repository

    Law, Kody

    2016-05-03

    The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d<2k. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. This is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.

  20. Online cross-validation-based ensemble learning.

    Science.gov (United States)

    Benkeser, David; Ju, Cheng; Lendle, Sam; van der Laan, Mark

    2018-01-30

    Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and, as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate excellent performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  1. Ensemble Kalman filtering with residual nudging

    Directory of Open Access Journals (Sweden)

    Xiaodong Luo

    2012-10-01

    Full Text Available Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF by (in effect adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.

  2. Deterministic Mean-Field Ensemble Kalman Filtering

    KAUST Repository

    Law, Kody; Tembine, Hamidou; Tempone, Raul

    2016-01-01

    The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d<2k. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. This is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.

  3. An Examination of Embedding Character Education into the Daily Functions of High School Instrumental Music Ensembles

    Science.gov (United States)

    Sours, James P.

    2009-01-01

    This study was conducted to examine the effectiveness of embedding character education into the daily functions of instrumental music ensembles at Franklin High School in Portland Oregon. The participants in the study were the students of the researcher which may have been a delimitation. Their ages were from 14 to 19 years. Students from…

  4. Exact solution for the inhomogeneous Dicke model in the canonical ensemble: Thermodynamical limit and finite-size corrections

    Energy Technology Data Exchange (ETDEWEB)

    Pogosov, W.V., E-mail: walter.pogosov@gmail.com [N.L. Dukhov All-Russia Research Institute of Automatics, Moscow (Russian Federation); Institute for Theoretical and Applied Electrodynamics, Russian Academy of Sciences, Moscow (Russian Federation); Moscow Institute of Physics and Technology, Dolgoprudny (Russian Federation); Shapiro, D.S. [N.L. Dukhov All-Russia Research Institute of Automatics, Moscow (Russian Federation); Moscow Institute of Physics and Technology, Dolgoprudny (Russian Federation); V.A. Kotel' nikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences, Moscow (Russian Federation); National University of Science and Technology MISIS, Moscow (Russian Federation); Bork, L.V. [N.L. Dukhov All-Russia Research Institute of Automatics, Moscow (Russian Federation); Institute for Theoretical and Experimental Physics, Moscow (Russian Federation); Onishchenko, A.I. [Bogoliubov Laboratory of Theoretical Physics, Joint Institute for Nuclear Research, Dubna (Russian Federation); Moscow Institute of Physics and Technology, Dolgoprudny (Russian Federation); Skobeltsyn Institute of Nuclear Physics, Moscow State University, Moscow (Russian Federation)

    2017-06-15

    We consider an exactly solvable inhomogeneous Dicke model which describes an interaction between a disordered ensemble of two-level systems with single mode boson field. The existing method for evaluation of Richardson–Gaudin equations in the thermodynamical limit is extended to the case of Bethe equations in Dicke model. Using this extension, we present expressions both for the ground state and lowest excited states energies as well as leading-order finite-size corrections to these quantities for an arbitrary distribution of individual spin energies. We then evaluate these quantities for an equally-spaced distribution (constant density of states). In particular, we study evolution of the spectral gap and other related quantities. We also reveal regions on the phase diagram, where finite-size corrections are of particular importance.

  5. Study of a twisted ATLAS SCT Barrel deformation as revealed by a photogrammetric survey

    CERN Document Server

    Dobson, E; Heinemann, F; Karagoz-Unel, M

    2007-01-01

    A photogrammetry survey on the SCT barrels was performed as an engineering check on the structure of the ATLAS Semiconductor Tracker (SCT) shortly after construction. Analysis of the data obtained revealed small scale elliptical deformation as well as a twist of the structure. The results of the survey are presented as well as interpolation of the measured targets to the module positions and a comparison with track based alignment measurements.

  6. How Do Multiple-Star Systems Form? VLA Study Reveals "Smoking Gun"

    Science.gov (United States)

    2006-12-01

    system, all the antennas could provide data for us. In addition, we improved the level of detail by using the Pie Town, NM, antenna of the Very Long Baseline Array, as part of an expanded system," Lim said. The implementation and improvement of the 43 GHz receiving system was a collaborative program among the German Max Planck Institute, the Mexican National Autonomous University, and the U.S. National Radio Astronomy Observatory. Two popular theoretical models for the formation of multiple-star systems are, first, that the two protostars and their surrounding dusty disks fragment from a larger parent disk, and, second, that the protostars form independently and then one captures the other into a mutual orbit. "Our new study shows that the disks of the two main protostars are aligned with each other, and also are aligned with the larger, surrounding disk. In addition, their orbital motion resembles the rotation of the larger disk. This is a 'smoking gun' supporting the fragmentation model," Lim said. However, the new study also revealed a third young star with a dust disk. "The disk of this one is misaligned with those of the other two, so it may be the result of either fragmentation or capture," Takakuwa said. The misalignment of the third disk could have come through gravitational interactions with the other two, larger, protostars, the scientists said. They plan further observations to try to resolve the question. "We have a very firm indication that two of these protostars and their dust disks formed from the same, larger disk-like cloud, then broke out from it in a fragmentation process. That strongly supports one theoretical model for how multiple-star systems are formed. The misalignment of the third protostar and its disk leaves open the possibility that it could have formed elsewhere and been captured, and we'll continue to work on reconstructing the history of this fascinating system," Lim summarized. The National Radio Astronomy Observatory is a facility of

  7. Strongly nonexponential time-resolved fluorescence of quantum-dot ensembles in three-dimensional photonic crystals

    DEFF Research Database (Denmark)

    Nikolaev, Ivan S.; Lodahl, Peter; van Driel, A. Floris

    2007-01-01

    We observe experimentally that ensembles of quantum dots in three-dimensional 3D photonic crystals reveal strongly nonexponential time-resolved emission. These complex emission decay curves are analyzed with a continuous distribution of decay rates. The log-normal distribution describes the decays...... parameter. This interpretation qualitatively agrees with the calculations of the 3D projected local density of states. We therefore conclude that fluorescence decay of ensembles of quantum dots is highly nonexponential to an extent that is controlled by photonic crystals....

  8. A new strategy for snow-cover mapping using remote sensing data and ensemble based systems techniques

    Science.gov (United States)

    Roberge, S.; Chokmani, K.; De Sève, D.

    2012-04-01

    The snow cover plays an important role in the hydrological cycle of Quebec (Eastern Canada). Consequently, evaluating its spatial extent interests the authorities responsible for the management of water resources, especially hydropower companies. The main objective of this study is the development of a snow-cover mapping strategy using remote sensing data and ensemble based systems techniques. Planned to be tested in a near real-time operational mode, this snow-cover mapping strategy has the advantage to provide the probability of a pixel to be snow covered and its uncertainty. Ensemble systems are made of two key components. First, a method is needed to build an ensemble of classifiers that is diverse as much as possible. Second, an approach is required to combine the outputs of individual classifiers that make up the ensemble in such a way that correct decisions are amplified, and incorrect ones are cancelled out. In this study, we demonstrate the potential of ensemble systems to snow-cover mapping using remote sensing data. The chosen classifier is a sequential thresholds algorithm using NOAA-AVHRR data adapted to conditions over Eastern Canada. Its special feature is the use of a combination of six sequential thresholds varying according to the day in the winter season. Two versions of the snow-cover mapping algorithm have been developed: one is specific for autumn (from October 1st to December 31st) and the other for spring (from March 16th to May 31st). In order to build the ensemble based system, different versions of the algorithm are created by varying randomly its parameters. One hundred of the versions are included in the ensemble. The probability of a pixel to be snow, no-snow or cloud covered corresponds to the amount of votes the pixel has been classified as such by all classifiers. The overall performance of ensemble based mapping is compared to the overall performance of the chosen classifier, and also with ground observations at meteorological

  9. Small angle X-ray scattering study of calreticulin reveals conformational plasticity

    DEFF Research Database (Denmark)

    Toft, Katrine Nørgaard; Larsen, Nanna; Jørgensen, Flemming Steen

    2008-01-01

    . The data from the calreticulin monomer reveal the shape of calreticulin in solution: The previously structurally un-described C-terminal is seen as a globular domain, and the P-domain beta-hairpin extends from the N-domain in a spiral like conformation. In the calreticulin solution dimer, the N-, C-, and P......-domains are easily identified, and the P-domain is in an extended conformation connecting to the second calreticulin molecule. The SAXS solution data enables the construction of a medium-resolution model of calreticulin. In the light of the unresolved chaperone mechanism of calreticulin and calnexin, we discuss...

  10. Real­-Time Ensemble Forecasting of Coronal Mass Ejections Using the Wsa-Enlil+Cone Model

    Science.gov (United States)

    Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; Odstrcil, D.; MacNeice, P. J.; Rastaetter, L.; LaSota, J. A.

    2014-12-01

    complete a parametric event case study of the sensitivity of the CME arrival time prediction to free parameters for ambient solar wind model and CME. The parameter sensitivity study suggests future directions for the system, such as running ensembles using various magnetogram inputs to the WSA model.

  11. Analyzing the uncertainty of ensemble-based gridded observations in land surface simulations and drought assessment

    Science.gov (United States)

    Ahmadalipour, Ali; Moradkhani, Hamid

    2017-12-01

    Hydrologic modeling is one of the primary tools utilized for drought monitoring and drought early warning systems. Several sources of uncertainty in hydrologic modeling have been addressed in the literature. However, few studies have assessed the uncertainty of gridded observation datasets from a drought monitoring perspective. This study provides a hydrologic modeling oriented analysis of the gridded observation data uncertainties over the Pacific Northwest (PNW) and its implications on drought assessment. We utilized a recently developed 100-member ensemble-based observed forcing data to simulate hydrologic fluxes at 1/8° spatial resolution using Variable Infiltration Capacity (VIC) model, and compared the results with a deterministic observation. Meteorological and hydrological droughts are studied at multiple timescales over the basin, and seasonal long-term trends and variations of drought extent is investigated for each case. Results reveal large uncertainty of observed datasets at monthly timescale, with systematic differences for temperature records, mainly due to different lapse rates. The uncertainty eventuates in large disparities of drought characteristics. In general, an increasing trend is found for winter drought extent across the PNW. Furthermore, a ∼3% decrease per decade is detected for snow water equivalent (SWE) over the PNW, with the region being more susceptible to SWE variations of the northern Rockies than the western Cascades. The agricultural areas of southern Idaho demonstrate decreasing trend of natural soil moisture as a result of precipitation decline, which implies higher appeal for anthropogenic water storage and irrigation systems.

  12. Ensemble-based flash-flood modelling: Taking into account hydrodynamic parameters and initial soil moisture uncertainties

    Science.gov (United States)

    Edouard, Simon; Vincendon, Béatrice; Ducrocq, Véronique

    2018-05-01

    Intense precipitation events in the Mediterranean often lead to devastating flash floods (FF). FF modelling is affected by several kinds of uncertainties and Hydrological Ensemble Prediction Systems (HEPS) are designed to take those uncertainties into account. The major source of uncertainty comes from rainfall forcing and convective-scale meteorological ensemble prediction systems can manage it for forecasting purpose. But other sources are related to the hydrological modelling part of the HEPS. This study focuses on the uncertainties arising from the hydrological model parameters and initial soil moisture with aim to design an ensemble-based version of an hydrological model dedicated to Mediterranean fast responding rivers simulations, the ISBA-TOP coupled system. The first step consists in identifying the parameters that have the strongest influence on FF simulations by assuming perfect precipitation. A sensitivity study is carried out first using a synthetic framework and then for several real events and several catchments. Perturbation methods varying the most sensitive parameters as well as initial soil moisture allow designing an ensemble-based version of ISBA-TOP. The first results of this system on some real events are presented. The direct perspective of this work will be to drive this ensemble-based version with the members of a convective-scale meteorological ensemble prediction system to design a complete HEPS for FF forecasting.

  13. Multi-criterion model ensemble of CMIP5 surface air temperature over China

    Science.gov (United States)

    Yang, Tiantian; Tao, Yumeng; Li, Jingjing; Zhu, Qian; Su, Lu; He, Xiaojia; Zhang, Xiaoming

    2018-05-01

    The global circulation models (GCMs) are useful tools for simulating climate change, projecting future temperature changes, and therefore, supporting the preparation of national climate adaptation plans. However, different GCMs are not always in agreement with each other over various regions. The reason is that GCMs' configurations, module characteristics, and dynamic forcings vary from one to another. Model ensemble techniques are extensively used to post-process the outputs from GCMs and improve the variability of model outputs. Root-mean-square error (RMSE), correlation coefficient (CC, or R) and uncertainty are commonly used statistics for evaluating the performances of GCMs. However, the simultaneous achievements of all satisfactory statistics cannot be guaranteed in using many model ensemble techniques. In this paper, we propose a multi-model ensemble framework, using a state-of-art evolutionary multi-objective optimization algorithm (termed MOSPD), to evaluate different characteristics of ensemble candidates and to provide comprehensive trade-off information for different model ensemble solutions. A case study of optimizing the surface air temperature (SAT) ensemble solutions over different geographical regions of China is carried out. The data covers from the period of 1900 to 2100, and the projections of SAT are analyzed with regard to three different statistical indices (i.e., RMSE, CC, and uncertainty). Among the derived ensemble solutions, the trade-off information is further analyzed with a robust Pareto front with respect to different statistics. The comparison results over historical period (1900-2005) show that the optimized solutions are superior over that obtained simple model average, as well as any single GCM output. The improvements of statistics are varying for different climatic regions over China. Future projection (2006-2100) with the proposed ensemble method identifies that the largest (smallest) temperature changes will happen in the

  14. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  15. Ensemble dispersion forecasting - Part 2. Application and evaluation

    DEFF Research Database (Denmark)

    Galmarini, S.; Bianconi, R.; Addis, R.

    2004-01-01

    of the dispersion of ETEX release 1 and the model ensemble is compared with the monitoring data. The scope of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results and represents a superior analysis of the process evolution. (C) 2004 Elsevier...

  16. Adaptive calibration of (u,v)‐wind ensemble forecasts

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2012-01-01

    of sufficient reliability. The original framework introduced here allows for an adaptive bivariate calibration of these ensemble forecasts. The originality of this methodology lies in the fact that calibrated ensembles still consist of a set of (space–time) trajectories, after translation and dilation...... of translation and dilation factors are discussed. Copyright © 2012 Royal Meteorological Society...

  17. Ensemble-based Probabilistic Forecasting at Horns Rev

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    2009-01-01

    forecasting methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power through a suitable power curve model. This modelemploys local polynomial regression, and is adoptively estimated with an orthogonal fitting method. The obtained ensemble forecasts of wind power...

  18. Programming in the Zone: Repertoire Selection for the Large Ensemble

    Science.gov (United States)

    Hopkins, Michael

    2013-01-01

    One of the great challenges ensemble directors face is selecting high-quality repertoire that matches the musical and technical levels of their ensembles. Thoughtful repertoire selection can lead to increased student motivation as well as greater enthusiasm for the music program from parents, administrators, teachers, and community members. Common…

  19. Preferences of and Attitudes toward Treble Choral Ensembles

    Science.gov (United States)

    Wilson, Jill M.

    2012-01-01

    In choral ensembles, a pursuit where females far outnumber males, concern exists that females are being devalued. Attitudes of female choral singers may be negatively affected by the gender imbalance that exists in mixed choirs and by the placement of the mixed choir as the most select ensemble in a program. The purpose of this research was to…

  20. Modality-Driven Classification and Visualization of Ensemble Variance

    Energy Technology Data Exchange (ETDEWEB)

    Bensema, Kevin; Gosink, Luke; Obermaier, Harald; Joy, Kenneth I.

    2016-10-01

    Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.