spatially correlated hierarchical: Topics by WorldWideScience.org

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Hierarchical clustering using correlation metric and spatial continuity constraint

Science.gov (United States)

Stork, Christopher L.; Brewer, Luke N.

2012-10-02

Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.
Hierarchical modeling and analysis for spatial data

CERN Document Server

Banerjee, Sudipto; Gelfand, Alan E

2003-01-01

Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and dat
Hierarchical spatial organization of geographical networks

International Nuclear Information System (INIS)

Travencolo, Bruno A N; Costa, Luciano da F

2008-01-01

In this work, we propose a hierarchical extension of the polygonality index as the means to characterize geographical planar networks. By considering successive neighborhoods around each node, it is possible to obtain more complete information about the spatial order of the network at progressive spatial scales. The potential of the methodology is illustrated with respect to synthetic and real geographical networks
Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots.

Science.gov (United States)

Hagiwara, Yoshinobu; Inoue, Masakazu; Kobayashi, Hiroyoshi; Taniguchi, Tadahiro

2018-01-01

In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., "I am in my home" and "I am in front of the table," a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA). Object recognition results using convolutional neural network (CNN), hierarchical k-means clustering result of self-position estimated by Monte Carlo localization (MCL), and a set of location names are used, respectively, as features in vision, position, and word information. Experiments in forming hierarchical spatial concepts and evaluating how the proposed method can predict unobserved location names and position categories are performed using a robot in the real world. Results verify that, relative to comparable baseline methods, the proposed method enables a robot to predict location names and position categories closer to predictions made by humans. As an application example of the proposed method in a home environment, a demonstration in which a human support robot moves to an instructed place based on human speech instructions is achieved based on the formed hierarchical spatial concept.
Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots

Directory of Open Access Journals (Sweden)

Yoshinobu Hagiwara

2018-03-01

Full Text Available In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., “I am in my home” and “I am in front of the table,” a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA. Object recognition results using convolutional neural network (CNN, hierarchical k-means clustering result of self-position estimated by Monte Carlo localization (MCL, and a set of location names are used, respectively, as features in vision, position, and word information. Experiments in forming hierarchical spatial concepts and evaluating how the proposed method can predict unobserved location names and position categories are performed using a robot in the real world. Results verify that, relative to comparable baseline methods, the proposed method enables a robot to predict location names and position categories closer to predictions made by humans. As an application example of the proposed method in a home environment, a demonstration in which a human support robot moves to an instructed place based on human speech instructions is achieved based on the formed hierarchical spatial concept.
Reduced Rank Mixed Effects Models for Spatially Correlated Hierarchical Functional Data

KAUST Repository

Zhou, Lan

2010-03-01

Hierarchical functional data are widely seen in complex studies where sub-units are nested within units, which in turn are nested within treatment groups. We propose a general framework of functional mixed effects model for such data: within unit and within sub-unit variations are modeled through two separate sets of principal components; the sub-unit level functions are allowed to be correlated. Penalized splines are used to model both the mean functions and the principal components functions, where roughness penalties are used to regularize the spline fit. An EM algorithm is developed to fit the model, while the specific covariance structure of the model is utilized for computational efficiency to avoid storage and inversion of large matrices. Our dimension reduction with principal components provides an effective solution to the difficult tasks of modeling the covariance kernel of a random function and modeling the correlation between functions. The proposed methodology is illustrated using simulations and an empirical data set from a colon carcinogenesis study. Supplemental materials are available online.
Bayesian disease mapping: hierarchical modeling in spatial epidemiology

National Research Council Canada - National Science Library

Lawson, Andrew

2013-01-01

.... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...
A hierarchical spatial framework for forest landscape planning.

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Pete Bettinger; Marie Lennette; K. Norman Johnson; Thomas A. Spies

2005-01-01

A hierarchical spatial framework for large-scale, long-term forest landscape planning is presented along with example policy analyses for a 560,000 ha area of the Oregon Coast Range. The modeling framework suggests utilizing the detail provided by satellite imagery to track forest vegetation condition and for representation of fine-scale features, such as riparian...
Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

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Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies
3D hierarchical spatial representation and memory of multimodal sensory data

Science.gov (United States)

Khosla, Deepak; Dow, Paul A.; Huber, David J.

2009-04-01

This paper describes an efficient method and system for representing, processing and understanding multi-modal sensory data. More specifically, it describes a computational method and system for how to process and remember multiple locations in multimodal sensory space (e.g., visual, auditory, somatosensory, etc.). The multimodal representation and memory is based on a biologically-inspired hierarchy of spatial representations implemented with novel analogues of real representations used in the human brain. The novelty of the work is in the computationally efficient and robust spatial representation of 3D locations in multimodal sensory space as well as an associated working memory for storage and recall of these representations at the desired level for goal-oriented action. We describe (1) A simple and efficient method for human-like hierarchical spatial representations of sensory data and how to associate, integrate and convert between these representations (head-centered coordinate system, body-centered coordinate, etc.); (2) a robust method for training and learning a mapping of points in multimodal sensory space (e.g., camera-visible object positions, location of auditory sources, etc.) to the above hierarchical spatial representations; and (3) a specification and implementation of a hierarchical spatial working memory based on the above for storage and recall at the desired level for goal-oriented action(s). This work is most useful for any machine or human-machine application that requires processing of multimodal sensory inputs, making sense of it from a spatial perspective (e.g., where is the sensory information coming from with respect to the machine and its parts) and then taking some goal-oriented action based on this spatial understanding. A multi-level spatial representation hierarchy means that heterogeneous sensory inputs (e.g., visual, auditory, somatosensory, etc.) can map onto the hierarchy at different levels. When controlling various machine
Hierarchical spatial segregation of two Mediterranean vole species: the role of patch-network structure and matrix composition.

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Pita, Ricardo; Lambin, Xavier; Mira, António; Beja, Pedro

2016-09-01

According to ecological theory, the coexistence of competitors in patchy environments may be facilitated by hierarchical spatial segregation along axes of environmental variation, but empirical evidence is limited. Cabrera and water voles show a metapopulation-like structure in Mediterranean farmland, where they are known to segregate along space, habitat, and time axes within habitat patches. Here, we assess whether segregation also occurs among and within landscapes, and how this is influenced by patch-network and matrix composition. We surveyed 75 landscapes, each covering 78 ha, where we mapped all habitat patches potentially suitable for Cabrera and water voles, and the area effectively occupied by each species (extent of occupancy). The relatively large water vole tended to be the sole occupant of landscapes with high habitat amount but relatively low patch density (i.e., with a few large patches), and with a predominantly agricultural matrix, whereas landscapes with high patch density (i.e., many small patches) and low agricultural cover, tended to be occupied exclusively by the small Cabrera vole. The two species tended to co-occur in landscapes with intermediate patch-network and matrix characteristics, though their extents of occurrence were negatively correlated after controlling for environmental effects. In combination with our previous studies on the Cabrera-water vole system, these findings illustrated empirically the occurrence of hierarchical spatial segregation, ranging from within-patches to among-landscapes. Overall, our study suggests that recognizing the hierarchical nature of spatial segregation patterns and their major environmental drivers should enhance our understanding of species coexistence in patchy environments.
Application of Parallel Hierarchical Matrices in Spatial Statistics and Parameter Identification

KAUST Repository

Litvinenko, Alexander

2018-04-20

Parallel H-matrices in spatial statistics 1. Motivation: improve statistical model 2. Tools: Hierarchical matrices [Hackbusch 1999] 3. Matern covariance function and joint Gaussian likelihood 4. Identification of unknown parameters via maximizing Gaussian log-likelihood 5. Implementation with HLIBPro
Hierarchical acquisition of visual specificity in spatial contextual cueing.

Science.gov (United States)

Lie, Kin-Pou

2015-01-01

Spatial contextual cueing refers to visual search performance's being improved when invariant associations between target locations and distractor spatial configurations are learned incidentally. Using the instance theory of automatization and the reverse hierarchy theory of visual perceptual learning, this study explores the acquisition of visual specificity in spatial contextual cueing. Two experiments in which detailed visual features were irrelevant for distinguishing between spatial contexts found that spatial contextual cueing was visually generic in difficult trials when the trials were not preceded by easy trials (Experiment 1) but that spatial contextual cueing progressed to visual specificity when difficult trials were preceded by easy trials (Experiment 2). These findings support reverse hierarchy theory, which predicts that even when detailed visual features are irrelevant for distinguishing between spatial contexts, spatial contextual cueing can progress to visual specificity if the stimuli remain constant, the task is difficult, and difficult trials are preceded by easy trials. However, these findings are inconsistent with instance theory, which predicts that when detailed visual features are irrelevant for distinguishing between spatial contexts, spatial contextual cueing will not progress to visual specificity. This study concludes that the acquisition of visual specificity in spatial contextual cueing is more plausibly hierarchical, rather than instance-based.
Hierarchical drivers of reef-fish metacommunity structure.

Science.gov (United States)

MacNeil, M Aaron; Graham, Nicholas A J; Polunin, Nicholas V C; Kulbicki, Michel; Galzin, René; Harmelin-Vivien, Mireille; Rushton, Steven P

2009-01-01

Coral reefs are highly complex ecological systems, where multiple processes interact across scales in space and time to create assemblages of exceptionally high biodiversity. Despite the increasing frequency of hierarchically structured sampling programs used in coral-reef science, little progress has been made in quantifying the relative importance of processes operating across multiple scales. The vast majority of reef studies are conducted, or at least analyzed, at a single spatial scale, ignoring the implicitly hierarchical structure of the overall system in favor of small-scale experiments or large-scale observations. Here we demonstrate how alpha (mean local number of species), beta diversity (degree of species dissimilarity among local sites), and gamma diversity (overall species richness) vary with spatial scale, and using a hierarchical, information-theoretic approach, we evaluate the relative importance of site-, reef-, and atoll-level processes driving the fish metacommunity structure among 10 atolls in French Polynesia. Process-based models, representing well-established hypotheses about drivers of reef-fish community structure, were assembled into a candidate set of 12 hierarchical linear models. Variation in fish abundance, biomass, and species richness were unevenly distributed among transect, reef, and atoll levels, establishing the relative contribution of variation at these spatial scales to the structure of the metacommunity. Reef-fish biomass, species richness, and the abundance of most functional-groups corresponded primarily with transect-level habitat diversity and atoll-lagoon size, whereas detritivore and grazer abundances were largely correlated with potential covariates of larval dispersal. Our findings show that (1) within-transect and among-atoll factors primarily drive the relationship between alpha and gamma diversity in this reef-fish metacommunity; (2) habitat is the primary correlate with reef-fish metacommunity structure at
Community turnover of wood-inhabiting fungi across hierarchical spatial scales.

Directory of Open Access Journals (Sweden)

Nerea Abrego

Full Text Available For efficient use of conservation resources it is important to determine how species diversity changes across spatial scales. In many poorly known species groups little is known about at which spatial scales the conservation efforts should be focused. Here we examined how the community turnover of wood-inhabiting fungi is realised at three hierarchical levels, and how much of community variation is explained by variation in resource composition and spatial proximity. The hierarchical study design consisted of management type (fixed factor, forest site (random factor, nested within management type and study plots (randomly placed plots within each study site. To examine how species richness varied across the three hierarchical scales, randomized species accumulation curves and additive partitioning of species richness were applied. To analyse variation in wood-inhabiting species and dead wood composition at each scale, linear and Permanova modelling approaches were used. Wood-inhabiting fungal communities were dominated by rare and infrequent species. The similarity of fungal communities was higher within sites and within management categories than among sites or between the two management categories, and it decreased with increasing distance among the sampling plots and with decreasing similarity of dead wood resources. However, only a small part of community variation could be explained by these factors. The species present in managed forests were in a large extent a subset of those species present in natural forests. Our results suggest that in particular the protection of rare species requires a large total area. As managed forests have only little additional value complementing the diversity of natural forests, the conservation of natural forests is the key to ecologically effective conservation. As the dissimilarity of fungal communities increases with distance, the conserved natural forest sites should be broadly distributed in space, yet
Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

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Wilson, T.L.; Odei, J.B.; Hooten, M.B.; Edwards, T.C.

2010-01-01

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.
Spatial patterns of breeding success of grizzly bears derived from hierarchical multistate models.

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Fisher, Jason T; Wheatley, Matthew; Mackenzie, Darryl

2014-10-01

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low-elevation wetlands or mid-elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy-herbaceous alpine ecotones-were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. © 2014 Society
Hierarchical spatial point process analysis for a plant community with high biodiversity

DEFF Research Database (Denmark)

Illian, Janine B.; Møller, Jesper; Waagepetersen, Rasmus

2009-01-01

A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maxim...
A hierarchical spatial model of avian abundance with application to Cerulean Warblers

Science.gov (United States)

Thogmartin, Wayne E.; Sauer, John R.; Knutson, Melinda G.

2004-01-01

Surveys collecting count data are the primary means by which abundance is indexed for birds. These counts are confounded, however, by nuisance effects including observer effects and spatial correlation between counts. Current methods poorly accommodate both observer and spatial effects because modeling these spatially autocorrelated counts within a hierarchical framework is not practical using standard statistical approaches. We propose a Bayesian approach to this problem and provide as an example of its implementation a spatial model of predicted abundance for the Cerulean Warbler (Dendroica cerulea) in the Prairie-Hardwood Transition of the upper midwestern United States. We used an overdispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods. We used 21 years of North American Breeding Bird Survey counts as the response in a loglinear function of explanatory variables describing habitat, spatial relatedness, year effects, and observer effects. The model included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land cover composition and configuration, climate, terrain heterogeneity, and human influence. The inherent hierarchy in the model was from counts occurring, in part, as a function of observers within survey routes within years. We found that the percentage of forested wetlands, an index of wetness potential, and an interaction between mean annual precipitation and deciduous forest patch size best described Cerulean Warbler abundance. Based on a map of relative abundance derived from the posterior parameter estimates, we estimated that only 15% of the species' population occurred on federal land, necessitating active engagement of public landowners and state agencies in the conservation of the breeding habitat for this species. Models of this type can be applied to any data in which the response
Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework

Science.gov (United States)

Omernik, James M.; Griffith, Glenn E.

2014-01-01

A map of ecological regions of the conterminous United States, first published in 1987, has been greatly refined and expanded into a hierarchical spatial framework in response to user needs, particularly by state resource management agencies. In collaboration with scientists and resource managers from numerous agencies and institutions in the United States, Mexico, and Canada, the framework has been expanded to cover North America, and the original ecoregions (now termed Level III) have been refined, subdivided, and aggregated to identify coarser as well as more detailed spatial units. The most generalized units (Level I) define 10 ecoregions in the conterminous U.S., while the finest-scale units (Level IV) identify 967 ecoregions. In this paper, we explain the logic underpinning the approach, discuss the evolution of the regional mapping process, and provide examples of how the ecoregions were distinguished at each hierarchical level. The variety of applications of the ecoregion framework illustrates its utility in resource assessment and management.

A new methodology of spatial cross-correlation analysis.

Science.gov (United States)

Chen, Yanguang

2015-01-01

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran's index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson's correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China's urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes.
On hierarchical solutions to the BBGKY hierarchy

Science.gov (United States)

Hamilton, A. J. S.

1988-01-01

It is thought that the gravitational clustering of galaxies in the universe may approach a scale-invariant, hierarchical form in the small separation, large-clustering regime. Past attempts to solve the Born-Bogoliubov-Green-Kirkwood-Yvon (BBGKY) hierarchy in this regime have assumed a certain separable hierarchical form for the higher order correlation functions of galaxies in phase space. It is shown here that such separable solutions to the BBGKY equations must satisfy the condition that the clustered component of the solution has cluster-cluster correlations equal to galaxy-galaxy correlations to all orders. The solutions also admit the presence of an arbitrary unclustered component, which plays no dyamical role in the large-clustering regime. These results are a particular property of the specific separable model assumed for the correlation functions in phase space, not an intrinsic property of spatially hierarchical solutions to the BBGKY hierarchy. The observed distribution of galaxies does not satisfy the required conditions. The disagreement between theory and observation may be traced, at least in part, to initial conditions which, if Gaussian, already have cluster correlations greater than galaxy correlations.
Application of Parallel Hierarchical Matrices and Low-Rank Tensors in Spatial Statistics and Parameter Identification

KAUST Repository

Litvinenko, Alexander

2018-03-12

Part 1: Parallel H-matrices in spatial statistics 1. Motivation: improve statistical model 2. Tools: Hierarchical matrices 3. Matern covariance function and joint Gaussian likelihood 4. Identification of unknown parameters via maximizing Gaussian log-likelihood 5. Implementation with HLIBPro. Part 2: Low-rank Tucker tensor methods in spatial statistics
A New Methodology of Spatial Cross-Correlation Analysis

Science.gov (United States)

Chen, Yanguang

2015-01-01

Spatial correlation modeling comprises both spatial autocorrelation and spatial cross-correlation processes. The spatial autocorrelation theory has been well-developed. It is necessary to advance the method of spatial cross-correlation analysis to supplement the autocorrelation analysis. This paper presents a set of models and analytical procedures for spatial cross-correlation analysis. By analogy with Moran’s index newly expressed in a spatial quadratic form, a theoretical framework is derived for geographical cross-correlation modeling. First, two sets of spatial cross-correlation coefficients are defined, including a global spatial cross-correlation coefficient and local spatial cross-correlation coefficients. Second, a pair of scatterplots of spatial cross-correlation is proposed, and the plots can be used to visually reveal the causality behind spatial systems. Based on the global cross-correlation coefficient, Pearson’s correlation coefficient can be decomposed into two parts: direct correlation (partial correlation) and indirect correlation (spatial cross-correlation). As an example, the methodology is applied to the relationships between China’s urbanization and economic development to illustrate how to model spatial cross-correlation phenomena. This study is an introduction to developing the theory of spatial cross-correlation, and future geographical spatial analysis might benefit from these models and indexes. PMID:25993120
Spatial-area selective retrieval of multiple object-place associations in a hierarchical cognitive map formed by theta phase coding.

Science.gov (United States)

Sato, Naoyuki; Yamaguchi, Yoko

2009-06-01

The human cognitive map is known to be hierarchically organized consisting of a set of perceptually clustered landmarks. Patient studies have demonstrated that these cognitive maps are maintained by the hippocampus, while the neural dynamics are still poorly understood. The authors have shown that the neural dynamic "theta phase precession" observed in the rodent hippocampus may be capable of forming hierarchical cognitive maps in humans. In the model, a visual input sequence consisting of object and scene features in the central and peripheral visual fields, respectively, results in the formation of a hierarchical cognitive map for object-place associations. Surprisingly, it is possible for such a complex memory structure to be formed in a few seconds. In this paper, we evaluate the memory retrieval of object-place associations in the hierarchical network formed by theta phase precession. The results show that multiple object-place associations can be retrieved with the initial cue of a scene input. Importantly, according to the wide-to-narrow unidirectional connections among scene units, the spatial area for object-place retrieval can be controlled by the spatial area of the initial cue input. These results indicate that the hierarchical cognitive maps have computational advantages on a spatial-area selective retrieval of multiple object-place associations. Theta phase precession dynamics is suggested as a fundamental neural mechanism of the human cognitive map.
Spatial photon correlations in multiple scattering media

DEFF Research Database (Denmark)

Smolka, Stephan; Muskens, O.; Lagendijk, A.

2010-01-01

We present the first angle-resolved measurements of spatial photon correlations that are induced by multiple scattering of light. The correlation relates multiple scattered photons at different spatial positions and depends on incident photon fluctuations.......We present the first angle-resolved measurements of spatial photon correlations that are induced by multiple scattering of light. The correlation relates multiple scattered photons at different spatial positions and depends on incident photon fluctuations....
Road network safety evaluation using Bayesian hierarchical joint model.

Science.gov (United States)

Wang, Jie; Huang, Helai

2016-05-01

Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model. A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well. Copyright © 2016 Elsevier Ltd. All rights reserved.
Couplings between hierarchical conformational dynamics from multi-time correlation functions and two-dimensional lifetime spectra: Application to adenylate kinase

Energy Technology Data Exchange (ETDEWEB)

Ono, Junichi [Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, Okazaki 444-8585 (Japan); Takada, Shoji [Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, Okazaki 444-8585 (Japan); Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502 (Japan); Saito, Shinji, E-mail: shinji@ims.ac.jp [Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, Okazaki 444-8585 (Japan); The Graduate University for Advanced Studies, Okazaki 444-8585 (Japan)

2015-06-07

An analytical method based on a three-time correlation function and the corresponding two-dimensional (2D) lifetime spectrum is developed to elucidate the time-dependent couplings between the multi-timescale (i.e., hierarchical) conformational dynamics in heterogeneous systems such as proteins. In analogy with 2D NMR, IR, electronic, and fluorescence spectroscopies, the waiting-time dependence of the off-diagonal peaks in the 2D lifetime spectra can provide a quantitative description of the dynamical correlations between the conformational motions with different lifetimes. The present method is applied to intrinsic conformational changes of substrate-free adenylate kinase (AKE) using long-time coarse-grained molecular dynamics simulations. It is found that the hierarchical conformational dynamics arise from the intra-domain structural transitions among conformational substates of AKE by analyzing the one-time correlation functions and one-dimensional lifetime spectra for the donor-acceptor distances corresponding to single-molecule Förster resonance energy transfer experiments with the use of the principal component analysis. In addition, the complicated waiting-time dependence of the off-diagonal peaks in the 2D lifetime spectra for the donor-acceptor distances is attributed to the fact that the time evolution of the couplings between the conformational dynamics depends upon both the spatial and temporal characters of the system. The present method is expected to shed light on the biological relationship among the structure, dynamics, and function.
Quantifying spatial scaling patterns and their local and regional correlates in headwater streams: Implications for resilience

Science.gov (United States)

Gothe, Emma; Sandin, Leonard; Allen, Craig R.; Angeler, David G.

2014-01-01

The distribution of functional traits within and across spatiotemporal scales has been used to quantify and infer the relative resilience across ecosystems. We use explicit spatial modeling to evaluate within- and cross-scale redundancy in headwater streams, an ecosystem type with a hierarchical and dendritic network structure. We assessed the cross-scale distribution of functional feeding groups of benthic invertebrates in Swedish headwater streams during two seasons. We evaluated functional metrics, i.e., Shannon diversity, richness, and evenness, and the degree of redundancy within and across modeled spatial scales for individual feeding groups. We also estimated the correlates of environmental versus spatial factors of both functional composition and the taxonomic composition of functional groups for each spatial scale identified. Measures of functional diversity and within-scale redundancy of functions were similar during both seasons, but both within- and cross-scale redundancy were low. This apparent low redundancy was partly attributable to a few dominant taxa explaining the spatial models. However, rare taxa with stochastic spatial distributions might provide additional information and should therefore be considered explicitly for complementing future resilience assessments. Otherwise, resilience may be underestimated. Finally, both environmental and spatial factors correlated with the scale-specific functional and taxonomic composition. This finding suggests that resilience in stream networks emerges as a function of not only local conditions but also regional factors such as habitat connectivity and invertebrate dispersal.
Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

Science.gov (United States)

Raykov, Tenko

2011-01-01

Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…
Estimating temporal trend in the presence of spatial complexity: a Bayesian hierarchical model for a wetland plant population undergoing restoration.

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Thomas J Rodhouse

Full Text Available Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas] population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones" with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity--a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.
Quantifying spatial scaling patterns and their local and regional correlates in headwater streams: implications for resilience

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Emma Göthe

2014-09-01

Full Text Available The distribution of functional traits within and across spatiotemporal scales has been used to quantify and infer the relative resilience across ecosystems. We use explicit spatial modeling to evaluate within- and cross-scale redundancy in headwater streams, an ecosystem type with a hierarchical and dendritic network structure. We assessed the cross-scale distribution of functional feeding groups of benthic invertebrates in Swedish headwater streams during two seasons. We evaluated functional metrics, i.e., Shannon diversity, richness, and evenness, and the degree of redundancy within and across modeled spatial scales for individual feeding groups. We also estimated the correlates of environmental versus spatial factors of both functional composition and the taxonomic composition of functional groups for each spatial scale identified. Measures of functional diversity and within-scale redundancy of functions were similar during both seasons, but both within- and cross-scale redundancy were low. This apparent low redundancy was partly attributable to a few dominant taxa explaining the spatial models. However, rare taxa with stochastic spatial distributions might provide additional information and should therefore be considered explicitly for complementing future resilience assessments. Otherwise, resilience may be underestimated. Finally, both environmental and spatial factors correlated with the scale-specific functional and taxonomic composition. This finding suggests that resilience in stream networks emerges as a function of not only local conditions but also regional factors such as habitat connectivity and invertebrate dispersal.
Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models

International Nuclear Information System (INIS)

Andrade, A.R.; Teixeira, P.F.

2015-01-01

Railway maintenance planners require a predictive model that can assess the railway track geometry degradation. The present paper uses a Hierarchical Bayesian model as a tool to model the main two quality indicators related to railway track geometry degradation: the standard deviation of longitudinal level defects and the standard deviation of horizontal alignment defects. Hierarchical Bayesian Models (HBM) are flexible statistical models that allow specifying different spatially correlated components between consecutive track sections, namely for the deterioration rates and the initial qualities parameters. HBM are developed for both quality indicators, conducting an extensive comparison between candidate models and a sensitivity analysis on prior distributions. HBM is applied to provide an overall assessment of the degradation of railway track geometry, for the main Portuguese railway line Lisbon–Oporto. - Highlights: • Rail track geometry degradation is analysed using Hierarchical Bayesian models. • A Gibbs sampling strategy is put forward to estimate the HBM. • Model comparison and sensitivity analysis find the most suitable model. • We applied the most suitable model to all the segments of the main Portuguese line. • Tackling spatial correlations using CAR structures lead to a better model fit
Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information

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Yi Wang

2018-03-01

Full Text Available In this paper, we introduce a novel classification framework for hyperspectral images (HSIs by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS, marker-based hierarchical segmentation (MHSEG and algebraic multigrid (AMG are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy.
A study of hierarchical structure on South China industrial electricity-consumption correlation

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Yao, Can-Zhong; Lin, Ji-Nan; Liu, Xiao-Feng

2016-02-01

Based on industrial electricity-consumption data of five southern provinces of China from 2005 to 2013, we study the industrial correlation mechanism with MST (minimal spanning tree) and HT (hierarchical tree) models. First, we comparatively analyze the industrial electricity-consumption correlation structure in pre-crisis and after-crisis period using MST model and Bootstrap technique of statistical reliability test of links. Results exhibit that all industrial electricity-consumption trees of five southern provinces of China in pre-crisis and after-crisis time are in formation of chain, and the "center-periphery structure" of those chain-like trees is consistent with industrial specialization in classical industrial chain theory. Additionally, the industrial structure of some provinces is reorganized and transferred in pre-crisis and after-crisis time. Further, the comparative analysis with hierarchical tree and Bootstrap technique demonstrates that as for both observations of GD and overall NF, the industrial electricity-consumption correlation is non-significant clustered in pre-crisis period, whereas it turns significant clustered in after-crisis time. Therefore we propose that in perspective of electricity-consumption, their industrial structures are directed to optimized organization and global correlation. Finally, the analysis of distance of HTs verifies that industrial reorganization and development may strengthen market integration, coordination and correlation of industrial production. Except GZ, other four provinces have a shorter distance of industrial electricity-consumption correlation in after-crisis period, revealing a better performance of regional specialization and integration.
WHEN THE DISTURBANCES ARE SPATIALLY CORRELATED

African Journals Online (AJOL)

correlation, spatial error process. INTRODUCTION. Consider the linear regression model for spatial correlation y=XB +u, u=Ce, (1) where y is a Txl observable random vector, X is a Txk matrix of known constants with full column rank k, B is a k xl vector of unknown parameters,. :2 is a Txl random vector with expectation zero ...
Spatial Patterns in Biofilm Diversity across Hierarchical Levels of River-Floodplain Landscapes.

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Marc Peipoch

Full Text Available River-floodplain systems are among the most diverse and productive ecosystems, but the effects of biophysical complexity at multiple scales on microbial biodiversity have not been studied. Here, we investigated how the hierarchical organization of river systems (i.e., region, floodplain, zone, habitats, and microhabitats influences epilithic biofilm community assemblage patterns by characterizing microbial communities using 16S rRNA gene sequence data and analyzing bacterial species distribution across local and regional scales. Results indicate that regional and local environmental filters concurrently sort bacterial species, suggesting that spatial configuration of epilithic biofilms resembles patterns of larger organisms in floodplain ecosystems. Along the hierarchical organization of fluvial systems, floodplains constitute a vector of maximum environmental heterogeneity and consequently act as a major landscape filter for biofilm species. Thus, river basins and associated floodplains may simply reflect very large scale 'patches' within which environmental conditions select for community composition of epilithic biofilms.
Topology of the correlation networks among major currencies using hierarchical structure methods

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Keskin, Mustafa; Deviren, Bayram; Kocakaplan, Yusuf

2011-02-01

We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 period. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.
Spatial Intensity Duration Frequency Relationships Using Hierarchical Bayesian Analysis for Urban Areas

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Rupa, Chandra; Mujumdar, Pradeep

2016-04-01

In urban areas, quantification of extreme precipitation is important in the design of storm water drains and other infrastructure. Intensity Duration Frequency (IDF) relationships are generally used to obtain design return level for a given duration and return period. Due to lack of availability of extreme precipitation data for sufficiently large number of years, estimating the probability of extreme events is difficult. Typically, a single station data is used to obtain the design return levels for various durations and return periods, which are used in the design of urban infrastructure for the entire city. In an urban setting, the spatial variation of precipitation can be high; the precipitation amounts and patterns often vary within short distances of less than 5 km. Therefore it is crucial to study the uncertainties in the spatial variation of return levels for various durations. In this work, the extreme precipitation is modeled spatially using the Bayesian hierarchical analysis and the spatial variation of return levels is studied. The analysis is carried out with Block Maxima approach for defining the extreme precipitation, using Generalized Extreme Value (GEV) distribution for Bangalore city, Karnataka state, India. Daily data for nineteen stations in and around Bangalore city is considered in the study. The analysis is carried out for summer maxima (March - May), monsoon maxima (June - September) and the annual maxima rainfall. In the hierarchical analysis, the statistical model is specified in three layers. The data layer models the block maxima, pooling the extreme precipitation from all the stations. In the process layer, the latent spatial process characterized by geographical and climatological covariates (lat-lon, elevation, mean temperature etc.) which drives the extreme precipitation is modeled and in the prior level, the prior distributions that govern the latent process are modeled. Markov Chain Monte Carlo (MCMC) algorithm (Metropolis Hastings
Evolution of Hierarchical Structure and Spatial Pattern of Coastal Cities in China – Based on the Data of Distribution of Marine-Related Enterprises

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Wang Lili

2017-11-01

Full Text Available In this paper, a comprehensive research of the evolution of the hierarchical structure and spatial pattern of coastal cities in China was conducted based on the data of distribution of the headquarters and subsidiaries of marine-related enterprises in 1995, 2005 and 2015 using the city network research method proposed by Taylor. The results of the empirical research showed: China’s coastal city network had an obvious hierarchical characteristics of “national coastal cityregional coastal city-sub-regional coastal city-local coastal city”, in the 20 years of development process, the hierarchies of coastal cities in China showed a hierarchical progressive evolution; in past 20 years, the spatial pattern and network structure of coastal cities in China tended to be complete, and the city network was more uniform, forming a “three tiers and three urban agglomerations” network structure; the strength of connection among the cities was obviously strengthened, and the efficiency of urban spatial connection was improved overall.

Hierarchical thermoplastic rippled nanostructures regulate Schwann cell adhesion, morphology and spatial organization.

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Masciullo, Cecilia; Dell'Anna, Rossana; Tonazzini, Ilaria; Böettger, Roman; Pepponi, Giancarlo; Cecchini, Marco

2017-10-12

Periodic ripples are a variety of anisotropic nanostructures that can be realized by ion beam irradiation on a wide range of solid surfaces. Only a few authors have investigated these surfaces for tuning the response of biological systems, probably because it is challenging to directly produce them in materials that well sustain long-term cellular cultures. Here, hierarchical rippled nanotopographies with a lateral periodicity of ∼300 nm are produced from a gold-irradiated germanium mold in polyethylene terephthalate (PET), a biocompatible polymer approved by the US Food and Drug Administration for clinical applications, by a novel three-step embossing process. The effects of nano-ripples on Schwann Cells (SCs) are studied in view of their possible use for nerve-repair applications. The data demonstrate that nano-ripples can enhance short-term SC adhesion and proliferation (3-24 h after seeding), drive their actin cytoskeleton spatial organization and sustain long-term cell growth. Notably, SCs are oriented perpendicularly with respect to the nanopattern lines. These results provide information about the possible use of hierarchical nano-rippled elements for nerve-regeneration protocols.
A model relating Eulerian spatial and temporal velocity correlations

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Cholemari, Murali R.; Arakeri, Jaywant H.

2006-03-01

In this paper we propose a model to relate Eulerian spatial and temporal velocity autocorrelations in homogeneous, isotropic and stationary turbulence. We model the decorrelation as the eddies of various scales becoming decorrelated. This enables us to connect the spatial and temporal separations required for a certain decorrelation through the ‘eddy scale’. Given either the spatial or the temporal velocity correlation, we obtain the ‘eddy scale’ and the rate at which the decorrelation proceeds. This leads to a spatial separation from the temporal correlation and a temporal separation from the spatial correlation, at any given value of the correlation relating the two correlations. We test the model using experimental data from a stationary axisymmetric turbulent flow with homogeneity along the axis.
Spatial correlation of probabilistic earthquake ground motion and loss

Science.gov (United States)

Wesson, R.L.; Perkins, D.M.

2001-01-01

Spatial correlation of annual earthquake ground motions and losses can be used to estimate the variance of annual losses to a portfolio of properties exposed to earthquakes A direct method is described for the calculations of the spatial correlation of earthquake ground motions and losses. Calculations for the direct method can be carried out using either numerical quadrature or a discrete, matrix-based approach. Numerical results for this method are compared with those calculated from a simple Monte Carlo simulation. Spatial correlation of ground motion and loss is induced by the systematic attenuation of ground motion with distance from the source, by common site conditions, and by the finite length of fault ruptures. Spatial correlation is also strongly dependent on the partitioning of the variability, given an event, into interevent and intraevent components. Intraevent variability reduces the spatial correlation of losses. Interevent variability increases spatial correlation of losses. The higher the spatial correlation, the larger the variance in losses to a port-folio, and the more likely extreme values become. This result underscores the importance of accurately determining the relative magnitudes of intraevent and interevent variability in ground-motion studies, because of the strong impact in estimating earthquake losses to a portfolio. The direct method offers an alternative to simulation for calculating the variance of losses to a portfolio, which may reduce the amount of calculation required.
Hierarchical structure of genetic distances: Effects of matrix size, spatial distribution and correlation structure among gene frequencies

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Flávia Melo Rodrigues

1998-06-01

Full Text Available Geographic structure of genetic distances among local populations within species, based on allozyme data, has usually been evaluated by estimating genetic distances clustered with hierarchical algorithms, such as the unweighted pair-group method by arithmetic averages (UPGMA. The distortion produced in the clustering process is estimated by the cophenetic correlation coefficient. This hierarchical approach, however, can fail to produce an accurate representation of genetic distances among populations in a low dimensional space, especially when continuous (clinal or reticulate patterns of variation exist. In the present study, we analyzed 50 genetic distance matrices from the literature, for animal taxa ranging from Platyhelminthes to Mammalia, in order to determine in which situations the UPGMA is useful to understand patterns of genetic variation among populations. The cophenetic correlation coefficients, derived from UPGMA based on three types of genetic distance coefficients, were correlated with other parameters of each matrix, including number of populations, loci, alleles, maximum geographic distance among populations, relative magnitude of the first eigenvalue of covariance matrix among alleles and logarithm of body size. Most cophenetic correlations were higher than 0.80, and the highest values appeared for Nei's and Rogers' genetic distances. The relationship between cophenetic correlation coefficients and the other parameters analyzed was defined by an "envelope space", forming triangles in which higher values of cophenetic correlations are found for higher values in the parameters, though low values do not necessarily correspond to high cophenetic correlations. We concluded that UPGMA is useful to describe genetic distances based on large distance matrices (both in terms of elevated number of populations or alleles, when dimensionality of the system is low (matrices with large first eigenvalues or when local populations are separated
Localization in a one-dimensional spatially correlated random potential

International Nuclear Information System (INIS)

Kasner, M.; Weller, W.

1986-01-01

The motion of an electron in a random one-dimensional spatially correlated potential is investigated. The spatial correlation is generated by a Markov chain. It is shown that the influence of the spatial correlation can be described by means of oscillating vertices usually neglected in the Berezinskii diagram technique. Correlation mainly leads to an increase of the localization length in comparison with an uncorrelated potential. However, there is a region of the parameter, where the localization decreases. (author)
Spatial Correlation Of Streamflows: An Analytical Approach

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Betterle, A.; Schirmer, M.; Botter, G.

2016-12-01

The interwoven space and time variability of climate and landscape properties results in complex and non-linear hydrological response of streamflow dynamics. Understanding how meteorologic and morphological characteristics of catchments affect similarity/dissimilarity of streamflow timeseries at their outlets represents a scientific challenge with application in water resources management, ecological studies and regionalization approaches aimed to predict streamflows in ungauged areas. In this study, we establish an analytical approach to estimate the spatial correlation of daily streamflows in two arbitrary locations within a given hydrologic district or river basin at seasonal and annual time scales. The method is based on a stochastic description of the coupled streamflow dynamics at the outlet of two catchments. The framework aims to express the correlation of daily streamflows at two locations along a river network as a function of a limited number of physical parameters characterizing the main underlying hydrological drivers, that include climate conditions, precipitation regime and catchment drainage rates. The proposed method portrays how heterogeneity of climate and landscape features affect the spatial variability of flow regimes along river systems. In particular, we show that frequency and intensity of synchronous effective rainfall events in the relevant contributing catchments are the main driver of the spatial correlation of daily discharge, whereas only pronounced differences in the drainage rate of the two basins bear a significant effect on the streamflow correlation. The topological arrangement of the two outlets also influences the underlying streamflow correlation, as we show that nested catchments tend to maximize the spatial correlation of flow regimes. The application of the method to a set of catchments in the South-Eastern US suggests the potential of the proposed tool for the characterization of spatial connections of flow regimes in the
Hierarchical spatial structure of stream fish colonization and extinction

Science.gov (United States)

Hitt, N.P.; Roberts, J.H.

2012-01-01

Spatial variation in extinction and colonization is expected to influence community composition over time. In stream fish communities, local species richness (alpha diversity) and species turnover (beta diversity) are thought to be regulated by high extinction rates in headwater streams and high colonization rates in downstream areas. We evaluated the spatiotemporal structure of fish communities in streams originally surveyed by Burton and Odum 1945 (Ecology 26: 182-194) in Virginia, USA and explored the effects of species traits on extinction and colonization dynamics. We documented dramatic changes in fish community structure at both the site and stream scales. Of the 34 fish species observed, 20 (59%) were present in both time periods, but 11 (32%) colonized the study area and three (9%) were extirpated over time. Within streams, alpha diversity increased in two of three streams but beta diversity decreased dramatically in all streams due to fish community homogenization caused by colonization of common species and extirpation of rare species. Among streams, however, fish communities differentiated over time. Regression trees indicated that reproductive life-history traits such as spawning mound construction, associations with mound-building species, and high fecundity were important predictors of species persistence or colonization. Conversely, native fishes not associated with mound-building exhibited the highest rates of extirpation from streams. Our results demonstrate that stream fish colonization and extinction dynamics exhibit hierarchical spatial structure and suggest that mound-building fishes serve as keystone species for colonization of headwater streams.
Fourth-Order Spatial Correlation of Thermal Light

International Nuclear Information System (INIS)

Wen Feng; Zhang Xun; Sun Jia; Song Jian-Ping; Zhang Yan-Peng; Xue Xin-Xin

2014-01-01

We investigate the fourth-order spatial correlation properties of pseudo-thermal light in the photon counting regime, and apply the Klyshko advanced-wave picture to describe the process of four-photon coincidence counting measurement. We deduce the theory of a proof-of-principle four-photon coincidence counting configuration, and find that if the four randomly radiated photons come from the same radiation area and are indistinguishable in principle, the fourth-order correlation of them is 24 times larger than that when four photons come from different radiation areas. In addition, we also show that the higher-order spatial correlation function can be decomposed into multiple lower-order correlation functions, and the contrast and visibility of low-order correlation peaks are less than those of higher orders, while the resolutions all are identical. This study may be useful for better understanding the four-photon interference and multi-channel correlation imaging
Kolmogorov-Smirnov test for spatially correlated data

Science.gov (United States)

Olea, R.A.; Pawlowsky-Glahn, V.

2009-01-01

The Kolmogorov-Smirnov test is a convenient method for investigating whether two underlying univariate probability distributions can be regarded as undistinguishable from each other or whether an underlying probability distribution differs from a hypothesized distribution. Application of the test requires that the sample be unbiased and the outcomes be independent and identically distributed, conditions that are violated in several degrees by spatially continuous attributes, such as topographical elevation. A generalized form of the bootstrap method is used here for the purpose of modeling the distribution of the statistic D of the Kolmogorov-Smirnov test. The innovation is in the resampling, which in the traditional formulation of bootstrap is done by drawing from the empirical sample with replacement presuming independence. The generalization consists of preparing resamplings with the same spatial correlation as the empirical sample. This is accomplished by reading the value of unconditional stochastic realizations at the sampling locations, realizations that are generated by simulated annealing. The new approach was tested by two empirical samples taken from an exhaustive sample closely following a lognormal distribution. One sample was a regular, unbiased sample while the other one was a clustered, preferential sample that had to be preprocessed. Our results show that the p-value for the spatially correlated case is always larger that the p-value of the statistic in the absence of spatial correlation, which is in agreement with the fact that the information content of an uncorrelated sample is larger than the one for a spatially correlated sample of the same size. ?? Springer-Verlag 2008.
Spatial correlation in precipitation trends in the Brazilian Amazon

Science.gov (United States)

Buarque, Diogo Costa; Clarke, Robin T.; Mendes, Carlos Andre Bulhoes

2010-06-01

A geostatistical analysis of variables derived from Amazon daily precipitation records (trends in annual precipitation totals, trends in annual maximum precipitation accumulated over 1-5 days, trend in length of dry spell, trend in number of wet days per year) gave results that are consistent with those previously reported. Averaged over the Brazilian Amazon region as a whole, trends in annual maximum precipitations were slightly negative, the trend in the length of dry spell was slightly positive, and the trend in the number of wet days in the year was slightly negative. For trends in annual maximum precipitation accumulated over 1-5 days, spatial correlation between trends was found to extend up to a distance equivalent to at least half a degree of latitude or longitude, with some evidence of anisotropic correlation. Time trends in annual precipitation were found to be spatially correlated up to at least ten degrees of separation, in both W-E and S-N directions. Anisotropic spatial correlation was strongly evident in time trends in length of dry spell with much stronger evidence of spatial correlation in the W-E direction, extending up to at least five degrees of separation, than in the S-N. Because the time trends analyzed are shown to be spatially correlated, it is argued that methods at present widely used to test the statistical significance of climate trends over time lead to erroneous conclusions if spatial correlation is ignored, because records from different sites are assumed to be statistically independent.
The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

Energy Technology Data Exchange (ETDEWEB)

Grasha, K.; Calzetti, D. [Astronomy Department, University of Massachusetts, Amherst, MA 01003 (United States); Adamo, A.; Messa, M. [Dept. of Astronomy, The Oskar Klein Centre, Stockholm University, Stockholm (Sweden); Kim, H. [Gemini Observatory, La Serena (Chile); Elmegreen, B. G. [IBM Research Division, T.J. Watson Research Center, Yorktown Hts., NY (United States); Gouliermis, D. A. [Zentrum für Astronomie der Universität Heidelberg, Institut für Theoretische Astrophysik, Albert-Ueberle-Str. 2, D-69120 Heidelberg (Germany); Dale, D. A. [Dept. of Physics and Astronomy, University of Wyoming, Laramie, WY (United States); Fumagalli, M. [Institute for Computational Cosmology and Centre for Extragalactic Astronomy, Durham University, Durham (United Kingdom); Grebel, E. K.; Shabani, F. [Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg, Mönchhofstr. 12-14, D-69120 Heidelberg (Germany); Johnson, K. E. [Dept. of Astronomy, University of Virginia, Charlottesville, VA (United States); Kahre, L. [Dept. of Astronomy, New Mexico State University, Las Cruces, NM (United States); Kennicutt, R. C. [Institute of Astronomy, University of Cambridge, Cambridge (United Kingdom); Pellerin, A. [Dept. of Physics and Astronomy, State University of New York at Geneseo, Geneseo NY (United States); Ryon, J. E.; Ubeda, L. [Space Telescope Science Institute, Baltimore, MD (United States); Smith, L. J. [European Space Agency/Space Telescope Science Institute, Baltimore, MD (United States); Thilker, D., E-mail: kgrasha@astro.umass.edu [Dept. of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD (United States)

2017-05-10

We present a study of the hierarchical clustering of the young stellar clusters in six local (3–15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ∼40–60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.
The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

Science.gov (United States)

Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Messa, M.; Pellerin, A.; Ryon, J. E.; Smith, L. J.; Shabani, F.; Thilker, D.; Ubeda, L.

2017-05-01

We present a study of the hierarchical clustering of the young stellar clusters in six local (3-15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ˜40-60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.
The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

International Nuclear Information System (INIS)

Grasha, K.; Calzetti, D.; Adamo, A.; Messa, M.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Shabani, F.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Pellerin, A.; Ryon, J. E.; Ubeda, L.; Smith, L. J.; Thilker, D.

2017-01-01

We present a study of the hierarchical clustering of the young stellar clusters in six local (3–15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ∼40–60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.
The pair correlation function of spatial Hawkes processes

DEFF Research Database (Denmark)

Møller, Jesper; Torrisi, Giovanni Luca

2007-01-01

Spatial Hawkes processes can be considered as spatial versions of classical Hawkes processes. We derive the pair correlation function of stationary spatial Hawkes processes and discuss the connection to the Bartlett spectrum and other summary statistics. Particularly, results for Gaussian fertility...... rates and the extension to spatial Hawkes processes with random fertility rates are discussed....
Spatial correlation between weed species densities and soil properties

DEFF Research Database (Denmark)

Walter, Mette; Christensen, Svend; Simmelsgaard, Svend Erik

2002-01-01

The spatial cross-correlation between weed species densities and six soil properties within fields was analysed using cross-semivariograms. The survey was carried out in three successive years in two fields. The most consistent relationship between weed species density (numbers m−2) and soil...... properties was negative cross-correlation between the density of Viola arvensis Murray and clay content. This correlation was found in both fields; however, the range of spatial dependence varied between fields. In one of the fields, the density of Lamium purpureum L. was positively cross......-correlated with the phosphorus content in the soil in all years. The density of Veronica spp. and Poa annua L. was negatively cross-correlated with pH in all three years. Other spatial cross-correlations that were found in this study were inconsistent over time or field site. The densities of some of the weed species were...
Multiview Bayesian Correlated Component Analysis

DEFF Research Database (Denmark)

Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai

2015-01-01

are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....
Spatial correlation length of normalized cone data in sand

DEFF Research Database (Denmark)

Firouzianbandpey, Sarah; Griffiths, D. V.; Ibsen, Lars Bo

2014-01-01

The main topic of this study is to assess the anisotropic spatial correlation lengths of a sand layer deposit based on cone penetration testing with pore pressure measurement (CPTu) data. Spatial correlation length can be an important factor in reliability analysis of geotechnical systems, yet it...
LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

Science.gov (United States)

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.
Correlation between hierarchical structure of crystal networks and macroscopic performance of mesoscopic soft materials and engineering principles.

Science.gov (United States)

Lin, Naibo; Liu, Xiang Yang

2015-11-07

This review examines how the concepts and ideas of crystallization can be extended further and applied to the field of mesoscopic soft materials. It concerns the structural characteristics vs. the macroscopic performance, and the formation mechanism of crystal networks. Although this subject can be discussed in a broad sense across the area of mesoscopic soft materials, our main focus is on supramolecular materials, spider and silkworm silks, and biominerals. First, the occurrence of a hierarchical structure, i.e. crystal network and domain network structures, will facilitate the formation kinetics of mesoscopic phases and boost up the macroscopic performance of materials in some cases (i.e. spider silk fibres). Second, the structure and performance of materials can be correlated in some way by the four factors: topology, correlation length, symmetry/ordering, and strength of association of crystal networks. Moreover, four different kinetic paths of crystal network formation are identified, namely, one-step process of assembly, two-step process of assembly, mixed mode of assembly and foreign molecule mediated assembly. Based on the basic mechanisms of crystal nucleation and growth, the formation of crystal networks, such as crystallographic mismatch (or noncrystallographic) branching (tip branching and fibre side branching) and fibre/polymeric side merging, are reviewed. This facilitates the rational design and construction of crystal networks in supramolecular materials. In this context, the (re-)construction of a hierarchical crystal network structure can be implemented by thermal, precipitate, chemical, and sonication stimuli. As another important class of soft materials, the unusual mechanical performance of spider and silkworm silk fibres are reviewed in comparison with the regenerated silk protein derivatives. It follows that the considerably larger breaking stress and unusual breaking strain of spider silk fibres vs. silkworm silk fibres can be interpreted
Spatial-Temporal Correlation Properties of the 3GPP Spatial Channel Model and the Kronecker MIMO Channel Model

Directory of Open Access Journals (Sweden)

Cheng-Xiang Wang

2007-02-01

Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.

Spatial variability of correlated color temperature of lightning channels

Directory of Open Access Journals (Sweden)

Nobuaki Shimoji

Full Text Available In this paper, we present the spatial variability of the correlated color temperature of lightning channel shown in a digital still image. In order to analyze the correlated color temperature, we calculated chromaticity coordinates of the lightning channels in the digital still image. From results, the spatial variation of the correlated color temperature of the lightning channel was confirmed. Moreover, the results suggest that the correlated color temperature and peak current of the lightning channels are related to each other. Keywords: Lightning, Color analysis, Correlated color temperature, Chromaticity coordinate, CIE 1931 xy-chromaticity diagram
Correlates of biological soil crust abundance across a continuum of spatial scales: Support for a hierarchical conceptual model

Science.gov (United States)

Bowker, M.A.; Belnap, J.; Davidson, D.W.; Goldstein, H.

2006-01-01

1. Desertification negatively impacts a large proportion of the global human population and > 30% of the terrestrial land surface. Better methods are needed to detect areas that are at risk of desertification and to ameliorate desertified areas. Biological soil crusts are an important soil lichen-moss-microbial community that can be used toward these goals, as (i) bioindicators of desertification damage and (ii) promoters of soil stability and fertility. 2. We identified environmental factors that correlate with soil crust occurrence on the landscape and might be manipulated to assist recovery of soil crusts in degraded areas. We conducted three studies on the Colorado Plateau, USA, to investigate the hypotheses that soil fertility [particularly phosphorus (P), manganese (Mn) and zinc (Zn)] and/or moisture limit soil crust lichens and mosses at four spatial scales. 3. In support of the soil fertility hypothesis, we found that lichen-moss crusts were positively correlated with several nutrients [Mn, Zn, potassium (K) and magnesium (Mg) were most consistent] at three of four spatial scales ranging from 3.5 cm2 in area to c. 800 km2. In contrast, P was negatively correlated with lichen-moss crusts at three scales. 4. Community composition varied with micro-aspect on ridges in the soil crust. Three micro-aspects [north-north-west (NNW), east-north-east (ENE) and TOP] supported greater lichen and moss cover than the warmer, windward and more xeric micro-aspects [west-south-west (WSW) and south-south-east (SSE)]. This pattern was poorly related to soil fertility; rather, it was consistent with the moisture limitation hypothesis. 5. Synthesis and application. Use of crusts as desertification bioindicators requires knowledge of a site's potential for crust cover in the absence of desertification. We present a multi-scale model of crust potential as a function of site properties. Future quantitative studies can use this model to guide sampling efforts. Also, our results
Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area

Directory of Open Access Journals (Sweden)

Alejandro Ivan Aguirre-Salado

2017-07-01

Full Text Available We implemented a spatial model for analysing PM 10 maxima across the Mexico City metropolitan area during the period 1995–2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM 10 maxima in space and time. We evaluated the statistical model’s performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM 10 maxima and the longitude and latitude. The relationship between time and the PM 10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM 10 maxima presenting levels above 1000 μ g/m 3 (return period: 25 yr was observed in the northwestern region of the study area.
Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

Science.gov (United States)

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep
Measurement-Based Spatial Correlation and Capacity of Indoor Distributed MIMO System

Directory of Open Access Journals (Sweden)

Yan Zhang

2013-01-01

Full Text Available Distributed MIMO (D-MIMO system is one of the candidates for future wireless access networks. In this study, the spatial correlation and capacity in indoor D-MIMO system are presented. All results are from the actual channel measurements in typical indoor scenarios, including office and corridor. Based on measured data, spatial correlation coefficients between distributed transmitting antennas are analyzed. Although the literature about D-MIMO system assumes the small scale fading between distributed antennas is independent, we find that spatial correlation may still exist in specific propagation scenario. This correlation can also degrade the performance of D-MIMO system. To mitigate the impact of spatial correlation, one efficient method is to use transmitting antenna selection technique.
Scaling local species-habitat relations to the larger landscape with a hierarchical spatial count model

Science.gov (United States)

Thogmartin, W.E.; Knutson, M.G.

2007-01-01

Much of what is known about avian species-habitat relations has been derived from studies of birds at local scales. It is entirely unclear whether the relations observed at these scales translate to the larger landscape in a predictable linear fashion. We derived habitat models and mapped predicted abundances for three forest bird species of eastern North America using bird counts, environmental variables, and hierarchical models applied at three spatial scales. Our purpose was to understand habitat associations at multiple spatial scales and create predictive abundance maps for purposes of conservation planning at a landscape scale given the constraint that the variables used in this exercise were derived from local-level studies. Our models indicated a substantial influence of landscape context for all species, many of which were counter to reported associations at finer spatial extents. We found land cover composition provided the greatest contribution to the relative explained variance in counts for all three species; spatial structure was second in importance. No single spatial scale dominated any model, indicating that these species are responding to factors at multiple spatial scales. For purposes of conservation planning, areas of predicted high abundance should be investigated to evaluate the conservation potential of the landscape in their general vicinity. In addition, the models and spatial patterns of abundance among species suggest locations where conservation actions may benefit more than one species. ?? 2006 Springer Science+Business Media B.V.
Spatial Correlation of PAN UWB-MIMO Channel Including User Dynamics

DEFF Research Database (Denmark)

Wang, Yu; Kovacs, Istvan Zsolt; Pedersen, Gert Frølund

. It is found the channel shows spatial correlated wideband power, and spatial uncorrelated complex channel coefficients at different frequencies and delays with respect to a correlation coefficient threshold of 0.7. The Kronecker model is proved not suitable for the investigated scenarios. The MIMO UWB channel......In this paper we present and analyze spatial correlation properties of indoor 4x2 MIMO UWB channels in personal area network (PAN) scenarios. The presented results are based on measurement of radio links between an access point like device and a hand held or belt mounted device with dynamic user...
Measurement of spatial correlation functions using image processing techniques

International Nuclear Information System (INIS)

Berryman, J.G.

1985-01-01

A procedure for using digital image processing techniques to measure the spatial correlation functions of composite heterogeneous materials is presented. Methods for eliminating undesirable biases and warping in digitized photographs are discussed. Fourier transform methods and array processor techniques for calculating the spatial correlation functions are treated. By introducing a minimal set of lattice-commensurate triangles, a method of sorting and storing the values of three-point correlation functions in a compact one-dimensional array is developed. Examples are presented at each stage of the analysis using synthetic photographs of cross sections of a model random material (the penetrable sphere model) for which the analytical form of the spatial correlations functions is known. Although results depend somewhat on magnification and on relative volume fraction, it is found that photographs digitized with 512 x 512 pixels generally have sufficiently good statistics for most practical purposes. To illustrate the use of the correlation functions, bounds on conductivity for the penetrable sphere model are calculated with a general numerical scheme developed for treating the singular three-dimensional integrals which must be evaluated
Two phase formation of massive elliptical galaxies: study through cross-correlation including spatial effect

Science.gov (United States)

Modak, Soumita; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar

2017-11-01

Area of study is the formation mechanism of the present-day population of elliptical galaxies, in the context of hierarchical cosmological models accompanied by accretion and minor mergers. The present work investigates the formation and evolution of several components of the nearby massive early-type galaxies (ETGs) through cross-correlation function (CCF), using the spatial parameters right ascension (RA) and declination (DEC), and the intrinsic parameters mass (M_{*}) and size. According to the astrophysical terminology, here these variables, namely mass, size, RA and DEC are termed as parameters, whereas the unknown constants involved in the kernel function are called hyperparameters. Throughout this paper, the parameter size is used to represent the effective radius (Re). Following Huang et al. (2013a), each nearby ETG is divided into three parts on the basis of its Re value. We study the CCF between each of these three components of nearby massive ETGs and the ETGs in the high redshift range, 0.5conflict raised in a previous work (De et al. 2014) suggesting other possibilities for the formation of the outermost part. A probable cause of this improvement is the inclusion of the spatial effects in addition to the other parameters in the study.
Spatial correlation genetic algorithm for fractal image compression

International Nuclear Information System (INIS)

Wu, M.-S.; Teng, W.-C.; Jeng, J.-H.; Hsieh, J.-G.

2006-01-01

Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In this paper, a spatial correlation genetic algorithm (SC-GA) is proposed to speed up the encoder. There are two stages for the SC-GA method. The first stage makes use of spatial correlations in images for both the domain pool and the range pool to exploit local optima. The second stage is operated on the whole image to explore more adequate similarities if the local optima are not satisfied. With the aid of spatial correlation in images, the encoding time is 1.5 times faster than that of traditional genetic algorithm method, while the quality of the retrieved image is almost the same. Moreover, about half of the matched blocks come from the correlated space, so fewer bits are required to represent the fractal transform and therefore the compression ratio is also improved
Correlation of Spatially Filtered Dynamic Speckles in Distance Measurement Application

International Nuclear Information System (INIS)

Semenov, Dmitry V.; Nippolainen, Ervin; Kamshilin, Alexei A.; Miridonov, Serguei V.

2008-01-01

In this paper statistical properties of spatially filtered dynamic speckles are considered. This phenomenon was not sufficiently studied yet while spatial filtering is an important instrument for speckles velocity measurements. In case of spatial filtering speckle velocity information is derived from the modulation frequency of filtered light power which is measured by photodetector. Typical photodetector output is represented by a narrow-band random noise signal which includes non-informative intervals. Therefore more or less precious frequency measurement requires averaging. In its turn averaging implies uncorrelated samples. However, conducting research we found that correlation is typical property not only of dynamic speckle patterns but also of spatially filtered speckles. Using spatial filtering the correlation is observed as a response of measurements provided to the same part of the object surface or in case of simultaneously using several adjacent photodetectors. Found correlations can not be explained using just properties of unfiltered dynamic speckles. As we demonstrate the subject of this paper is important not only from pure theoretical point but also from the point of applied speckle metrology. E.g. using single spatial filter and an array of photodetector can greatly improve accuracy of speckle velocity measurements
Hierarchically Ordered Nanopatterns for Spatial Control of Biomolecules

Science.gov (United States)

2015-01-01

The development and study of a benchtop, high-throughput, and inexpensive fabrication strategy to obtain hierarchical patterns of biomolecules with sub-50 nm resolution is presented. A diblock copolymer of polystyrene-b-poly(ethylene oxide), PS-b-PEO, is synthesized with biotin capping the PEO block and 4-bromostyrene copolymerized within the polystyrene block at 5 wt %. These two handles allow thin films of the block copolymer to be postfunctionalized with biotinylated biomolecules of interest and to obtain micropatterns of nanoscale-ordered films via photolithography. The design of this single polymer further allows access to two distinct superficial nanopatterns (lines and dots), where the PEO cylinders are oriented parallel or perpendicular to the substrate. Moreover, we present a strategy to obtain hierarchical mixed morphologies: a thin-film coating of cylinders both parallel and perpendicular to the substrate can be obtained by tuning the solvent annealing and irradiation conditions. PMID:25363506
A Hidden Markov Model Representing the Spatial and Temporal Correlation of Multiple Wind Farms

DEFF Research Database (Denmark)

Fang, Jiakun; Su, Chi; Hu, Weihao

2015-01-01

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

Science.gov (United States)

Kocakaplan, Yusuf; Deviren, Bayram; Keskin, Mustafa

2012-12-01

We have examined the hierarchical structures of correlations networks among Turkey’s exports and imports by currencies for the 1996-2010 periods, using the concept of a minimal spanning tree (MST) and hierarchical tree (HT) which depend on the concept of ultrametricity. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial markets. We derived a hierarchical organization and build the MSTs and HTs during the 1996-2001 and 2002-2010 periods. The reason for studying two different sub-periods, namely 1996-2001 and 2002-2010, is that the Euro (EUR) came into use in 2001, and some countries have made their exports and imports with Turkey via the EUR since 2002, and in order to test various time-windows and observe temporal evolution. We have carried out bootstrap analysis to associate a value of the statistical reliability to the links of the MSTs and HTs. We have also used the average linkage cluster analysis (ALCA) to observe the cluster structure more clearly. Moreover, we have obtained the bidimensional minimal spanning tree (BMST) due to economic trade being a bidimensional problem. From the structural topologies of these trees, we have identified different clusters of currencies according to their proximity and economic ties. Our results show that some currencies are more important within the network, due to a tighter connection with other currencies. We have also found that the obtained currencies play a key role for Turkey’s exports and imports and have important implications for the design of portfolio and investment strategies.
Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

Directory of Open Access Journals (Sweden)

Hamed Faroqi

2017-01-01

Full Text Available The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers’ spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.
Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

Directory of Open Access Journals (Sweden)

Zhensheng Wang

2017-02-01

Full Text Available The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.
Hierarchical imaging of the human knee

Science.gov (United States)

Schulz, Georg; Götz, Christian; Deyhle, Hans; Müller-Gerbl, Magdalena; Zanette, Irene; Zdora, Marie-Christine; Khimchenko, Anna; Thalmann, Peter; Rack, Alexander; Müller, Bert

2016-10-01

Among the clinically relevant imaging techniques, computed tomography (CT) reaches the best spatial resolution. Sub-millimeter voxel sizes are regularly obtained. For investigations on true micrometer level lab-based μCT has become gold standard. The aim of the present study is the hierarchical investigation of a human knee post mortem using hard X-ray μCT. After the visualization of the entire knee using a clinical CT with a spatial resolution on the sub-millimeter range, a hierarchical imaging study was performed using a laboratory μCT system nanotom m. Due to the size of the whole knee the pixel length could not be reduced below 65 μm. These first two data sets were directly compared after a rigid registration using a cross-correlation algorithm. The μCT data set allowed an investigation of the trabecular structures of the bones. The further reduction of the pixel length down to 25 μm could be achieved by removing the skin and soft tissues and measuring the tibia and the femur separately. True micrometer resolution could be achieved after extracting cylinders of several millimeters diameters from the two bones. The high resolution scans revealed the mineralized cartilage zone including the tide mark line as well as individual calcified chondrocytes. The visualization of soft tissues including cartilage, was arranged by X-ray grating interferometry (XGI) at ESRF and Diamond Light Source. Whereas the high-energy measurements at ESRF allowed the simultaneous visualization of soft and hard tissues, the low-energy results from Diamond Light Source made individual chondrocytes within the cartilage visual.
Incrementally Detecting Change Types of Spatial Area Object: A Hierarchical Matching Method Considering Change Process

Directory of Open Access Journals (Sweden)

Yanhui Wang

2018-01-01

Full Text Available Detecting and extracting the change types of spatial area objects can track area objects’ spatiotemporal change pattern and provide the change backtracking mechanism for incrementally updating spatial datasets. To respond to the problems of high complexity of detection methods, high redundancy rate of detection factors, and the low automation degree during incrementally update process, we take into account the change process of area objects in an integrated way and propose a hierarchical matching method to detect the nine types of changes of area objects, while minimizing the complexity of the algorithm and the redundancy rate of detection factors. We illustrate in details the identification, extraction, and database entry of change types, and how we achieve a close connection and organic coupling of incremental information extraction and object type-of-change detection so as to characterize the whole change process. The experimental results show that this method can successfully detect incremental information about area objects in practical applications, with the overall accuracy reaching above 90%, which is much higher than the existing weighted matching method, making it quite feasible and applicable. It helps establish the corresponding relation between new-version and old-version objects, and facilitate the linked update processing and quality control of spatial data.
Continuous-wave spatial quantum correlations of light induced by multiple scattering

DEFF Research Database (Denmark)

Smolka, Stephan; Ott, Johan Raunkjær; Huck, Alexander

2012-01-01

and reflectance. Utilizing frequency-resolved quantum noise measurements, we observe that the strength of the spatial quantum correlation function can be controlled by changing the quantum state of an incident bright squeezed-light source. Our results are found to be in excellent agreement with the developed......We present theoretical and experimental results on spatial quantum correlations induced by multiple scattering of nonclassical light. A continuous-mode quantum theory is derived that enables determining the spatial quantum correlation function from the fluctuations of the total transmittance...... theory and form a basis for future research on, e. g., quantum interference of multiple quantum states in a multiple scattering medium....
Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

KAUST Repository

Sang, Huiyan; Jun, Mikyoung; Huang, Jianhua Z.

2011-01-01

This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models

Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models

Science.gov (United States)

Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.

2017-12-01

Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream
The correlation function for density perturbations in an expanding universe. III The three-point and predictions of the four-point and higher order correlation functions

Science.gov (United States)

Mcclelland, J.; Silk, J.

1978-01-01

Higher-order correlation functions for the large-scale distribution of galaxies in space are investigated. It is demonstrated that the three-point correlation function observed by Peebles and Groth (1975) is not consistent with a distribution of perturbations that at present are randomly distributed in space. The two-point correlation function is shown to be independent of how the perturbations are distributed spatially, and a model of clustered perturbations is developed which incorporates a nonuniform perturbation distribution and which explains the three-point correlation function. A model with hierarchical perturbations incorporating the same nonuniform distribution is also constructed; it is found that this model also explains the three-point correlation function, but predicts different results for the four-point and higher-order correlation functions than does the model with clustered perturbations. It is suggested that the model of hierarchical perturbations might be explained by the single assumption of having density fluctuations or discrete objects all of the same mass randomly placed at some initial epoch.
Cell Matrix Remodeling Ability Shown by Image Spatial Correlation

Science.gov (United States)

Chiu, Chi-Li; Digman, Michelle A.; Gratton, Enrico

2013-01-01

Extracellular matrix (ECM) remodeling is a critical step of many biological and pathological processes. However, most of the studies to date lack a quantitative method to measure ECM remodeling at a scale comparable to cell size. Here, we applied image spatial correlation to collagen second harmonic generation (SHG) images to quantitatively evaluate the degree of collagen remodeling by cells. We propose a simple statistical method based on spatial correlation functions to determine the size of high collagen density area around cells. We applied our method to measure collagen remodeling by two breast cancer cell lines (MDA-MB-231 and MCF-7), which display different degrees of invasiveness, and a fibroblast cell line (NIH/3T3). We found distinct collagen compaction levels of these three cell lines by applying the spatial correlation method, indicating different collagen remodeling ability. Furthermore, we quantitatively measured the effect of Latrunculin B and Marimastat on MDA-MB-231 cell line collagen remodeling ability and showed that significant collagen compaction level decreases with these treatments. PMID:23935614
Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood

Energy Technology Data Exchange (ETDEWEB)

Price, Oliver R., E-mail: oliver.price@unilever.co [Warwick-HRI, University of Warwick, Wellesbourne, Warwick, CV32 6EF (United Kingdom); University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom); Oliver, Margaret A. [University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom); Walker, Allan [Warwick-HRI, University of Warwick, Wellesbourne, Warwick, CV32 6EF (United Kingdom); Wood, Martin [University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom)

2009-05-15

An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field. - Estimating the spatial scale of herbicide and soil interactions by nested sampling.
Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood

International Nuclear Information System (INIS)

Price, Oliver R.; Oliver, Margaret A.; Walker, Allan; Wood, Martin

2009-01-01

An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field. - Estimating the spatial scale of herbicide and soil interactions by nested sampling.
Crucial nesting habitat for gunnison sage-grouse: A spatially explicit hierarchical approach

Science.gov (United States)

Aldridge, Cameron L.; Saher, D.J.; Childers, T.M.; Stahlnecker, K.E.; Bowen, Z.H.

2012-01-01

Gunnison sage-grouse (Centrocercus minimus) is a species of special concern and is currently considered a candidate species under Endangered Species Act. Careful management is therefore required to ensure that suitable habitat is maintained, particularly because much of the species' current distribution is faced with exurban development pressures. We assessed hierarchical nest site selection patterns of Gunnison sage-grouse inhabiting the western portion of the Gunnison Basin, Colorado, USA, at multiple spatial scales, using logistic regression-based resource selection functions. Models were selected using Akaike Information Criterion corrected for small sample sizes (AIC c) and predictive surfaces were generated using model averaged relative probabilities. Landscape-scale factors that had the most influence on nest site selection included the proportion of sagebrush cover >5%, mean productivity, and density of 2 wheel-drive roads. The landscape-scale predictive surface captured 97% of known Gunnison sage-grouse nests within the top 5 of 10 prediction bins, implicating 57% of the basin as crucial nesting habitat. Crucial habitat identified by the landscape model was used to define the extent for patch-scale modeling efforts. Patch-scale variables that had the greatest influence on nest site selection were the proportion of big sagebrush cover >10%, distance to residential development, distance to high volume paved roads, and mean productivity. This model accurately predicted independent nest locations. The unique hierarchical structure of our models more accurately captures the nested nature of habitat selection, and allowed for increased discrimination within larger landscapes of suitable habitat. We extrapolated the landscape-scale model to the entire Gunnison Basin because of conservation concerns for this species. We believe this predictive surface is a valuable tool which can be incorporated into land use and conservation planning as well the assessment of
Spatial correlations between browsing on balsam fir by white-tailed deer and the nutritional value of neighboring winter forage.

Science.gov (United States)

Champagne, Emilie; Moore, Ben D; Côté, Steeve D; Tremblay, Jean-Pierre

2018-03-01

Associational effects, that is, the influence of neighboring plants on herbivory suffered by a plant, are an outcome of forage selection. Although forage selection is a hierarchical process, few studies have investigated associational effects at multiple spatial scales. Because the nutritional quality of plants can be spatially structured, it might differently influence associational effects across multiple scales. Our objective was to determine the radius of influence of neighbor density and nutritional quality on balsam fir ( Abies balsamea ) herbivory by white-tailed deer ( Odocoileus virginianus ) in winter. We quantified browsing rates on fir and the density and quality of neighboring trees in a series of 10-year-old cutovers on Anticosti Island (Canada). We used cross-correlations to investigate relationships between browsing rates and the density and nutritional quality of neighboring trees at distances up to 1,000 m. Balsam fir and white spruce ( Picea glauca ) fiber content and dry matter in vitro true digestibility were correlated with fir browsing rate at the finest extra-patch scale (across distance of up to 50 m) and between cutover areas (300-400 m). These correlations suggest associational effects, that is, low nutritional quality of neighbors reduces the likelihood of fir herbivory (associational defense). Our results may indicate associational effects mediated by intraspecific variation in plant quality and suggest that these effects could occur at scales from tens to hundreds of meters. Understanding associational effects could inform strategies for restoration or conservation; for example, planting of fir among existing natural regeneration could be concentrated in areas of low nutritional quality.
Observation of spatial quantum correlations induced by multiple scattering of nonclassical light

DEFF Research Database (Denmark)

Smolka, Stephan; Huck, Alexander; Andersen, Ulrik Lund

2009-01-01

and negative spatial quantum correlations are observed when varying the quantum state incident to the multiple scattering medium, and the strength of the correlations is controlled by the number of photons. The experimental results are in excellent agreement with recent theoretical proposals by implementing......We present the experimental realization of spatial quantum correlations of photons that are induced by multiple scattering of squeezed light. The quantum correlation relates photons propagating along two different light paths through the random medium and is infinite in range. Both positive...... the full quantum model of multiple scattering....
Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets

KAUST Repository

Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.

2017-01-01

algebra, we approximate the discretized covariance function in the hierarchical (H-) matrix format. The H-matrix format has a log-linear computational cost and storage O(kn log n), where the rank k is a small integer and n is the number of locations. The H
MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches.

Science.gov (United States)

Chowdhury, Rasheda Arman; Lina, Jean Marc; Kobayashi, Eliane; Grova, Christophe

2013-01-01

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2) to 30 cm(2), whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.
MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches.

Directory of Open Access Journals (Sweden)

Rasheda Arman Chowdhury

Full Text Available Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG or Magneto-EncephaloGraphy (MEG signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i brain activity may be modeled using cortical parcels and (ii brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM and the Hierarchical Bayesian (HB source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2 to 30 cm(2, whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.
Spatial correlation analysis of urban traffic state under a perspective of community detection

Science.gov (United States)

Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

2018-05-01

Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.
Revealing Spatial Variation and Correlation of Urban Travels from Big Trajectory Data

Science.gov (United States)

Li, X.; Tu, W.; Shen, S.; Yue, Y.; Luo, N.; Li, Q.

2017-09-01

With the development of information and communication technology, spatial-temporal data that contain rich human mobility information are growing rapidly. However, the consistency of multi-mode human travel behind multi-source spatial-temporal data is not clear. To this aim, we utilized a week of taxies' and buses' GPS trajectory data and smart card data in Shenzhen, China to extract city-wide travel information of taxi, bus and metro and tested the correlation of multi-mode travel characteristics. Both the global correlation and local correlation of typical travel indicator were examined. The results show that: (1) Significant differences exist in of urban multi-mode travels. The correlation between bus travels and taxi travels, metro travel and taxi travels are globally low but locally high. (2) There are spatial differences of the correlation relationship between bus, metro and taxi travel. These findings help us understanding urban travels deeply therefore facilitate both the transport policy making and human-space interaction research.
REVEALING SPATIAL VARIATION AND CORRELATION OF URBAN TRAVELS FROM BIG TRAJECTORY DATA

Directory of Open Access Journals (Sweden)

X. Li

2017-09-01

Full Text Available With the development of information and communication technology, spatial-temporal data that contain rich human mobility information are growing rapidly. However, the consistency of multi-mode human travel behind multi-source spatial-temporal data is not clear. To this aim, we utilized a week of taxies’ and buses’ GPS trajectory data and smart card data in Shenzhen, China to extract city-wide travel information of taxi, bus and metro and tested the correlation of multi-mode travel characteristics. Both the global correlation and local correlation of typical travel indicator were examined. The results show that: (1 Significant differences exist in of urban multi-mode travels. The correlation between bus travels and taxi travels, metro travel and taxi travels are globally low but locally high. (2 There are spatial differences of the correlation relationship between bus, metro and taxi travel. These findings help us understanding urban travels deeply therefore facilitate both the transport policy making and human-space interaction research.
Hierarchical structure of correlation functions for single jets

International Nuclear Information System (INIS)

Lupia, S.; Giovannini, A.; Ugoccioni, R.

1993-01-01

Theoretical basis of void scaling function properties of hierarchical structure in rapidity and p T intervals are explored. Their phenomenological consequences are analyzed at single jet level by using Monte Carlo methods in e + e - annihilation. It is found that void scaling function study provides an interesting alternative approach for characterizing single jets of different origin. (orig.)
Comparison of Extreme Precipitation Return Levels using Spatial Bayesian Hierarchical Modeling versus Regional Frequency Analysis

Science.gov (United States)

Love, C. A.; Skahill, B. E.; AghaKouchak, A.; Karlovits, G. S.; England, J. F.; Duren, A. M.

2017-12-01

We compare gridded extreme precipitation return levels obtained using spatial Bayesian hierarchical modeling (BHM) with their respective counterparts from a traditional regional frequency analysis (RFA) using the same set of extreme precipitation data. Our study area is the 11,478 square mile Willamette River basin (WRB) located in northwestern Oregon, a major tributary of the Columbia River whose 187 miles long main stem, the Willamette River, flows northward between the Coastal and Cascade Ranges. The WRB contains approximately two thirds of Oregon's population and 20 of the 25 most populous cities in the state. The U.S. Army Corps of Engineers (USACE) Portland District operates thirteen dams and extreme precipitation estimates are required to support risk informed hydrologic analyses as part of the USACE Dam Safety Program. Our intent is to profile for the USACE an alternate methodology to an RFA that was developed in 2008 due to the lack of an official NOAA Atlas 14 update for the state of Oregon. We analyze 24-hour annual precipitation maxima data for the WRB utilizing the spatial BHM R package "spatial.gev.bma", which has been shown to be efficient in developing coherent maps of extreme precipitation by return level. Our BHM modeling analysis involved application of leave-one-out cross validation (LOO-CV), which not only supported model selection but also a comprehensive assessment of location specific model performance. The LOO-CV results will provide a basis for the BHM RFA comparison.
Spatial and temporal correlation in dynamic, multi-electron quantum systems

Energy Technology Data Exchange (ETDEWEB)

Godunov, A.L.; McGuire, J.H.; Shakov, Kh.Kh. [Department of Physics, Tulane University, New Orleans, LA (United States); Ivanov, P.B.; Shipakov, V.A. [Troitsk Institute for Innovation and Fusion Research, Troitsk (Russian Federation); Merabet, H.; Bruch, R.; Hanni, J. [Department of Physics, University of Nevada Reno, Reno, NV (United States)

2001-12-28

Cross sections for ionization with excitation and for double excitation in helium are evaluated in a full second Born calculation. These full second Born calculations are compared to calculations in the independent electron approximation, where spatial correlation between the electrons is removed. Comparison is also made to calculations in the independent time approximation, where time correlation between the electrons is removed. The two-electron transitions considered here are caused by interactions with incident protons and electrons with velocities ranging between 2 and 10 au. Good agreement is found between our full calculations and experiment, except for the lowest velocities, where higher Born terms are expected to be significant. Spatial electron correlation, arising from internal electron-electron interactions, and time correlation, arising from time ordering of the external interactions, can both give rise to observable effects. Our method may be used for photon impact. (author)
Accounting for connectivity and spatial correlation in the optimal placement of wildlife habitat

Science.gov (United States)

John Hof; Curtis H. Flather

1996-01-01

This paper investigates optimization approaches to simultaneously modelling habitat fragmentation and spatial correlation between patch populations. The problem is formulated with habitat connectivity affecting population means and variances, with spatial correlations accounted for in covariance calculations. Population with a pre-specifled confidence level is then...
Hierarchical structure of correlation functions for single jets

Energy Technology Data Exchange (ETDEWEB)

Lupia, S. (Dipt. di Fisica Teorica, Univ. di Torino, and INFN, Sezione di Torino (Italy)); Giovannini, A. (Dipt. di Fisica Teorica, Univ. di Torino, and INFN, Sezione di Torino (Italy)); Ugoccioni, R. (Dipt. di Fisica Teorica, Univ. di Torino, and INFN, Sezione di Torino (Italy))

1993-08-01

Theoretical basis of void scaling function properties of hierarchical structure in rapidity and p[sub T] intervals are explored. Their phenomenological consequences are analyzed at single jet level by using Monte Carlo methods in e[sup +]e[sup -] annihilation. It is found that void scaling function study provides an interesting alternative approach for characterizing single jets of different origin. (orig.)
SPATIAL CORRELATION BETWEEN PHYSICAL PROPERTIES OF SOIL AND WEEDS IN TWO MANAGEMENT SYSTEMS

Directory of Open Access Journals (Sweden)

Valter Roberto Schaffrath

2015-02-01

Full Text Available The spatial correlation between soil properties and weeds is relevant in agronomic and environmental terms. The analysis of this correlation is crucial for the interpretation of its meaning, for influencing factors such as dispersal mechanisms, seed production and survival, and the range of influence of soil management techniques. This study aimed to evaluate the spatial correlation between the physical properties of soil and weeds in no-tillage (NT and conventional tillage (CT systems. The following physical properties of soil and weeds were analyzed: soil bulk density, macroporosity, microporosity, total porosity, aeration capacity of soil matrix, soil water content at field capacity, weed shoot biomass, weed density, Commelina benghalensis density, and Bidens pilosa density. Generally, the ranges of the spatial correlations were higher in NT than in CT. The cross-variograms showed that many variables have a structure of combined spatial variation and can therefore be mapped from one another by co-kriging. This combined variation also allows inferences about the physical and biological meanings of the study variables. Results also showed that soil management systems influence the spatial dependence structure significantly.

Hierarchical population monitoring of greater sage-grouse (Centrocercus urophasianus) in Nevada and California—Identifying populations for management at the appropriate spatial scale

Science.gov (United States)

Coates, Peter S.; Prochazka, Brian G.; Ricca, Mark A.; Wann, Gregory T.; Aldridge, Cameron L.; Hanser, Steven E.; Doherty, Kevin E.; O'Donnell, Michael S.; Edmunds, David R.; Espinosa, Shawn P.

2017-08-10

Population ecologists have long recognized the importance of ecological scale in understanding processes that guide observed demographic patterns for wildlife species. However, directly incorporating spatial and temporal scale into monitoring strategies that detect whether trajectories are driven by local or regional factors is challenging and rarely implemented. Identifying the appropriate scale is critical to the development of management actions that can attenuate or reverse population declines. We describe a novel example of a monitoring framework for estimating annual rates of population change for greater sage-grouse (Centrocercus urophasianus) within a hierarchical and spatially nested structure. Specifically, we conducted Bayesian analyses on a 17-year dataset (2000–2016) of lek counts in Nevada and northeastern California to estimate annual rates of population change, and compared trends across nested spatial scales. We identified leks and larger scale populations in immediate need of management, based on the occurrence of two criteria: (1) crossing of a destabilizing threshold designed to identify significant rates of population decline at a particular nested scale; and (2) crossing of decoupling thresholds designed to identify rates of population decline at smaller scales that decouple from rates of population change at a larger spatial scale. This approach establishes how declines affected by local disturbances can be separated from those operating at larger scales (for example, broad-scale wildfire and region-wide drought). Given the threshold output from our analysis, this adaptive management framework can be implemented readily and annually to facilitate responsive and effective actions for sage-grouse populations in the Great Basin. The rules of the framework can also be modified to identify populations responding positively to management action or demonstrating strong resilience to disturbance. Similar hierarchical approaches might be beneficial
Spatially correlated disorder in striped precursor magnetic modulations

International Nuclear Information System (INIS)

Porta, Marcel; Castan, Teresa; LLoveras, Pol; Planes, Antoni; Saxena, Avadh

2007-01-01

We use a Ginzburg-Landau model that includes long-range dipolar interactions and spatially correlated quenched-in disorder coupled to the local magnetization to study the properties of the precursor magnetic modulations as a function of the characteristics of the disorder. We find that although the modulation pattern is very robust and does not depend on details of the pair correlation function G(r), the scaling behaviour of the characteristic length of the striped magnetic modulations depends on the behaviour of G(r) for small values of r
Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows

Science.gov (United States)

Gay-Balmaz, François; Holm, Darryl D.

2018-01-01

Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.
Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows

Science.gov (United States)

Gay-Balmaz, François; Holm, Darryl D.

2018-06-01

Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.
HiPS - Hierarchical Progressive Survey Version 1.0

Science.gov (United States)

Fernique, Pierre; Allen, Mark; Boch, Thomas; Donaldson, Tom; Durand, Daniel; Ebisawa, Ken; Michel, Laurent; Salgado, Jesus; Stoehr, Felix; Fernique, Pierre

2017-05-01

This document presents HiPS, a hierarchical scheme for the description, storage and access of sky survey data. The system is based on hierarchical tiling of sky regions at finer and finer spatial resolution which facilitates a progressive view of a survey, and supports multi-resolution zooming and panning. HiPS uses the HEALPix tessellation of the sky as the basis for the scheme and is implemented as a simple file structure with a direct indexing scheme that leads to practical implementations.
Spatially varying cross-correlation coefficients in the presence of nugget effects

KAUST Repository

Kleiber, William; Genton, Marc G.

2012-01-01

We derive sufficient conditions for the cross-correlation coefficient of a multivariate spatial process to vary with location when the spatial model is augmented with nugget effects. The derived class is valid for any choice of covariance functions, and yields substantial flexibility between multiple processes. The key is to identify the cross-correlation coefficient matrix with a contraction matrix, which can be either diagonal, implying a parsimonious formulation, or a fully general contraction matrix, yielding greater flexibility but added model complexity. We illustrate the approach with a bivariate minimum and maximum temperature dataset in Colorado, allowing the two variables to be positively correlated at low elevations and nearly independent at high elevations, while still yielding a positive definite covariance matrix. © 2012 Biometrika Trust.
Spatially varying cross-correlation coefficients in the presence of nugget effects

KAUST Repository

Kleiber, William

2012-11-29

We derive sufficient conditions for the cross-correlation coefficient of a multivariate spatial process to vary with location when the spatial model is augmented with nugget effects. The derived class is valid for any choice of covariance functions, and yields substantial flexibility between multiple processes. The key is to identify the cross-correlation coefficient matrix with a contraction matrix, which can be either diagonal, implying a parsimonious formulation, or a fully general contraction matrix, yielding greater flexibility but added model complexity. We illustrate the approach with a bivariate minimum and maximum temperature dataset in Colorado, allowing the two variables to be positively correlated at low elevations and nearly independent at high elevations, while still yielding a positive definite covariance matrix. © 2012 Biometrika Trust.
Horizontal Residual Mean Circulation: Evaluation of Spatial Correlations in Coarse Resolution Ocean Models

Science.gov (United States)

Li, Y.; McDougall, T. J.

2016-02-01

Coarse resolution ocean models lack knowledge of spatial correlations between variables on scales smaller than the grid scale. Some researchers have shown that these spatial correlations play a role in the poleward heat flux. In order to evaluate the poleward transport induced by the spatial correlations at a fixed horizontal position, an equation is obtained to calculate the approximate transport from velocity gradients. The equation involves two terms that can be added to the quasi-Stokes streamfunction (based on temporal correlations) to incorporate the contribution of spatial correlations. Moreover, these new terms do not need to be parameterized and is ready to be evaluated by using model data directly. In this study, data from a high resolution ocean model have been used to estimate the accuracy of this HRM approach for improving the horizontal property fluxes in coarse-resolution ocean models. A coarse grid is formed by sub-sampling and box-car averaging the fine grid scale. The transport calculated on the coarse grid is then compared to the transport on original high resolution grid scale accumulated over a corresponding number of grid boxes. The preliminary results have shown that the estimate on coarse resolution grids roughly match the corresponding transports on high resolution grids.
Hierarchical structure in the distribution of galaxies

International Nuclear Information System (INIS)

Schulman, L.S.; Seiden, P.E.; Technion - Israel Institute of Technology, Haifa; IBM Thomas J. Watson Research Center, Yorktown Heights, NY)

1986-01-01

The distribution of galaxies has a hierarchical structure with power-law correlations. This is usually thought to arise from gravity alone acting on an originally uniform distributioon. If, however, the original process of galaxy formation occurs through the stimulated birth of one galaxy due to a nearby recently formed galaxy, and if this process occurs near its percolation threshold, then a hierarchical structure with power-law correlations arises at the time of galaxy formation. If subsequent gravitational evolution within an expanding cosmology is such as to retain power-law correlations, the initial r exp -1 dropoff can steepen to the observed r exp -1.8. The distribution of galaxies obtained by this process produces clustering and voids, as observed. 23 references
On the hierarchical lattices approximation of Bravais lattices: Specific heat and correlation length

International Nuclear Information System (INIS)

Tsallis, C.

1984-01-01

Certain types of real-space renormalization groups (which essentially approximate Bravais lattices through hierarchical ones) do not preserve standard thermodynamic convexity properties. It is pointed out that this serious defect is not intrinsic to any real-space renormalization. It can be avoided if form-invariance (under uniform translation of the energy scale) of the equation connecting the Bravais lattice (which is intended to study) to the hierarchical one (which approximates it) is demanded. In addition to that expressions for the critical exponentes ν and α corresponding to hierarchical lattices are analysed; these are consistent with Melrose recent analysis of the fractal intrinsic dimensionality. (Author) [pt
Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models.

Science.gov (United States)

Wang, Xinlei; Zang, Miao; Xiao, Guanghua

2013-06-15

Epigenetics is the study of changes to the genome that can switch genes on or off and determine which proteins are transcribed without altering the DNA sequence. Recently, epigenetic changes have been linked to the development and progression of disease such as psychiatric disorders. High-throughput epigenetic experiments have enabled researchers to measure genome-wide epigenetic profiles and yield data consisting of intensity ratios of immunoprecipitation versus reference samples. The intensity ratios can provide a view of genomic regions where protein binding occur under one experimental condition and further allow us to detect epigenetic alterations through comparison between two different conditions. However, such experiments can be expensive, with only a few replicates available. Moreover, epigenetic data are often spatially correlated with high noise levels. In this paper, we develop a Bayesian hierarchical model, combined with hidden Markov processes with four states for modeling spatial dependence, to detect genomic sites with epigenetic changes from two-sample experiments with paired internal control. One attractive feature of the proposed method is that the four states of the hidden Markov process have well-defined biological meanings and allow us to directly call the change patterns based on the corresponding posterior probabilities. In contrast, none of existing methods can offer this advantage. In addition, the proposed method offers great power in statistical inference by spatial smoothing (via hidden Markov modeling) and information pooling (via hierarchical modeling). Both simulation studies and real data analysis in a cocaine addiction study illustrate the reliability and success of this method. Copyright © 2012 John Wiley & Sons, Ltd.
A composite likelihood approach for spatially correlated survival data

Science.gov (United States)

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory. PMID:24223450
A composite likelihood approach for spatially correlated survival data.

Science.gov (United States)

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.
Correlated density matrix theory of spatially inhomogeneous Bose fluids

International Nuclear Information System (INIS)

Gernoth, K.A.; Clark, J.W.; Ristig, M.L.

1994-06-01

In this paper, the variational Hartree-Jastrow theory of the ground state of spatially inhomogeneous Bose systems is extended to finite temperatures. The theory presented here is a generalization also in the sense that it extends the correlated density matrix approach, formulated previously for uniform Bose fluids, to systems with nonuniform density profiles. The method provides a framework in which the effects of thermal excitations on the spatial structure of a Bose fluid, as represented by the density profile and the two-body distribution functions, may be discussed on the basis on an ab initio microscopic description of the system. Thermal excitations make their appearance through self-consistently determined one-body and two-body potentials which enter the nonlinear, coupled Euler-Lagrange equations for the one-body density and for the pair distribution function. Since back-flow correlations are neglected, the excitations are described by a Feynman eigenvalue equation, suitably generalized to nonzero temperatures. The only external quantities entering the correlated density matrix theory elaborated here are the bare two-body interaction potential and, in actual applications, the boundary conditions to be imposed on the one-body density. 30 refs
Spatially heterogeneous dynamics investigated via a time-dependent four-point density correlation function

DEFF Research Database (Denmark)

Lacevic, N.; Starr, F. W.; Schrøder, Thomas

2003-01-01

correlation function g4(r,t) and corresponding "structure factor" S4(q,t) which measure the spatial correlations between the local liquid density at two points in space, each at two different times, and so are sensitive to dynamical heterogeneity. We study g4(r,t) and S4(q,t) via molecular dynamics......Relaxation in supercooled liquids above their glass transition and below the onset temperature of "slow" dynamics involves the correlated motion of neighboring particles. This correlated motion results in the appearance of spatially heterogeneous dynamics or "dynamical heterogeneity." Traditional...... two-point time-dependent density correlation functions, while providing information about the transient "caging" of particles on cooling, are unable to provide sufficiently detailed information about correlated motion and dynamical heterogeneity. Here, we study a four-point, time-dependent density...
Fast methods for spatially correlated multilevel functional data

KAUST Repository

Staicu, A.-M.

2010-01-19

We propose a new methodological framework for the analysis of hierarchical functional data when the functions at the lowest level of the hierarchy are correlated. For small data sets, our methodology leads to a computational algorithm that is orders of magnitude more efficient than its closest competitor (seconds versus hours). For large data sets, our algorithm remains fast and has no current competitors. Thus, in contrast to published methods, we can now conduct routine simulations, leave-one-out analyses, and nonparametric bootstrap sampling. Our methods are inspired by and applied to data obtained from a state-of-the-art colon carcinogenesis scientific experiment. However, our models are general and will be relevant to many new data sets where the object of inference are functions or images that remain dependent even after conditioning on the subject on which they are measured. Supplementary materials are available at Biostatistics online.
Spatial correlations, clustering and percolation-like transitions in homicide crimes

Science.gov (United States)

Alves, L. G. A.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2015-07-01

The spatial dynamics of criminal activities has been recently studied through statistical physics methods; however, models and results have been focusing on local scales (city level) and much less is known about these patterns at larger scales, e.g. at a country level. Here we report on a characterization of the spatial dynamics of the homicide crimes along the Brazilian territory using data from all cities (˜5000) in a period of more than thirty years. Our results show that the spatial correlation function in the per capita homicides decays exponentially with the distance between cities and that the characteristic correlation length displays an acute increasing trend in the latest years. We also investigate the formation of spatial clusters of cities via a percolation-like analysis, where clustering of cities and a phase-transition-like behavior describing the size of the largest cluster as a function of a homicide threshold are observed. This transition-like behavior presents evolutive features characterized by an increasing in the homicide threshold (where the transitions occur) and by a decreasing in the transition magnitudes (length of the jumps in the cluster size). We believe that our work sheds new light on the spatial patterns of criminal activities at large scales, which may contribute for better political decisions and resources allocation as well as opens new possibilities for modeling criminal activities by setting up fundamental empirical patterns at large scales.
Hierarchical species distribution models

Science.gov (United States)

Hefley, Trevor J.; Hooten, Mevin B.

2016-01-01

Determining the distribution pattern of a species is important to increase scientific knowledge, inform management decisions, and conserve biodiversity. To infer spatial and temporal patterns, species distribution models have been developed for use with many sampling designs and types of data. Recently, it has been shown that count, presence-absence, and presence-only data can be conceptualized as arising from a point process distribution. Therefore, it is important to understand properties of the point process distribution. We examine how the hierarchical species distribution modeling framework has been used to incorporate a wide array of regression and theory-based components while accounting for the data collection process and making use of auxiliary information. The hierarchical modeling framework allows us to demonstrate how several commonly used species distribution models can be derived from the point process distribution, highlight areas of potential overlap between different models, and suggest areas where further research is needed.
Two-particle spatial correlations in superfluid nuclei

International Nuclear Information System (INIS)

Pillet, N.; Berger, J.-F.; Sandulescu, N.; Schuck, P.

2010-01-01

We discuss the effect of pairing on two-neutron space correlations in deformed nuclei. The spatial correlations are described by the pairing tensor in coordinate space calculated in the HFB approach. Calculations are done using the D1S Gogny force. We show that the pairing tensor has a rather small extension in the relative coordinate, a feature observed earlier in spherical nuclei. It is pointed out that in deformed nuclei the coherence length corresponding to the pairing tensor has a pattern similar to what we have found previously in spherical nuclei; that is, it is maximal in the interior of the nucleus and then it decreases rather rapidly in the surface region, where it reaches a minimal value of about 2 fm. This minimal value of the coherence length in the surface is essentially determined by the finite size properties of single-particle states in the vicinity of the chemical potential and has little to do with enhanced pairing correlations in the nuclear surface. It is shown that in nuclei the coherence length is not a good indicator of the intensity of pairing correlations. This feature is contrasted with the situation in infinite matter.
A DATA FIELD METHOD FOR URBAN REMOTELY SENSED IMAGERY CLASSIFICATION CONSIDERING SPATIAL CORRELATION

Directory of Open Access Journals (Sweden)

Y. Zhang

2016-06-01

Full Text Available Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM for the classification of multi-features (e.g. the spectral feature and spatial correlation feature. In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

Learning with hierarchical-deep models.

Science.gov (United States)

Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio

2013-08-01

We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.
Hierarchical Factoring Based On Image Analysis And Orthoblique Rotations.

Science.gov (United States)

Stankov, L

1979-07-01

The procedure for hierarchical factoring suggested by Schmid and Leiman (1957) is applied within the framework of image analysis and orthoblique rotational procedures. It is shown that this approach necessarily leads to correlated higher order factors. Also, one can obtain a smaller number of factors than produced by typical hierarchical procedures.
Characterizing the spatial variations and correlations of large rainstorms for landslide study

Directory of Open Access Journals (Sweden)

L. Gao

2017-09-01

Full Text Available Rainfall is the primary trigger of landslides in Hong Kong; hence, rainstorm spatial distribution is an important piece of information in landslide hazard analysis. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a rotated ellipsoid trend surface and a random field of residuals. The maximum rolling 4, 12, 24, and 36 h rainfall amounts of these storms are assessed via surface trend fitting, and the spatial correlation of the detrended residuals is determined through studying the scales of fluctuation along eight directions. The principal directions of the surface trend are between 19 and 43°, and the major and minor axis lengths are 83–386 and 55–79 km, respectively. The scales of fluctuation of the residuals are found between 5 and 30 km. The spatial distribution parameters for the three large rainstorms are found to be similar to those for four ordinary rainfall events. The proposed rainfall spatial distribution model and parameters help define the impact area, rainfall intensity and local topographic effects for landslide hazard evaluation in the future.
Leadership styles across hierarchical levels in nursing departments.

Science.gov (United States)

Stordeur, S; Vandenberghe, C; D'hoore, W

2000-01-01

Some researchers have reported on the cascading effect of transformational leadership across hierarchical levels. One study examined this effect in nursing, but it was limited to a single hospital. To examine the cascading effect of leadership styles across hierarchical levels in a sample of nursing departments and to investigate the effect of hierarchical level on the relationships between leadership styles and various work outcomes. Based on a sample of eight hospitals, the cascading effect was tested using correlation analysis. The main sources of variation among leadership scores were determined with analyses of variance (ANOVA), and the interaction effect of hierarchical level and leadership styles on criterion variables was tested with moderated regression analysis. No support was found for a cascading effect of leadership across hierarchical levels. Rather, the variation of leadership scores was explained primarily by the organizational context. Transformational leadership had a stronger impact on criterion variables than transactional leadership. Interaction effects between leadership styles and hierarchical level were observed only for perceived unit effectiveness. The hospital's structure and culture are major determinants of leadership styles.
Probing heterogeneous dynamics from spatial density correlation in glass-forming liquids.

Science.gov (United States)

Li, Yan-Wei; Zhu, You-Liang; Sun, Zhao-Yan

2016-12-01

We numerically investigate the connection between spatial density correlation and dynamical heterogeneity in glass-forming liquids. We demonstrate that the cluster size defined by the spatial aggregation of densely packed particles (DPPs) can better capture the difference between the dynamics of the Lennard-Jones glass model and the Weeks-Chandler-Andersen truncation model than the commonly used pair correlation functions. More interestingly, we compare the mobility of DPPs and loosely packed particles, and we find that high local density correlates well with slow dynamics in systems with relatively hard repulsive interactions but links to mobile ones in the system with soft repulsive interactions at one relaxation time scale. Our results show clear evidence that the above model dependence behavior stems from the hopping motion of DPPs at the end of the caging stage due to the compressive nature of soft repulsive spheres, which activates the dynamics of DPPs in the α relaxation stage.
Inter-subject Functional Correlation Reveal a Hierarchical Organization of Extrinsic and Intrinsic Systems in the Brain.

Science.gov (United States)

Ren, Yudan; Nguyen, Vinh Thai; Guo, Lei; Guo, Christine Cong

2017-09-07

The brain is constantly monitoring and integrating both cues from the external world and signals generated intrinsically. These extrinsically and intrinsically-driven neural processes are thought to engage anatomically distinct regions, which are thought to constitute the extrinsic and intrinsic systems of the brain. While the specialization of extrinsic and intrinsic system is evident in primary and secondary sensory cortices, a systematic mapping of the whole brain remains elusive. Here, we characterized the extrinsic and intrinsic functional activities in the brain during naturalistic movie-viewing. Using a novel inter-subject functional correlation (ISFC) analysis, we found that the strength of ISFC shifts along the hierarchical organization of the brain. Primary sensory cortices appear to have strong inter-subject functional correlation, consistent with their role in processing exogenous information, while heteromodal regions that attend to endogenous processes have low inter-subject functional correlation. Those brain systems with higher intrinsic tendency show greater inter-individual variability, likely reflecting the aspects of brain connectivity architecture unique to individuals. Our study presents a novel framework for dissecting extrinsically- and intrinsically-driven processes, as well as examining individual differences in brain function during naturalistic stimulation.
A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders.

Directory of Open Access Journals (Sweden)

Robert M Dorazio

Full Text Available Several spatial capture-recapture (SCR models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in
Effect of spatially correlated noise on coherence resonance in a network of excitable cells

International Nuclear Information System (INIS)

Kwon, Okyu; Jo, Hang-Hyun; Moon, Hie-Tae

2005-01-01

We study the effect of spatially correlated noise on coherence resonance (CR) in a Watts-Strogatz small-world network of Fitz Hugh-Nagumo neurons, where the noise correlation decays exponentially with distance between neurons. It is found that CR is considerably improved just by a small fraction of long-range connections for an intermediate coupling strength. For other coupling strengths, an abrupt change in CR occurs following the drastic fracture of the clustered structures in the network. Our study shows that spatially correlated noise plays a significant role in the phenomenon of CR reinforcing the role of the clustered structure of the system
Quantum diffraction and interference of spatially correlated photon pairs and its Fourier-optical analysis

International Nuclear Information System (INIS)

Shimizu, Ryosuke; Edamatsu, Keiichi; Itoh, Tadashi

2006-01-01

We present one- and two-photon diffraction and interference experiments involving parametric down-converted photon pairs. By controlling the divergence of the pump beam in parametric down-conversion, the diffraction-interference pattern produced by an object changes from a quantum (perfectly correlated) case to a classical (uncorrelated) one. The observed diffraction and interference patterns are accurately reproduced by Fourier-optical analysis taking into account the quantum spatial correlation. We show that the relation between the spatial correlation and the object size plays a crucial role in the formation of both one- and two-photon diffraction-interference patterns
Likelihood Approximation With Parallel Hierarchical Matrices For Large Spatial Datasets

KAUST Repository

Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.

2017-01-01

The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Matérn covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\H$-) matrix format with computational cost $\\mathcal{O}(k^2n \\log^2 n/p)$ and storage $\\mathcal{O}(kn \\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.
Likelihood Approximation With Parallel Hierarchical Matrices For Large Spatial Datasets

KAUST Repository

Litvinenko, Alexander

2017-11-01

The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Matérn covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\\\H$-) matrix format with computational cost $\\\\mathcal{O}(k^2n \\\\log^2 n/p)$ and storage $\\\\mathcal{O}(kn \\\\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.
What Do They Have in Common? Drivers of Streamflow Spatial Correlation and Prediction of Flow Regimes in Ungauged Locations

Science.gov (United States)

Betterle, A.; Radny, D.; Schirmer, M.; Botter, G.

2017-12-01

The spatial correlation of daily streamflows represents a statistical index encapsulating the similarity between hydrographs at two arbitrary catchment outlets. In this work, a process-based analytical framework is utilized to investigate the hydrological drivers of streamflow spatial correlation through an extensive application to 78 pairs of stream gauges belonging to 13 unregulated catchments in the eastern United States. The analysis provides insight on how the observed heterogeneity of the physical processes that control flow dynamics ultimately affect streamflow correlation and spatial patterns of flow regimes. Despite the variability of recession properties across the study catchments, the impact of heterogeneous drainage rates on the streamflow spatial correlation is overwhelmed by the spatial variability of frequency and intensity of effective rainfall events. Overall, model performances are satisfactory, with root mean square errors between modeled and observed streamflow spatial correlation below 10% in most cases. We also propose a method for estimating streamflow correlation in the absence of discharge data, which proves useful to predict streamflow regimes in ungauged areas. The method consists in setting a minimum threshold on the modeled flow correlation to individuate hydrologically similar sites. Catchment outlets that are most correlated (ρ>0.9) are found to be characterized by analogous streamflow distributions across a broad range of flow regimes.
Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing.

Science.gov (United States)

Gundogdu, Erhan; Ozkan, Huseyin; Alatan, A Aydin

2017-11-01

Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [of complexity O(D) ] based on a large ensemble of CFB trackers. The ensemble [of size O(2 D ) ] is organized over a binary tree (depth D ), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing.
Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models.

Science.gov (United States)

Felleki, M; Lee, D; Lee, Y; Gilmour, A R; Rönnegård, L

2012-12-01

The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being -0·52 for IRWLS and -0·62 in Sorensen & Waagepetersen (2003).
Estimates of spatial correlation in volcanic tuff, Yucca Mountain, Nevada: Yucca Mountain Site Characterization Project

International Nuclear Information System (INIS)

Rautman, C.A.

1991-02-01

The spatial correlation structure of volcanic tuffs at and near the site of the proposed high-level nuclear waste repository at Yucca Mountain, Nevada, is estimated using samples obtained from surface outcrops and drill holes. Data are examined for four rock properties: porosity, air permeability, saturated hydraulic conductivity, and dry bulk density. Spatial continuity patterns are identified in both lateral and vertical (stratigraphic) dimensions. The data are examined for the Calico Hills tuff stratigraphic unit and also without regard for stratigraphy. Variogram models fitted to the sample data from the tuffs of Calico Hills indicate that porosity is correlated laterally over distances of up to 3000 feet. If air permeability and saturated conductivity values are viewed as semi-interchangeable for purposes of identifying spatial structure, the data suggest a maximum range of correlation of 300 to 500 feet without any obvious horizontal to vertical anisotropy. Continuity exists over vertical distances of roughly 200 feet. Similar variogram models fitted to sample data taken from vertical drill holes without regard for stratigraphy suggest that correlation exists over distances of 500 to 800 feet for each rock property examined. Spatial correlation of rock properties violates the sample-independence assumptions of classical statistics to a degree not usually acknowledged. In effect, the existence of spatial structure reduces the ''equivalent'' number of samples below the number of physical samples. This reduction in the effective sampling density has important implications for site characterization for the Yucca Mountain Project. 19 refs., 43 figs., 5 tabs
Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure

Science.gov (United States)

Skvortsova, Elena B.; Mallants, Dirk

2015-01-01

Structural features of porous materials such as soil define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, or gas exchange between biologically active soil root zone and atmosphere) and solute transport. To characterize soil microstructure, conventional soil science uses such metrics as pore size and pore-size distributions and thin section-derived morphological indicators. However, these descriptors provide only limited amount of information about the complex arrangement of soil structure and have limited capability to reconstruct structural features or predict physical properties. We introduce three different spatial correlation functions as a comprehensive tool to characterize soil microstructure: 1) two-point probability functions, 2) linear functions, and 3) two-point cluster functions. This novel approach was tested on thin-sections (2.21×2.21 cm2) representing eight soils with different pore space configurations. The two-point probability and linear correlation functions were subsequently used as a part of simulated annealing optimization procedures to reconstruct soil structure. Comparison of original and reconstructed images was based on morphological characteristics, cluster correlation functions, total number of pores and pore-size distribution. Results showed excellent agreement for soils with isolated pores, but relatively poor correspondence for soils exhibiting dual-porosity features (i.e. superposition of pores and micro-cracks). Insufficient information content in the correlation function sets used for reconstruction may have contributed to the observed discrepancies. Improved reconstructions may be obtained by adding cluster and other correlation functions into reconstruction sets. Correlation functions and the associated stochastic reconstruction algorithms introduced here are universally applicable in soil science, such as for soil classification
Spatial Correlation in the Ambient Core Noise Field of a Turbofan Engine

Science.gov (United States)

Miles, Jeffrey Hilton

2012-01-01

An acoustic transfer function relating combustion noise and turbine exit noise in the presence of enclosed ambient core noise is investigated using a dynamic system model and an acoustic system model for the particular turbofan engine studied and for a range of operating conditions. Measurements of cross-spectra magnitude and phase between the combustor and turbine exit and auto-spectra at the turbine exit and combustor are used to show the presence of indirect and direct combustion noise over the frequency range of 0 400 Hz. The procedure used evaluates the ratio of direct to indirect combustion noise. The procedure used also evaluates the post-combustion residence time in the combustor which is a factor in the formation of thermal NOx and soot in this region. These measurements are masked by the ambient core noise sound field in this frequency range which is observable since the transducers are situated within an acoustic wavelength of one another. An ambient core noise field model based on one and two dimensional spatial correlation functions is used to replicate the spatially correlated response of the pair of transducers. The spatial correlation function increases measured attenuation due to destructive interference and masks the true attenuation of the turbine.
The Effect of Spatial Interference Correlation and Jamming on Secrecy in Cellular Networks

KAUST Repository

Ali, Konpal S.

2017-06-02

Recent studies on secure wireless communication have shed light on a scenario where interference has a desirable impact on network performance. Particularly, assuming independent interference-power fluctuations at the eavesdropper and the receiver, opportunistic secure-information transfer can occur on the legitimate-link. However, interference is spatially correlated due to the common set of interfering sources, which may diminish the opportunistic-secure-spectrum-access (OSSA) probability. We study and quantify the effect of spatial interference correlation on OSSA in cellular-networks and investigate the potential of full-duplex jamming (FDJ) solutions. The results highlight the scenarios where FDJ improves OSSA performance.
The Effect of Spatial Interference Correlation and Jamming on Secrecy in Cellular Networks

KAUST Repository

Ali, Konpal S.; Elsawy, Hesham; Haenggi, Martin; Alouini, Mohamed-Slim

2017-01-01

Recent studies on secure wireless communication have shed light on a scenario where interference has a desirable impact on network performance. Particularly, assuming independent interference-power fluctuations at the eavesdropper and the receiver, opportunistic secure-information transfer can occur on the legitimate-link. However, interference is spatially correlated due to the common set of interfering sources, which may diminish the opportunistic-secure-spectrum-access (OSSA) probability. We study and quantify the effect of spatial interference correlation on OSSA in cellular-networks and investigate the potential of full-duplex jamming (FDJ) solutions. The results highlight the scenarios where FDJ improves OSSA performance.
Hierarchical multi-scale classification of nearshore aquatic habitats of the Great Lakes: Western Lake Erie

Science.gov (United States)

McKenna, J.E.; Castiglione, C.

2010-01-01

Classification is a valuable conservation tool for examining natural resource status and problems and is being developed for coastal aquatic habitats. We present an objective, multi-scale hydrospatial framework for nearshore areas of the Great Lakes. The hydrospatial framework consists of spatial units at eight hierarchical scales from the North American Continent to the individual 270-m spatial cell. Characterization of spatial units based on fish abundance and diversity provides a fish-guided classification of aquatic areas at each spatial scale and demonstrates how classifications may be generated from that framework. Those classification units then provide information about habitat, as well as biotic conditions, which can be compared, contrasted, and hierarchically related spatially. Examples within several representative coastal or open water zones of the Western Lake Erie pilot area highlight potential application of this classification system to management problems. This classification system can assist natural resource managers with planning and establishing priorities for aquatic habitat protection, developing rehabilitation strategies, or identifying special management actions.

An improved spatial contour tree constructed method

Science.gov (United States)

Zheng, Yi; Zhang, Ling; Guilbert, Eric; Long, Yi

2018-05-01

Contours are important data to delineate the landform on a map. A contour tree provides an object-oriented description of landforms and can be used to enrich the topological information. The traditional contour tree is used to store topological relationships between contours in a hierarchical structure and allows for the identification of eminences and depressions as sets of nested contours. This research proposes an improved contour tree so-called spatial contour tree that contains not only the topological but also the geometric information. It can be regarded as a terrain skeleton in 3-dimention, and it is established based on the spatial nodes of contours which have the latitude, longitude and elevation information. The spatial contour tree is built by connecting spatial nodes from low to high elevation for a positive landform, and from high to low elevation for a negative landform to form a hierarchical structure. The connection between two spatial nodes can provide the real distance and direction as a Euclidean vector in 3-dimention. In this paper, the construction method is tested in the experiment, and the results are discussed. The proposed hierarchical structure is in 3-demintion and can show the skeleton inside a terrain. The structure, where all nodes have geo-information, can be used to distinguish different landforms and applied for contour generalization with consideration of geographic characteristics.
Monte Carlo power iteration: Entropy and spatial correlations

International Nuclear Information System (INIS)

Nowak, Michel; Miao, Jilang; Dumonteil, Eric; Forget, Benoit; Onillon, Anthony; Smith, Kord S.; Zoia, Andrea

2016-01-01

Highlights: • We show that the entropy function might be misleading in criticality simulations. • We interpret the spatial fluctuations of the fission chains in terms of the key parameters of the simulated system. • We show that the behavior of the entropy function is related to the theory of neutron clustering. - Abstract: The behavior of Monte Carlo criticality simulations is often assessed by examining the convergence of the so-called entropy function. In this work, we shall show that the entropy function may lead to a misleading interpretation, and that potential issues occur when spatial correlations induced by fission events are important. We will support our analysis by examining the higher-order moments of the entropy function and the center of mass of the neutron population. Within the framework of a simplified model based on branching processes, we will relate the behavior of the spatial fluctuations of the fission chains to the key parameters of the simulated system, namely, the number of particles per generation, the reactor size and the migration area. Numerical simulations of a fuel rod and of a whole core suggest that the obtained results are quite general and hold true also for real-world applications.
Attention-spreading based on hierarchical spatial representations for connected objects.

Science.gov (United States)

Kasai, Tetsuko

2010-01-01

Attention selects objects or groups as the most fundamental unit, and this may be achieved through a process in which attention automatically spreads throughout their entire region. Previously, we found that a lateralized potential relative to an attended hemifield at occipito-temporal electrode sites reflects attention-spreading in response to connected bilateral stimuli [Kasai, T., & Kondo, M. Electrophysiological correlates of attention-spreading in visual grouping. NeuroReport, 18, 93-98, 2007]. The present study examined the nature of object representations by manipulating the extent of grouping through connectedness, while controlling the symmetrical structure of bilateral stimuli. The electrophysiological results of two experiments consistently indicated that attention was guided twice in association with perceptual grouping in the early phase (N1, 150-200 msec poststimulus) and with the unity of an object in the later phase (N2pc, 310/330-390 msec). This suggests that there are two processes in object-based spatial selection, and these are discussed with regard to their cognitive mechanisms and object representations.
Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data

DEFF Research Database (Denmark)

Lenzi, Amanda; Pinson, Pierre; Clemmensen, Line Katrine Harder

2017-01-01

average wind power generation, and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial correlation is captured by a latent Gaussian field. We explore how such models can be handled...... with stochastic partial differential approximations of Matérn Gaussian fields together with Integrated Nested Laplace Approximations. We demonstrate the proposed methods on wind farm data from Western Denmark, and compare the results to those obtained with standard geostatistical methods. The results show...
Tools for Multimode Quantum Information: Modulation, Detection, and Spatial Quantum Correlations

DEFF Research Database (Denmark)

Lassen, Mikael Østergaard; Delaubert, Vincent; Janousek, Jirí

2007-01-01

We present here all the tools required for continuous variable parallel quantum information protocols based on spatial multi-mode quantum correlations and entanglement. We describe techniques for encoding and detecting this quantum information with high efficiency in the individual modes. We use ...
Understanding structure of urban traffic network based on spatial-temporal correlation analysis

Science.gov (United States)

Yang, Yanfang; Jia, Limin; Qin, Yong; Han, Shixiu; Dong, Honghui

2017-08-01

Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.
Spatial Correlation Characterization of a Full Dimension Massive MIMO System

KAUST Repository

Nadeem, Qurrat-Ul-Ain

2017-02-07

Elevation beamforming and Full Dimension MIMO (FD-MIMO) are currently active areas of research and standardization in 3GPP LTE-Advanced. FD-MIMO utilizes an active antenna array system (AAS), that provides the ability of adaptive electronic beam control over the elevation dimension, resulting in a better system performance as compared to the conventional 2D MIMO systems. FD-MIMO is more advantageous when amalgamated with massive MIMO systems, in that it exploits the additional degrees of freedom offered by a large number of antennas in the elevation. To facilitate the evaluation of these systems, a large effort in 3D channel modeling is needed. This paper aims at providing a summary of the recent 3GPP activity around 3D channel modeling. The 3GPP proposed approach to model antenna radiation pattern is compared with the ITU approach. A closed-form expression is then worked out for the spatial correlation function (SCF) for channels constituted by individual antenna elements in the array by exploiting results on spherical harmonics and Legendre polynomials. The proposed expression can be used to obtain correlation coefficients for any arbitrary 3D propagation environment. Simulation results corroborate and study the derived spatial correlation expression. The results are directly applicable to the analysis of future 5G 3D massive MIMO systems.
Modelling the distribution of fish accounting for spatial correlation and overdispersion

DEFF Research Database (Denmark)

Lewy, Peter; Kristensen, Kasper

2009-01-01

correlation between observations. It is therefore possible to predict and interpolate unobserved densities at any location in the area. This is important for obtaining unbiased estimates of stock concentration and other measures depending on the distribution in the entire area. Results show that the spatial...
Effect of spatially correlated noise on stochastic synchronization in globally coupled FitzHugh-Nagumo neuron systems

Directory of Open Access Journals (Sweden)

Yange Shao

2014-01-01

Full Text Available The phenomenon of stochastic synchronization in globally coupled FitzHugh-Nagumo (FHN neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation (DMA and direct simulation (DS. Results from DMA are in good quantitative or qualitative agreement with those from DS for weak noise intensity and larger system size. Whether the consisting single FHN neuron is staying at the resting state, subthreshold oscillatory regime, or the spiking state, our investigation shows that the synchronization ratio of the globally coupled system becomes higher as the noise correlation coefficient increases, and thus we conclude that spatial correlation has an active effect on stochastic synchronization, and the neurons can achieve complete synchronization in the sense of statistics when the noise correlation coefficient tends to one. Our investigation also discloses that the noise spatial correlation plays the same beneficial role as the global coupling strength in enhancing stochastic synchronization in the ensemble. The result might be useful in understanding the information coding mechanism in neural systems.
Managing the spatial properties and photon correlations in squeezed non-classical twisted light

Science.gov (United States)

Zakharov, R. V.; Tikhonova, O. V.

2018-05-01

Spatial photon correlations and mode content of the squeezed vacuum light generated in a system of two separated nonlinear crystals is investigated. The contribution of both the polar and azimuthal modes with non-zero orbital angular momentum is analyzed. The control and engineering of the spatial properties and degree of entanglement of the non-classical squeezed light by changing the distance between crystals and pump parameters is demonstrated. Methods for amplification of certain spatial modes and managing the output mode content and intensity profile of quantum twisted light are suggested.
Hierarchical processing in the prefrontal cortex in a variety of cognitive domains

Directory of Open Access Journals (Sweden)

Hyeon-Ae eJeon

2014-11-01

Full Text Available This review scrutinizes several findings on human hierarchical processing within the prefrontal cortex (PFC in diverse cognitive domains. Converging evidence from previous studies has shown that the PFC, specifically Brodmann area (BA 44, may function as the essential region for hierarchical processing across the domains. In language fMRI studies, BA 44 was significantly activated for the hierarchical processing of center-embedded sentences and this pattern of activations was also observed in artificial grammar. The same pattern was observed in the visuo-spatial domain where BA44 was actively involved in the processing of hierarchy for the visual symbol. Musical syntax, which is the rule-based arrangement of musical sets, has also been construed as hierarchical processing as in the language domain such that the activation in BA44 was observed in a chord sequence paradigm. P600 ERP was also engendered during the processing of musical hierarchy. Along with a longstanding idea that a human’s number faculty is developed as a by-product of language faculty, BA44 was closely involved in hierarchical processing in mental arithmetic. This review extended its discussion of hierarchical processing to hierarchical behavior, that is, human action which has been referred to as being hierarchically composed. Several lesion and TMS studies supported the involvement of BA44 for hierarchical processing in the action domain. Lastly, the hierarchical organization of cognitive controls was discussed within the PFC, forming a cascade of top-down hierarchical processes operating along a posterior-to-anterior axis of the lateral PFC including BA44 within the network. It is proposed that PFC is actively involved in different forms of hierarchical processing and specifically BA44 may play an integral role in the process. Taking levels of proficiency and subcortical areas into consideration may provide further insight into the functional role of BA44 for hierarchical
Ergodic channel capacity of spatial correlated multiple-input multiple-output free space optical links using multipulse pulse-position modulation

Science.gov (United States)

Wang, Huiqin; Wang, Xue; Cao, Minghua

2017-02-01

The spatial correlation extensively exists in the multiple-input multiple-output (MIMO) free space optical (FSO) communication systems due to the channel fading and the antenna space limitation. Wilkinson's method was utilized to investigate the impact of spatial correlation on the MIMO FSO communication system employing multipulse pulse-position modulation. Simulation results show that the existence of spatial correlation reduces the ergodic channel capacity, and the reception diversity is more competent to resist this kind of performance degradation.
Spatial correlation of the ionsphere total electron content at the equatorial anomaly crest

International Nuclear Information System (INIS)

Huang, Y.

1984-01-01

The spatial correlation of the ionospheric total electron content (TEC) at the equatorial anomaly crest was studied by recording Faraday rotation angle of the ETS-II geostationary satellite at Lunping and Kaohsiung whose subionospheric points are located at 23.0 0 N, 121.0 0 N, and 20.9 0 N, 121.1 0 E, respectively, and are about 280 km apart. The results show that the spatial correlation of TEC at the equatorial crest region is smaller than that at other places. The day-to-day variabilities of TEC differences between two subionospheric points are quite large. The day-to-day variabilities of the fountain effect seem to play an important role
iHAT: interactive Hierarchical Aggregation Table for Genetic Association Data

Directory of Open Access Journals (Sweden)

Heinrich Julian

2012-05-01

Full Text Available Abstract In the search for single-nucleotide polymorphisms which influence the observable phenotype, genome wide association studies have become an important technique for the identification of associations between genotype and phenotype of a diverse set of sequence-based data. We present a methodology for the visual assessment of single-nucleotide polymorphisms using interactive hierarchical aggregation techniques combined with methods known from traditional sequence browsers and cluster heatmaps. Our tool, the interactive Hierarchical Aggregation Table (iHAT, facilitates the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Different color maps and aggregation strategies as well as filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT relies on the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. We demonstrate iHAT using artificial and real-world datasets for DNA and protein association studies as well as expression Quantitative Trait Locus data.
Generating Improved Experimental Designs with Spatially and Genetically Correlated Observations Using Mixed Models

Directory of Open Access Journals (Sweden)

Lazarus K. Mramba

2018-03-01

Full Text Available The aim of this study was to generate and evaluate the efficiency of improved field experiments while simultaneously accounting for spatial correlations and different levels of genetic relatedness using a mixed models framework for orthogonal and non-orthogonal designs. Optimality criteria and a search algorithm were implemented to generate randomized complete block (RCB, incomplete block (IB, augmented block (AB and unequally replicated (UR designs. Several conditions were evaluated including size of the experiment, levels of heritability, and optimality criteria. For RCB designs with half-sib or full-sib families, the optimization procedure yielded important improvements under the presence of mild to strong spatial correlation levels and relatively low heritability values. Also, for these designs, improvements in terms of overall design efficiency (ODE% reached values of up to 8.7%, but these gains varied depending on the evaluated conditions. In general, for all evaluated designs, higher ODE% values were achieved from genetically unrelated individuals compared to experiments with half-sib and full-sib families. As expected, accuracy of prediction of genetic values improved as levels of heritability and spatial correlations increased. This study has demonstrated that important improvements in design efficiency and prediction accuracies can be achieved by optimizing how the levels of a treatment are assigned to the experimental units.
Nearest neighbor imputation using spatial-temporal correlations in wireless sensor networks.

Science.gov (United States)

Li, YuanYuan; Parker, Lynne E

2014-01-01

Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications. There are many reasons for this phenomenon, such as unstable wireless communications, synchronization issues, and unreliable sensors. Unfortunately, missing data creates a number of problems for WSNs. First, since most sensor nodes in the network are battery-powered, it is too expensive to have the nodes retransmit missing data across the network. Data re-transmission may also cause time delays when detecting abnormal changes in an environment. Furthermore, localized reasoning techniques on sensor nodes (such as machine learning algorithms to classify states of the environment) are generally not robust enough to handle missing data. Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly improve the network's performance. However, our studies show that it is important to determine which nodes are spatially and temporally correlated with each other. Simple techniques based on Euclidean distance are not sufficient for complex environmental deployments. Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes. To improve the search time, we utilize a k d-tree data structure, which is a non-parametric, data-driven binary search tree. Instead of using traditional mean and variance of each dimension for k d-tree construction, and Euclidean distance for k d-tree search, we use weighted variances and weighted Euclidean distances based on measured percentages of missing data. We have evaluated this approach through experiments on sensor data from a volcano dataset collected by a network of Crossbow motes, as well as experiments using sensor data from a highway traffic monitoring application. Our experimental
Spatial- and Time-Correlated Detection of Fission Fragments

Directory of Open Access Journals (Sweden)

Platkevic M.

2012-02-01

Full Text Available With the goal to measure angular correlations of fission fragments in rare fission decay (e.g. ternary and quaternary fission, a multi-detector coincidence system based on two and up to four position sensitive pixel detectors Timepix has been built. In addition to the high granularity, wide dynamic range and per pixel signal threshold, these devices are equipped with per pixel energy and time sensitivity providing more information (position, energy, time, enhances particle-type identification and selectivity of event-by-event detection. Operation of the device with the integrated USB 2.0 based readout interface FITPix and the control and data acquisition software tool Pixelman enables online visualization and flexible/adjustable operation for a different type of experiments. Spatially correlated fission fragments can be thus registered in coincidence. Similarly triggered measurements are performed using an integrated spectrometric module with analogue signal chain electronics. The current status of development together with demonstration of the technique with a 252Cf source is presented.
Long-term memory of hierarchical relationships in free-living greylag geese

NARCIS (Netherlands)

Weiss, Brigitte M.; Scheiber, Isabella B. R.

Animals may memorise spatial and social information for many months and even years. Here, we investigated long-term memory of hierarchically ordered relationships, where the position of a reward depended on the relationship of a stimulus relative to other stimuli in the hierarchy. Seventeen greylag
Correlation of spatial climate/weather maps and the advantages of using the Mahalanobis metric in predictions

OpenAIRE

Stephenson, D. B.

2011-01-01

he skill in predicting spatially varying weather/climate maps depends on the definition of the measure of similarity between the maps. Under the justifiable approximation that the anomaly maps are distributed multinormally, it is shown analytically that the choice of weighting metric, used in defining the anomaly correlation between spatial maps, can change the resulting probability distribution of the correlation coefficient. The estimate of the numbers of degrees of freedom based on the var...
Anti-hierarchical evolution of the active galactic nucleus space density in a hierarchical universe

International Nuclear Information System (INIS)

Enoki, Motohiro; Ishiyama, Tomoaki; Kobayashi, Masakazu A. R.; Nagashima, Masahiro

2014-01-01

Recent observations show that the space density of luminous active galactic nuclei (AGNs) peaks at higher redshifts than that of faint AGNs. This downsizing trend in the AGN evolution seems to be contradictory to the hierarchical structure formation scenario. In this study, we present the AGN space density evolution predicted by a semi-analytic model of galaxy and AGN formation based on the hierarchical structure formation scenario. We demonstrate that our model can reproduce the downsizing trend of the AGN space density evolution. The reason for the downsizing trend in our model is a combination of the cold gas depletion as a consequence of star formation, the gas cooling suppression in massive halos, and the AGN lifetime scaling with the dynamical timescale. We assume that a major merger of galaxies causes a starburst, spheroid formation, and cold gas accretion onto a supermassive black hole (SMBH). We also assume that this cold gas accretion triggers AGN activity. Since the cold gas is mainly depleted by star formation and gas cooling is suppressed in massive dark halos, the amount of cold gas accreted onto SMBHs decreases with cosmic time. Moreover, AGN lifetime increases with cosmic time. Thus, at low redshifts, major mergers do not always lead to luminous AGNs. Because the luminosity of AGNs is correlated with the mass of accreted gas onto SMBHs, the space density of luminous AGNs decreases more quickly than that of faint AGNs. We conclude that the anti-hierarchical evolution of the AGN space density is not contradictory to the hierarchical structure formation scenario.

Anti-hierarchical evolution of the active galactic nucleus space density in a hierarchical universe

Energy Technology Data Exchange (ETDEWEB)

Enoki, Motohiro [Faculty of Business Administration, Tokyo Keizai University, Kokubunji, Tokyo 185-8502 (Japan); Ishiyama, Tomoaki [Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577 (Japan); Kobayashi, Masakazu A. R. [Research Center for Space and Cosmic Evolution, Ehime University, Matsuyama, Ehime 790-8577 (Japan); Nagashima, Masahiro, E-mail: enokimt@tku.ac.jp [Faculty of Education, Nagasaki University, Nagasaki, Nagasaki 852-8521 (Japan)

2014-10-10

Recent observations show that the space density of luminous active galactic nuclei (AGNs) peaks at higher redshifts than that of faint AGNs. This downsizing trend in the AGN evolution seems to be contradictory to the hierarchical structure formation scenario. In this study, we present the AGN space density evolution predicted by a semi-analytic model of galaxy and AGN formation based on the hierarchical structure formation scenario. We demonstrate that our model can reproduce the downsizing trend of the AGN space density evolution. The reason for the downsizing trend in our model is a combination of the cold gas depletion as a consequence of star formation, the gas cooling suppression in massive halos, and the AGN lifetime scaling with the dynamical timescale. We assume that a major merger of galaxies causes a starburst, spheroid formation, and cold gas accretion onto a supermassive black hole (SMBH). We also assume that this cold gas accretion triggers AGN activity. Since the cold gas is mainly depleted by star formation and gas cooling is suppressed in massive dark halos, the amount of cold gas accreted onto SMBHs decreases with cosmic time. Moreover, AGN lifetime increases with cosmic time. Thus, at low redshifts, major mergers do not always lead to luminous AGNs. Because the luminosity of AGNs is correlated with the mass of accreted gas onto SMBHs, the space density of luminous AGNs decreases more quickly than that of faint AGNs. We conclude that the anti-hierarchical evolution of the AGN space density is not contradictory to the hierarchical structure formation scenario.
3D spatially-adaptive canonical correlation analysis: Local and global methods.

Science.gov (United States)

Yang, Zhengshi; Zhuang, Xiaowei; Sreenivasan, Karthik; Mishra, Virendra; Curran, Tim; Byrd, Richard; Nandy, Rajesh; Cordes, Dietmar

2018-04-01

Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data. Copyright © 2017 Elsevier Inc. All rights reserved.
The Hierarchical Trend Model for property valuation and local price indices

NARCIS (Netherlands)

Francke, M.K.; Vos, G.A.

2002-01-01

This paper presents a hierarchical trend model (HTM) for selling prices of houses, addressing three main problems: the spatial and temporal dependence of selling prices and the dependency of price index changes on housing quality. In this model the general price trend, cluster-level price trends,
Hierarchical analysis of urban space

OpenAIRE

Kataeva, Y.

2014-01-01

Multi-level structure of urban space, multitude of subjects of its transformation, which follow asymmetric interests, multilevel system of institutions which regulate interaction in the "population business government -public organizations" system, determine the use of hierarchic approach to the analysis of urban space. The article observes theoretical justification of using this approach to study correlations and peculiarities of interaction in urban space as in an intricately organized syst...
Potts ferromagnet correlation length in hypercubic lattices: Renormalization - group approach

International Nuclear Information System (INIS)

Curado, E.M.F.; Hauser, P.R.

1984-01-01

Through a real space renormalization group approach, the q-state Potts ferromagnet correlation length on hierarchical lattices is calculated. These hierarchical lattices are build in order to simulate hypercubic lattices. The high-and-low temperature correlation length asymptotic behaviours tend (in the Ising case) to the Bravais lattice correlation length ones when the size of the hierarchical lattice cells tends to infinity. It is conjectured that the asymptotic behaviours several values of q and d (dimensionality) so obtained are correct. Numerical results are obtained for the full temperature range of the correlation length. (Author) [pt
Bayesian disease mapping: hierarchical modeling in spatial epidemiology

National Research Council Canada - National Science Library

Lawson, Andrew

2013-01-01

Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas...
The conversion of spatial planning in Denmark

DEFF Research Database (Denmark)

Galland, Daniel

2014-01-01

Danish spatial planning has been increasingly subjected to significant structural, functional and conceptual reorientations over the past two decades. The comprehensive hierarchical framework wherein planning policies and practices used to operate became considerably modified after the implementa......Danish spatial planning has been increasingly subjected to significant structural, functional and conceptual reorientations over the past two decades. The comprehensive hierarchical framework wherein planning policies and practices used to operate became considerably modified after...... the implementation of a local government reform back in 2007. The reform abolished the county level, which implied the re-scaling of planning policies, functions and responsibilities to municipal and national levels. This situation brought about radical shifts concerning the implementation of land-use tasks...
Hierarchical ordering with partial pairwise hierarchical relationships on the macaque brain data sets.

Directory of Open Access Journals (Sweden)

Woosang Lim

Full Text Available Hierarchical organizations of information processing in the brain networks have been known to exist and widely studied. To find proper hierarchical structures in the macaque brain, the traditional methods need the entire pairwise hierarchical relationships between cortical areas. In this paper, we present a new method that discovers hierarchical structures of macaque brain networks by using partial information of pairwise hierarchical relationships. Our method uses a graph-based manifold learning to exploit inherent relationship, and computes pseudo distances of hierarchical levels for every pair of cortical areas. Then, we compute hierarchy levels of all cortical areas by minimizing the sum of squared hierarchical distance errors with the hierarchical information of few cortical areas. We evaluate our method on the macaque brain data sets whose true hierarchical levels are known as the FV91 model. The experimental results show that hierarchy levels computed by our method are similar to the FV91 model, and its errors are much smaller than the errors of hierarchical clustering approaches.
Organization of excitable dynamics in hierarchical biological networks.

Directory of Open Access Journals (Sweden)

Mark Müller-Linow

Full Text Available This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.
Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets

KAUST Repository

Litvinenko, Alexander

2017-09-03

We use available measurements to estimate the unknown parameters (variance, smoothness parameter, and covariance length) of a covariance function by maximizing the joint Gaussian log-likelihood function. To overcome cubic complexity in the linear algebra, we approximate the discretized covariance function in the hierarchical (H-) matrix format. The H-matrix format has a log-linear computational cost and storage O(kn log n), where the rank k is a small integer and n is the number of locations. The H-matrix technique allows us to work with general covariance matrices in an efficient way, since H-matrices can approximate inhomogeneous covariance functions, with a fairly general mesh that is not necessarily axes-parallel, and neither the covariance matrix itself nor its inverse have to be sparse. We demonstrate our method with Monte Carlo simulations and an application to soil moisture data. The C, C++ codes and data are freely available.
Attention to Hierarchical Level Influences Attentional Selection of Spatial Scale

Science.gov (United States)

Flevaris, Anastasia V.; Bentin, Shlomo; Robertson, Lynn C.

2011-01-01

Ample evidence suggests that global perception may involve low spatial frequency (LSF) processing and that local perception may involve high spatial frequency (HSF) processing (Shulman, Sullivan, Gish, & Sakoda, 1986; Shulman & Wilson, 1987; Robertson, 1996). It is debated whether SF selection is a low-level mechanism associating global…
Spatial Decomposition of Translational Water–Water Correlation Entropy in Binding Pockets

Science.gov (United States)

2015-01-01

A number of computational tools available today compute the thermodynamic properties of water at surfaces and in binding pockets by using inhomogeneous solvation theory (IST) to analyze explicit-solvent simulations. Such methods enable qualitative spatial mappings of both energy and entropy around a solute of interest and can also be applied quantitatively. However, the entropy estimates of existing methods have, to date, been almost entirely limited to the first-order terms in the IST’s entropy expansion. These first-order terms account for localization and orientation of water molecules in the field of the solute but not for the modification of water–water correlations by the solute. Here, we present an extension of the Grid Inhomogeneous Solvation Theory (GIST) approach which accounts for water–water translational correlations. The method involves rewriting the two-point density of water in terms of a conditional density and utilizes the efficient nearest-neighbor entropy estimation approach. Spatial maps of this second order term, for water in and around the synthetic host cucurbit[7]uril and in the binding pocket of the enzyme Factor Xa, reveal mainly negative contributions, indicating solute-induced water–water correlations relative to bulk water; particularly strong signals are obtained for sites at the entrances of cavities or pockets. This second-order term thus enters with the same, negative, sign as the first order translational and orientational terms. Numerical and convergence properties of the methodology are examined. PMID:26636620
The Realized Hierarchical Archimedean Copula in Risk Modelling

Directory of Open Access Journals (Sweden)

Ostap Okhrin

2017-06-01

Full Text Available This paper introduces the concept of the realized hierarchical Archimedean copula (rHAC. The proposed approach inherits the ability of the copula to capture the dependencies among financial time series, and combines it with additional information contained in high-frequency data. The considered model does not suffer from the curse of dimensionality, and is able to accurately predict high-dimensional distributions. This flexibility is obtained by using a hierarchical structure in the copula. The time variability of the model is provided by daily forecasts of the realized correlation matrix, which is used to estimate the structure and the parameters of the rHAC. Extensive simulation studies show the validity of the estimator based on this realized correlation matrix, and its performance, in comparison to the benchmark models. The application of the estimator to one-day-ahead Value at Risk (VaR prediction using high-frequency data exhibits good forecasting properties for a multivariate portfolio.
Quantum metrology subject to spatially correlated Markovian noise: restoring the Heisenberg limit

International Nuclear Information System (INIS)

Jeske, Jan; Cole, Jared H; Huelga, Susana F

2014-01-01

Environmental noise can hinder the metrological capabilities of entangled states. While the use of entanglement allows for Heisenberg-limited resolution, the largest permitted by quantum mechanics, deviations from strictly unitary dynamics quickly restore the standard scaling dictated by the central limit theorem. Product and maximally entangled states become asymptotically equivalent when the noisy evolution is both local and strictly Markovian. However, temporal correlations in the noise have been shown to lift this equivalence while fully (spatially) correlated noise allows for the identification of decoherence-free subspaces. Here we analyze precision limits in the presence of noise with finite correlation length and show that there exist robust entangled state preparations which display persistent Heisenberg scaling despite the environmental decoherence, even for small correlation length. Our results emphasize the relevance of noise correlations in the study of quantum advantage and could be relevant beyond metrological applications. (paper)
Considering built environment and spatial correlation in modeling pedestrian injury severity.

Science.gov (United States)

Prato, Carlo G; Kaplan, Sigal; Patrier, Alexandre; Rasmussen, Thomas K

2018-01-02

This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes. Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user. This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash. This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.
The change in spatial distribution of upper trapezius muscle activity is correlated to contraction duration.

Science.gov (United States)

Farina, Dario; Leclerc, Frédéric; Arendt-Nielsen, Lars; Buttelli, Olivier; Madeleine, Pascal

2008-02-01

The aim of the study was to confirm the hypothesis that the longer a contraction is sustained, the larger are the changes in the spatial distribution of muscle activity. For this purpose, surface electromyographic (EMG) signals were recorded with a 13 x 5 grid of electrodes from the upper trapezius muscle of 11 healthy male subjects during static contractions with shoulders 90 degrees abducted until endurance. The entropy (degree of uniformity) and center of gravity of the EMG root mean square map were computed to assess spatial inhomogeneity in muscle activation and changes over time in EMG amplitude spatial distribution. At the endurance time, entropy decreased (mean+/-SD, percent change 2.0+/-1.6%; Pgrid) root mean square was positively correlated with the shift in the center of gravity (R(2)=0.51, P<0.05). Moreover, the shift in the center of gravity was negatively correlated to both initial and final (at the endurance) entropy (R(2)=0.54 and R(2)=0.56, respectively; P<0.01 in both cases), indicating that subjects with less uniform root mean square maps had larger shift of the center of gravity over time. The spatial changes in root mean square EMG were likely due to spatially-dependent changes in motor unit activation during the sustained contraction. It was concluded that the changes in spatial muscle activity distribution play a role in the ability to maintain a static contraction.
Exploiting large-scale correlations to detect continuous gravitational waves.

Science.gov (United States)

Pletsch, Holger J; Allen, Bruce

2009-10-30

Fully coherent searches (over realistic ranges of parameter space and year-long observation times) for unknown sources of continuous gravitational waves are computationally prohibitive. Less expensive hierarchical searches divide the data into shorter segments which are analyzed coherently, then detection statistics from different segments are combined incoherently. The novel method presented here solves the long-standing problem of how best to do the incoherent combination. The optimal solution exploits large-scale parameter-space correlations in the coherent detection statistic. Application to simulated data shows dramatic sensitivity improvements compared with previously available (ad hoc) methods, increasing the spatial volume probed by more than 2 orders of magnitude at lower computational cost.
Sparsey^TM: Spatiotemporal Event Recognition via Deep Hierarchical Sparse Distributed Codes

Directory of Open Access Journals (Sweden)

Gerard J Rinkus

2014-12-01

Full Text Available The visual cortex’s hierarchical, multi-level organization is captured in many biologically inspired computational vision models, the general idea being that progressively larger scale (spatially/temporally and more complex visual features are represented in progressively higher areas. However, most earlier models use localist representations (codes in each representational field (which we equate with the cortical macrocolumn, mac, at each level. In localism, each represented feature/concept/event (hereinafter item is coded by a single unit. The model we describe, Sparsey, is hierarchical as well but crucially, it uses sparse distributed coding (SDC in every mac in all levels. In SDC, each represented item is coded by a small subset of the mac’s units. The SDCs of different items can overlap and the size of overlap between items can be used to represent their similarity. The difference between localism and SDC is crucial because SDC allows the two essential operations of associative memory, storing a new item and retrieving the best-matching stored item, to be done in fixed time for the life of the model. Since the model’s core algorithm, which does both storage and retrieval (inference, makes a single pass over all macs on each time step, the overall model’s storage/retrieval operation is also fixed-time, a criterion we consider essential for scalability to the huge (Big Data problems. A 2010 paper described a non-hierarchical version of this model in the context of purely spatial pattern processing. Here, we elaborate a fully hierarchical model (arbitrary numbers of levels and macs per level, describing novel model principles like progressive critical periods, dynamic modulation of principal cells’ activation functions based on a mac-level familiarity measure, representation of multiple simultaneously active hypotheses, a novel method of time warp invariant recognition, and we report results showing learning/recognition of
Characterizing short-range vs. long-range spatial correlations in dislocation distributions

International Nuclear Information System (INIS)

Chevy, Juliette; Fressengeas, Claude; Lebyodkin, Mikhail; Taupin, Vincent; Bastie, Pierre; Duval, Paul

2010-01-01

Hard X-ray diffraction experiments have provided evidence of a strongly heterogeneous distribution of dislocation densities along the axis of cylindrical ice single crystals oriented for basal slip in torsion creep. The dislocation arrangements showed a complex scale-invariant character, which was analyzed by means of statistical and multifractal techniques. A trend to decreasing autocorrelation of the dislocation distribution was observed as deformation proceeds. At low strain levels, long-range spatial correlations control the distribution, but short-range correlations in relation with cross-slip progressively prevail when strain increases. This trend was reproduced by a model based on field dislocation dynamics, a theory accounting for both long-range elastic interactions and short-range interactions through transport of dislocation densities.
Characterizing short-range vs. long-range spatial correlations in dislocation distributions

Energy Technology Data Exchange (ETDEWEB)

Chevy, Juliette, E-mail: juliette.chevy@gmail.com [Laboratoire de Glaciologie et Geophysique de l' Environnement-CNRS, 54 rue Moliere, 38402 St. Martin d' Heres (France)] [Laboratoire Science et Ingenierie des Materiaux et Procedes, Grenoble INP-CNRS-UJF, BP 75, 38402 St. Martin d' Heres Cedex (France); Fressengeas, Claude; Lebyodkin, Mikhail; Taupin, Vincent [Laboratoire de Physique et Mecanique des Materiaux, Universite Paul Verlaine-Metz/CNRS, Ile du Saulcy, 57045 Metz Cedex (France); Bastie, Pierre [Laboratoire de Spectrometrie Physique, BP 87, 38402 St. Martin d' Heres Cedex (France)] [Institut Laue Langevin, BP 156, 38042 Grenoble Cedex 9 (France); Duval, Paul [Laboratoire de Glaciologie et Geophysique de l' Environnement-CNRS, 54 rue Moliere, 38402 St. Martin d' Heres (France)

2010-03-15

Hard X-ray diffraction experiments have provided evidence of a strongly heterogeneous distribution of dislocation densities along the axis of cylindrical ice single crystals oriented for basal slip in torsion creep. The dislocation arrangements showed a complex scale-invariant character, which was analyzed by means of statistical and multifractal techniques. A trend to decreasing autocorrelation of the dislocation distribution was observed as deformation proceeds. At low strain levels, long-range spatial correlations control the distribution, but short-range correlations in relation with cross-slip progressively prevail when strain increases. This trend was reproduced by a model based on field dislocation dynamics, a theory accounting for both long-range elastic interactions and short-range interactions through transport of dislocation densities.

Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

Science.gov (United States)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.
Spatial occupancy models for large data sets

Science.gov (United States)

Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.

2013-01-01

Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.
A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

Directory of Open Access Journals (Sweden)

Shaoning Li

2017-01-01

Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.
The Effect of Velocity Correlation on the Spatial Evolution of Breakthrough Curves in Heterogeneous Media

Science.gov (United States)

Massoudieh, A.; Dentz, M.; Le Borgne, T.

2017-12-01

In heterogeneous media, the velocity distribution and the spatial correlation structure of velocity for solute particles determine the breakthrough curves and how they evolve as one moves away from the solute source. The ability to predict such evolution can help relating the spatio-statistical hydraulic properties of the media to the transport behavior and travel time distributions. While commonly used non-local transport models such as anomalous dispersion and classical continuous time random walk (CTRW) can reproduce breakthrough curve successfully by adjusting the model parameter values, they lack the ability to relate model parameters to the spatio-statistical properties of the media. This in turns limits the transferability of these models. In the research to be presented, we express concentration or flux of solutes as a distribution over their velocity. We then derive an integrodifferential equation that governs the evolution of the particle distribution over velocity at given times and locations for a particle ensemble, based on a presumed velocity correlation structure and an ergodic cross-sectional velocity distribution. This way, the spatial evolution of breakthrough curves away from the source is predicted based on cross-sectional velocity distribution and the connectivity, which is expressed by the velocity transition probability density. The transition probability is specified via a copula function that can help construct a joint distribution with a given correlation and given marginal velocities. Using this approach, we analyze the breakthrough curves depending on the velocity distribution and correlation properties. The model shows how the solute transport behavior evolves from ballistic transport at small spatial scales to Fickian dispersion at large length scales relative to the velocity correlation length.
Investigation of spatial correlation in MR images of human cerebral white matter using geostatistical methods

International Nuclear Information System (INIS)

Keil, Fabian

2014-01-01

Investigating the structure of human cerebral white matter is gaining interest in the neurological as well as in the neuroscientific community. It has been demonstrated in many studies that white matter is a very dynamic structure, rather than a static construct which does not change for a lifetime. That means, structural changes within white matter can be observed even on short timescales, e.g. in the course of normal ageing, neurodegenerative diseases or even during learning processes. To investigate these changes, one method of choice is the texture analysis of images obtained from white matter. In this regard, MRI plays a distinguished role as it provides a completely non-invasive way of acquiring in vivo images of human white matter. This thesis adapted a statistical texture analysis method, known as variography, to quantify the spatial correlation of human cerebral white matter based on MR images. This method, originally introduced in geoscience, relies on the idea of spatial correlation in geological phenomena: in naturally grown structures near things are correlated stronger to each other than distant things. This work reveals that the geological principle of spatial correlation can be applied to MR images of human cerebral white matter and proves that variography is an adequate method to quantify alterations therein. Since the process of MRI data acquisition is completely different to the measuring process used to quantify geological phenomena, the variographic analysis had to be adapted carefully to MR methods in order to provide a correctly working methodology. Therefore, theoretical considerations were evaluated with numerical samples in a first, and validated with real measurements in a second step. It was shown that MR variography facilitates to reduce the information stored in the texture of a white matter image to a few highly significant parameters, thereby quantifying heterogeneity and spatial correlation distance with an accuracy better than 5
Frequency and spatial correlation functions in a fading communication channel through the ionosphere

International Nuclear Information System (INIS)

Liu, C.H.; Yeh, K.C.

1975-01-01

Equations for the two-frequency two-position mutual coherence functions are derived under the usual parabolic and Markov approximations. These equations are then solved numerically. It is shown that the mutual coherence functions occur naturally in the study of pulse distortion through a random communication channel and in the investigation of signal correlations. Contour plots of correlation functions show the possibility of having equal values at two frequency separations for a given spatial separation. This behavior is explainable in terms of overlapping Fresnel zones
Spatial Random Effects Survival Models to Assess Geographical Inequalities in Dengue Fever Using Bayesian Approach: a Case Study

Science.gov (United States)

Astuti Thamrin, Sri; Taufik, Irfan

2018-03-01

Dengue haemorrhagic fever (DHF) is an infectious disease caused by dengue virus. The increasing number of people with DHF disease correlates with the neighbourhood, for example sub-districts, and the characteristics of the sub-districts are formed from individuals who are domiciled in the sub-districts. Data containing individuals and sub-districts is a hierarchical data structure, called multilevel analysis. Frequently encountered response variable of the data is the time until an event occurs. Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in DHF survival. Using a case study approach, we report on the implications of using multilevel with spatial survival models to study geographical inequalities in all cause survival.
An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

Science.gov (United States)

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.
IMPROVING NEAREST NEIGHBOUR SEARCH IN 3D SPATIAL ACCESS METHOD

Directory of Open Access Journals (Sweden)

A. Suhaibaha

2016-10-01

Full Text Available Nearest Neighbour (NN is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN search. The tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized.
Langevin Dynamics with Spatial Correlations as a Model for Electron-Phonon Coupling

Science.gov (United States)

Tamm, A.; Caro, M.; Caro, A.; Samolyuk, G.; Klintenberg, M.; Correa, A. A.

2018-05-01

Stochastic Langevin dynamics has been traditionally used as a tool to describe nonequilibrium processes. When utilized in systems with collective modes, traditional Langevin dynamics relaxes all modes indiscriminately, regardless of their wavelength. We propose a generalization of Langevin dynamics that can capture a differential coupling between collective modes and the bath, by introducing spatial correlations in the random forces. This allows modeling the electronic subsystem in a metal as a generalized Langevin bath endowed with a concept of locality, greatly improving the capabilities of the two-temperature model. The specific form proposed here for the spatial correlations produces a physical wave-vector and polarization dependency of the relaxation produced by the electron-phonon coupling in a solid. We show that the resulting model can be used for describing the path to equilibration of ions and electrons and also as a thermostat to sample the equilibrium canonical ensemble. By extension, the family of models presented here can be applied in general to any dense system, solids, alloys, and dense plasmas. As an example, we apply the model to study the nonequilibrium dynamics of an electron-ion two-temperature Ni crystal.
LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

Science.gov (United States)

Chad Babcock; Andrew O. Finley; John B. Bradford; Randy Kolka; Richard Birdsey; Michael G. Ryan

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both...
Spatial and statistical methods for correlating the interaction between groundwater contamination and tap water exposure in karst regions

Science.gov (United States)

Padilla, I. Y.; Rivera, V. L.; Macchiavelli, R. E.; Torres Torres, N. I.

2016-12-01

Groundwater systems in karst regions are highly vulnerable to contamination and have an enormous capacity to store and rapidly convey pollutants to potential exposure zones over long periods of time. Contaminants in karst aquifers used for drinking water purposes can, therefore, enter distributions lines and the tap water point of use. This study applies spatial and statistical analytical methods to assess potential correlations between contaminants in a karst groundwater system in northern Puerto Rico and exposure in the tap water. It focuses on chlorinated volatile organic compounds (CVOC) and phthalates because of their ubiquitous presence in the environment and the potential public health impacts. The work integrates historical data collected from regulatory agencies and current field measurements involving groundwater and tap water sampling and analysis. Contaminant distributions and cluster analysis is performed with Geographic Information System technology. Correlations between detection frequencies and contaminants concentration in source groundwater and tap water point of use are assessed using Pearson's Chi Square and T-Test analysis. Although results indicate that correlations are contaminant-specific, detection frequencies are generally higher for total CVOC in groundwater than tap water samples, but greater for phthalates in tap water than groundwater samples. Spatial analysis shows widespread distribution of CVOC and phthalates in both groundwater and tap water, suggesting that contamination comes from multiple sources. Spatial correlation analysis indicates that association between tap water and groundwater contamination depends on the source and type of contaminants, spatial location, and time. Full description of the correlations may, however, need to take into consideration variable anthropogenic interventions.
Flow distributions and spatial correlations in human brain capillary networks

Science.gov (United States)

Lorthois, Sylvie; Peyrounette, Myriam; Larue, Anne; Le Borgne, Tanguy

2015-11-01

The vascular system of the human brain cortex is composed of a space filling mesh-like capillary network connected upstream and downstream to branched quasi-fractal arterioles and venules. The distribution of blood flow rates in these networks may affect the efficiency of oxygen transfer processes. Here, we investigate the distribution and correlation properties of blood flow velocities from numerical simulations in large 3D human intra-cortical vascular network (10000 segments) obtained from an anatomical database. In each segment, flow is solved from a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation to deduce blood pressure, blood flow and red blood cell volume fraction distributions throughout the network. The network structural complexity is found to impart broad and spatially correlated Lagrangian velocity distributions, leading to power law transit time distributions. The origins of this behavior (existence of velocity correlations in capillary networks, influence of the coupling with the feeding arterioles and draining veins, topological disorder, complex blood rheology) are studied by comparison with results obtained in various model capillary networks of controlled disorder. ERC BrainMicroFlow GA615102, ERC ReactiveFronts GA648377.
Investigation of spatial correlation in MR images of human cerebral white matter using geostatistical methods

Energy Technology Data Exchange (ETDEWEB)

Keil, Fabian

2014-03-20

Investigating the structure of human cerebral white matter is gaining interest in the neurological as well as in the neuroscientific community. It has been demonstrated in many studies that white matter is a very dynamic structure, rather than a static construct which does not change for a lifetime. That means, structural changes within white matter can be observed even on short timescales, e.g. in the course of normal ageing, neurodegenerative diseases or even during learning processes. To investigate these changes, one method of choice is the texture analysis of images obtained from white matter. In this regard, MRI plays a distinguished role as it provides a completely non-invasive way of acquiring in vivo images of human white matter. This thesis adapted a statistical texture analysis method, known as variography, to quantify the spatial correlation of human cerebral white matter based on MR images. This method, originally introduced in geoscience, relies on the idea of spatial correlation in geological phenomena: in naturally grown structures near things are correlated stronger to each other than distant things. This work reveals that the geological principle of spatial correlation can be applied to MR images of human cerebral white matter and proves that variography is an adequate method to quantify alterations therein. Since the process of MRI data acquisition is completely different to the measuring process used to quantify geological phenomena, the variographic analysis had to be adapted carefully to MR methods in order to provide a correctly working methodology. Therefore, theoretical considerations were evaluated with numerical samples in a first, and validated with real measurements in a second step. It was shown that MR variography facilitates to reduce the information stored in the texture of a white matter image to a few highly significant parameters, thereby quantifying heterogeneity and spatial correlation distance with an accuracy better than 5
Changes of hierarchical network in local and world stock market

Science.gov (United States)

Patwary, Enayet Ullah; Lee, Jong Youl; Nobi, Ashadun; Kim, Doo Hwan; Lee, Jae Woo

2017-10-01

We consider the cross-correlation coefficients of the daily returns in the local and global stock markets. We generate the minimal spanning tree (MST) using the correlation matrix. We observe that the MSTs change their structure from chain-like networks to star-like networks during periods of market uncertainty. We quantify the measure of the hierarchical network utilizing the value of the hierarchy measured by the hierarchical path. The hierarchy and betweenness centrality characterize the state of the market regarding the impact of crises. During crises, the non-financial company is established as the central node of the MST. However, before the crisis and during stable periods, the financial company is occupying the central node of the MST in the Korean and the U.S. stock markets. The changes in the network structure and the central node are good indicators of an upcoming crisis.
A new maximum likelihood blood velocity estimator incorporating spatial and temporal correlation

DEFF Research Database (Denmark)

Schlaikjer, Malene; Jensen, Jørgen Arendt

2001-01-01

and space. This paper presents a new estimator (STC-MLE), which incorporates the correlation property. It is an expansion of the maximum likelihood estimator (MLE) developed by Ferrara et al. With the MLE a cross-correlation analysis between consecutive RF-lines on complex form is carried out for a range...... of possible velocities. In the new estimator an additional similarity investigation for each evaluated velocity and the available velocity estimates in a temporal (between frames) and spatial (within frames) neighborhood is performed. An a priori probability density term in the distribution...... of the observations gives a probability measure of the correlation between the velocities. Both the MLE and the STC-MLE have been evaluated on simulated and in-vivo RF-data obtained from the carotid artery. Using the MLE 4.1% of the estimates deviate significantly from the true velocities, when the performance...
Impact of Non-Idealities System on Spatial Correlation in a Multi-Probe Based MIMO OTA Setup

DEFF Research Database (Denmark)

Fan, Wei; Nielsen, Jesper Ødum; Carreño, Xavier

2013-01-01

MIMO OTA testing methodologies are being intensively investigated by CTIA and 3GPP, where a multi-probe anechoic chamber based solution is an important candidate for future standardized testing. In this paper, the probes located on an OTA ring are used to reproduce the channel spatial information....... This paper investigates the extent to which we can emulate the channel spatial characteristics inside the test zone where the device under test is located. The focus is on performance deterioration introduced by system non-idealities on spatial correlation emulation in practical MIMO OTA test systems....
Impact of spatially correlated pore-scale heterogeneity on drying porous media

Science.gov (United States)

Borgman, Oshri; Fantinel, Paolo; Lühder, Wieland; Goehring, Lucas; Holtzman, Ran

2017-07-01

We study the effect of spatially-correlated heterogeneity on isothermal drying of porous media. We combine a minimal pore-scale model with microfluidic experiments with the same pore geometry. Our simulated drying behavior compares favorably with experiments, considering the large sensitivity of the emergent behavior to the uncertainty associated with even small manufacturing errors. We show that increasing the correlation length in particle sizes promotes preferential drying of clusters of large pores, prolonging liquid connectivity and surface wetness and thus higher drying rates for longer periods. Our findings improve our quantitative understanding of how pore-scale heterogeneity impacts drying, which plays a role in a wide range of processes ranging from fuel cells to curing of paints and cements to global budgets of energy, water and solutes in soils.
Spatial correlation characterization of a uniform circular array in 3D MIMO systems

KAUST Repository

Nadeem, Qurrat-Ul-Ain; Kammoun, Abla; Debbah, Merouane; Alouini, Mohamed-Slim

2016-01-01

In this paper, we consider a uniform circular array (UCA) of directional antennas at the base station (BS) and the mobile station (MS) and derive an exact closed-form expression for the spatial correlation present in the 3D multiple-input multiple-output (MIMO) channel constituted by these arrays. The underlying method leverages the mathematical convenience of the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre and associated Legendre polynomials. In contrast to the existing results, this generalized closed-form expression is independent of the form of the underlying angular distributions and antenna patterns. Moreover, the incorporation of the elevation dimension into the antenna pattern and channel model renders the proposed expression extremely useful for the performance evaluation of 3D MIMO systems in the future. Verification is achieved with the help of simulation results, which highlight the dependence of the spatial correlation on channel and array parameters. An interesting interplay between the mean angle of departure (AoD), angular spread and the positioning of antennas in the array is demonstrated. © 2016 IEEE.
Spatial correlation characterization of a uniform circular array in 3D MIMO systems

KAUST Repository

Nadeem, Qurrat-Ul-Ain

2016-08-11

In this paper, we consider a uniform circular array (UCA) of directional antennas at the base station (BS) and the mobile station (MS) and derive an exact closed-form expression for the spatial correlation present in the 3D multiple-input multiple-output (MIMO) channel constituted by these arrays. The underlying method leverages the mathematical convenience of the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre and associated Legendre polynomials. In contrast to the existing results, this generalized closed-form expression is independent of the form of the underlying angular distributions and antenna patterns. Moreover, the incorporation of the elevation dimension into the antenna pattern and channel model renders the proposed expression extremely useful for the performance evaluation of 3D MIMO systems in the future. Verification is achieved with the help of simulation results, which highlight the dependence of the spatial correlation on channel and array parameters. An interesting interplay between the mean angle of departure (AoD), angular spread and the positioning of antennas in the array is demonstrated. © 2016 IEEE.

Spatial Vertical Directionality and Correlation of Low-Frequency Ambient Noise in Deep Ocean Direct-Arrival Zones

Directory of Open Access Journals (Sweden)

Qiulong Yang

2018-01-01

Full Text Available Wind-driven and distant shipping noise sources contribute to the total noise field in the deep ocean direct-arrival zones. Wind-driven and distant shipping noise sources may significantly and simultaneously affect the spatial characteristics of the total noise field to some extent. In this work, a ray approach and parabolic equation solution method were jointly utilized to model the low-frequency ambient noise field in a range-dependent deep ocean environment by considering their calculation accuracy and efficiency in near-field wind-driven and far-field distant shipping noise fields. The reanalysis databases of National Center of Environment Prediction (NCEP and Volunteer Observation System (VOS were used to model the ambient noise source intensity and distribution. Spatial vertical directionality and correlation were analyzed in three scenarios that correspond to three wind speed conditions. The noise field was dominated by distant shipping noise sources when the wind speed was less than 3 m/s, and then the spatial vertical directionality and vertical correlation of the total noise field were nearly consistent with those of distant shipping noise field. The total noise field was completely dominated by near field wind generated noise sources when the wind speed was greater than 12 m/s at 150 Hz, and then the spatial vertical correlation coefficient and directionality pattern of the total noise field was approximately consistent with that of the wind-driven noise field. The spatial characteristics of the total noise field for wind speeds between 3 m/s and 12 m/s were the weighted results of wind-driven and distant shipping noise fields. Furthermore, the spatial characteristics of low-frequency ambient noise field were compared with the classical Cron/Sherman deep water noise field coherence function. Simulation results with the described modeling method showed good agreement with the experimental measurement results based on the vertical line
Spatial Vertical Directionality and Correlation of Low-Frequency Ambient Noise in Deep Ocean Direct-Arrival Zones

Science.gov (United States)

Yang, Qiulong; Yang, Kunde; Cao, Ran; Duan, Shunli

2018-01-01

Wind-driven and distant shipping noise sources contribute to the total noise field in the deep ocean direct-arrival zones. Wind-driven and distant shipping noise sources may significantly and simultaneously affect the spatial characteristics of the total noise field to some extent. In this work, a ray approach and parabolic equation solution method were jointly utilized to model the low-frequency ambient noise field in a range-dependent deep ocean environment by considering their calculation accuracy and efficiency in near-field wind-driven and far-field distant shipping noise fields. The reanalysis databases of National Center of Environment Prediction (NCEP) and Volunteer Observation System (VOS) were used to model the ambient noise source intensity and distribution. Spatial vertical directionality and correlation were analyzed in three scenarios that correspond to three wind speed conditions. The noise field was dominated by distant shipping noise sources when the wind speed was less than 3 m/s, and then the spatial vertical directionality and vertical correlation of the total noise field were nearly consistent with those of distant shipping noise field. The total noise field was completely dominated by near field wind generated noise sources when the wind speed was greater than 12 m/s at 150 Hz, and then the spatial vertical correlation coefficient and directionality pattern of the total noise field was approximately consistent with that of the wind-driven noise field. The spatial characteristics of the total noise field for wind speeds between 3 m/s and 12 m/s were the weighted results of wind-driven and distant shipping noise fields. Furthermore, the spatial characteristics of low-frequency ambient noise field were compared with the classical Cron/Sherman deep water noise field coherence function. Simulation results with the described modeling method showed good agreement with the experimental measurement results based on the vertical line array deployed near
Catalytic Fast Pyrolysis of Kraft Lignin over Hierarchical HZSM-5 and Hβ Zeolites

Directory of Open Access Journals (Sweden)

Yadong Bi

2018-02-01

Full Text Available The hierarchical HZSM-5 and Hβ zeolites were prepared by alkaline post-treatment methods adopting Na2CO3, TMAOH/NaOH mixture, and NaOH as desilication sources, respectively. More mesopores are produced over two kinds of zeolites, while the micropores portion is well preserved. The mesopores formed in hierarchical Hβ zeolites were directly related to the basicity of the alkaline solution, indicating that Hβ zeolite is more sensitive to the alkaline post-treatment. The hierarchical HZSM-5 and Hβ zeolites are more active than the parent one for catalytic fast pyrolysis (CFP of Kraft lignin. Hierarchical zeolites retained the function of acid catalysis, while additionally creating larger mesopores to ensure the entry of bulkier reactant molecules. The increase of the condensable volatiles yield can be attributed to the improvement of the mass transfer performance, which correlates well with the change of mesoporous surface area. In particular, the condensable volatiles yield for the optimized hierarchical Hβ reached approximately two times that of the parent Hβ zeolites. In contrast to the parent HZSM-5, the optimized hierarchical HZSM-5 zeolite significantly reduced the selectivity of oxygenates from 27.2% to 3.3%.
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Directory of Open Access Journals (Sweden)

Joel Saltz

2018-04-01

Full Text Available Summary: Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. : Tumor-infiltrating lymphocytes (TILs were identified from standard pathology cancer images by a deep-learning-derived “computational stain” developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles. Keywords: digital pathology, immuno-oncology, machine learning, lymphocytes, tumor microenvironment, deep learning, tumor-infiltrating lymphocytes, artificial intelligence, bioinformatics, computer vision
Designs of Optoelectronic Trinary Signed-Digit Multiplication by use of Joint Spatial Encodings and Optical Correlation

Science.gov (United States)

Cherri, Abdallah K.

1999-02-01

Trinary signed-digit (TSD) symbolic-substitution-based (SS-based) optical adders, which were recently proposed, are used as the basic modules for designing highly parallel optical multiplications by use of cascaded optical correlators. The proposed multiplications perform carry-free generation of the multiplication partial products of two words in constant time. Also, three different multiplication designs are presented, and new joint spatial encodings for the TSD numbers are introduced. The proposed joint spatial encodings allow one to reduce the SS computation rules involved in optical multiplication. In addition, the proposed joint spatial encodings increase the space bandwidth product of the spatial light modulators of the optical system. This increase is achieved by reduction of the numbers of pixels in the joint spatial encodings for the input TSD operands as well as reduction of the number of pixels used in the proposed matched spatial filters for the optical multipliers.
Hierarchical Network Design

DEFF Research Database (Denmark)

Thomadsen, Tommy

2005-01-01

Communication networks are immensely important today, since both companies and individuals use numerous services that rely on them. This thesis considers the design of hierarchical (communication) networks. Hierarchical networks consist of layers of networks and are well-suited for coping...... with changing and increasing demands. Two-layer networks consist of one backbone network, which interconnects cluster networks. The clusters consist of nodes and links, which connect the nodes. One node in each cluster is a hub node, and the backbone interconnects the hub nodes of each cluster and thus...... the clusters. The design of hierarchical networks involves clustering of nodes, hub selection, and network design, i.e. selection of links and routing of ows. Hierarchical networks have been in use for decades, but integrated design of these networks has only been considered for very special types of networks...
Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

Science.gov (United States)

Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

2016-08-18

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.
Spatial dependence and correlation of rainfall in the Danube catchment and its role in flood risk assessment.

Science.gov (United States)

Martina, M. L. V.; Vitolo, R.; Todini, E.; Stephenson, D. B.; Cook, I. M.

2009-04-01

The possibility that multiple catastrophic events occur within a given timespan and affect the same portfolio of insured properties may induce enhanced risk. For this reason, in the insurance industry it is of interest to characterise not only the point probability of catastrophic events, but also their spatial structure. As far as floods are concerned it is important to determine the probability of having multiple simultaneous events in different parts of the same basin: in this case, indeed, the loss in a portfolio can be significantly different. Understanding the spatial structure of the precipitation field is a necessary step for the proper modelling of the spatial dependence and correlation of river discharge. Several stochastic models are available in the scientific literature for the multi-site generation of precipitation. Although most models achieve good performance in modelling mean values, temporal variability and inter-site dependence of extremes are still delicate issues. In this work we aim at identifying the main spatial characteristics of the precipitation structure and then at analysing them in a real case. We consider data from a large network of raingauges in the Danube catchment. This catchment is a good example of a large-scale catchment where the spatial correlation of flood events can radically change the effect in term of flood damage.
Spatially resolved D-T(2) correlation NMR of porous media.

Science.gov (United States)

Zhang, Yan; Blümich, Bernhard

2014-05-01

Within the past decade, 2D Laplace nuclear magnetic resonance (NMR) has been developed to analyze pore geometry and diffusion of fluids in porous media on the micrometer scale. Many objects like rocks and concrete are heterogeneous on the macroscopic scale, and an integral analysis of microscopic properties provides volume-averaged information. Magnetic resonance imaging (MRI) resolves this spatial average on the contrast scale set by the particular MRI technique. Desirable contrast parameters for studies of fluid transport in porous media derive from the pore-size distribution and the pore connectivity. These microscopic parameters are accessed by 1D and 2D Laplace NMR techniques. It is therefore desirable to combine MRI and 2D Laplace NMR to image functional information on fluid transport in porous media. Because 2D Laplace resolved MRI demands excessive measuring time, this study investigates the possibility to restrict the 2D Laplace analysis to the sum signals from low-resolution pixels, which correspond to pixels of similar amplitude in high-resolution images. In this exploratory study spatially resolved D-T2 correlation maps from glass beads and mortar are analyzed. Regions of similar contrast are first identified in high-resolution images to locate corresponding pixels in low-resolution images generated with D-T2 resolved MRI for subsequent pixel summation to improve the signal-to-noise ratio of contrast-specific D-T2 maps. This method is expected to contribute valuable information on correlated sample heterogeneity from the macroscopic and the microscopic scales in various types of porous materials including building materials and rock. Copyright © 2014 Elsevier Inc. All rights reserved.
Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?. -based on different credit correlations using hierarchical methods

Science.gov (United States)

He, Fang; Chen, Xi

2016-11-01

The accelerating accumulation and risk concentration of Chinese local financing platforms debts have attracted wide attention throughout the world. Due to the network of financial exposures among institutions, the failure of several platforms or regions of systemic importance will probably trigger systemic risk and destabilize the financial system. However, the complex network of credit relationships in Chinese local financing platforms at the state level remains unknown. To fill this gap, we presented the first complex networks and hierarchical cluster analysis of the credit market of Chinese local financing platforms using the ;bottom up; method from firm-level data. Based on balance-sheet channel, we analyzed the topology and taxonomy by applying the analysis paradigm of subdominant ultra-metric space to an empirical data in 2013. It is remarked that we chose to extract the network of co-financed financing platforms in order to evaluate the effect of risk contagion from platforms to bank system. We used the new credit similarity measure by combining the factor of connectivity and size, to extract minimal spanning trees (MSTs) and hierarchical trees (HTs). We found that: (1) the degree distributions of credit correlation backbone structure of Chinese local financing platforms are fat tailed, and the structure is unstable with respect to targeted failures; (2) the backbone is highly hierarchical, and largely explained by the geographic region; (3) the credit correlation backbone structure based on connectivity and size is significantly heterogeneous; (4) key platforms and regions of systemic importance, and contagion path of systemic risk are obtained, which are contributed to preventing systemic risk and regional risk of Chinese local financing platforms and preserving financial stability under the framework of macro prudential supervision. Our approach of credit similarity measure provides a means of recognizing ;systemically important; institutions and regions
Spatial correlations of Diceroprocta apache and its host plants: Evidence for a negative impact from Tamarix invasion

Science.gov (United States)

Ellingson, A.R.; Andersen, D.C.

2002-01-01

1. The hypothesis that the habitat-scale spatial distribution of the Apache cicada Diceroprocta apache Davis is unaffected by the presence of the invasive exotic saltcedar Tamarix ramosissima was tested using data from 205 1-m2 quadrats placed within the flood-plain of the Bill Williams River, Arizona, U.S.A. Spatial dependencies within and between cicada density and habitat variables were estimated using Moran's I and its bivariate analogue to discern patterns and associations at spatial scales from 1 to 30 m.2. Apache cicadas were spatially aggregated in high-density clusters averaging 3 m in diameter. A positive association between cicada density, estimated by exuvial density, and the per cent canopy cover of a native tree, Goodding's willow Salix gooddingii, was detected in a non-spatial correlation analysis. No non-spatial association between cicada density and saltcedar canopy cover was detected.3. Tests for spatial cross-correlation using the bivariate IYZ indicated the presence of a broad-scale negative association between cicada density and saltcedar canopy cover. This result suggests that large continuous stands of saltcedar are associated with reduced cicada density. In contrast, positive associations detected at spatial scales larger than individual quadrats suggested a spill-over of high cicada density from areas featuring Goodding's willow canopy into surrounding saltcedar monoculture.4. Taken together and considered in light of the Apache cicada's polyphagous habits, the observed spatial patterns suggest that broad-scale factors such as canopy heterogeneity affect cicada habitat use more than host plant selection. This has implications for management of lower Colorado River riparian woodlands to promote cicada presence and density through maintenance or creation of stands of native trees as well as manipulation of the characteristically dense and homogeneous saltcedar canopies.
Discrete capacity limits and neuroanatomical correlates of visual short-term memory for objects and spatial locations.

Science.gov (United States)

Konstantinou, Nikos; Constantinidou, Fofi; Kanai, Ryota

2017-02-01

Working memory is responsible for keeping information in mind when it is no longer in view, linking perception with higher cognitive functions. Despite such crucial role, short-term maintenance of visual information is severely limited. Research suggests that capacity limits in visual short-term memory (VSTM) are correlated with sustained activity in distinct brain areas. Here, we investigated whether variability in the structure of the brain is reflected in individual differences of behavioral capacity estimates for spatial and object VSTM. Behavioral capacity estimates were calculated separately for spatial and object information using a novel adaptive staircase procedure and were found to be unrelated, supporting domain-specific VSTM capacity limits. Voxel-based morphometry (VBM) analyses revealed dissociable neuroanatomical correlates of spatial versus object VSTM. Interindividual variability in spatial VSTM was reflected in the gray matter density of the inferior parietal lobule. In contrast, object VSTM was reflected in the gray matter density of the left insula. These dissociable findings highlight the importance of considering domain-specific estimates of VSTM capacity and point to the crucial brain regions that limit VSTM capacity for different types of visual information. Hum Brain Mapp 38:767-778, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Diffuse correlation tomography reveals spatial and temporal difference in blood flow changes among murine femoral grafts

Science.gov (United States)

Han, Songfeng; Proctor, Ashley R.; Benoit, Danielle S. W.; Choe, Regine

2017-07-01

Diffuse correlation tomography was utilized to noninvasively monitor 3D blood flow changes in three types of healing mouse femoral grafts. Results reveal the spatial and temporal difference among the groups.
An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

Science.gov (United States)

Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

2015-01-01

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.
Creating Hierarchical Pores by Controlled Linker Thermolysis in Multivariate Metal-Organic Frameworks.

Science.gov (United States)

Feng, Liang; Yuan, Shuai; Zhang, Liang-Liang; Tan, Kui; Li, Jia-Luo; Kirchon, Angelo; Liu, Ling-Mei; Zhang, Peng; Han, Yu; Chabal, Yves J; Zhou, Hong-Cai

2018-02-14

Sufficient pore size, appropriate stability, and hierarchical porosity are three prerequisites for open frameworks designed for drug delivery, enzyme immobilization, and catalysis involving large molecules. Herein, we report a powerful and general strategy, linker thermolysis, to construct ultrastable hierarchically porous metal-organic frameworks (HP-MOFs) with tunable pore size distribution. Linker instability, usually an undesirable trait of MOFs, was exploited to create mesopores by generating crystal defects throughout a microporous MOF crystal via thermolysis. The crystallinity and stability of HP-MOFs remain after thermolabile linkers are selectively removed from multivariate metal-organic frameworks (MTV-MOFs) through a decarboxylation process. A domain-based linker spatial distribution was found to be critical for creating hierarchical pores inside MTV-MOFs. Furthermore, linker thermolysis promotes the formation of ultrasmall metal oxide nanoparticles immobilized in an open framework that exhibits high catalytic activity for Lewis acid-catalyzed reactions. Most importantly, this work provides fresh insights into the connection between linker apportionment and vacancy distribution, which may shed light on probing the disordered linker apportionment in multivariate systems, a long-standing challenge in the study of MTV-MOFs.
Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

Science.gov (United States)

Royle, J. Andrew; Dorazio, Robert M.

2008-01-01

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.
Detecting Hierarchical Structure in Networks

DEFF Research Database (Denmark)

Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

2012-01-01

Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose...... a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....
Planning paths through a spatial hierarchy - Eliminating stair-stepping effects

Science.gov (United States)

Slack, Marc G.

1989-01-01

Stair-stepping effects are a result of the loss of spatial continuity resulting from the decomposition of space into a grid. This paper presents a path planning algorithm which eliminates stair-stepping effects induced by the grid-based spatial representation. The algorithm exploits a hierarchical spatial model to efficiently plan paths for a mobile robot operating in dynamic domains. The spatial model and path planning algorithm map to a parallel machine, allowing the system to operate incrementally, thereby accounting for unexpected events in the operating space.
Many-body correlation effects in the spatially separated electron and hole layers in the coupled quantum wells

Energy Technology Data Exchange (ETDEWEB)

Babichenko, V.S. [RRC Kurchatov Institute, Kurchatov Sq., 1, 123182 Moscow (Russian Federation); Polishchuk, I.Ya., E-mail: iyppolishchuk@gmail.com [RRC Kurchatov Institute, Kurchatov Sq., 1, 123182 Moscow (Russian Federation); Moscow Institute of Physics and Technology, 141700, 9, Institutskii per., Dolgoprudny, Moscow Region (Russian Federation)

2014-11-15

The many-body correlation effects in the spatially separated electron and hole layers in the coupled quantum wells are investigated. A special case of the many-component electron–hole system is considered. It is shown that if the hole mass is much greater than the electron mass, the negative correlation energy is mainly determined by the holes. The ground state of the system is found to be the 2D electron–hole liquid with the energy smaller than the exciton phase. It is shown that the system decays into the spatially separated neutral electron–hole drops if the initially created charge density in the layers is smaller than the certain critical value n{sub eq}.
Functional annotation of hierarchical modularity.

Directory of Open Access Journals (Sweden)

Kanchana Padmanabhan

Full Text Available In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function-hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology and the association of individual genes or proteins with these concepts (e.g., GO terms, our method will assign a Hierarchical Modularity Score (HMS to each node in the hierarchy of functional modules; the HMS score and its p-value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of "enriched" functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our

Spatial genetic structure across a hybrid zone between European rabbit subspecies

Directory of Open Access Journals (Sweden)

Fernando Alda

2014-09-01

Full Text Available The Iberian Peninsula is the only region in the world where the two existing subspecies of the European rabbit (Oryctolagus cuniculus naturally occur and hybridize. In this study we explore the relative roles of historical and contemporary processes in shaping the spatial genetic structure of the rabbit across its native distribution range, and how they differently affect each subspecies and the hybrid zone. For that purpose we obtained multilocus genotypes and mitochondrial DNA data from 771 rabbits across most of the distribution range of the European rabbit in Spain. Based on the nuclear markers we observed a hierarchical genetic structure firstly comprised by two genetic groups, largely congruent with the mitochondrial lineages and subspecies distributions (O. c. algirus and O. c. cuniculus, which were subsequently subdivided into seven genetic groups. Geographic distance alone emerged as an important factor explaining genetic differentiation across the whole range, without the need to invoke for the effect for geographical barriers. Additionally, the significantly positive spatial correlation up to a distance of only 100 km supported the idea that differentiation at a local level is of greater importance when considering the species overall genetic structure. When looking at the subspecies, northern populations of O. c. cuniculus showed more spatial genetic structure and differentiation than O. c. algirus. This could be due to local geographic barriers, limited resources, soil type and/or social behavior limiting dispersal. The hybrid zone showed similar genetic structure to the southern populations but a larger introgression from the northern lineage genome. These differences have been attributed to selection against the hybrids rather than to behavioral differences between subspecies. Ultimately, the genetic structure of the rabbit in its native distribution range is the result of an ensemble of factors, from geographical and ecological
Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model

Directory of Open Access Journals (Sweden)

Joon‐young Jung

2018-02-01

Full Text Available This paper proposes a hierarchical dual filtering (HDF algorithm to estimate the spatial region between a Cloud of Things (CoT gateway and an Internet of Things (IoT device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM with a raw Bluetooth received signal strength indicator (RSSI. However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high‐frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.
Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model

Science.gov (United States)

Graves, T.A.; Kendall, Katherine C.; Royle, J. Andrew; Stetz, J.B.; Macleod, A.C.

2011-01-01

Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system.
Epitaxial Growth of Hetero-Ln-MOF Hierarchical Single Crystals for Domain- and Orientation-Controlled Multicolor Luminescence 3D Coding Capability.

Science.gov (United States)

Pan, Mei; Zhu, Yi-Xuan; Wu, Kai; Chen, Ling; Hou, Ya-Jun; Yin, Shao-Yun; Wang, Hai-Ping; Fan, Ya-Nan; Su, Cheng-Yong

2017-11-13

Core-shell or striped heteroatomic lanthanide metal-organic framework hierarchical single crystals were obtained by liquid-phase anisotropic epitaxial growth, maintaining identical periodic organization while simultaneously exhibiting spatially segregated structure. Different types of domain and orientation-controlled multicolor photophysical models are presented, which show either visually distinguishable or visible/near infrared (NIR) emissive colors. This provides a new bottom-up strategy toward the design of hierarchical molecular systems, offering high-throughput and multiplexed luminescence color tunability and readability. The unique capability of combining spectroscopic coding with 3D (three-dimensional) microscale spatial coding is established, providing potential applications in anti-counterfeiting, color barcoding, and other types of integrated and miniaturized optoelectronic materials and devices. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Epitaxial growth of hetero-Ln-MOF hierarchical single crystals for domain- and orientation-controlled multicolor luminescence 3D coding capability

International Nuclear Information System (INIS)

Pan, Mei; Zhu, Yi-Xuan; Wu, Kai; Chen, Ling; Hou, Ya-Jun; Yin, Shao-Yun; Wang, Hai-Ping; Fan, Ya-Nan; Su, Cheng-Yong

2017-01-01

Core-shell or striped heteroatomic lanthanide metal-organic framework hierarchical single crystals were obtained by liquid-phase anisotropic epitaxial growth, maintaining identical periodic organization while simultaneously exhibiting spatially segregated structure. Different types of domain and orientation-controlled multicolor photophysical models are presented, which show either visually distinguishable or visible/near infrared (NIR) emissive colors. This provides a new bottom-up strategy toward the design of hierarchical molecular systems, offering high-throughput and multiplexed luminescence color tunability and readability. The unique capability of combining spectroscopic coding with 3D (three-dimensional) microscale spatial coding is established, providing potential applications in anti-counterfeiting, color barcoding, and other types of integrated and miniaturized optoelectronic materials and devices. (copyright 2017 Wiley-VCH Verlag GmbH and Co. KGaA, Weinheim)
Epitaxial growth of hetero-Ln-MOF hierarchical single crystals for domain- and orientation-controlled multicolor luminescence 3D coding capability

Energy Technology Data Exchange (ETDEWEB)

Pan, Mei; Zhu, Yi-Xuan; Wu, Kai; Chen, Ling; Hou, Ya-Jun; Yin, Shao-Yun; Wang, Hai-Ping; Fan, Ya-Nan [MOE Laboratory of Bioinorganic and Synthetic Chemistry, Lehn Institute of Functional Materials, School of Chemistry, Sun Yat-Sen University, Guangzhou (China); Su, Cheng-Yong [MOE Laboratory of Bioinorganic and Synthetic Chemistry, Lehn Institute of Functional Materials, School of Chemistry, Sun Yat-Sen University, Guangzhou (China); State Key Laboratory of Applied Organic Chemistry, Lanzhou University, Lanzhou (China)

2017-11-13

Core-shell or striped heteroatomic lanthanide metal-organic framework hierarchical single crystals were obtained by liquid-phase anisotropic epitaxial growth, maintaining identical periodic organization while simultaneously exhibiting spatially segregated structure. Different types of domain and orientation-controlled multicolor photophysical models are presented, which show either visually distinguishable or visible/near infrared (NIR) emissive colors. This provides a new bottom-up strategy toward the design of hierarchical molecular systems, offering high-throughput and multiplexed luminescence color tunability and readability. The unique capability of combining spectroscopic coding with 3D (three-dimensional) microscale spatial coding is established, providing potential applications in anti-counterfeiting, color barcoding, and other types of integrated and miniaturized optoelectronic materials and devices. (copyright 2017 Wiley-VCH Verlag GmbH and Co. KGaA, Weinheim)
Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

Science.gov (United States)

Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

2018-01-01

Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.
Catalysis with hierarchical zeolites

DEFF Research Database (Denmark)

Holm, Martin Spangsberg; Taarning, Esben; Egeblad, Kresten

2011-01-01

Hierarchical (or mesoporous) zeolites have attracted significant attention during the first decade of the 21st century, and so far this interest continues to increase. There have already been several reviews giving detailed accounts of the developments emphasizing different aspects of this research...... topic. Until now, the main reason for developing hierarchical zeolites has been to achieve heterogeneous catalysts with improved performance but this particular facet has not yet been reviewed in detail. Thus, the present paper summaries and categorizes the catalytic studies utilizing hierarchical...... zeolites that have been reported hitherto. Prototypical examples from some of the different categories of catalytic reactions that have been studied using hierarchical zeolite catalysts are highlighted. This clearly illustrates the different ways that improved performance can be achieved with this family...
Analysis of individual brain activation maps using hierarchical description and multiscale detection

International Nuclear Information System (INIS)

Poline, J.B.; Mazoyer, B.M.

1994-01-01

The authors propose a new method for the analysis of brain activation images that aims at detecting activated volumes rather than pixels. The method is based on Poisson process modeling, hierarchical description, and multiscale detection (MSD). Its performances have been assessed using both Monte Carlo simulated images and experimental PET brain activation data. As compared to other methods, the MSD approach shows enhanced sensitivity with a controlled overall type I error, and has the ability to provide an estimate of the spatial limits of the detected signals. It is applicable to any kind of difference image for which the spatial autocorrelation function can be approximated by a stationary Gaussian function
Spatial variation of peat soil properties in the oil-producing region of northeastern Sakhalin

Science.gov (United States)

Lipatov, D. N.; Shcheglov, A. I.; Manakhov, D. V.; Zavgorodnyaya, Yu. A.; Rozanova, M. S.; Brekhov, P. T.

2017-07-01

Morphology and properties of medium-deep oligotrophic peat, oligotrophic peat gley, pyrogenic oligotrophic peat gley, and peat gley soils on subshrub-cotton grass-sphagnum bogs and in swampy larch forests of northeastern Sakhalin have been studied. Variation in the thickness and reserves of litters in the studied bog and forest biogeocenoses has been analyzed. The profile distribution and spatial variability of moisture, density, ash, and pHKCl in separate groups of peat soils have been described. The content and spatial variability of petroleum hydrocarbons have been considered in relation to the accumulation of natural bitumoids by peat soils and the technogenic pressing in the oil-producing region. Variation of each parameter at different distances (10, 50, and 1000 m) has been estimated using a hierarchical sampling scheme. The spatial conjugation of soil parameters has been studied by factor analysis using the principal components method and Spearman correlation coefficients. Regression equations have been proposed to describe relationships of ash content with soil density and content of petroleum hydrocarbons in peat horizons.
Hierarchically nested river landform sequences

Science.gov (United States)

Pasternack, G. B.; Weber, M. D.; Brown, R. A.; Baig, D.

2017-12-01

River corridors exhibit landforms nested within landforms repeatedly down spatial scales. In this study we developed, tested, and implemented a new way to create river classifications by mapping domains of fluvial processes with respect to the hierarchical organization of topographic complexity that drives fluvial dynamism. We tested this approach on flow convergence routing, a morphodynamic mechanism with different states depending on the structure of nondimensional topographic variability. Five nondimensional landform types with unique functionality (nozzle, wide bar, normal channel, constricted pool, and oversized) represent this process at any flow. When this typology is nested at base flow, bankfull, and floodprone scales it creates a system with up to 125 functional types. This shows how a single mechanism produces complex dynamism via nesting. Given the classification, we answered nine specific scientific questions to investigate the abundance, sequencing, and hierarchical nesting of these new landform types using a 35-km gravel/cobble river segment of the Yuba River in California. The nested structure of flow convergence routing landforms found in this study revealed that bankfull landforms are nested within specific floodprone valley landform types, and these types control bankfull morphodynamics during moderate to large floods. As a result, this study calls into question the prevailing theory that the bankfull channel of a gravel/cobble river is controlled by in-channel, bankfull, and/or small flood flows. Such flows are too small to initiate widespread sediment transport in a gravel/cobble river with topographic complexity.
A Multi-layer, Hierarchical Information Management System for the Smart Grid

Energy Technology Data Exchange (ETDEWEB)

Lu, Ning; Du, Pengwei; Paulson, Patrick R.; Greitzer, Frank L.; Guo, Xinxin; Hadley, Mark D.

2011-10-10

This paper presents the modeling approach, methodologies, and initial results of setting up a multi-layer, hierarchical information management system (IMS) for the smart grid. The IMS allows its users to analyze the data collected by multiple control and communication networks to characterize the states of the smart grid. Abnormal, corrupted, or erroneous measurement data and outliers are detected and analyzed to identify whether they are caused by random equipment failures, unintentional human errors, or deliberate tempering attempts. Data collected from different information networks are crosschecked for data integrity based on redundancy, dependency, correlation, or cross-correlations, which reveal the interdependency between data sets. A hierarchically structured reasoning mechanism is used to rank possible causes of an event to aid the system operators to proactively respond or provide mitigation recommendations to remove or neutralize the threats. The model provides satisfactory performance on identifying the cause of an event and significantly reduces the need of processing myriads of data collected.
On the role of spatial phase and phase correlation in vision, illusion, and cognition.

Science.gov (United States)

Gladilin, Evgeny; Eils, Roland

2015-01-01

Numerous findings indicate that spatial phase bears an important cognitive information. Distortion of phase affects topology of edge structures and makes images unrecognizable. In turn, appropriately phase-structured patterns give rise to various illusions of virtual image content and apparent motion. Despite a large body of phenomenological evidence not much is known yet about the role of phase information in neural mechanisms of visual perception and cognition. Here, we are concerned with analysis of the role of spatial phase in computational and biological vision, emergence of visual illusions and pattern recognition. We hypothesize that fundamental importance of phase information for invariant retrieval of structural image features and motion detection promoted development of phase-based mechanisms of neural image processing in course of evolution of biological vision. Using an extension of Fourier phase correlation technique, we show that the core functions of visual system such as motion detection and pattern recognition can be facilitated by the same basic mechanism. Our analysis suggests that emergence of visual illusions can be attributed to presence of coherently phase-shifted repetitive patterns as well as the effects of acuity compensation by saccadic eye movements. We speculate that biological vision relies on perceptual mechanisms effectively similar to phase correlation, and predict neural features of visual pattern (dis)similarity that can be used for experimental validation of our hypothesis of "cognition by phase correlation."
On the role of spatial phase and phase correlation in vision, illusion and cognition

Directory of Open Access Journals (Sweden)

Evgeny eGladilin

2015-04-01

Full Text Available Numerous findings indicate that spatial phase bears an important cognitive information. Distortion of phase affects topology of edge structures and makes images unrecognizable. In turn, appropriately phase-structured patterns give rise to various illusions of virtual image content and apparent motion. Despite a large body of phenomenological evidence not much is known yet about the role of phase information in neural mechanisms of visual perception and cognition. Here, we are concerned with analysis of the role of spatial phase in computational and biological vision, emergence of visual illusions and pattern recognition. We hypothesize that fundamental importance of phase information for invariant retrieval of structural image features and motion detection promoted development of phase-based mechanisms of neural image processing in course of evolution of biological vision. Using an extension of Fourier phase correlation technique, we show that the core functions of visual system such as motion detection and pattern recognition can be facilitated by the same basic mechanism. Our analysis suggests that emergence of visual illusions can be attributed to presence of coherently phase-shifted repetitive patterns as well as the effects of acuity compensation by saccadic eye movements. We speculate that biological vision relies on perceptual mechanisms effectively similar to phase correlation, and predict neural features of visual pattern (dissimilarity that can be used for experimental validation of our hypothesis of 'cognition by phase correlation'.
Parallel hierarchical radiosity rendering

Energy Technology Data Exchange (ETDEWEB)

Carter, Michael [Iowa State Univ., Ames, IA (United States)

1993-07-01

In this dissertation, the step-by-step development of a scalable parallel hierarchical radiosity renderer is documented. First, a new look is taken at the traditional radiosity equation, and a new form is presented in which the matrix of linear system coefficients is transformed into a symmetric matrix, thereby simplifying the problem and enabling a new solution technique to be applied. Next, the state-of-the-art hierarchical radiosity methods are examined for their suitability to parallel implementation, and scalability. Significant enhancements are also discovered which both improve their theoretical foundations and improve the images they generate. The resultant hierarchical radiosity algorithm is then examined for sources of parallelism, and for an architectural mapping. Several architectural mappings are discussed. A few key algorithmic changes are suggested during the process of making the algorithm parallel. Next, the performance, efficiency, and scalability of the algorithm are analyzed. The dissertation closes with a discussion of several ideas which have the potential to further enhance the hierarchical radiosity method, or provide an entirely new forum for the application of hierarchical methods.
Quantifying the Hierarchical Order in Self-Aligned Carbon Nanotubes from Atomic to Micrometer Scale.

Science.gov (United States)

Meshot, Eric R; Zwissler, Darwin W; Bui, Ngoc; Kuykendall, Tevye R; Wang, Cheng; Hexemer, Alexander; Wu, Kuang Jen J; Fornasiero, Francesco

2017-06-27

Fundamental understanding of structure-property relationships in hierarchically organized nanostructures is crucial for the development of new functionality, yet quantifying structure across multiple length scales is challenging. In this work, we used nondestructive X-ray scattering to quantitatively map the multiscale structure of hierarchically self-organized carbon nanotube (CNT) "forests" across 4 orders of magnitude in length scale, from 2.0 Å to 1.5 μm. Fully resolved structural features include the graphitic honeycomb lattice and interlayer walls (atomic), CNT diameter (nano), as well as the greater CNT ensemble (meso) and large corrugations (micro). Correlating orientational order across hierarchical levels revealed a cascading decrease as we probed finer structural feature sizes with enhanced sensitivity to small-scale disorder. Furthermore, we established qualitative relationships for single-, few-, and multiwall CNT forest characteristics, showing that multiscale orientational order is directly correlated with number density spanning 10 9 -10 12 cm -2 , yet order is inversely proportional to CNT diameter, number of walls, and atomic defects. Lastly, we captured and quantified ultralow-q meridional scattering features and built a phenomenological model of the large-scale CNT forest morphology, which predicted and confirmed that these features arise due to microscale corrugations along the vertical forest direction. Providing detailed structural information at multiple length scales is important for design and synthesis of CNT materials as well as other hierarchically organized nanostructures.
Hierarchical prisoner’s dilemma in hierarchical game for resource competition

Science.gov (United States)

Fujimoto, Yuma; Sagawa, Takahiro; Kaneko, Kunihiko

2017-07-01

Dilemmas in cooperation are one of the major concerns in game theory. In a public goods game, each individual cooperates by paying a cost or defecting without paying it, and receives a reward from the group out of the collected cost. Thus, defecting is beneficial for each individual, while cooperation is beneficial for the group. Now, groups (say, countries) consisting of individuals also play games. To study such a multi-level game, we introduce a hierarchical game in which multiple groups compete for limited resources by utilizing the collected cost in each group, where the power to appropriate resources increases with the population of the group. Analyzing this hierarchical game, we found a hierarchical prisoner’s dilemma, in which groups choose the defecting policy (say, armament) as a Nash strategy to optimize each group’s benefit, while cooperation optimizes the total benefit. On the other hand, for each individual, refusing to pay the cost (say, tax) is a Nash strategy, which turns out to be a cooperation policy for the group, thus leading to a hierarchical dilemma. Here the group reward increases with the group size. However, we find that there exists an optimal group size that maximizes the individual payoff. Furthermore, when the population asymmetry between two groups is large, the smaller group will choose a cooperation policy (say, disarmament) to avoid excessive response from the larger group, and the prisoner’s dilemma between the groups is resolved. Accordingly, the relevance of this hierarchical game on policy selection in society and the optimal size of human or animal groups are discussed.
Fluid intellingence and spatial reasoning as predictors of pilot training performance in the South African Air Force (SAAF

Directory of Open Access Journals (Sweden)

François de Kock

2009-04-01

Full Text Available Pilot selection is a form of high-stakes selection due to the massive costs of training, high trainee ability requirements and costly repercussions of poor selection decisions. This criterion-related validation study investigated the predictive ability of fluid intelligence and spatial reasoning in predicting three criteria of pilot training performance, using an accumulated sample of South African Air Force pilots (N = 108. Hierarchical multiple regression analyses with training grade achieved as criterion were performed for each of the phases of training, namely practical flight training, ground school training, and officers’ formative training. Multiple correlations of 0.35 (p 0.05 and 0.23 (p > 0.05 were obtained for flight, ground school and formative training results, respectively. Spatial ability had incremental validity over fluid intelligence for predicting flight training performance.
Velocity correlations and spatial dependencies between neighbors in a unidirectional flow of pedestrians

Science.gov (United States)

Porzycki, Jakub; WÄ s, Jarosław; Hedayatifar, Leila; Hassanibesheli, Forough; Kułakowski, Krzysztof

2017-08-01

The aim of the paper is an analysis of self-organization patterns observed in the unidirectional flow of pedestrians. On the basis of experimental data from Zhang et al. [J. Zhang et al., J. Stat. Mech. (2011) P06004, 10.1088/1742-5468/2011/06/P06004], we analyze the mutual positions and velocity correlations between pedestrians when walking along a corridor. The angular and spatial dependencies of the mutual positions reveal a spatial structure that remains stable during the crowd motion. This structure differs depending on the value of n , for the consecutive n th -nearest-neighbor position set. The preferred position for the first-nearest neighbor is on the side of the pedestrian, while for further neighbors, this preference shifts to the axis of movement. The velocity correlations vary with the angle formed by the pair of neighboring pedestrians and the direction of motion and with the time delay between pedestrians' movements. The delay dependence of the correlations shows characteristic oscillations, produced by the velocity oscillations when striding; however, a filtering of the main frequency of individual striding out reduces the oscillations only partially. We conclude that pedestrians select their path directions so as to evade the necessity of continuously adjusting their speed to their neighbors'. They try to keep a given distance, but follow the person in front of them, as well as accepting and observing pedestrians on their sides. Additionally, we show an empirical example that illustrates the shape of a pedestrian's personal space during movement.
Micromechanics of hierarchical materials

DEFF Research Database (Denmark)

Mishnaevsky, Leon, Jr.

2012-01-01

A short overview of micromechanical models of hierarchical materials (hybrid composites, biomaterials, fractal materials, etc.) is given. Several examples of the modeling of strength and damage in hierarchical materials are summarized, among them, 3D FE model of hybrid composites...... with nanoengineered matrix, fiber bundle model of UD composites with hierarchically clustered fibers and 3D multilevel model of wood considered as a gradient, cellular material with layered composite cell walls. The main areas of research in micromechanics of hierarchical materials are identified, among them......, the investigations of the effects of load redistribution between reinforcing elements at different scale levels, of the possibilities to control different material properties and to ensure synergy of strengthening effects at different scale levels and using the nanoreinforcement effects. The main future directions...

HIERARCHICAL FRAGMENTATION OF THE ORION MOLECULAR FILAMENTS

International Nuclear Information System (INIS)

Takahashi, Satoko; Ho, Paul T. P.; Su, Yu-Nung; Teixeira, Paula S.; Zapata, Luis A.

2013-01-01

We present a high angular resolution map of the 850 μm continuum emission of the Orion Molecular Cloud-3 (OMC 3) obtained with the Submillimeter Array (SMA); the map is a mosaic of 85 pointings covering an approximate area of 6.'5 × 2.'0 (0.88 × 0.27 pc). We detect 12 spatially resolved continuum sources, each with an H 2 mass between 0.3-5.7 M ☉ and a projected source size between 1400-8200 AU. All the detected sources are on the filamentary main ridge (n H 2 ≥10 6 cm –3 ), and analysis based on the Jeans theorem suggests that they are most likely gravitationally unstable. Comparison of multi-wavelength data sets indicates that of the continuum sources, 6/12 (50%) are associated with molecular outflows, 8/12 (67%) are associated with infrared sources, and 3/12 (25%) are associated with ionized jets. The evolutionary status of these sources ranges from prestellar cores to protostar phase, confirming that OMC-3 is an active region with ongoing embedded star formation. We detect quasi-periodical separations between the OMC-3 sources of ≈17''/0.035 pc. This spatial distribution is part of a large hierarchical structure that also includes fragmentation scales of giant molecular cloud (≈35 pc), large-scale clumps (≈1.3 pc), and small-scale clumps (≈0.3 pc), suggesting that hierarchical fragmentation operates within the Orion A molecular cloud. The fragmentation spacings are roughly consistent with the thermal fragmentation length in large-scale clumps, while for small-scale cores it is smaller than the local fragmentation length. These smaller spacings observed with the SMA can be explained by either a helical magnetic field, cloud rotation, or/and global filament collapse. Finally, possible evidence for sequential fragmentation is suggested in the northern part of the OMC-3 filament.
Spatial variations of storm runoff pollution and their correlation with land-use in a rapidly urbanizing catchment in China.

Science.gov (United States)

Qin, Hua-Peng; Khu, Soon-Thiam; Yu, Xiang-Ying

2010-09-15

The composition of land use for a rapidly urbanizing catchment is usually heterogeneous, and this may result in significant spatial variations of storm runoff pollution and increase the difficulties of water quality management. The Shiyan Reservoir catchment, a typical rapidly urbanizing area in China, is chosen as a study area, and temporary monitoring sites were set at the downstream of its 6 sub-catchments to synchronously measure rainfall, runoff and water quality during 4 storm events in 2007 and 2009. Due to relatively low frequency monitoring, the IHACRES and exponential pollutant wash-off simulation models are used to interpolate the measured data to compensate for data insufficiency. Three indicators, event pollutant loads per unit area (EPL), event mean concentration (EMC) and pollutant loads transported by the first 50% of runoff volume (FF50), were used to describe the runoff pollution for different pollutants in each sub-catchment during the storm events, and the correlations between runoff pollution spatial variations and land-use patterns were tested by Spearman's rank correlation analysis. The results indicated that similar spatial variation trends were found for different pollutants (EPL or EMC) in light storm events, which strongly correlate with the proportion of residential land use; however, they have different trends in heavy storm events, which correlate with not only the residential land use, but also agricultural and bare land use. And some pairs of pollutants (such as COD/BOD, NH(3)-N/TN) might have the similar source because they have strong or moderate positive spatial correlation. Moreover, the first flush intensity (FF50) varies with impervious land areas and different interception ratio of initial storm runoff volume should be adopted in different sub-catchments. Copyright 2010 Elsevier B.V. All rights reserved.
Hybrid optical CDMA-FSO communications network under spatially correlated gamma-gamma scintillation.

Science.gov (United States)

Jurado-Navas, Antonio; Raddo, Thiago R; Garrido-Balsells, José María; Borges, Ben-Hur V; Olmos, Juan José Vegas; Monroy, Idelfonso Tafur

2016-07-25

In this paper, we propose a new hybrid network solution based on asynchronous optical code-division multiple-access (OCDMA) and free-space optical (FSO) technologies for last-mile access networks, where fiber deployment is impractical. The architecture of the proposed hybrid OCDMA-FSO network is thoroughly described. The users access the network in a fully asynchronous manner by means of assigned fast frequency hopping (FFH)-based codes. In the FSO receiver, an equal gain-combining technique is employed along with intensity modulation and direct detection. New analytical formalisms for evaluating the average bit error rate (ABER) performance are also proposed. These formalisms, based on the spatially correlated gamma-gamma statistical model, are derived considering three distinct scenarios, namely, uncorrelated, totally correlated, and partially correlated channels. Numerical results show that users can successfully achieve error-free ABER levels for the three scenarios considered as long as forward error correction (FEC) algorithms are employed. Therefore, OCDMA-FSO networks can be a prospective alternative to deliver high-speed communication services to access networks with deficient fiber infrastructure.
Spatial correlations in intense ionospheric scintillations - comparison between numerical computation and observation

International Nuclear Information System (INIS)

Kumagai, H.

1987-01-01

The spatial correlations in intense ionospheric scintillations were analyzed by comparing numerical results with observational ones. The observational results were obtained by spaced-receiver scintillation measurements of VHF satellite radiowave. The numerical computation was made by using the fourth-order moment equation with fairly realistic ionospheric irregularity models, in which power-law irregularities with spectral index 4, both thin and thick slabs, and both isotropic and anisotropic irregularities, were considered. Evolution of the S(4) index and the transverse correlation function was computed. The numerical result that the transverse correlation distance decreases with the increase in S(4) was consistent with that obtained in the observation, suggesting that multiple scattering plays an important role in the intense scintillations observed. The anisotropy of irregularities proved to act as if the density fluctuation increased. This effect, as well as the effect of slab thickness, was evaluated by the total phase fluctuations that the radiowave experienced in the slab. On the basis of the comparison, the irregularity height and electron-density fluctuation which is necessary to produce a particular strength of scintillation were estimated. 30 references
Spatial correlation of conductive filaments for multiple switching cycles in CBRAM

KAUST Repository

Pey, K. L.

2014-06-01

Conducting bridge random access memory (CBRAM) is one of the potential technologies being considered for replacement of Flash memory for non-volatile data storage. CBRAM devices operate on the principle of nucleation and rupture of metallic filaments. One key concern for commercializing this technology is the question of variability which could arise due to nucleation of multiple filaments across the device at spatially different locations. The spatial spread of the filament location may cause long tails at the low and high percentile regions for the switching parameter distribution as the new filament that nucleates may have a completely different shape and size. It is therefore essential to probe whether switching in CBRAM occurs every time at the same filament location or whether there are other new filaments that could nucleate during repeated cycling with some spatial correlation (if any) to the original filament. To investigate this issue, we make use of a metal-insulator-semiconductor (M-I-S) transistor test structure with Ni as the top electrode and HfOx/SiOx as the dielectric stack. In-situ stressing using a nano-tip on the M-I-S stack is performed and the filament is imaged in real-time using a high resolution transmission electron microscope (TEM). We also extract the location of the filament (LFIL) along the channel of the transistor after the nucleation stage using the weighted proportion of the source and drain currents. © 2014 IEEE.
Density dependence of the fine-differential disturbed gamma-gamma-spatial correlation in gaseous 111InI-sources

International Nuclear Information System (INIS)

Schuetter, K.

1985-01-01

An instrument for measuring a time-differential disturbed angular correlation was developed. Using this instrument the disturbance of the spatial correlation of the γ-quanta of the 171-245 keV γ-γ-cascade in 111 Cd was examined in dependence of the density of the gaseous 111 InI-systems and the time difference between the emission of the both γ-quanta. (BBOE)
How hierarchical is language use?

Science.gov (United States)

Frank, Stefan L.; Bod, Rens; Christiansen, Morten H.

2012-01-01

It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science. PMID:22977157
Creating Hierarchical Pores by Controlled Linker Thermolysis in Multivariate Metal-Organic Frameworks

KAUST Repository

Feng, Liang

2018-01-18

Sufficient pore size, appropriate stability and hierarchical porosity are three prerequisites for open frameworks designed for drug delivery, enzyme immobilization and catalysis involving large molecules. Herein, we report a powerful and general strate-gy, linker thermolysis, to construct ultra-stable hierarchically porous metal−organic frameworks (HP-MOFs) with tunable pore size distribution. Linker instability, usually an undesirable trait of MOFs, was exploited to create mesopores by generating crystal defects throughout a microporous MOF crystal via thermolysis. The crystallinity and stability of HP-MOFs remain after thermolabile linkers are selectively removed from multivariate metal-organic frameworks (MTV-MOFs) through a decarboxyla-tion process. A domain-based linker spatial distribution was found to be critical for creating hierarchical pores inside MTV-MOFs. Furthermore, linker thermolysis promotes the formation of ultra-small metal oxide (MO) nanoparticles immobilized in an open framework that exhibits high catalytic activity for Lewis acid catalyzed reactions. Most importantly, this work pro-vides fresh insights into the connection between linker apportionment and vacancy distribution, which may shed light on prob-ing the disordered linker apportionment in multivariate systems, a long-standing challenge in the study of MTV-MOFs.
Fractal analytical approach of urban form based on spatial correlation function

International Nuclear Information System (INIS)

Chen, Yanguang

2013-01-01

Highlights: ► Many fractal parameter relations of cities can be derived by scaling analysis. ► The area-radius scaling of cities suggests a spatial correlation function. ► Spectral analysis can be used to estimate fractal dimension values of urban form. ► The valid range of fractal dimension of urban form comes between 1.5 and 2. ► The traditional scale concept will be replaced by scaling concept in geography. -- Abstract: Urban form has been empirically demonstrated to be of scaling invariance and can be described with fractal geometry. However, the rational range of fractal dimension value and the relationships between various fractal indicators of cities are not yet revealed in theory. By mathematical deduction and transform (e.g., Fourier transform), I find that scaling analysis, spectral analysis, and spatial correlation analysis are all associated with fractal concepts and can be integrated into a new approach to fractal analysis of cities. This method can be termed ‘3S analyses’ of urban form. Using the 3S analysis, I derived a set of fractal parameter equations, by which different fractal parameters of cities can be linked up with one another. Each fractal parameter has its own reasonable extent of values. According to the fractal parameter equations, the intersection of the rational ranges of different fractal parameters suggests the proper scale of the fractal dimension of urban patterns, which varies from 1.5 to 2. The fractal dimension equations based on the 3S analysis and the numerical relationships between different fractal parameters are useful for geographers to understand urban evolution and potentially helpful for future city planning
Single nucleotide polymorphisms unravel hierarchical divergence and signatures of selection among Alaskan sockeye salmon (Oncorhynchus nerka populations

Directory of Open Access Journals (Sweden)

Habicht Christopher

2011-02-01

Full Text Available Abstract Background Disentangling the roles of geography and ecology driving population divergence and distinguishing adaptive from neutral evolution at the molecular level have been common goals among evolutionary and conservation biologists. Using single nucleotide polymorphism (SNP multilocus genotypes for 31 sockeye salmon (Oncorhynchus nerka populations from the Kvichak River, Alaska, we assessed the relative roles of geography (discrete boundaries or continuous distance and ecology (spawning habitat and timing driving genetic divergence in this species at varying spatial scales within the drainage. We also evaluated two outlier detection methods to characterize candidate SNPs responding to environmental selection, emphasizing which mechanism(s may maintain the genetic variation of outlier loci. Results For the entire drainage, Mantel tests suggested a greater role of geographic distance on population divergence than differences in spawn timing when each variable was correlated with pairwise genetic distances. Clustering and hierarchical analyses of molecular variance indicated that the largest genetic differentiation occurred between populations from distinct lakes or subdrainages. Within one population-rich lake, however, Mantel tests suggested a greater role of spawn timing than geographic distance on population divergence when each variable was correlated with pairwise genetic distances. Variable spawn timing among populations was linked to specific spawning habitats as revealed by principal coordinate analyses. We additionally identified two outlier SNPs located in the major histocompatibility complex (MHC class II that appeared robust to violations of demographic assumptions from an initial pool of eight candidates for selection. Conclusions First, our results suggest that geography and ecology have influenced genetic divergence between Alaskan sockeye salmon populations in a hierarchical manner depending on the spatial scale. Second
Fabrication of three-dimensional poly(ε-caprolactone) scaffolds with hierarchical pore structures for tissue engineering

Energy Technology Data Exchange (ETDEWEB)

Zhang, Qingchun [Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China); Luo, Houyong [State Key Laboratory of Bioreactor Engineering, School of Bioengineering, East China University of Science and Technology, Shanghai 200237 (China); Zhang, Yan [Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China); Zhou, Yan [State Key Laboratory of Bioreactor Engineering, School of Bioengineering, East China University of Science and Technology, Shanghai 200237 (China); Ye, Zhaoyang, E-mail: zhaoyangye@ecust.edu.cn [State Key Laboratory of Bioreactor Engineering, School of Bioengineering, East China University of Science and Technology, Shanghai 200237 (China); Tan, Wensong [State Key Laboratory of Bioreactor Engineering, School of Bioengineering, East China University of Science and Technology, Shanghai 200237 (China); Lang, Meidong, E-mail: mdlang@ecust.edu.cn [Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237 (China)

2013-05-01

The physical properties of tissue engineering scaffolds such as microstructures play important roles in controlling cellular behaviors and neotissue formation. Among them, the pore size stands out as a key determinant factor. In the present study, we aimed to fabricate porous scaffolds with pre-defined hierarchical pore sizes, followed by examining cell growth in these scaffolds. This hierarchical porous microstructure was implemented via integrating different pore-generating methodologies, including salt leaching and thermal induced phase separation (TIPS). Specifically, large (L, 200–300 μm), medium (M, 40–50 μm) and small (S, < 10 μm) pores were able to be generated. As such, three kinds of porous scaffolds with a similar porosity of ∼ 90% creating pores of either two (LS or MS) or three (LMS) different sizes were successfully prepared. The number fractions of different pores in these scaffolds were determined to confirm the hierarchical organization of pores. It was found that the interconnectivity varied due to the different pore structures. Besides, these scaffolds demonstrated similar compressive moduli under dry and hydrated states. The adhesion, proliferation, and spatial distribution of human fibroblasts within the scaffolds during a 14-day culture were evaluated with MTT assay and fluorescence microscopy. While all three scaffolds well supported the cell attachment and proliferation, the best cell spatial distribution inside scaffolds was achieved with LMS, implicating that such a controlled hierarchical microstructure would be advantageous in tissue engineering applications. Highlights: ► The scaffolds with dual-pore and triple-pore structures were fabricated. ► Triple-pore structure had better interconnectivity than dual-pore structures. ► Better cell migration and distribution were found on the triple-pore structures. ► The medium pore size (45–50 μm) was appropriate for cell migration. ► Scaffolds with triple-pore structure
Energy flow in plate assembles by hierarchical version of finite element method

DEFF Research Database (Denmark)

Wachulec, Marcin; Kirkegaard, Poul Henning

method has been proposed. In this paper a modified hierarchical version of finite element method is used for modelling of energy flow in plate assembles. The formulation includes description of in-plane forces so that planes lying in different planes can be modelled. Two examples considered are: L......The dynamic analysis of structures in medium and high frequencies are usually focused on frequency and spatial averages of energy of components, and not the displacement/velocity fields. This is especially true for structure-borne noise modelling. For the analysis of complicated structures...... the finite element method has been used to study the energy flow. The finite element method proved its usefulness despite the computational expense. Therefore studies have been conducted in order to simplify and reduce the computations required. Among others, the use of hierarchical version of finite element...
Introduction to the transverse spatial correlations in spontaneous parametric down-conversion through the biphoton birth zone

International Nuclear Information System (INIS)

Schneeloch, James; Howell, John C

2016-01-01

As a tutorial to the spatial aspects of spontaneous parametric downconversion (SPDC), we present a detailed first-principles derivation of the transverse correlation width of photon pairs in degenerate collinear SPDC. This width defines the size of a biphoton birth zone, the region where the signal and idler photons are likely to be found when conditioning on the position of the destroyed pump photon. Along the way, we discuss the quantum-optical calculation of the amplitude for the SPDC process, as well as its simplified form for nearly collinear degenerate phase matching. Following this, we show how this biphoton amplitude can be approximated with a double-Gaussian wavefunction, and give a brief discussion of the measurement statistics (and subsequent convenience) of such double-Gaussian wavefunctions. Next, we use this approximation to get a simplified estimation of the transverse correlation width, and compare it to more accurate calculations as well as experimental results. We then conclude with a discussion of the concept of a biphoton birth zone, using it to develop intuition for the tradeoff between the first-order spatial coherence and bipohoton correlations in SPDC. (tutorial)
Prospective surveillance of multivariate spatial disease data

Science.gov (United States)

Corberán-Vallet, A

2012-01-01

Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. PMID:22534429
Spatial correlation in 3D MIMO channels using fourier coefficients of power spectrums

KAUST Repository

Nadeem, Qurrat-Ul-Ain

2015-03-01

In this paper, an exact closed-form expression for the Spatial Correlation Function (SCF) is derived for the standardized three-dimensional (3D) multiple-input multiple-output (MIMO) channel. This novel SCF is developed for a uniform linear array of antennas with non-isotropic antenna patterns. The proposed method resorts to the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre and associated Legendre polynomials to obtain a closed-form expression for the SCF for arbitrary angular distributions and antenna patterns. The resulting expression depends on the underlying angular distributions and antenna patterns through the Fourier Series (FS) coefficients of power azimuth and elevation spectrums. The novelty of the proposed method lies in the SCF being valid for any 3D propagation environment. Numerical results validate the proposed analytical expression and study the impact of angular spreads on the correlation. The derived SCF will help evaluate the performance of correlated 3D MIMO channels in the future. © 2015 IEEE.
Directed Hierarchical Patterning of Polycarbonate Bisphenol A Glass Surface along Predictable Sites

Directory of Open Access Journals (Sweden)

Mazen Khaled

2015-01-01

Full Text Available This paper reports a new approach in designing textured and hierarchical surfaces on polycarbonate bisphenol A type glass to improve hydrophobicity and dust repellent application for solar panels. Solvent- and vapor-induced crystallization of thermoplastic glass polycarbonate bisphenol A (PC is carried out to create hierarchically structured surfaces. In this approach dichloromethane (DCM and acetone are used in sequence. Samples are initially immersed in DCM liquid to generate nanopores, followed by exposing to acetone vapor resulting in the generation of hierarchical structure along the interporous sites. The effects of exposure time on the size, density, and distance of the generated spherules and gaps are studied and correlated with the optical transmittance and contact angle measurements at the surface. At optimized exposure time a contact angle of 98° was achieved with 80% optical transmittance. To further increase the hydrophobicity while maintaining optical properties, the hierarchical surfaces were coated with a transparent composite of tetraethyl orthosilicate as precursor and hexamethyldisilazane as silylation agent resulting in an average contact angle of 135.8° and transmittance of around 70%. FTIR and AFM characterization techniques are employed to study the composition and morphology of the generated surfaces.
Hierarchical architecture of active knits

International Nuclear Information System (INIS)

Abel, Julianna; Luntz, Jonathan; Brei, Diann

2013-01-01

Nature eloquently utilizes hierarchical structures to form the world around us. Applying the hierarchical architecture paradigm to smart materials can provide a basis for a new genre of actuators which produce complex actuation motions. One promising example of cellular architecture—active knits—provides complex three-dimensional distributed actuation motions with expanded operational performance through a hierarchically organized structure. The hierarchical structure arranges a single fiber of active material, such as shape memory alloys (SMAs), into a cellular network of interlacing adjacent loops according to a knitting grid. This paper defines a four-level hierarchical classification of knit structures: the basic knit loop, knit patterns, grid patterns, and restructured grids. Each level of the hierarchy provides increased architectural complexity, resulting in expanded kinematic actuation motions of active knits. The range of kinematic actuation motions are displayed through experimental examples of different SMA active knits. The results from this paper illustrate and classify the ways in which each level of the hierarchical knit architecture leverages the performance of the base smart material to generate unique actuation motions, providing necessary insight to best exploit this new actuation paradigm. (paper)
Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains.

Science.gov (United States)

Dettmer, Jan; Dosso, Stan E

2012-10-01

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea.
Nested and Hierarchical Archimax copulas

KAUST Repository

Hofert, Marius

2017-07-03

The class of Archimax copulas is generalized to nested and hierarchical Archimax copulas in several ways. First, nested extreme-value copulas or nested stable tail dependence functions are introduced to construct nested Archimax copulas based on a single frailty variable. Second, a hierarchical construction of d-norm generators is presented to construct hierarchical stable tail dependence functions and thus hierarchical extreme-value copulas. Moreover, one can, by itself or additionally, introduce nested frailties to extend Archimax copulas to nested Archimax copulas in a similar way as nested Archimedean copulas extend Archimedean copulas. Further results include a general formula for the density of Archimax copulas.
Nested and Hierarchical Archimax copulas

KAUST Repository

Hofert, Marius; Huser, Raphaë l; Prasad, Avinash

2017-01-01

The class of Archimax copulas is generalized to nested and hierarchical Archimax copulas in several ways. First, nested extreme-value copulas or nested stable tail dependence functions are introduced to construct nested Archimax copulas based on a single frailty variable. Second, a hierarchical construction of d-norm generators is presented to construct hierarchical stable tail dependence functions and thus hierarchical extreme-value copulas. Moreover, one can, by itself or additionally, introduce nested frailties to extend Archimax copulas to nested Archimax copulas in a similar way as nested Archimedean copulas extend Archimedean copulas. Further results include a general formula for the density of Archimax copulas.

Spatial and temporal correlation between beach and wave processes: implications for bar-berm sediment transition

Science.gov (United States)

Joevivek, V.; Chandrasekar, N.; Saravanan, S.; Anandakumar, H.; Thanushkodi, K.; Suguna, N.; Jaya, J.

2018-06-01

Investigation of a beach and its wave conditions is highly requisite for understanding the physical processes in a coast. This study composes spatial and temporal correlation between beach and nearshore processes along the extensive sandy beach of Nagapattinam coast, southeast peninsular India. The data collection includes beach profile, wave data, and intertidal sediment samples for 2 years from January 2011 to January 2013. The field data revealed significant variability in beach and wave morphology during the northeast (NE) and southwest (SW) monsoon. However, the beach has been stabilized by the reworking of sediment distribution during the calm period. The changes in grain sorting and longshore sediment transport serve as a clear evidence of the sediment migration that persevered between foreshore and nearshore regions. The Empirical Orthogonal Function (EOF) analysis and Canonical Correlation Analysis (CCA) were utilized to investigate the spatial and temporal linkages between beach and nearshore criterions. The outcome of the multivariate analysis unveiled that the seasonal variations in the wave climate tends to influence the bar-berm sediment transition that is discerned in the coast.
Spatial and temporal correlation between beach and wave processes: implications for bar-berm sediment transition

Science.gov (United States)

Joevivek, V.; Chandrasekar, N.; Saravanan, S.; Anandakumar, H.; Thanushkodi, K.; Suguna, N.; Jaya, J.

2017-06-01

Investigation of a beach and its wave conditions is highly requisite for understanding the physical processes in a coast. This study composes spatial and temporal correlation between beach and nearshore processes along the extensive sandy beach of Nagapattinam coast, southeast peninsular India. The data collection includes beach profile, wave data, and intertidal sediment samples for 2 years from January 2011 to January 2013. The field data revealed significant variability in beach and wave morphology during the northeast (NE) and southwest (SW) monsoon. However, the beach has been stabilized by the reworking of sediment distribution during the calm period. The changes in grain sorting and longshore sediment transport serve as a clear evidence of the sediment migration that persevered between foreshore and nearshore regions. The Empirical Orthogonal Function (EOF) analysis and Canonical Correlation Analysis (CCA) were utilized to investigate the spatial and temporal linkages between beach and nearshore criterions. The outcome of the multivariate analysis unveiled that the seasonal variations in the wave climate tends to influence the bar-berm sediment transition that is discerned in the coast.
Learning to echolocate in sighted people: a correlational study on attention, working memory and spatial abilities.

Science.gov (United States)

Ekkel, M R; van Lier, R; Steenbergen, B

2017-03-01

Echolocation can be beneficial for the orientation and mobility of visually impaired people. Research has shown considerable individual differences for acquiring this skill. However, individual characteristics that affect the learning of echolocation are largely unknown. In the present study, we examined individual factors that are likely to affect learning to echolocate: sustained and divided attention, working memory, and spatial abilities. To that aim, sighted participants with normal hearing performed an echolocation task that was adapted from a previously reported size-discrimination task. In line with existing studies, we found large individual differences in echolocation ability. We also found indications that participants were able to improve their echolocation ability. Furthermore, we found a significant positive correlation between improvement in echolocation and sustained and divided attention, as measured in the PASAT. No significant correlations were found with our tests regarding working memory and spatial abilities. These findings may have implications for the development of guidelines for training echolocation that are tailored to the individual with a visual impairment.
Planar spatial correlations, anisotropy, and specific surface area of stationary random porous media

International Nuclear Information System (INIS)

Berryman, J.G.

1998-01-01

An earlier result of the author showed that an anisotropic spatial correlation function of a random porous medium could be used to compute the specific surface area when it is stationary as well as anisotropic by first performing a three-dimensional radial average and then taking the first derivative with respect to lag at the origin. This result generalized the earlier result for isotropic porous media of Debye et al. [J. Appl. Phys. 28, 679 (1957)]. The present article provides more detailed information about the use of spatial correlation functions for anisotropic porous media and in particular shows that, for stationary anisotropic media, the specific surface area can be related to the derivative of the two-dimensional radial average of the correlation function measured from cross sections taken through the anisotropic medium. The main concept is first illustrated using a simple pedagogical example for an anisotropic distribution of spherical voids. Then, a general derivation of formulas relating the derivative of the planar correlation functions to surface integrals is presented. When the surface normal is uniformly distributed (as is the case for any distribution of spherical voids), our formulas can be used to relate a specific surface area to easily measurable quantities from any single cross section. When the surface normal is not distributed uniformly (as would be the case for an oriented distribution of ellipsoidal voids), our results show how to obtain valid estimates of specific surface area by averaging measurements on three orthogonal cross sections. One important general observation for porous media is that the surface area from nearly flat cracks may be underestimated from measurements on orthogonal cross sections if any of the cross sections happen to lie in the plane of the cracks. This result is illustrated by taking the very small aspect ratio (penny-shaped crack) limit of an oblate spheroid, but holds for other types of flat surfaces as well
Correlation of spatial climate/weather maps and the advantages of using the Mahalanobis metric in predictions

Science.gov (United States)

Stephenson, D. B.

1997-10-01

The skill in predicting spatially varying weather/climate maps depends on the definition of the measure of similarity between the maps. Under the justifiable approximation that the anomaly maps are distributed multinormally, it is shown analytically that the choice of weighting metric, used in defining the anomaly correlation between spatial maps, can change the resulting probability distribution of the correlation coefficient. The estimate of the numbers of degrees of freedom based on the variance of the correlation distribution can vary from unity up to the number of grid points depending on the choice of weighting metric. The (pseudo-) inverse of the sample covariance matrix acts as a special choice for the metric in that it gives a correlation distribution which has minimal kurtosis and maximum dimension. Minimal kurtosis suggests that the average predictive skill might be improved due to the rarer occurrence of troublesome outlier patterns far from the mean state. Maximum dimension has a disadvantage for analogue prediction schemes in that it gives the minimum number of analogue states. This metric also has an advantage in that it allows one to powerfully test the null hypothesis of multinormality by examining the second and third moments of the correlation coefficient which were introduced by Mardia as invariant measures of multivariate kurtosis and skewness. For these reasons, it is suggested that this metric could be usefully employed in the prediction of weather/climate and in fingerprinting anthropogenic climate change. The ideas are illustrated using the bivariate example of the observed monthly mean sea-level pressures at Darwin and Tahitifrom 1866 1995.
Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

Science.gov (United States)

Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

2018-01-01

Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.
Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

KAUST Repository

Sang, Huiyan

2011-12-01

This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.
Utilisation of spatial and temporal correlations in positron emission tomography

International Nuclear Information System (INIS)

Sureau, F.

2008-06-01

In this thesis we propose, implement, and evaluate algorithms improving spatial resolution in reconstructed images and reducing data noise in positron emission tomography imaging. These algorithms have been developed for a high resolution tomograph (HRRT) and applied to brain imaging, but can be used for other tomographs or studies. We first developed an iterative reconstruction algorithm including a stationary and isotropic model of resolution in image space, experimentally measured. We evaluated the impact of such a model of resolution in Monte-Carlo simulations, physical phantom experiments and in two clinical studies by comparing our algorithm with a reference reconstruction algorithm. This study suggests that biases due to partial volume effects are reduced, in particular in the clinical studies. Better spatial and temporal correlations are also found at the voxel level. However, other methods should be developed to further reduce data noise. We then proposed a maximum a posteriori de-noising algorithm that can be used for dynamic data to de-noise temporally raw data (sino-grams) or reconstructed images. The a priori modeled the coefficients in a wavelet basis of all the signals without noise (in an image or sinogram). We compared this technique with a reference de-noising method on replicated simulations. This illustrates the potential benefits of our approach of sinogram de-noising. (author)
Effects of fading and spatial correlation on node selection for estimation in Wireless Sensor Networks

KAUST Repository

Al-Murad, Tamim M.

2010-06-01

In densely deployed sensor networks, correlation among measurements may be high. Spatial sampling through node selection is usually used to minimize this correlation and to save energy consumption. However because of the fading nature of the wireless channels, extra care should be taken when performing this sampling. In this paper, we develop expressions for the distortion which include the channel effects. The asymptotic behavior of the distortion as the number of sensors or total transmit power increase without bound is also investigated. Further, based on the channel and position information we propose and test several node selection schemes.
Continuous Spatial Process Models for Spatial Extreme Values

KAUST Repository

Sang, Huiyan

2010-01-28

We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.
DUST SPECTRAL ENERGY DISTRIBUTIONS IN THE ERA OF HERSCHEL AND PLANCK: A HIERARCHICAL BAYESIAN-FITTING TECHNIQUE

International Nuclear Information System (INIS)

Kelly, Brandon C.; Goodman, Alyssa A.; Shetty, Rahul; Stutz, Amelia M.; Launhardt, Ralf; Kauffmann, Jens

2012-01-01

We present a hierarchical Bayesian method for fitting infrared spectral energy distributions (SEDs) of dust emission to observed fluxes. Under the standard assumption of optically thin single temperature (T) sources, the dust SED as represented by a power-law-modified blackbody is subject to a strong degeneracy between T and the spectral index β. The traditional non-hierarchical approaches, typically based on χ 2 minimization, are severely limited by this degeneracy, as it produces an artificial anti-correlation between T and β even with modest levels of observational noise. The hierarchical Bayesian method rigorously and self-consistently treats measurement uncertainties, including calibration and noise, resulting in more precise SED fits. As a result, the Bayesian fits do not produce any spurious anti-correlations between the SED parameters due to measurement uncertainty. We demonstrate that the Bayesian method is substantially more accurate than the χ 2 fit in recovering the SED parameters, as well as the correlations between them. As an illustration, we apply our method to Herschel and submillimeter ground-based observations of the star-forming Bok globule CB244. This source is a small, nearby molecular cloud containing a single low-mass protostar and a starless core. We find that T and β are weakly positively correlated—in contradiction with the χ 2 fits, which indicate a T-β anti-correlation from the same data set. Additionally, in comparison to the χ 2 fits the Bayesian SED parameter estimates exhibit a reduced range in values.
Task Switching in a Hierarchical Task Structure: Evidence for the Fragility of the Task Repetition Benefit

Science.gov (United States)

Lien, Mei-Ching; Ruthruff, Eric

2004-01-01

This study examined how task switching is affected by hierarchical task organization. Traditional task-switching studies, which use a constant temporal and spatial distance between each task element (defined as a stimulus requiring a response), promote a flat task structure. Using this approach, Experiment 1 revealed a large switch cost of 238 ms.…
Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

Science.gov (United States)

Žukovič, Milan; Hristopulos, Dionissios T.

2009-02-01

A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the Nc-state Potts model, each point is assigned to one of Nc classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of
Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping.

Science.gov (United States)

Goungounga, Juste Aristide; Gaudart, Jean; Colonna, Marc; Giorgi, Roch

2016-10-12

The reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status (e.g., the Townsend index) on cancer incidence. Moran's I, the empirical Bayes index (EBI), and Potthoff-Whittinghill test were used to investigate the general clustering. The local cluster detection methods were: i) the spatial oblique decision tree (SpODT); ii) the spatial scan statistic of Kulldorff (SaTScan); and, iii) the hierarchical Bayesian spatial modeling (HBSM) in a univariate and multivariate setting. These methods were used with and without introducing the Townsend index of socioeconomic deprivation known to be related to the distribution of cancer incidence. Incidence data stemmed from the Cancer Registry of Isère and were limited to prostate, lung, colon-rectum, and bladder cancers diagnosed between 1999 and 2007 in men only. The study found a spatial heterogeneity (p 1.2). The multivariate HBSM found a spatial correlation between lung and bladder cancers (r = 0.6). In spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.
Spatial correlation structure of the ionosphere predicted by geomagnetic indices and application to global field modelling

Science.gov (United States)

Holschneider, M.; Ferrat, K.; Lesur, V.; Stolle, C.

2017-12-01

Ionospheric fields are modelled in terms of random structures taking into account a mean behaviour as well as random fluctuations which are described through two point correlation kernels. These kernels are estimated from long time series of numerical simulations from various models. These correlations are best expressed in SM system of coordinates. For the moment we limit ourselves to spatial correlations only in this coordinate system. We study the influence of various indices as possible predictor parameters for these correlations as well as seasonal effects. The various time series of ionospheric fields are stored in a HDF5 database which is accessible via a web interface. The obtained correlation structures serve as prior information to separate external and internal field components from observatory based measurements. We present a model that predicts the correlations as a function of time and some geomagnetic indices. First results of the inversion from observatory data are presented.
[Spatial distribution of COD and the correlations with other parameters in the northern region of Lake Taihu].

Science.gov (United States)

Zhang, Yun-lin; Yang, Long-yuan; Qin, Bo-qiang; Gao, Guang; Luo, Lian-cong; Zhu, Guang-wei; Liu, Ming-liang

2008-06-01

Spatial variation of chemical oxygen demand (COD) concentration was documented and significant correlations between COD concentration and chromophoric dissolved organic matter (CDOM) absorption, fluorescence, DOC concentration were found based on a cruise sampling in the northern region of Lake Taihu in summer including 42 samplings. The possible source of COD was also discussed using every two cruise samplings in summer and winter, respectively. The COD concentration ranged from 3.77 to 7.96 mg x L(-1) with a mean value of (5.90 +/- 1.54) mg x L(-1). The mean COD concentrations in Meiliang Bay and the central lake basin were (6.93 +/- 0.89) mg x L(-1) and (4.21 +/- 0.49) mg x L(-1) respectively. A significant spatial difference was found between Meiliang Bay and the central lake basin in COD concentration, CDOM absorption coefficient, fluorescence, DOC and phytoplankton pigment concentrations, decreasing from the river mouth to inner bay, outer bay and the central lake basin. Significant correlations between COD concentration and CDOM absorption, fluorescence, DOC concentration, suggested that COD concentration could be estimated and organic pollution could be assessed using CDOM absorption retrieved from remote sensing images. Significant and positive correlation was found between COD concentration and chlorophyll a concentration in summer. However, the correlation was weak or no correlation was found in winter. Furthermore, a significant higher COD concentration was found in summer than in winter (p summer, except for river terrestrial input.
Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

Science.gov (United States)

Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

2017-08-01

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2 = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.
Evaluation of Deep Learning Representations of Spatial Storm Data

Science.gov (United States)

Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

2017-12-01

The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being
Restoring method for missing data of spatial structural stress monitoring based on correlation

Science.gov (United States)

Zhang, Zeyu; Luo, Yaozhi

2017-07-01

Long-term monitoring of spatial structures is of great importance for the full understanding of their performance and safety. The missing part of the monitoring data link will affect the data analysis and safety assessment of the structure. Based on the long-term monitoring data of the steel structure of the Hangzhou Olympic Center Stadium, the correlation between the stress change of the measuring points is studied, and an interpolation method of the missing stress data is proposed. Stress data of correlated measuring points are selected in the 3 months of the season when missing data is required for fitting correlation. Data of daytime and nighttime are fitted separately for interpolation. For a simple linear regression when single point's correlation coefficient is 0.9 or more, the average error of interpolation is about 5%. For multiple linear regression, the interpolation accuracy is not significantly increased after the number of correlated points is more than 6. Stress baseline value of construction step should be calculated before interpolating missing data in the construction stage, and the average error is within 10%. The interpolation error of continuous missing data is slightly larger than that of the discrete missing data. The data missing rate of this method should better not exceed 30%. Finally, a measuring point's missing monitoring data is restored to verify the validity of the method.
Kendall’s tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas

Directory of Open Access Journals (Sweden)

Górecki J.

2017-01-01

Full Text Available Several successful approaches to structure determination of hierarchical Archimedean copulas (HACs proposed in the literature rely on agglomerative clustering and Kendall’s correlation coefficient. However, there has not been presented any theoretical proof justifying such approaches. This work fills this gap and introduces a theorem showing that, given the matrix of the pairwise Kendall correlation coefficients corresponding to a HAC, its structure can be recovered by an agglomerative clustering technique.

Memory-induced sign reversals of the spatial cross-correlation for particles in viscoelastic shear flows

International Nuclear Information System (INIS)

Sauga, Ako; Laas, Katrin; Mankin, Romi

2015-01-01

Highlights: • Cross-correlation (CC) of coordinates of particles in viscoelastic shear flows is discussed. • Expressions for CC functions subjected to both internal and external noises are presented. • Impact of internal and external noises on CC functions are compared. • Memory-induced reentrant sign reversals of the spatial cross-moment are established. - Abstract: The behavior of shear-induced cross-correlation functions between particle fluctuations along orthogonal directions in the shear plane for harmonically trapped Brownian particles in a viscoelastic shear flow is studied. A generalized Langevin equation with a power-law-type memory kernel is used to model the complex structure of the viscoelastic media. Interaction with fluctuations of environmental parameters is modeled by a multiplicative white Gaussian noise, by an internal fractional Gaussian noise, and by an additive external white noise. It is shown that the presence of a memory has a profound effect on the behavior of the cross-correlation functions. Particularly, memory-induced reentrant sign reversals of the spatial cross-moment between orthogonal random displacements of a particle are established, i.e., an increase of the memory exponent can cause the sign reversal from positive to negative, but by a further increase of the memory exponent a reentrant transition from negative to positive values appears. Similarities and differences between the behavior of the models with additive internal and external noises are considered. It is shown that additive external and internal noises cause qualitatively different dependencies of the cross-correlation functions on the time lag. The occurrence of energetic instability due to the influence of multiplicative noise is also discussed.
The Hierarchical Factor Model of ADHD: Invariant across Age and National Groupings?

Science.gov (United States)

Toplak, Maggie E.; Sorge, Geoff B.; Flora, David B.; Chen, Wai; Banaschewski, Tobias; Buitelaar, Jan; Ebstein, Richard; Eisenberg, Jacques; Franke, Barbara; Gill, Michael; Miranda, Ana; Oades, Robert D.; Roeyers, Herbert; Rothenberger, Aribert; Sergeant, Joseph; Sonuga-Barke, Edmund; Steinhausen, Hans-Christoph; Thompson, Margaret; Tannock, Rosemary; Asherson, Philip; Faraone, Stephen V.

2012-01-01

Objective: To examine the factor structure of attention-deficit/hyperactivity disorder (ADHD) in a clinical sample of 1,373 children and adolescents with ADHD and their 1,772 unselected siblings recruited from different countries across a large age range. Hierarchical and correlated factor analytic models were compared separately in the ADHD and…
The Correlation between Pre-Service Science Teachers' Astronomy Achievement, Attitudes towards Astronomy and Spatial Thinking Skills

Science.gov (United States)

Türk, Cumhur

2016-01-01

The purpose of this study was to examine the changes in pre-service Science teachers' astronomy achievement, attitudes towards astronomy and skills for spatial thinking in terms of their years of study. Another purpose of the study was to find out whether there was correlation between pre-service teachers' astronomy achievement, attitudes towards…
Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis

Directory of Open Access Journals (Sweden)

Jianhua Ni

2016-08-01

Full Text Available The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.
Performance Analysis of a Two-Hop MIMO Mobile-to-Mobile via Stratospheric-Relay Link Employing Hierarchical Modulation

Directory of Open Access Journals (Sweden)

Nikolaos Nomikos

2013-01-01

Full Text Available Next generation wireless communication networks intend to take advantage of the integration of terrestrial and aerospace infrastructures. Besides, multiple-input multiple-output (MIMO architecture is the key technology, which has brought the wireless gigabit vision closer to reality. In this direction, high-altitude platforms (HAPs could act as relay stations in the stratosphere transferring information from an uplink to a downlink MIMO channel. This paper investigates the performance of a novel transmission scheme for the delivery of mobile-to-mobile (M-to-M services via a stratospheric relay. It is assumed that the source, relay, and destination nodes are equipped with multiple antennas and that amplify-and-forward (AF relaying is adopted. The performance is analyzed through rigorous simulations in terms of the bit-error rate (BER by using a recently proposed 3D geometry-based reference model in spatially correlated flat-fading MIMO channels, employing a hierarchical broadcast technique and minimum mean square error (MMSE receivers.
Correlation Between Hierarchical Bayesian and Aerosol Optical Depth PM2.5 Data and Respiratory-Cardiovascular Chronic Diseases

Science.gov (United States)

Tools to estimate PM2.5 mass have expanded in recent years, and now include: 1) stationary monitor readings, 2) Community Multi-Scale Air Quality (CMAQ) model estimates, 3) Hierarchical Bayesian (HB) estimates from combined stationary monitor readings and CMAQ model output; and, ...
Action recognition using mined hierarchical compound features.

Science.gov (United States)

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical
Neutrosophic Hierarchical Clustering Algoritms

Directory of Open Access Journals (Sweden)

Rıdvan Şahin

2014-03-01

Full Text Available Interval neutrosophic set (INS is a generalization of interval valued intuitionistic fuzzy set (IVIFS, whose the membership and non-membership values of elements consist of fuzzy range, while single valued neutrosophic set (SVNS is regarded as extension of intuitionistic fuzzy set (IFS. In this paper, we extend the hierarchical clustering techniques proposed for IFSs and IVIFSs to SVNSs and INSs respectively. Based on the traditional hierarchical clustering procedure, the single valued neutrosophic aggregation operator, and the basic distance measures between SVNSs, we define a single valued neutrosophic hierarchical clustering algorithm for clustering SVNSs. Then we extend the algorithm to classify an interval neutrosophic data. Finally, we present some numerical examples in order to show the effectiveness and availability of the developed clustering algorithms.
The Hierarchical Perspective

Directory of Open Access Journals (Sweden)

Daniel Sofron

2015-05-01

Full Text Available This paper is focused on the hierarchical perspective, one of the methods for representing space that was used before the discovery of the Renaissance linear perspective. The hierarchical perspective has a more or less pronounced scientific character and its study offers us a clear image of the way the representatives of the cultures that developed it used to perceive the sensitive reality. This type of perspective is an original method of representing three-dimensional space on a flat surface, which characterises the art of Ancient Egypt and much of the art of the Middle Ages, being identified in the Eastern European Byzantine art, as well as in the Western European Pre-Romanesque and Romanesque art. At the same time, the hierarchical perspective is also present in naive painting and infantile drawing. Reminiscences of this method can be recognised also in the works of some precursors of the Italian Renaissance. The hierarchical perspective can be viewed as a subjective ranking criterion, according to which the elements are visually represented by taking into account their relevance within the image while perception is ignored. This paper aims to show how the main objective of the artists of those times was not to faithfully represent the objective reality, but rather to emphasize the essence of the world and its perennial aspects. This may represent a possible explanation for the refusal of perspective in the Egyptian, Romanesque and Byzantine painting, characterised by a marked two-dimensionality.
Comparisons of Flow Patterns over a Hierarchical and a Non-hierarchical Surface in Relation to Biofouling Control

Directory of Open Access Journals (Sweden)

Bin Ahmad Fawzan Mohammed Ridha

2018-01-01

Full Text Available Biofouling can be defined as unwanted deposition and development of organisms on submerged surfaces. It is a major problem as it causes water contamination, infrastructures damage and increase in maintenance and operational cost especially in the shipping industry. There are a few methods that can prevent this problem. One of the most effective methods which is using chemicals particularly Tributyltin has been banned due to adverse effects on the environment. One of the non-toxic methods found to be effective is surface modification which involves altering the surface topography so that it becomes a low-fouling or a non-stick surface to biofouling organisms. Current literature suggested that non-hierarchical topographies has lower antifouling performance compared to hierarchical topographies. It is still unclear if the effects of the flow on these topographies could have aided in their antifouling properties. This research will use Computational Fluid Dynamics (CFD simulations to study the flow on these two topographies which also involves comparison study of the topographies used. According to the results obtained, it is shown that hierarchical topography has higher antifouling performance compared to non-hierarchical topography. This is because the fluid characteristics at the hierarchical topography is more favorable in controlling biofouling. In addition, hierarchical topography has higher wall shear stress distribution compared to non-hierarchical topography
Spatial correlations in compressible granular flows

OpenAIRE

Van Noije, T. P. C.; Ernst, M. H.; Brito López, Ricardo

1998-01-01

The clustering instability in freely evolving granular fluids manifests itself in the density-density correlation function and structure factor. These functions are calculated from fluctuating hydrodynamics. As time increases, the structure factor of density fluctuations develops a maximum, which shifts to smaller wave numbers (growing correlation length). Furthermore, the inclusion of longitudinal velocity fluctuations changes long-range correlations in the flow field qualitatively and exten...
Epidemic spreading in a hierarchical social network.

Science.gov (United States)

Grabowski, A; Kosiński, R A

2004-09-01

A model of epidemic spreading in a population with a hierarchical structure of interpersonal interactions is described and investigated numerically. The structure of interpersonal connections is based on a scale-free network. Spatial localization of individuals belonging to different social groups, and the mobility of a contemporary community, as well as the effectiveness of different interpersonal interactions, are taken into account. Typical relations characterizing the spreading process, like a range of epidemic and epidemic curves, are discussed. The influence of preventive vaccinations on the spreading process is investigated. The critical value of preventively vaccinated individuals that is sufficient for the suppression of an epidemic is calculated. Our results are compared with solutions of the master equation for the spreading process and good agreement of the character of this process is found.
Hierarchical Image Segmentation of Remotely Sensed Data using Massively Parallel GNU-LINUX Software

Science.gov (United States)

Tilton, James C.

2003-01-01

A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). In addition, HSEG optionally interjects between HSWO region growing iterations, merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the utility of the segmentation results, especially for larger images, it also significantly increases HSEG s computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic
Spatial but not temporal numerosity thresholds correlate with formal math skills in children.

Science.gov (United States)

Anobile, Giovanni; Arrighi, Roberto; Castaldi, Elisa; Grassi, Eleonora; Pedonese, Lara; Moscoso, Paula A M; Burr, David C

2018-03-01

Humans and other animals are able to make rough estimations of quantities using what has been termed the approximate number system (ANS). Much evidence suggests that sensitivity to numerosity correlates with symbolic math capacity, leading to the suggestion that the ANS may serve as a start-up tool to develop symbolic math. Many experiments have demonstrated that numerosity perception transcends the sensory modality of stimuli and their presentation format (sequential or simultaneous), but it remains an open question whether the relationship between numerosity and math generalizes over stimulus format and modality. Here we measured precision for estimating the numerosity of clouds of dots and sequences of flashes or clicks, as well as for paired comparisons of the numerosity of clouds of dots. Our results show that in children, formal math abilities correlate positively with sensitivity for estimation and paired-comparisons of the numerosity of visual arrays of dots. However, precision of numerosity estimation for sequences of flashes or sounds did not correlate with math, although sensitivities in all estimations tasks (for sequential or simultaneous stimuli) were strongly correlated with each other. In adults, we found no significant correlations between math scores and sensitivity to any of the psychophysical tasks. Taken together these results support the existence of a generalized number sense, and go on to demonstrate an intrinsic link between mathematics and perception of spatial, but not temporal numerosity. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Adaptive hierarchical multi-agent organizations

NARCIS (Netherlands)

Ghijsen, M.; Jansweijer, W.N.H.; Wielinga, B.J.; Babuška, R.; Groen, F.C.A.

2010-01-01

In this chapter, we discuss the design of adaptive hierarchical organizations for multi-agent systems (MAS). Hierarchical organizations have a number of advantages such as their ability to handle complex problems and their scalability to large organizations. By introducing adaptivity in the
Correlated cryo-fluorescence and cryo-electron microscopy with high spatial precision and improved sensitivity

International Nuclear Information System (INIS)

Schorb, Martin; Briggs, John A.G.

2014-01-01

Performing fluorescence microscopy and electron microscopy on the same sample allows fluorescent signals to be used to identify and locate features of interest for subsequent imaging by electron microscopy. To carry out such correlative microscopy on vitrified samples appropriate for structural cryo-electron microscopy it is necessary to perform fluorescence microscopy at liquid-nitrogen temperatures. Here we describe an adaptation of a cryo-light microscopy stage to permit use of high-numerical aperture objectives. This allows high-sensitivity and high-resolution fluorescence microscopy of vitrified samples. We describe and apply a correlative cryo-fluorescence and cryo-electron microscopy workflow together with a fiducial bead-based image correlation procedure. This procedure allows us to locate fluorescent bacteriophages in cryo-electron microscopy images with an accuracy on the order of 50 nm, based on their fluorescent signal. It will allow the user to precisely and unambiguously identify and locate objects and events for subsequent high-resolution structural study, based on fluorescent signals. - Highlights: • Workflow for correlated cryo-fluorescence and cryo-electron microscopy. • Cryo-fluorescence microscopy setup incorporating a high numerical aperture objective. • Fluorescent signals located in cryo-electron micrographs with 50 nm spatial precision
Correlated cryo-fluorescence and cryo-electron microscopy with high spatial precision and improved sensitivity

Energy Technology Data Exchange (ETDEWEB)

Schorb, Martin [Structural and Computational Biology Unit, European Molecular Biology Laboratory, D-69117 Heidelberg (Germany); Briggs, John A.G., E-mail: john.briggs@embl.de [Structural and Computational Biology Unit, European Molecular Biology Laboratory, D-69117 Heidelberg (Germany); Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, D-69117 Heidelberg (Germany)

2014-08-01

Performing fluorescence microscopy and electron microscopy on the same sample allows fluorescent signals to be used to identify and locate features of interest for subsequent imaging by electron microscopy. To carry out such correlative microscopy on vitrified samples appropriate for structural cryo-electron microscopy it is necessary to perform fluorescence microscopy at liquid-nitrogen temperatures. Here we describe an adaptation of a cryo-light microscopy stage to permit use of high-numerical aperture objectives. This allows high-sensitivity and high-resolution fluorescence microscopy of vitrified samples. We describe and apply a correlative cryo-fluorescence and cryo-electron microscopy workflow together with a fiducial bead-based image correlation procedure. This procedure allows us to locate fluorescent bacteriophages in cryo-electron microscopy images with an accuracy on the order of 50 nm, based on their fluorescent signal. It will allow the user to precisely and unambiguously identify and locate objects and events for subsequent high-resolution structural study, based on fluorescent signals. - Highlights: • Workflow for correlated cryo-fluorescence and cryo-electron microscopy. • Cryo-fluorescence microscopy setup incorporating a high numerical aperture objective. • Fluorescent signals located in cryo-electron micrographs with 50 nm spatial precision.
A hierarchical spatial framework and database for the national river fish habitat condition assessment

Science.gov (United States)

Wang, L.; Infante, D.; Esselman, P.; Cooper, A.; Wu, D.; Taylor, W.; Beard, D.; Whelan, G.; Ostroff, A.

2011-01-01

Fisheries management programs, such as the National Fish Habitat Action Plan (NFHAP), urgently need a nationwide spatial framework and database for health assessment and policy development to protect and improve riverine systems. To meet this need, we developed a spatial framework and database using National Hydrography Dataset Plus (I-.100,000-scale); http://www.horizon-systems.com/nhdplus). This framework uses interconfluence river reaches and their local and network catchments as fundamental spatial river units and a series of ecological and political spatial descriptors as hierarchy structures to allow users to extract or analyze information at spatial scales that they define. This database consists of variables describing channel characteristics, network position/connectivity, climate, elevation, gradient, and size. It contains a series of catchment-natural and human-induced factors that are known to influence river characteristics. Our framework and database assembles all river reaches and their descriptors in one place for the first time for the conterminous United States. This framework and database provides users with the capability of adding data, conducting analyses, developing management scenarios and regulation, and tracking management progresses at a variety of spatial scales. This database provides the essential data needs for achieving the objectives of NFHAP and other management programs. The downloadable beta version database is available at http://ec2-184-73-40-15.compute-1.amazonaws.com/nfhap/main/.
Basin Assessment Spatial Planning Platform

Energy Technology Data Exchange (ETDEWEB)

2017-07-26

The tool is intended to facilitate hydropower development and water resource planning by improving synthesis and interpretation of disparate spatial datasets that are considered in development actions (e.g., hydrological characteristics, environmentally and culturally sensitive areas, existing or proposed water power resources, climate-informed forecasts). The tool enables this capability by providing a unique framework for assimilating, relating, summarizing, and visualizing disparate spatial data through the use of spatial aggregation techniques, relational geodatabase platforms, and an interactive web-based Geographic Information Systems (GIS). Data are aggregated and related based on shared intersections with a common spatial unit; in this case, industry-standard hydrologic drainage areas for the U.S. (National Hydrography Dataset) are used as the spatial unit to associate planning data. This process is performed using all available scalar delineations of drainage areas (i.e., region, sub-region, basin, sub-basin, watershed, sub-watershed, catchment) to create spatially hierarchical relationships among planning data and drainages. These entity-relationships are stored in a relational geodatabase that provides back-end structure to the web GIS and its widgets. The full technology stack was built using all open-source software in modern programming languages. Interactive widgets that function within the viewport are also compatible with all modern browsers.
Panel data models with spatial correlation: Estimation theory and an empirical investigation of the United States wholesale gasoline industry

Science.gov (United States)

Kapoor, Mudit

The first part of my dissertation considers the estimation of a panel data model with error components that are both spatially and time-wise correlated. The dissertation combines widely used model for spatial correlation (Cliff and Ord (1973, 1981)) with the classical error component panel data model. I introduce generalizations of the generalized moments (GM) procedure suggested in Kelejian and Prucha (1999) for estimating the spatial autoregressive parameter in case of a single cross section. I then use those estimators to define feasible generalized least squares (GLS) procedures for the regression parameters. I give formal large sample results concerning the consistency of the proposed GM procedures, as well as the consistency and asymptotic normality of the proposed feasible GLS procedures. The new estimators remain computationally feasible even in large samples. The second part of my dissertation employs a Cliff-Ord-type model to empirically estimate the nature and extent of price competition in the US wholesale gasoline industry. I use data on average weekly wholesale gasoline price for 289 terminals (distribution facilities) in the US. Data on demand factors, cost factors and market structure that affect price are also used. I consider two time periods, a high demand period (August 1999) and a low demand period (January 2000). I find a high level of competition in prices between neighboring terminals. In particular, price in one terminal is significantly and positively correlated to the price of its neighboring terminal. Moreover, I find this to be much higher during the low demand period, as compared to the high demand period. In contrast to previous work, I include for each terminal the characteristics of the marginal customer by controlling for demand factors in the neighboring location. I find these demand factors to be important during period of high demand and insignificant during the low demand period. Furthermore, I have also considered spatial

Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

International Nuclear Information System (INIS)

Žukovič, Milan; Hristopulos, Dionissios T

2009-01-01

A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the N c -state Potts model, each point is assigned to one of N c classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of
Large-scale model of flow in heterogeneous and hierarchical porous media

Science.gov (United States)

Chabanon, Morgan; Valdés-Parada, Francisco J.; Ochoa-Tapia, J. Alberto; Goyeau, Benoît

2017-11-01

Heterogeneous porous structures are very often encountered in natural environments, bioremediation processes among many others. Reliable models for momentum transport are crucial whenever mass transport or convective heat occurs in these systems. In this work, we derive a large-scale average model for incompressible single-phase flow in heterogeneous and hierarchical soil porous media composed of two distinct porous regions embedding a solid impermeable structure. The model, based on the local mechanical equilibrium assumption between the porous regions, results in a unique momentum transport equation where the global effective permeability naturally depends on the permeabilities at the intermediate mesoscopic scales and therefore includes the complex hierarchical structure of the soil. The associated closure problem is numerically solved for various configurations and properties of the heterogeneous medium. The results clearly show that the effective permeability increases with the volume fraction of the most permeable porous region. It is also shown that the effective permeability is sensitive to the dimensionality spatial arrangement of the porous regions and in particular depends on the contact between the impermeable solid and the two porous regions.
Hierarchical video summarization

Science.gov (United States)

Ratakonda, Krishna; Sezan, M. Ibrahim; Crinon, Regis J.

1998-12-01

We address the problem of key-frame summarization of vide in the absence of any a priori information about its content. This is a common problem that is encountered in home videos. We propose a hierarchical key-frame summarization algorithm where a coarse-to-fine key-frame summary is generated. A hierarchical key-frame summary facilitates multi-level browsing where the user can quickly discover the content of the video by accessing its coarsest but most compact summary and then view a desired segment of the video with increasingly more detail. At the finest level, the summary is generated on the basis of color features of video frames, using an extension of a recently proposed key-frame extraction algorithm. The finest level key-frames are recursively clustered using a novel pairwise K-means clustering approach with temporal consecutiveness constraint. We also address summarization of MPEG-2 compressed video without fully decoding the bitstream. We also propose efficient mechanisms that facilitate decoding the video when the hierarchical summary is utilized in browsing and playback of video segments starting at selected key-frames.
Mineralogy of the clay fraction of alfisols in two slope curvatures: IV - spatial correlation with physical properties

Directory of Open Access Journals (Sweden)

Livia Arantes Camargo

2013-04-01

Full Text Available Although the influence of clay mineralogy on soil physical properties has been widely studied, spatial relationships between these features in Alfisols have rarely been examined. The purpose of this work was to relate the clay minerals and physical properties of an Alfisol of sandstone origin in two slope curvatures. The crystallographic properties such as mean crystallite size (MCS and width at half height (WHH of hematite, goethite, kaolinite and gibbsite; contents of hematite and goethite; aluminium substitution (AS and specific surface area (SSA of hematite and goethite; the goethite/(goethite+hematite and kaolinite/(kaolinite+gibbsite ratios; and the citrate/bicarbonate/dithionite extractable Fe (Fe d were correlated with the soil physical properties through Pearson correlation coefficients and cross-semivariograms. The correlations found between aluminium substitution in goethite and the soil physical properties suggest that the degree of crystallinity of this mineral influences soil properties used as soil quality indicators. Thus, goethite with a high aluminium substitution resulted in large aggregate sizes and a high porosity, and also in a low bulk density and soil penetration resistance. The presence of highly crystalline gibbsite resulted in a high density and micropore content, as well as in smaller aggregates. Interpretation of the cross-semivariogram and classification of landscape compartments in terms of the spatial dependence pattern for the relief-dependent physical and mineralogical properties of the soil proved an effective supplementary method for assessing Pearson correlations between the soil physical and mineralogical properties.
Considering built environment and spatial correlation in modelling pedestrian injury severity

DEFF Research Database (Denmark)

Prato, Carlo G.; Kaplan, Sigal; Patrier, Alexandre

traffic calming measures, illumination solutions, road maintenance programs and speed limit reductions. Moreover, this study emphasises the role of the built environment, as shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity......This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by posing attention on the built environment and considering spatial correlation...... of pedestrians to sustain a severe or fatal injury conditional on the occurrence of a crash with another road user. This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users, and crashes with heavy vehicles and in roads with higher speed limits...
Considering built environment and spatial correlation in modelling pedestrian injury severity

DEFF Research Database (Denmark)

Prato, Carlo G.; Kaplan, Sigal; Patrier, Alexandre

2018-01-01

traffic calming measures, illumination solutions, road maintenance programs and speed limit reductions. Moreover, this study emphasises the role of the built environment, as shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity......This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by posing attention on the built environment and considering spatial correlation...... of pedestrians to sustain a severe or fatal injury conditional on the occurrence of a crash with another road user. This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users, and crashes with heavy vehicles and in roads with higher speed limits...
Motion Tree Delineates Hierarchical Structure of Protein Dynamics Observed in Molecular Dynamics Simulation.

Directory of Open Access Journals (Sweden)

Kei Moritsugu

Full Text Available Molecular dynamics (MD simulations of proteins provide important information to understand their functional mechanisms, which are, however, likely to be hidden behind their complicated motions with a wide range of spatial and temporal scales. A straightforward and intuitive analysis of protein dynamics observed in MD simulation trajectories is therefore of growing significance with the large increase in both the simulation time and system size. In this study, we propose a novel description of protein motions based on the hierarchical clustering of fluctuations in the inter-atomic distances calculated from an MD trajectory, which constructs a single tree diagram, named a "Motion Tree", to determine a set of rigid-domain pairs hierarchically along with associated inter-domain fluctuations. The method was first applied to the MD trajectory of substrate-free adenylate kinase to clarify the usefulness of the Motion Tree, which illustrated a clear-cut dynamics picture of the inter-domain motions involving the ATP/AMP lid and the core domain together with the associated amplitudes and correlations. The comparison of two Motion Trees calculated from MD simulations of ligand-free and -bound glutamine binding proteins clarified changes in inherent dynamics upon ligand binding appeared in both large domains and a small loop that stabilized ligand molecule. Another application to a huge protein, a multidrug ATP binding cassette (ABC transporter, captured significant increases of fluctuations upon binding a drug molecule observed in both large scale inter-subunit motions and a motion localized at a transmembrane helix, which may be a trigger to the subsequent structural change from inward-open to outward-open states to transport the drug molecule. These applications demonstrated the capabilities of Motion Trees to provide an at-a-glance view of various sizes of functional motions inherent in the complicated MD trajectory.
Methane fugitive emissions quantification using the novel 'plume camera' (spatial correlation) method

Science.gov (United States)

Crosson, E.; Rella, C.

2012-12-01

Fugitive emissions of methane into the atmosphere are a major concern facing the natural gas production industry. Given that the global warming potential of methane is many times greater than that of carbon dioxide, the importance of quantifying methane emissions becomes clear. The rapidly increasing reliance on shale gas (or other unconventional sources) is only intensifying the interest in fugitive methane releases. Natural gas (which is predominantly methane) is an attractive energy source, as it emits 40% less carbon dioxide per Joule of energy generated than coal. However, if just a small percentage of the natural gas consumed is lost due to fugitive emissions during production, processing, or transport, this global warming benefit is lost (Howarth et al. 2012). It is therefore imperative, as production of natural gas increases, that the fugitive emissions of methane are quantified accurately. Traditional direct measurement techniques often involve physical access of the leak itself to quantify the emissions rate, and are generally require painstaking effort to first find the leak and then quantify the emissions rate. With over half a million natural gas producing wells in the U.S. (U.S. Energy Information Administration), not including the associated processing, storage, and transport facilities, and with each facility having hundreds or even thousands of fittings that can potentially leak, the need is clear to develop methodologies that can provide a rapid and accurate assessment of the total emissions rate on a per-well head basis. In this paper we present a novel method for emissions quantification which uses a 'plume camera' with three 'pixels' to quantify emissions using direct measurements of methane concentration in the downwind plume. By analyzing the spatial correlation between the pixels, the spatial extent of the instantaneous plume can be inferred. This information, when combined with the wind speed through the measurement plane, provides a direct
Brain correlates of the orientation of auditory spatial attention onto speaker location in a "cocktail-party" situation.

Science.gov (United States)

Lewald, Jörg; Hanenberg, Christina; Getzmann, Stephan

2016-10-01

Successful speech perception in complex auditory scenes with multiple competing speakers requires spatial segregation of auditory streams into perceptually distinct and coherent auditory objects and focusing of attention toward the speaker of interest. Here, we focused on the neural basis of this remarkable capacity of the human auditory system and investigated the spatiotemporal sequence of neural activity within the cortical network engaged in solving the "cocktail-party" problem. Twenty-eight subjects localized a target word in the presence of three competing sound sources. The analysis of the ERPs revealed an anterior contralateral subcomponent of the N2 (N2ac), computed as the difference waveform for targets to the left minus targets to the right. The N2ac peaked at about 500 ms after stimulus onset, and its amplitude was correlated with better localization performance. Cortical source localization for the contrast of left versus right targets at the time of the N2ac revealed a maximum in the region around left superior frontal sulcus and frontal eye field, both of which are known to be involved in processing of auditory spatial information. In addition, a posterior-contralateral late positive subcomponent (LPCpc) occurred at a latency of about 700 ms. Both these subcomponents are potential correlates of allocation of spatial attention to the target under cocktail-party conditions. © 2016 Society for Psychophysiological Research.
The Problem of English Spatial, Non-spatial and Idiomatic Adpositions in Iranian EFL Environment: A Prototypical Approach

Directory of Open Access Journals (Sweden)

Nassim Golaghaei

2010-11-01

Full Text Available Several studies of L2 learners’ interlanguage have addressed the complexity of the English adpositional system due to several reasons like L1 transfer, lack of knowledge in L2 and the strong collocational relations of prepositions with other elements of the English language. The major purpose of the present study is to evaluate the performance of Iranian students in dealing with three broad categories of spatial, non-spatial and idiomatic adpositions in English. To achieve the inclinations of the research project, 60 students majoring in TEFL at Roudehen University were selected. A paper-based TOEFL test of English Proficiency was administered to obtain some information about the participants’ general language proficiency. Three completion tasks with the division of spatial, non-spatial (nominal, adjectival and verbal and idiomatic adpositions were administered. The obtained results represented the fact that the Iranian participants were considerably inclined to transfer their L1 adpositional patterns to their L2 production. The correlational analyses indicated that whereas the scores related to adposition task in general, non-spatial as well as the idiomatic subtests were strongly correlated with the scores obtained from the TOEFL test, there was a moderate correlation between the spatial subtest and the TOEFL one. The independent sample t-test results between the freshmen and sophomores dealing with spatial, nominal and adjectival subtests were considered to be significant. However, in reference to the verbal subtest, the difference between the two groups was not significant. The results obtained from the independent sample t-test indicated no significant differences between the freshmen and sophomores in their performance on idiomatic adpositions. Finally, the result of the correlation coefficients showed high correlation coefficients between the whole adposition test and the three subtests of spatial, non-spatial and idiomatic ones
The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments

Science.gov (United States)

Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan

2018-04-01

Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.
Investigation of major international and Turkish companies via hierarchical methods and bootstrap approach

Science.gov (United States)

Kantar, E.; Deviren, B.; Keskin, M.

2011-11-01

We present a study, within the scope of econophysics, of the hierarchical structure of 98 among the largest international companies including 18 among the largest Turkish companies, namely Banks, Automobile, Software-hardware, Telecommunication Services, Energy and the Oil-Gas sectors, viewed as a network of interacting companies. We analyze the daily time series data of the Boerse-Frankfurt and Istanbul Stock Exchange. We examine the topological properties among the companies over the period 2006-2010 by using the concept of hierarchical structure methods (the minimal spanning tree (MST) and the hierarchical tree (HT)). The period is divided into three subperiods, namely 2006-2007, 2008 which was the year of global economic crisis, and 2009-2010, in order to test various time-windows and observe temporal evolution. We carry out bootstrap analyses to associate the value of statistical reliability to the links of the MSTs and HTs. We also use average linkage clustering analysis (ALCA) in order to better observe the cluster structure. From these studies, we find that the interactions among the Banks/Energy sectors and the other sectors were reduced after the global economic crisis; hence the effects of the Banks and Energy sectors on the correlations of all companies were decreased. Telecommunication Services were also greatly affected by the crisis. We also observed that the Automobile and Banks sectors, including Turkish companies as well as some companies from the USA, Japan and Germany were strongly correlated with each other in all periods.
Redefining territorial scales and the strategic role of spatial planning

DEFF Research Database (Denmark)

Galland, Daniel; Elinbaum, Pablo

2015-01-01

This paper argues that spatial planning systems tend to redefine and reinterpret conventional territorial scales through the dual adoption and articulation of legal instruments and spatial strategies at different levels of planning administration. In depicting such redefinition, this paper delves...... into the cases of Denmark and Catalonia through an analysis concerned with: i) the strategic spatial role attributed to each level of planning; and ii) the redefinition of territorial scales as a result of changing political objectives and spatial relationships occurring between planning levels. The assessment...... pertaining to the strategic roles of spatial planning instruments as well as the evolving redefinition of territorial scales in both Denmark and Catalonia suggests that the conventional, hierarchical ‘cascade-shaped’ ideal of policy implementation is superseded. While both cases tend to converge...
TRENDS IN FLOODS AND LOW FLOWS IN THE UNITED STATES: IMPACT OF SPATIAL CORRELATION. (R824992,R826888)

Science.gov (United States)

Trends in flood and low flows in the US were evaluated using a regional average Kendall's S trend test at two spatial scales and over two timeframes. Field significance was assessed using a bootstrap methodology to account for the observed regional cross-correlation of streamflow...
Hierarchical modeling of cluster size in wildlife surveys

Science.gov (United States)

Royle, J. Andrew

2008-01-01

Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).
Improved Side Information Generation for Distributed Video Coding by Exploiting Spatial and Temporal Correlations

Directory of Open Access Journals (Sweden)

Ye Shuiming

2009-01-01

Full Text Available Distributed video coding (DVC is a video coding paradigm allowing low complexity encoding for emerging applications such as wireless video surveillance. Side information (SI generation is a key function in the DVC decoder, and plays a key-role in determining the performance of the codec. This paper proposes an improved SI generation for DVC, which exploits both spatial and temporal correlations in the sequences. Partially decoded Wyner-Ziv (WZ frames, based on initial SI by motion compensated temporal interpolation, are exploited to improve the performance of the whole SI generation. More specifically, an enhanced temporal frame interpolation is proposed, including motion vector refinement and smoothing, optimal compensation mode selection, and a new matching criterion for motion estimation. The improved SI technique is also applied to a new hybrid spatial and temporal error concealment scheme to conceal errors in WZ frames. Simulation results show that the proposed scheme can achieve up to 1.0 dB improvement in rate distortion performance in WZ frames for video with high motion, when compared to state-of-the-art DVC. In addition, both the objective and perceptual qualities of the corrupted sequences are significantly improved by the proposed hybrid error concealment scheme, outperforming both spatial and temporal concealments alone.
The Usaquén Cemetery – A Case Study about the Spacial Manifestations of Society’s Hierarchical Order

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Juan Camilo González Vargas

2007-07-01

Full Text Available Beginning with an analysis of the spatial organization of the Usaquén Cemetery in Bogotá, this text proposes that the space of the cemetery functions like a scenario in which the hierarchical order of society is represented and reinforced. This order, however, is not admitted and interiorized totally by cemetery users. Through practices like the Cult to the Holy Souls the hierarchical social order is denied, in a cycle that repeats itself every week when this ritual is carried out. With the exploration of this case study it is proposed that investigations concerning social practices related to death are very useful for understanding the societies that produce them.
Evaluating Hierarchical Structure in Music Annotations.

Science.gov (United States)

McFee, Brian; Nieto, Oriol; Farbood, Morwaread M; Bello, Juan Pablo

2017-01-01

Music exhibits structure at multiple scales, ranging from motifs to large-scale functional components. When inferring the structure of a piece, different listeners may attend to different temporal scales, which can result in disagreements when they describe the same piece. In the field of music informatics research (MIR), it is common to use corpora annotated with structural boundaries at different levels. By quantifying disagreements between multiple annotators, previous research has yielded several insights relevant to the study of music cognition. First, annotators tend to agree when structural boundaries are ambiguous. Second, this ambiguity seems to depend on musical features, time scale, and genre. Furthermore, it is possible to tune current annotation evaluation metrics to better align with these perceptual differences. However, previous work has not directly analyzed the effects of hierarchical structure because the existing methods for comparing structural annotations are designed for "flat" descriptions, and do not readily generalize to hierarchical annotations. In this paper, we extend and generalize previous work on the evaluation of hierarchical descriptions of musical structure. We derive an evaluation metric which can compare hierarchical annotations holistically across multiple levels. sing this metric, we investigate inter-annotator agreement on the multilevel annotations of two different music corpora, investigate the influence of acoustic properties on hierarchical annotations, and evaluate existing hierarchical segmentation algorithms against the distribution of inter-annotator agreement.
Evaluating Hierarchical Structure in Music Annotations

Directory of Open Access Journals (Sweden)

Brian McFee

2017-08-01

Full Text Available Music exhibits structure at multiple scales, ranging from motifs to large-scale functional components. When inferring the structure of a piece, different listeners may attend to different temporal scales, which can result in disagreements when they describe the same piece. In the field of music informatics research (MIR, it is common to use corpora annotated with structural boundaries at different levels. By quantifying disagreements between multiple annotators, previous research has yielded several insights relevant to the study of music cognition. First, annotators tend to agree when structural boundaries are ambiguous. Second, this ambiguity seems to depend on musical features, time scale, and genre. Furthermore, it is possible to tune current annotation evaluation metrics to better align with these perceptual differences. However, previous work has not directly analyzed the effects of hierarchical structure because the existing methods for comparing structural annotations are designed for “flat” descriptions, and do not readily generalize to hierarchical annotations. In this paper, we extend and generalize previous work on the evaluation of hierarchical descriptions of musical structure. We derive an evaluation metric which can compare hierarchical annotations holistically across multiple levels. sing this metric, we investigate inter-annotator agreement on the multilevel annotations of two different music corpora, investigate the influence of acoustic properties on hierarchical annotations, and evaluate existing hierarchical segmentation algorithms against the distribution of inter-annotator agreement.
Process-based modelling of tree and stand growth: towards a hierarchical treatment of multiscale processes

International Nuclear Information System (INIS)

Makela, A.

2003-01-01

A generally accepted method has not emerged for managing the different temporal and spatial scales in a forest ecosystem. This paper reviews a hierarchical-modular modelling tradition, with the main focus on individual tree growth throughout the rotation. At this scale, model performance requires (i) realistic long-term dynamic properties, (ii) realistic responses of growth and mortality of competing individuals, and (iii) realistic responses to ecophysio-logical inputs. Model development and validation are illustrated through allocation patterns, height growth, and size-related feedbacks. Empirical work to test the approach is reviewed. In this approach, finer scale effects are embedded in parameters calculated using more detailed, interacting modules. This is exemplified by (i) the within-year effect of weather on annual photosynthesis, (ii) the effects of fast soil processes on carbon allocation and photosynthesis, and (iii) the utilization of detailed stem structure to predict wood quality. Prevailing management paradigms are reflected in growth modelling. A shift of emphasis has occurred from productivity in homogeneous canopies towards, e.g., wood quality versus total yield, spatially more explicit models, and growth decline in old-growth forests. The new problems emphasize the hierarchy of the system and interscale interactions, suggesting that the hierarchical-modular approach could prove constructive. (author)

Domains and facets: hierarchical personality assessment using the revised NEO personality inventory.

Science.gov (United States)

Costa, P T; McCrae, R R

1995-02-01

Personality traits are organized hierarchically, with narrow, specific traits combining to define broad, global factors. The Revised NEO Personality Inventory (NEO-PI-R; Costa & McCrae, 1992c) assesses personality at both levels, with six specific facet scales in each of five broad domains. This article describes conceptual issues in specifying facets of a domain and reports evidence on the validity of NEO-PI-R facet scales. Facet analysis-the interpretation of a scale in terms of the specific facets with which it correlates-is illustrated using alternative measures of the five-factor model and occupational scales. Finally, the hierarchical interpretation of personality profiles is discussed. Interpretation on the domain level yields a rapid understanding of the individual interpretation of specific facet scales gives a more detailed assessment.
Multivariate Receptor Models for Spatially Correlated Multipollutant Data

KAUST Repository

Jun, Mikyoung

2013-08-01

The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.
The Neural Correlates of Spatial and Object Working Memory in Elderly and Parkinson's Disease Subjects

OpenAIRE

Caminiti, Silvia P.; Siri, Chiara; Guidi, Lucia; Antonini, Angelo; Perani, Daniela

2015-01-01

This fMRI study deals with the neural correlates of spatial and objects working memory (SWM and OWM) in elderly subjects (ESs) and idiopathic Parkinson’s disease (IPD). Normal aging and IPD can be associated with a WM decline. In IPD population, some studies reported similar SWM and OWM deficits; others reported a greater SWM than OWM impairment. In the present fMRI research, we investigated whether compensated IPD patients and elderly subjects with comparable performance during the execution...
Hierarchically Nanostructured Materials for Sustainable Environmental Applications

Directory of Open Access Journals (Sweden)

Zheng eRen

2013-11-01

Full Text Available This article presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions and multiple functionalities towards water remediation, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology.
Hierarchically structured, nitrogen-doped carbon membranes

KAUST Repository

Wang, Hong; Wu, Tao

2017-01-01

The present invention is a structure, method of making and method of use for a novel macroscopic hierarchically structured, nitrogen-doped, nano-porous carbon membrane (HNDCMs) with asymmetric and hierarchical pore architecture that can be produced
Attention to local and global levels of hierarchical Navon figures affects rapid scene categorization.

Science.gov (United States)

Brand, John; Johnson, Aaron P

2014-01-01

In four experiments, we investigated how attention to local and global levels of hierarchical Navon figures affected the selection of diagnostic spatial scale information used in scene categorization. We explored this issue by asking observers to classify hybrid images (i.e., images that contain low spatial frequency (LSF) content of one image, and high spatial frequency (HSF) content from a second image) immediately following global and local Navon tasks. Hybrid images can be classified according to either their LSF, or HSF content; thus, making them ideal for investigating diagnostic spatial scale preference. Although observers were sensitive to both spatial scales (Experiment 1), they overwhelmingly preferred to classify hybrids based on LSF content (Experiment 2). In Experiment 3, we demonstrated that LSF based hybrid categorization was faster following global Navon tasks, suggesting that LSF processing associated with global Navon tasks primed the selection of LSFs in hybrid images. In Experiment 4, replicating Experiment 3 but suppressing the LSF information in Navon letters by contrast balancing the stimuli examined this hypothesis. Similar to Experiment 3, observers preferred to classify hybrids based on LSF content; however and in contrast, LSF based hybrid categorization was slower following global than local Navon tasks.
Hierarchically organized layout for visualization of biochemical pathways.

Science.gov (United States)

Tsay, Jyh-Jong; Wu, Bo-Liang; Jeng, Yu-Sen

2010-01-01

Many complex pathways are described as hierarchical structures in which a pathway is recursively partitioned into several sub-pathways, and organized hierarchically as a tree. The hierarchical structure provides a natural way to visualize the global structure of a complex pathway. However, none of the previous research on pathway visualization explores the hierarchical structures provided by many complex pathways. In this paper, we aim to develop algorithms that can take advantages of hierarchical structures, and give layouts that explore the global structures as well as local structures of pathways. We present a new hierarchically organized layout algorithm to produce layouts for hierarchically organized pathways. Our algorithm first decomposes a complex pathway into sub-pathway groups along the hierarchical organization, and then partition each sub-pathway group into basic components. It then applies conventional layout algorithms, such as hierarchical layout and force-directed layout, to compute the layout of each basic component. Finally, component layouts are joined to form a final layout of the pathway. Our main contribution is the development of algorithms for decomposing pathways and joining layouts. Experiment shows that our algorithm is able to give comprehensible visualization for pathways with hierarchies, cycles as well as complex structures. It clearly renders the global component structures as well as the local structure in each component. In addition, it runs very fast, and gives better visualization for many examples from previous related research. 2009 Elsevier B.V. All rights reserved.
Hierarchical screening for multiple mental disorders.

Science.gov (United States)

Batterham, Philip J; Calear, Alison L; Sunderland, Matthew; Carragher, Natacha; Christensen, Helen; Mackinnon, Andrew J

2013-10-01

There is a need for brief, accurate screening when assessing multiple mental disorders. Two-stage hierarchical screening, consisting of brief pre-screening followed by a battery of disorder-specific scales for those who meet diagnostic criteria, may increase the efficiency of screening without sacrificing precision. This study tested whether more efficient screening could be gained using two-stage hierarchical screening than by administering multiple separate tests. Two Australian adult samples (N=1990) with high rates of psychopathology were recruited using Facebook advertising to examine four methods of hierarchical screening for four mental disorders: major depressive disorder, generalised anxiety disorder, panic disorder and social phobia. Using K6 scores to determine whether full screening was required did not increase screening efficiency. However, pre-screening based on two decision tree approaches or item gating led to considerable reductions in the mean number of items presented per disorder screened, with estimated item reductions of up to 54%. The sensitivity of these hierarchical methods approached 100% relative to the full screening battery. Further testing of the hierarchical screening approach based on clinical criteria and in other samples is warranted. The results demonstrate that a two-phase hierarchical approach to screening multiple mental disorders leads to considerable increases efficiency gains without reducing accuracy. Screening programs should take advantage of prescreeners based on gating items or decision trees to reduce the burden on respondents. © 2013 Elsevier B.V. All rights reserved.
Self-assembled biomimetic superhydrophobic hierarchical arrays.

Science.gov (United States)

Yang, Hongta; Dou, Xuan; Fang, Yin; Jiang, Peng

2013-09-01

Here, we report a simple and inexpensive bottom-up technology for fabricating superhydrophobic coatings with hierarchical micro-/nano-structures, which are inspired by the binary periodic structure found on the superhydrophobic compound eyes of some insects (e.g., mosquitoes and moths). Binary colloidal arrays consisting of exemplary large (4 and 30 μm) and small (300 nm) silica spheres are first assembled by a scalable Langmuir-Blodgett (LB) technology in a layer-by-layer manner. After surface modification with fluorosilanes, the self-assembled hierarchical particle arrays become superhydrophobic with an apparent water contact angle (CA) larger than 150°. The throughput of the resulting superhydrophobic coatings with hierarchical structures can be significantly improved by templating the binary periodic structures of the LB-assembled colloidal arrays into UV-curable fluoropolymers by a soft lithography approach. Superhydrophobic perfluoroether acrylate hierarchical arrays with large CAs and small CA hysteresis can be faithfully replicated onto various substrates. Both experiments and theoretical calculations based on the Cassie's dewetting model demonstrate the importance of the hierarchical structure in achieving the final superhydrophobic surface states. Copyright © 2013 Elsevier Inc. All rights reserved.
Twin boundary spacing effects on shock response and spall behaviors of hierarchically nanotwinned fcc metals

International Nuclear Information System (INIS)

Yuan, Fuping; Chen, Liu; Jiang, Ping; Wu, Xiaolei

2014-01-01

Atomistic deformation mechanisms of hierarchically nano-twinned (NT) Ag under shock conditions have been investigated using a series of large-scale molecular dynamics simulations. For the same grain size d and the same spacing of primary twins λ 1 , the average flow stress behind the shock front in hierarchically NT Ag first increases with decreasing spacing of secondary twins λ 2 , achieving a maximum at a critical λ 2 , and then drops as λ 2 decreases further. Above the critical λ 2 , the deformation mechanisms are dominated by three type strengthening mechanisms: (a) partial dislocations emitted from grain boundaries (GBs) travel across other boundaries; (b) partial dislocations emitted from twin boundaries (TBs) travel across other TBs; (c) formation of tertiary twins. Below the critical λ 2 , the deformation mechanism are dominated by two softening mechanisms: (a) detwinning of secondary twins; (b) formation of new grains by cross slip of partial dislocations. Moreover, the twin-free nanocrystalline (NC) Ag is found to have lower average flow stress behind the shock front than those of all hierarchically NT Ag samples except the one with the smallest λ 2 of 0.71 nm. No apparent correlation between the spall strength and λ 2 is observed in hierarchically NT Ag, since voids always nucleate at both GBs and boundaries of the primary twins. However, twin-free NC Ag is found to have higher spall strength than hierarchically NT Ag. Voids can only nucleate from GBs for twin-free NC Ag, therefore, twin-free NC Ag has less nucleation sources along the shock direction when compared to hierarchically NT Ag, which requiring higher tensile stress to create spallation. These findings should contribute to the understandings of deformation mechanisms of hierarchically NT fcc metals under extreme deformation conditions
Hierarchically structured, nitrogen-doped carbon membranes

KAUST Repository

Wang, Hong

2017-08-03

The present invention is a structure, method of making and method of use for a novel macroscopic hierarchically structured, nitrogen-doped, nano-porous carbon membrane (HNDCMs) with asymmetric and hierarchical pore architecture that can be produced on a large-scale approach. The unique HNDCM holds great promise as components in separation and advanced carbon devices because they could offer unconventional ﬂuidic transport phenomena on the nanoscale. Overall, the invention set forth herein covers a hierarchically structured, nitrogen-doped carbon membranes and methods of making and using such a membranes.
Hierarchical Rhetorical Sentence Categorization for Scientific Papers

Science.gov (United States)

Rachman, G. H.; Khodra, M. L.; Widyantoro, D. H.

2018-03-01

Important information in scientific papers can be composed of rhetorical sentences that is structured from certain categories. To get this information, text categorization should be conducted. Actually, some works in this task have been completed by employing word frequency, semantic similarity words, hierarchical classification, and the others. Therefore, this paper aims to present the rhetorical sentence categorization from scientific paper by employing TF-IDF and Word2Vec to capture word frequency and semantic similarity words and employing hierarchical classification. Every experiment is tested in two classifiers, namely Naïve Bayes and SVM Linear. This paper shows that hierarchical classifier is better than flat classifier employing either TF-IDF or Word2Vec, although it increases only almost 2% from 27.82% when using flat classifier until 29.61% when using hierarchical classifier. It shows also different learning model for child-category can be built by hierarchical classifier.
A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China.

Science.gov (United States)

Wang, Wenqiao; Ying, Yangyang; Wu, Quanyuan; Zhang, Haiping; Ma, Dedong; Xiao, Wei

2015-03-01

workplace associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD. Ambient air pollution is correlated with AECOPD hospitalizations spatially. A 10 μg/m(3) increase of PM10 at workplace was associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD in Jinan, 2009. As a spatial data processing tool, GIS has novel and great potential on air pollutants exposure assessment and spatial analysis in AECOPD research. Copyright © 2015 Elsevier Ltd. All rights reserved.
Assessment of surface water quality using hierarchical cluster analysis

Directory of Open Access Journals (Sweden)

Dheeraj Kumar Dabgerwal

2016-02-01

Full Text Available This study was carried out to assess the physicochemical quality river Varuna inVaranasi,India. Water samples were collected from 10 sites during January-June 2015. Pearson correlation analysis was used to assess the direction and strength of relationship between physicochemical parameters. Hierarchical Cluster analysis was also performed to determine the sources of pollution in the river Varuna. The result showed quite high value of DO, Nitrate, BOD, COD and Total Alkalinity, above the BIS permissible limit. The results of correlation analysis identified key water parameters as pH, electrical conductivity, total alkalinity and nitrate, which influence the concentration of other water parameters. Cluster analysis identified three major clusters of sampling sites out of total 10 sites, according to the similarity in water quality. This study illustrated the usefulness of correlation and cluster analysis for getting better information about the river water quality.International Journal of Environment Vol. 5 (1 2016, pp: 32-44
Systems View on Spatial Planning and Perception Based on Invariants in Agent-Environment Dynamics

Directory of Open Access Journals (Sweden)

Berenice eMettler

2015-01-01

Full Text Available Modeling agile and versatile spatial behavior remains a challenging task, due to the intricate coupling of planning, control, and perceptual processes. Previous results have shown that humans plan and organize their guidance behavior by exploiting patterns in the interactions between agent or organism and the environment. These patterns, described under the concept of Interaction Patterns (IPs, capture invariants arising from equivalences and symmetries in the interaction with the environment, as well as effects arising from intrinsic properties of human control and guidance processes, such as perceptual guidance mechanisms. The paper takes a systems' perspective, considering the IP as a unit of organization, and builds on its properties to present a hierarchical model that delineates the planning, control, and perceptual processes and their integration. The model's planning process is further elaborated by showing that the IP can be abstracted, using spatial time-to-go functions. The perceptual processes are elaborated from the hierarchical model. The paper provides experimental support for the model's ability to predict the spatial organization of behavior and the perceptual processes.
Attentional selection of levels within hierarchically organized figures is mediated by object-files

Directory of Open Access Journals (Sweden)

Mitchell Joseph Valdes-Sosa

2014-12-01

Full Text Available Objects frequently have a hierarchical organization (tree-branch-leaf. How do we select the level to be attended? This has been explored with compound letters: a global letter built from local letters. One explanation, backed by much empirical support, is that attentional competition is biased towards certain spatial frequency (SF bands across all locations and objects (a SF filter. This view assumes that the global and local letters are carried respectively by low and high SF bands, and that the bias can persist over time. Here we advocate a complementary view in which perception of hierarchical level is determined by how we represent each object-file. Although many properties bound to an object-file (i.e. position, color, even shape can mutate without affecting its persistence over time, we posit that same object-file cannot be used to store information from different hierarchical levels. Thus selection of level would be independent from locations but not from the way objects are represented at each moment. These views were contrasted via an attentional blink paradigm that presented letters within compound figures, but only one level at a time. Attending to two letters in rapid succession was easier if they were at the same- compared to different-levels, as predicted by both accounts. However, only the object-file account was able to explain why it was easier to report two targets on the same moving object compared to the same targets on distinct objects. The interference of different masks on target recognition was also easier to predict by the object-file account than by the SF filter. The methods introduced here allowed us to investigate attention to hierarchical levels and to objects within the same empirical framework. The data suggests that SF information is used to structure the internal organization of object representations, a process understood best by integrating object-file theory with previous models of hierarchical perception.
Photon propagation in heterogeneous optical media with spatial correlations: enhanced mean-free-paths and wider-than-exponential free-path distributions

International Nuclear Information System (INIS)

Davis, A.B.; Marshak, Alexander

2004-01-01

Beer's law of exponential decay in direct transmission is well-known but its break-down in spatially variable optical media has been discussed only sporadically in the literature. We document here this break-down in three-dimensional (3D) media with complete generality and explore its ramifications for photon propagation. We show that effective transmission laws and their associated free-path distributions (FPDs) are in fact never exactly exponential in variable media of any kind. Moreover, if spatial correlations in the extinction field extend at least to the scale of the mean-free-path (MFP), FPDs are necessarily wider-than-exponential in the sense that all higher-order moments of the relevant mean-field FPDs exceed those of the exponential FPD, even if it is tuned to yield the proper MFP. The MFP itself is always larger than the inverse of average extinction in a variable medium. In a vast and important class of spatially-correlated random media, the MFP is indeed the average of the inverse of extinction. We translate these theoretical findings into a practical method for deciding a priori when 3D effects become important. Finally, we discuss an obvious but limited analogy between our analysis of spatial variability and the well-known effects of strong spectral variability in gaseous media when observed or modeled at moderate resolution
Zeolitic materials with hierarchical porous structures.

Science.gov (United States)

Lopez-Orozco, Sofia; Inayat, Amer; Schwab, Andreas; Selvam, Thangaraj; Schwieger, Wilhelm

2011-06-17

During the past several years, different kinds of hierarchical structured zeolitic materials have been synthesized due to their highly attractive properties, such as superior mass/heat transfer characteristics, lower restriction of the diffusion of reactants in the mesopores, and low pressure drop. Our contribution provides general information regarding types and preparation methods of hierarchical zeolitic materials and their relative advantages and disadvantages. Thereafter, recent advances in the preparation and characterization of hierarchical zeolitic structures within the crystallites by post-synthetic treatment methods, such as dealumination or desilication; and structured devices by in situ and ex situ zeolite coatings on open-cellular ceramic foams as (non-reactive as well as reactive) supports are highlighted. Specific advantages of using hierarchical zeolitic catalysts/structures in selected catalytic reactions, such as benzene to phenol (BTOP) and methanol to olefins (MTO) are presented. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Processing of hierarchical syntactic structure in music.

Science.gov (United States)

Koelsch, Stefan; Rohrmeier, Martin; Torrecuso, Renzo; Jentschke, Sebastian

2013-09-17

Hierarchical structure with nested nonlocal dependencies is a key feature of human language and can be identified theoretically in most pieces of tonal music. However, previous studies have argued against the perception of such structures in music. Here, we show processing of nonlocal dependencies in music. We presented chorales by J. S. Bach and modified versions in which the hierarchical structure was rendered irregular whereas the local structure was kept intact. Brain electric responses differed between regular and irregular hierarchical structures, in both musicians and nonmusicians. This finding indicates that, when listening to music, humans apply cognitive processes that are capable of dealing with long-distance dependencies resulting from hierarchically organized syntactic structures. Our results reveal that a brain mechanism fundamental for syntactic processing is engaged during the perception of music, indicating that processing of hierarchical structure with nested nonlocal dependencies is not just a key component of human language, but a multidomain capacity of human cognition.
A Generalized Spatial Correlation Model for 3D MIMO Channels based on the Fourier Coefficients of Power Spectrums

KAUST Repository

Nadeem, Qurrat-Ul-Ain

2015-05-07

Previous studies have confirmed the adverse impact of fading correlation on the mutual information (MI) of two-dimensional (2D) multiple-input multiple-output (MIMO) systems. More recently, the trend is to enhance the system performance by exploiting the channel’s degrees of freedom in the elevation, which necessitates the derivation and characterization of three-dimensional (3D) channels in the presence of spatial correlation. In this paper, an exact closed-form expression for the Spatial Correlation Function (SCF) is derived for 3D MIMO channels. This novel SCF is developed for a uniform linear array of antennas with nonisotropic antenna patterns. The proposed method resorts to the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre and associated Legendre polynomials. The resulting expression depends on the underlying arbitrary angular distributions and antenna patterns through the Fourier Series (FS) coefficients of power azimuth and elevation spectrums. The novelty of the proposed method lies in the SCF being valid for any 3D propagation environment. The developed SCF determines the covariance matrices at the transmitter and the receiver that form the Kronecker channel model. In order to quantify the effects of correlation on the system performance, the information-theoretic deterministic equivalents of the MI for the Kronecker model are utilized in both mono-user and multi-user cases. Numerical results validate the proposed analytical expressions and elucidate the dependence of the system performance on azimuth and elevation angular spreads and antenna patterns. Some useful insights into the behaviour of MI as a function of downtilt angles are provided. The derived model will help evaluate the performance of correlated 3D MIMO channels in the future.

Hierarchical Nanoceramics for Industrial Process Sensors

Energy Technology Data Exchange (ETDEWEB)

Ruud, James, A.; Brosnan, Kristen, H.; Striker, Todd; Ramaswamy, Vidya; Aceto, Steven, C.; Gao, Yan; Willson, Patrick, D.; Manoharan, Mohan; Armstrong, Eric, N., Wachsman, Eric, D.; Kao, Chi-Chang

2011-07-15

This project developed a robust, tunable, hierarchical nanoceramics materials platform for industrial process sensors in harsh-environments. Control of material structure at multiple length scales from nano to macro increased the sensing response of the materials to combustion gases. These materials operated at relatively high temperatures, enabling detection close to the source of combustion. It is anticipated that these materials can form the basis for a new class of sensors enabling widespread use of efficient combustion processes with closed loop feedback control in the energy-intensive industries. The first phase of the project focused on materials selection and process development, leading to hierarchical nanoceramics that were evaluated for sensing performance. The second phase focused on optimizing the materials processes and microstructures, followed by validation of performance of a prototype sensor in a laboratory combustion environment. The objectives of this project were achieved by: (1) synthesizing and optimizing hierarchical nanostructures; (2) synthesizing and optimizing sensing nanomaterials; (3) integrating sensing functionality into hierarchical nanostructures; (4) demonstrating material performance in a sensing element; and (5) validating material performance in a simulated service environment. The project developed hierarchical nanoceramic electrodes for mixed potential zirconia gas sensors with increased surface area and demonstrated tailored electrocatalytic activity operable at high temperatures enabling detection of products of combustion such as NOx close to the source of combustion. Methods were developed for synthesis of hierarchical nanostructures with high, stable surface area, integrated catalytic functionality within the structures for gas sensing, and demonstrated materials performance in harsh lab and combustion gas environments.
Mapping fields of 137Cs contamination in soils in the context of their stability and hierarchical spatial structure

Science.gov (United States)

Korobova, E.; Romanov, S.

2009-04-01

Technogenic radioisotopes now dispersed in the environment are involved in natural and technogenic processes forming specific geochemical fields and serving as tracers of modern mass migration and geofield transformation. Cs-137 radioisotopes having a comparatively long life time are known for a fast fixation by the top soil layer; radiocesium activity can be measured in the surface layer in field conditions. This makes 137Cs rather convenient for the study and modeling a behavior of toxic elements in soils [1-3, 5] and for the investigation of relative stability and hierarchical fractal structures of the soil contamination of the atmospheric origin [2]. The objective of the experimental study performed on the test site in Bryansk region was to find and prove polycentric regularities in the structure of 137Cs contamination field formed after the Chernobyl accident in natural conditions. Such a character of spatial variability can be seen on the maps showing different soil parameters and chemical element distribution measured in grids [3-5]. The research was undertaken to support our idea of the regular patterns in the contamination field structure that enables to apply a mathematical theory of the field to the geochemical fields modeling on the basis of a limited number of direct measurements sufficient to reproduce the configuration and main parameters of the geochemical field structure on the level of the elementary landscape geochemical system (top-slope-bottom). Cs-137 field measurements were verified by a direct soil sampling. Soil cores dissected into subsamples with increments of 2, 5 and 10 cm, were taken to the depth of 40 cm at points with various surface activity located at different elements of relief. According to laboratory measurements 137Cs inventory in soils varied from 344 to 3448 kBq/m2 (983 kBq/m2 on the average). From 95,1% to 98,0% to of the total inventory was retained in the top 20-cm soil layer. This confirmed that field gamma spectrometry
Correlation analysis of the urban heat island effect and the spatial and temporal distribution of atmospheric particulates using TM images in Beijing

International Nuclear Information System (INIS)

Xu, L.Y.; Xie, X.D.; Li, S.

2013-01-01

This study combines the methods of observation statistics and remote sensing retrieval, using remote sensing information including the urban heat island (UHI) intensity index, the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the difference vegetation index (DVI) to analyze the correlation between the urban heat island effect and the spatial and temporal concentration distributions of atmospheric particulates in Beijing. The analysis establishes (1) a direct correlation between UHI and DVI; (2) an indirect correlation among UHI, NDWI and DVI; and (3) an indirect correlation among UHI, NDVI, and DVI. The results proved the existence of three correlation types with regional and seasonal effects and revealed an interesting correlation between UHI and DVI, that is, if UHI is below 0.1, then DVI increases with the increase in UHI, and vice versa. Also, DVI changes more with UHI in the two middle zones of Beijing. -- Highlights: •We analyze the correlation from the spatial and temporal views. •We present correlation analyses among UHI, NDWI, NDVI, and DVI from three perspectives. •Three correlations are proven to exist with regional and seasonal effects. •If UHI is below 0.1, then DVI increases with the increase in UHI, and vice versa. •The DVI changes more with UHI in the two middle zones of Beijing. -- Generally, if UHI is below 0.1 in the weak heat island or green island range, then DVI increases with the increase in UHI, and vice versa
Resolving the Framework Position of Organic Structure-Directing Agents in Hierarchical Zeolites via Polarized Stimulated Raman Scattering.

Science.gov (United States)

Fleury, Guillaume; Steele, Julian A; Gerber, Iann C; Jolibois, F; Puech, P; Muraoka, Koki; Keoh, Sye Hoe; Chaikittisilp, Watcharop; Okubo, Tatsuya; Roeffaers, Maarten B J

2018-04-05

The direct synthesis of hierarchically intergrown silicalite-1 can be achieved using a specific diquaternary ammonium agent. However, the location of these molecules in the zeolite framework, which is critical to understand the formation of the material, remains unclear. Where traditional characterization tools have previously failed, herein we use polarized stimulated Raman scattering (SRS) microscopy to resolve molecular organization inside few-micron-sized crystals. Through a combination of experiment and first-principles calculations, our investigation reveals the preferential location of the templating agent inside the linear pores of the MFI framework. Besides illustrating the attractiveness of SRS microscopy in the field of material science to study and spatially resolve local molecular distribution as well as orientation, these results can be exploited in the design of new templating agents for the preparation of hierarchical zeolites.
The Case for a Hierarchical Cosmology

Science.gov (United States)

Vaucouleurs, G. de

1970-01-01

The development of modern theoretical cosmology is presented and some questionable assumptions of orthodox cosmology are pointed out. Suggests that recent observations indicate that hierarchical clustering is a basic factor in cosmology. The implications of hierarchical models of the universe are considered. Bibliography. (LC)
Classification using Hierarchical Naive Bayes models

DEFF Research Database (Denmark)

Langseth, Helge; Dyhre Nielsen, Thomas

2006-01-01

Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe......, termed Hierarchical Naïve Bayes models. Hierarchical Naïve Bayes models extend the modeling flexibility of Naïve Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naïve Bayes models...
Improved Adhesion and Compliancy of Hierarchical Fibrillar Adhesives.

Science.gov (United States)

Li, Yasong; Gates, Byron D; Menon, Carlo

2015-08-05

The gecko relies on van der Waals forces to cling onto surfaces with a variety of topography and composition. The hierarchical fibrillar structures on their climbing feet, ranging from mesoscale to nanoscale, are hypothesized to be key elements for the animal to conquer both smooth and rough surfaces. An epoxy-based artificial hierarchical fibrillar adhesive was prepared to study the influence of the hierarchical structures on the properties of a dry adhesive. The presented experiments highlight the advantages of a hierarchical structure despite a reduction of overall density and aspect ratio of nanofibrils. In contrast to an adhesive containing only nanometer-size fibrils, the hierarchical fibrillar adhesives exhibited a higher adhesion force and better compliancy when tested on an identical substrate.
Construction of 3D Arrays of Cylindrically Hierarchical Structures with ZnO Nanorods Hydrothermally Synthesized on Optical Fiber Cores

Directory of Open Access Journals (Sweden)

Weixuan Jing

2014-01-01

Full Text Available With ZnO nanorods hydrothermally synthesized on manually assembled arrays of optical fiber cores, 3D arrays of ZnO nanorod-based cylindrically hierarchical structures with nominal pitch 250 μm or 375 μm were constructed. Based on micrographs of scanning electron microscopy and image processing operators of MATLAB software, the 3D arrays of cylindrically hierarchical structures were quantitatively characterized. The values of the actual diameters, the actual pitches, and the parallelism errors suggest that the process capability of the manual assembling is sufficient and the quality of the 3D arrays of cylindrically hierarchical structures is acceptable. The values of the characteristic parameters such as roughness, skewness, kurtosis, correlation length, and power spectrum density show that the surface morphologies of the cylindrically hierarchical structures not only were affected significantly by Zn2+ concentration of the growth solution but also were anisotropic due to different curvature radii of the optical fiber core at side and front view.
Nonparametric Bayesian models for a spatial covariance.

Science.gov (United States)

Reich, Brian J; Fuentes, Montserrat

2012-01-01

A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.
The use of hierarchical clustering for the design of optimized monitoring networks

Science.gov (United States)

Soares, Joana; Makar, Paul Andrew; Aklilu, Yayne; Akingunola, Ayodeji

2018-05-01

Associativity analysis is a powerful tool to deal with large-scale datasets by clustering the data on the basis of (dis)similarity and can be used to assess the efficacy and design of air quality monitoring networks. We describe here our use of Kolmogorov-Zurbenko filtering and hierarchical clustering of NO2 and SO2 passive and continuous monitoring data to analyse and optimize air quality networks for these species in the province of Alberta, Canada. The methodology applied in this study assesses dissimilarity between monitoring station time series based on two metrics: 1 - R, R being the Pearson correlation coefficient, and the Euclidean distance; we find that both should be used in evaluating monitoring site similarity. We have combined the analytic power of hierarchical clustering with the spatial information provided by deterministic air quality model results, using the gridded time series of model output as potential station locations, as a proxy for assessing monitoring network design and for network optimization. We demonstrate that clustering results depend on the air contaminant analysed, reflecting the difference in the respective emission sources of SO2 and NO2 in the region under study. Our work shows that much of the signal identifying the sources of NO2 and SO2 emissions resides in shorter timescales (hourly to daily) due to short-term variation of concentrations and that longer-term averages in data collection may lose the information needed to identify local sources. However, the methodology identifies stations mainly influenced by seasonality, if larger timescales (weekly to monthly) are considered. We have performed the first dissimilarity analysis based on gridded air quality model output and have shown that the methodology is capable of generating maps of subregions within which a single station will represent the entire subregion, to a given level of dissimilarity. We have also shown that our approach is capable of identifying different
Hierarchical patch-based co-registration of differently stained histopathology slides

Science.gov (United States)

Yigitsoy, Mehmet; Schmidt, Günter

2017-03-01

Over the past decades, digital pathology has emerged as an alternative way of looking at the tissue at subcellular level. It enables multiplexed analysis of different cell types at micron level. Information about cell types can be extracted by staining sections of a tissue block using different markers. However, robust fusion of structural and functional information from different stains is necessary for reproducible multiplexed analysis. Such a fusion can be obtained via image co-registration by establishing spatial correspondences between tissue sections. Spatial correspondences can then be used to transfer various statistics about cell types between sections. However, the multi-modal nature of images and sparse distribution of interesting cell types pose several challenges for the registration of differently stained tissue sections. In this work, we propose a co-registration framework that efficiently addresses such challenges. We present a hierarchical patch-based registration of intensity normalized tissue sections. Preliminary experiments demonstrate the potential of the proposed technique for the fusion of multi-modal information from differently stained digital histopathology sections.
Wiener spectral effects of spatial correlation between the sites of characteristic x-ray emission and reabsorption in radiographic screen-film systems

Energy Technology Data Exchange (ETDEWEB)

Metz, C E; Vyborny, C J [Chicago Univ., IL (USA). Dept. of Radiology

1983-05-01

When characteristic x-rays are generated and reabsorbed in the phosphor of a radiographic screen-film system, the positions at which light is emitted from the initial and secondary interactions are correlated. A simple statistical model is developed to account for the effect of this correlation on the Wiener spectrum of quantum mottle. Unlike previous models, which ignore spatial correlation, the new model predicts that not only noise magnitude but also noise texture is changed as the incident x-ray energy exceeds the phosphor K-edge.
Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach

Science.gov (United States)

Ulusoy, Tolga; Keskin, Mustafa; Shirvani, Ayoub; Deviren, Bayram; Kantar, Ersin; Çaǧrı Dönmez, Cem

2012-11-01

This study reports on topology of the top 40 UK companies that have been analysed for predictive verification of markets for the period 2006-2010, applying the concept of minimal spanning tree and hierarchical tree (HT) analysis. Construction of the minimal spanning tree (MST) and the hierarchical tree (HT) is confined to a brief description of the methodology and a definition of the correlation function between a pair of companies based on the London Stock Exchange (LSE) index in order to quantify synchronization between the companies. A derivation of hierarchical organization and the construction of minimal-spanning and hierarchical trees for the 2006-2008 and 2008-2010 periods have been used and the results validate the predictive verification of applied semantics. The trees are known as useful tools to perceive and detect the global structure, taxonomy and hierarchy in financial data. From these trees, two different clusters of companies in 2006 were detected. They also show three clusters in 2008 and two between 2008 and 2010, according to their proximity. The clusters match each other as regards their common production activities or their strong interrelationship. The key companies are generally given by major economic activities as expected. This work gives a comparative approach between MST and HT methods from statistical physics and information theory with analysis of financial markets that may give new valuable and useful information of the financial market dynamics.
Spatially resolved vertical vorticity in solar supergranulation using helioseismology and local correlation tracking

Science.gov (United States)

Langfellner, J.; Gizon, L.; Birch, A. C.

2015-09-01

Flow vorticity is a fundamental property of turbulent convection in rotating systems. Solar supergranules exhibit a preferred sense of rotation, which depends on the hemisphere. This is due to the Coriolis force acting on the diverging horizontal flows. We aim to spatially resolve the vertical flow vorticity of the average supergranule at different latitudes, both for outflow and inflow regions. To measure the vertical vorticity, we use two independent techniques: time-distance helioseismology (TD) and local correlation tracking of granules in intensity images (LCT) using data from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Both maps are corrected for center-to-limb systematic errors. We find that 8 h TD and LCT maps of vertical vorticity are highly correlated at large spatial scales. Associated with the average supergranule outflow, we find tangential (vortical) flows that reach about 10 m s-1 in the clockwise direction at 40° latitude. In average inflow regions, the tangential flow reaches the same magnitude, but in the anticlockwise direction. These tangential velocities are much smaller than the radial (diverging) flow component (300 m s-1 for the average outflow and 200 m s-1 for the average inflow). The results for TD and LCT as measured from HMI are in excellent agreement for latitudes between -60° and 60°. From HMI LCT, we measure the vorticity peak of the average supergranule to have a full width at half maximum of about 13 Mm for outflows and 8 Mm for inflows. This is larger than the spatial resolution of the LCT measurements (about 3 Mm). On the other hand, the vorticity peak in outflows is about half the value measured at inflows (e.g., 4 × 10-6 s-1 clockwise compared to 8 × 10-6 s-1 anticlockwise at 40° latitude). Results from the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) obtained in 2010 are biased compared to the HMI/SDO results for the same period
Attention to local and global levels of hierarchical Navon figures affects rapid scene categorization

Science.gov (United States)

Brand, John; Johnson, Aaron P.

2014-01-01

In four experiments, we investigated how attention to local and global levels of hierarchical Navon figures affected the selection of diagnostic spatial scale information used in scene categorization. We explored this issue by asking observers to classify hybrid images (i.e., images that contain low spatial frequency (LSF) content of one image, and high spatial frequency (HSF) content from a second image) immediately following global and local Navon tasks. Hybrid images can be classified according to either their LSF, or HSF content; thus, making them ideal for investigating diagnostic spatial scale preference. Although observers were sensitive to both spatial scales (Experiment 1), they overwhelmingly preferred to classify hybrids based on LSF content (Experiment 2). In Experiment 3, we demonstrated that LSF based hybrid categorization was faster following global Navon tasks, suggesting that LSF processing associated with global Navon tasks primed the selection of LSFs in hybrid images. In Experiment 4, replicating Experiment 3 but suppressing the LSF information in Navon letters by contrast balancing the stimuli examined this hypothesis. Similar to Experiment 3, observers preferred to classify hybrids based on LSF content; however and in contrast, LSF based hybrid categorization was slower following global than local Navon tasks. PMID:25520675
Does pop music exist? Hierarchical structure in phonographic markets

Science.gov (United States)

Buda, Andrzej

2012-11-01

I find a topological arrangement of assets traded in phonographic markets which has associated a meaningful economic taxonomy. I continue using the Minimal Spanning Tree and the correlations between assets, but now outside the stock markets. This is the first attempt to use these methods on phonographic markets where we have artists instead of stocks. The value of an artist is defined by record sales. The graph is obtained starting from the matrix of correlation coefficients computed between the world’s most popular 30 artists by considering the synchronous time evolution of the difference of the logarithm of weekly record sales. This method provides the hierarchical structure of the phonographic market and information on which music genre is meaningful according to customers. Statistical properties (including the Hurst exponent) of weekly record sales in the phonographic market are also discussed.
Validating spatial structure in canopy water content using geostatistics

Science.gov (United States)

Sanderson, E. W.; Zhang, M. H.; Ustin, S. L.; Rejmankova, E.; Haxo, R. S.

1995-01-01

Heterogeneity in ecological phenomena are scale dependent and affect the hierarchical structure of image data. AVIRIS pixels average reflectance produced by complex absorption and scattering interactions between biogeochemical composition, canopy architecture, view and illumination angles, species distributions, and plant cover as well as other factors. These scales affect validation of pixel reflectance, typically performed by relating pixel spectra to ground measurements acquired at scales of 1m(exp 2) or less (e.g., field spectra, foilage and soil samples, etc.). As image analysis becomes more sophisticated, such as those for detection of canopy chemistry, better validation becomes a critical problem. This paper presents a methodology for bridging between point measurements and pixels using geostatistics. Geostatistics have been extensively used in geological or hydrogeolocial studies but have received little application in ecological studies. The key criteria for kriging estimation is that the phenomena varies in space and that an underlying controlling process produces spatial correlation between the measured data points. Ecological variation meets this requirement because communities vary along environmental gradients like soil moisture, nutrient availability, or topography.
Visual Spatial Attention Training Improve Spatial Attention and Motor Control for Unilateral Neglect Patients.

Science.gov (United States)

Wang, Wei; Ji, Xiangtong; Ni, Jun; Ye, Qian; Zhang, Sicong; Chen, Wenli; Bian, Rong; Yu, Cui; Zhang, Wenting; Shen, Guangyu; Machado, Sergio; Yuan, Tifei; Shan, Chunlei

2015-01-01

To compare the effect of visual spatial training on the spatial attention to that on motor control and to correlate the improvement of spatial attention to motor control progress after visual spatial training in subjects with unilateral spatial neglect (USN). 9 cases with USN after right cerebral stroke were randomly divided into Conventional treatment group + visual spatial attention and Conventional treatment group. The Conventional treatment group + visual spatial attention received conventional rehabilitation therapy (physical and occupational therapy) and visual spatial attention training (optokinetic stimulation and right half-field eye patching). The Conventional treatment group was only treated with conventional rehabilitation training (physical and occupational therapy). All patients were assessed by behavioral inattention test (BIT), Fugl-Meyer Assessment of motor function (FMA), equilibrium coordination test (ECT) and non-equilibrium coordination test (NCT) before and after 4 weeks treatment. Total scores in both groups (without visual spatial attention/with visual spatial attention) improved significantly (BIT: P=0.021/P=0.000, d=1.667/d=2.116, power=0.69/power=0.98, 95%CI[-0.8839,45.88]/95%CI=[16.96,92.64]; FMA: P=0.002/P=0.000, d=2.521/d=2.700, power=0.93/power=0.98, 95%CI[5.707,30.79]/95%CI=[16.06,53.94]; ECT: P=0.002/ P=0.000, d=2.031/d=1.354, power=0.90/power=0.17, 95%CI[3.380,42.61]/95%CI=[-1.478,39.08]; NCT: P=0.013/P=0.000, d=1.124/d=1.822, power=0.41/power=0.56, 95%CI[-7.980,37.48]/95%CI=[4.798,43.60],) after treatment. Among the 2 groups, the group with visual spatial attention significantly improved in BIT (P=0.003, d=3.103, power=1, 95%CI[15.68,48.92]), FMA of upper extremity (P=0.006, d=2.771, power=1, 95%CI[5.061,20.14]) and NCT (P=0.010, d=2.214, power=0.81-0.90, 95%CI[3.018,15.88]). Correlative analysis shows that the change of BIT scores is positively correlated to the change of FMA total score (r=0.77, Pvisual spatial training could
Impact of Spatial Scales on the Intercomparison of Climate Scenarios

Energy Technology Data Exchange (ETDEWEB)

Luo, Wei; Steptoe, Michael; Chang, Zheng; Link, Robert; Clarke, Leon; Maciejewski, Ross

2017-01-01

Scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment, but intercomparison and similarity analysis of different climate scenarios based on multiple simulation runs remain challenging. Although spatial heterogeneity plays a key role in modeling climate and human systems, little research has been performed to understand the impact of spatial variations and scales on similarity analysis of climate scenarios. To address this issue, the authors developed a geovisual analytics framework that lets users perform similarity analysis of climate scenarios from the Global Change Assessment Model (GCAM) using a hierarchical clustering approach.
TYPE Ia SUPERNOVA LIGHT CURVE INFERENCE: HIERARCHICAL MODELS IN THE OPTICAL AND NEAR-INFRARED

International Nuclear Information System (INIS)

Mandel, Kaisey S.; Narayan, Gautham; Kirshner, Robert P.

2011-01-01

We have constructed a comprehensive statistical model for Type Ia supernova (SN Ia) light curves spanning optical through near-infrared (NIR) data. A hierarchical framework coherently models multiple random and uncertain effects, including intrinsic supernova (SN) light curve covariances, dust extinction and reddening, and distances. An improved BAYESN Markov Chain Monte Carlo code computes probabilistic inferences for the hierarchical model by sampling the global probability density of parameters describing individual SNe and the population. We have applied this hierarchical model to optical and NIR data of 127 SNe Ia from PAIRITEL, CfA3, Carnegie Supernova Project, and the literature. We find an apparent population correlation between the host galaxy extinction A V and the ratio of total-to-selective dust absorption R V . For SNe with low dust extinction, A V ∼ V ∼ 2.5-2.9, while at high extinctions, A V ∼> 1, low values of R V < 2 are favored. The NIR luminosities are excellent standard candles and are less sensitive to dust extinction. They exhibit low correlation with optical peak luminosities, and thus provide independent information on distances. The combination of NIR and optical data constrains the dust extinction and improves the predictive precision of individual SN Ia distances by about 60%. Using cross-validation, we estimate an rms distance modulus prediction error of 0.11 mag for SNe with optical and NIR data versus 0.15 mag for SNe with optical data alone. Continued study of SNe Ia in the NIR is important for improving their utility as precise and accurate cosmological distance indicators.

Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach

International Nuclear Information System (INIS)

Musciotto, Federico; Marotta, Luca; Miccichè, Salvatore; Piilo, Jyrki; Mantegna, Rosario N.

2016-01-01

We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that is able to detect a hierarchical structure of the trading profiles of heterogeneous individual investors. We verify that the detected hierarchical structure is highly overlapping with the cluster structure obtained with the approach of statistically validated networks when an appropriate threshold of the hierarchical trees is used. We also show that the combination of the correlation based method and of the statistically validated method provides a way to expand the information about the clusters of investors with similar trading profiles in a robust and reliable way.
Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.

Science.gov (United States)

Syed Abdul Mutalib, Sharifah Norsukhairin; Juahir, Hafizan; Azid, Azman; Mohd Sharif, Sharifah; Latif, Mohd Talib; Aris, Ahmad Zaharin; Zain, Sharifuddin M; Dominick, Doreena

2013-09-01

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
Spatial correlation in matter-wave interference as a measure of decoherence, dephasing, and entropy

Science.gov (United States)

Chen, Zilin; Beierle, Peter; Batelaan, Herman

2018-04-01

The loss of contrast in double-slit electron diffraction due to dephasing and decoherence processes is studied. It is shown that the spatial intensity correlation function of diffraction patterns can be used to distinguish between dephasing and decoherence. This establishes a measure of time reversibility that does not require the determination of coherence terms of the density matrix, while von Neumann entropy, another measure of time reversibility, does require coherence terms. This technique is exciting in view of the need to understand and control the detrimental experimental effect of contrast loss and for fundamental studies on the transition from the classical to the quantum regime.
Hierarchical assembly of viral nanotemplates with encoded microparticles via nucleic acid hybridization.

Science.gov (United States)

Tan, Wui Siew; Lewis, Christina L; Horelik, Nicholas E; Pregibon, Daniel C; Doyle, Patrick S; Yi, Hyunmin

2008-11-04

We demonstrate hierarchical assembly of tobacco mosaic virus (TMV)-based nanotemplates with hydrogel-based encoded microparticles via nucleic acid hybridization. TMV nanotemplates possess a highly defined structure and a genetically engineered high density thiol functionality. The encoded microparticles are produced in a high throughput microfluidic device via stop-flow lithography (SFL) and consist of spatially discrete regions containing encoded identity information, an internal control, and capture DNAs. For the hybridization-based assembly, partially disassembled TMVs were programmed with linker DNAs that contain sequences complementary to both the virus 5' end and a selected capture DNA. Fluorescence microscopy, atomic force microscopy (AFM), and confocal microscopy results clearly indicate facile assembly of TMV nanotemplates onto microparticles with high spatial and sequence selectivity. We anticipate that our hybridization-based assembly strategy could be employed to create multifunctional viral-synthetic hybrid materials in a rapid and high-throughput manner. Additionally, we believe that these viral-synthetic hybrid microparticles may find broad applications in high capacity, multiplexed target sensing.
Hierarchical analysis of acceptable use policies

Directory of Open Access Journals (Sweden)

P. A. Laughton

2008-01-01

Full Text Available Acceptable use policies (AUPs are vital tools for organizations to protect themselves and their employees from misuse of computer facilities provided. A well structured, thorough AUP is essential for any organization. It is impossible for an effective AUP to deal with every clause and remain readable. For this reason, some sections of an AUP carry more weight than others, denoting importance. The methodology used to develop the hierarchical analysis is a literature review, where various sources were consulted. This hierarchical approach to AUP analysis attempts to highlight important sections and clauses dealt with in an AUP. The emphasis of the hierarchal analysis is to prioritize the objectives of an AUP.
Inheritance rules for Hierarchical Metadata Based on ISO 19115

Science.gov (United States)

Zabala, A.; Masó, J.; Pons, X.

2012-04-01

Mainly, ISO19115 has been used to describe metadata for datasets and services. Furthermore, ISO19115 standard (as well as the new draft ISO19115-1) includes a conceptual model that allows to describe metadata at different levels of granularity structured in hierarchical levels, both in aggregated resources such as particularly series, datasets, and also in more disaggregated resources such as types of entities (feature type), types of attributes (attribute type), entities (feature instances) and attributes (attribute instances). In theory, to apply a complete metadata structure to all hierarchical levels of metadata, from the whole series to an individual feature attributes, is possible, but to store all metadata at all levels is completely impractical. An inheritance mechanism is needed to store each metadata and quality information at the optimum hierarchical level and to allow an ease and efficient documentation of metadata in both an Earth observation scenario such as a multi-satellite mission multiband imagery, as well as in a complex vector topographical map that includes several feature types separated in layers (e.g. administrative limits, contour lines, edification polygons, road lines, etc). Moreover, and due to the traditional split of maps in tiles due to map handling at detailed scales or due to the satellite characteristics, each of the previous thematic layers (e.g. 1:5000 roads for a country) or band (Landsat-5 TM cover of the Earth) are tiled on several parts (sheets or scenes respectively). According to hierarchy in ISO 19115, the definition of general metadata can be supplemented by spatially specific metadata that, when required, either inherits or overrides the general case (G.1.3). Annex H of this standard states that only metadata exceptions are defined at lower levels, so it is not necessary to generate the full registry of metadata for each level but to link particular values to the general value that they inherit. Conceptually the metadata
Virtual timers in hierarchical real-time systems

NARCIS (Netherlands)

Heuvel, van den M.M.H.P.; Holenderski, M.J.; Cools, W.A.; Bril, R.J.; Lukkien, J.J.; Zhu, D.

2009-01-01

Hierarchical scheduling frameworks (HSFs) provide means for composing complex real-time systems from welldefined subsystems. This paper describes an approach to provide hierarchically scheduled real-time applications with virtual event timers, motivated by the need for integrating priority
Neighborhood social capital and crime victimization: comparison of spatial regression analysis and hierarchical regression analysis.

Science.gov (United States)

Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro

2012-11-01

Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright
Hierarchical organization versus self-organization

OpenAIRE

Busseniers, Evo

2014-01-01

In this paper we try to define the difference between hierarchical organization and self-organization. Organization is defined as a structure with a function. So we can define the difference between hierarchical organization and self-organization both on the structure as on the function. In the next two chapters these two definitions are given. For the structure we will use some existing definitions in graph theory, for the function we will use existing theory on (self-)organization. In the t...
Structural changes in the minimal spanning tree and the hierarchical network in the Korean stock market around the global financial crisis

Science.gov (United States)

Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

2015-04-01

This paper considers stock prices in the Korean stock market during the 2008 global financial crisis by focusing on three time periods: before, during, and after the crisis. Complex networks are extracted from cross-correlation coefficients between the normalized logarithmic return of the stock price time series of firms. The minimal spanning trees (MSTs) and the hierarchical network (HN) are generated from cross-correlation coefficients. Before and after the crisis, securities firms are located at the center of the MST. During the crisis, however, the center of the MST changes to a firm in heavy industry and construction. During the crisis, the MST shrinks in comparison to that before and that after the crisis. This topological change in the MST during the crisis reflects a distinct effect of the global financial crisis. The cophenetic correlation coefficient increases during the crisis, indicating an increase in the hierarchical structure during in this period. When crisis hits the market, firms behave synchronously, and their correlations are higher than those during a normal period.
Hollow Carbon Nanopolyhedra for Enhanced Electrocatalysis via Confined Hierarchical Porosity.

Science.gov (United States)

Song, Xiaokai; Guo, Linli; Liao, Xuemei; Liu, Jian; Sun, Jianhua; Li, Xiaopeng

2017-06-01

A novel strategy for the fabrication of hollow Co and N-codoped carbon nanopolyhedra (H-CoNC) from metal-organic framework (MOF) using in situ evaporation of ZnO nanosphere templates is proposed. The excess Zn supply during the pyrolysis process is found beneficial in terms of high nitrogen (≈9.75 at%), relatively homogenous CoN bonding, and the electrochemically accessible hierarchical porous system. Compared with other reported "solid" CoNC of identical surface areas, the newly developed H-CoNC shows enhanced kinetic current in 0.1 m KOH electrolyte and elevated oxygen reduction reaction (ORR) performance in 6 m KOH. The latter exceeds results obtained with the benchmark 20 wt% Pt/C, which is related to the strong confinement of O 2 molecules in the H-CoNC hierarchical porous system. Furthermore, the H-CoNC displays great tolerance toward the methanol crossover and KSCN poisoning. Finally, the assembled Zn-air batteries with H-CoNC yield a record open circuit potential (1.59 V vs Zn, stabilized at 1.52 V), high power density (331.0 mW cm -2 ), and promising rate performance. This work provides a new guideline for the design of MOF-derived carbon materials, as well as novel insights into spatial confinement effect toward the ORR activity. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Generating spatial precipitation ensembles: impact of temporal correlation structure

Science.gov (United States)

Rakovec, O.; Hazenberg, P.; Torfs, P. J. J. F.; Weerts, A. H.; Uijlenhoet, R.

2012-09-01

Sound spatially distributed rainfall fields including a proper spatial and temporal error structure are of key interest for hydrologists to force hydrological models and to identify uncertainties in the simulated and forecasted catchment response. The current paper presents a temporally coherent error identification method based on time-dependent multivariate spatial conditional simulations, which are conditioned on preceding simulations. A sensitivity analysis and real-world experiment are carried out within the hilly region of the Belgian Ardennes. Precipitation fields are simulated for pixels of 10 km × 10 km resolution. Uncertainty analyses in the simulated fields focus on (1) the number of previous simulation hours on which the new simulation is conditioned, (2) the advection speed of the rainfall event, (3) the size of the catchment considered, and (4) the rain gauge density within the catchment. The results for a sensitivity analysis show for typical advection speeds >20 km h-1, no uncertainty is added in terms of across ensemble spread when conditioned on more than one or two previous hourly simulations. However, for the real-world experiment, additional uncertainty can still be added when conditioning on a larger number of previous simulations. This is because for actual precipitation fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by thinning the observation network with 50%, the added uncertainty increases only slightly and the cross-validation shows that the simulations at the unobserved locations are unbiased. Finally, the first-order autocorrelation coefficients show clear temporal coherence in the time series of the areal precipitation using the time-dependent multivariate conditional simulations, which was not the case using the time-independent univariate conditional simulations. The presented work can be easily implemented within a hydrological calibration and data assimilation framework and can be used as an
DESIGN AND CONSTRUCTION OF A FOREST SPATIAL DATABASE: AN APPLICATION

Directory of Open Access Journals (Sweden)

Turan Sönmez

2006-11-01

Full Text Available General Directorate of Forests (GDF has not yet created the spatial forest database to manage forest and catch the developed countries in forestry. The lack of spatial forest database results in collection of the spatial data redundancy, communication problems among the forestry organizations. Also it causes Turkish forestry to be backward of informatics’ era. To solve these problems; GDF should establish spatial forest database supported Geographic Information System (GIS. To design the spatial database, supported GIS, which provides accurate, on time and current data/info for decision makers and operators in forestry, and to develop sample interface program to apply and monitor classical forest management plans is paramount in contemporary forest management planning process. This research is composed of three major stages: (i spatial rototype database design considering required by the three hierarchical organizations of GDF (regional directorate of forests, forest enterprise, and territorial division, (ii user interface program developed to apply and monitor classical management plans based on the designed database, (iii the implementation of the designed database and its user interface in Artvin Central Planning Unit.
Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data.

Science.gov (United States)

Kumar, Dushyant; Hariharan, Hari; Faizy, Tobias D; Borchert, Patrick; Siemonsen, Susanne; Fiehler, Jens; Reddy, Ravinder; Sedlacik, Jan

2018-05-12

We present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1 + -inhomogeneity or flip angle inhomogeneity to enhance the noise robustness of the reconstruction while intrinsically accounting for stimulated echo contributions using T2-distribution data alone. Simulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well. In comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs. The proposed algorithm provides more noise
Outage probability analysis of wireless sensor networks in the presence of channel fading and spatial correlation

KAUST Repository

Al-Murad, Tamim M.

2011-07-01

Evaluating the reliability of wireless sensor networks is becoming more important as theses networks are being used in crucial applications. The outage probability defined as the probability that the error in the system exceeds a maximum acceptable threshold has recently been used as a measure of the reliability of such systems. In this work we find the outage probability of wireless sensor network in different scenarios of distributed sensing where sensors\\' readings are affected by spatial correlation and in the presence of channel fading. © 2011 IEEE.
Auxiliary variables for the mapping of the drainage network: spatial correlation between relieve units, lithotypes and springs in Benevente River basin-ES

Directory of Open Access Journals (Sweden)

Tony Vinicius Moreira Sampaio

2014-12-01

Full Text Available Process of the drainage network mapping present methodological limitations re- sulting in inaccurate maps, restricting their use in environmental studies. Such problems demand the realization of long field surveys to verify the error and the search for auxiliary variables to optimize this works and turn possible the analysis of map accuracy. This research aims at the measurement of the correlation be- tween springs, lithotypes and relieve units, characterized by Roughness Concentration Index (RCI in River Basin Benevente-ES, focusing on the operations of map algebra and the use of spatial statistical techniques. These procedures have identified classes of RCI and lithotypes that present the highest and the lowest correlation with the spatial distribution of springs, indicating its potential use as auxiliary variables to verify the map accuracy.
Hierarchical Model for the Similarity Measurement of a Complex Holed-Region Entity Scene

Directory of Open Access Journals (Sweden)

Zhanlong Chen

2017-11-01

Full Text Available Complex multi-holed-region entity scenes (i.e., sets of random region with holes are common in spatial database systems, spatial query languages, and the Geographic Information System (GIS. A multi-holed-region (region with an arbitrary number of holes is an abstraction of the real world that primarily represents geographic objects that have more than one interior boundary, such as areas that contain several lakes or lakes that contain islands. When the similarity of the two complex holed-region entity scenes is measured, the number of regions in the scenes and the number of holes in the regions are usually different between the two scenes, which complicates the matching relationships of holed-regions and holes. The aim of this research is to develop several holed-region similarity metrics and propose a hierarchical model to measure comprehensively the similarity between two complex holed-region entity scenes. The procedure first divides a complex entity scene into three layers: a complex scene, a micro-spatial-scene, and a simple entity (hole. The relationships between the adjacent layers are considered to be sets of relationships, and each level of similarity measurements is nested with the adjacent one. Next, entity matching is performed from top to bottom, while the similarity results are calculated from local to global. In addition, we utilize position graphs to describe the distribution of the holed-regions and subsequently describe the directions between the holes using a feature matrix. A case study that uses the Great Lakes in North America in 1986 and 2015 as experimental data illustrates the entire similarity measurement process between two complex holed-region entity scenes. The experimental results show that the hierarchical model accounts for the relationships of the different layers in the entire complex holed-region entity scene. The model can effectively calculate the similarity of complex holed-region entity scenes, even if the
Deliberate change without hierarchical influence?

DEFF Research Database (Denmark)

Nørskov, Sladjana; Kesting, Peter; Ulhøi, John Parm

2017-01-01

reveals that deliberate change is indeed achievable in a non-hierarchical collaborative OSS community context. However, it presupposes the presence and active involvement of informal change agents. The paper identifies and specifies four key drivers for change agents’ influence. Originality....../value The findings contribute to organisational analysis by providing a deeper understanding of the importance of leadership in making deliberate change possible in non-hierarchical settings. It points to the importance of “change-by-conviction”, essentially based on voluntary behaviour. This can open the door...
Multiparty hierarchical quantum-information splitting

International Nuclear Information System (INIS)

Wang Xinwen; Zhang Dengyu; Tang Shiqing; Xie Lijun

2011-01-01

We propose a scheme for multiparty hierarchical quantum-information splitting (QIS) with a multipartite entangled state, where a boss distributes a secret quantum state to two grades of agents asymmetrically. The agents who belong to different grades have different authorities for recovering the boss's secret. Except for the boss's Bell-state measurement, no nonlocal operation is involved. The presented scheme is also shown to be secure against eavesdropping. Such a hierarchical QIS is expected to find useful applications in the field of modern multipartite quantum cryptography.
Biased trapping issue on weighted hierarchical networks

Indian Academy of Sciences (India)

archical networks which are based on the classic scale-free hierarchical networks. ... Weighted hierarchical networks; weight-dependent walks; mean first passage ..... The weighted networks can mimic some real-world natural and social systems to ... the Priority Academic Program Development of Jiangsu Higher Education ...

Hydrodeoxygenation of Methyl Laurate over Ni Catalysts Supported on Hierarchical HZSM-5 Zeolite

Directory of Open Access Journals (Sweden)

Nana Li

2017-12-01

Full Text Available The hierarchical HZSM-5 zeolite was prepared successfully by a simple NaOH treatment method. The concentration of NaOH solution was carefully tuned to optimal the zeolite acidity and pore structure. Under NaOH treatment conditions, a large number of mesopores, which interconnected with the retained micropores, were created to facilitate mass transfer performance. There are very good correlations between the decline of the relative zeolite crystallinity and the loss of micropores volume. The Ni nanoclusters were uniformly confined in the mesopores of hierarchical HZSM-5 by the excessive impregnation method. The direct deoxygenation in N2 and hydrodeoxygenation in H2 of the methyl laurate were compared respectively over the Ni/HZSM-5 catalysts. In the N2 atmosphere, the deoxygenation rate of the methyl laurate on the Ni/HZSM-5 catalyst is relatively slow. In the presence of H2, the synergistic effect between the hydrogenation function of the metal and the acid function of the zeolite supports can make the deoxygenation level more obvious. The yield of hydrocarbon products gradually reached the maximum with the appropriate treatment concentration of 1M NaOH, which could be attributed to the improved mass transfer in the hierarchical HZSM-5 supports.
Hierarchical Linked Views

Energy Technology Data Exchange (ETDEWEB)

Erbacher, Robert; Frincke, Deb

2007-07-02

Coordinated views have proven critical to the development of effective visualization environments. This results from the fact that a single view or representation of the data cannot show all of the intricacies of a given data set. Additionally, users will often need to correlate more data parameters than can effectively be integrated into a single visual display. Typically, development of multiple-linked views results in an adhoc configuration of views and associated interactions. The hierarchical model we are proposing is geared towards more effective organization of such environments and the views they encompass. At the same time, this model can effectively integrate much of the prior work on interactive and visual frameworks. Additionally, we expand the concept of views to incorporate perceptual views. This is related to the fact that visual displays can have information encoded at various levels of focus. Thus, a global view of the display provides overall trends of the data while focusing in on individual elements provides detailed specifics. By integrating interaction and perception into a single model, we show how one impacts the other. Typically, interaction and perception are considered separately, however, when interaction is being considered at a fundamental level and allowed to direct/modify the visualization directly we must consider them simultaneously and how they impact one another.
Thermal conductivity from hierarchical heat sinks using carbon nanotubes and graphene nanosheets.

Science.gov (United States)

Hsieh, Chien-Te; Lee, Cheng-En; Chen, Yu-Fu; Chang, Jeng-Kuei; Teng, Hsi-sheng

2015-11-28

The in-plane (kip) and through-plane (ktp) thermal conductivities of heat sinks using carbon nanotubes (CNTs), graphene nanosheets (GNs), and CNT/GN composites are extracted from two experimental setups within the 323-373 K temperature range. Hierarchical three-dimensional CNT/GN frameworks display higher kip and ktp values, as compared to the CNT- and GN-based heat sinks. The kip and ktp values of the CNT/GN-based heat sink reach as high as 1991 and 76 W m(-1) K(-1) at 323 K, respectively. This improved thermal conductivity is attributed to the fact that the hierarchical heat sink offers a stereo thermal conductive network that combines point, line, and plane contact, leading to better heat transport. Furthermore, the compression treatment provided an efficient route to increase both kip and ktp values. This result reveals that the hierarchical carbon structures become denser, inducing more thermal conductive area and less thermal resistivity, i.e., a reduced possibility of phonon-boundary scattering. The correlation between thermal and electrical conductivity (ε) can be well described by two empirical equations: kip = 567 ln(ε) + 1120 and ktp = 20.6 ln(ε) + 36.1. The experimental results are obtained within the temperature range of 323-373 K, suitably complementing the thermal management of chips for consumer electronics.
A study on the spatial characteristics and correlation of migrant workers' urban integration and well-being: A case study of Xi’an (China)

Science.gov (United States)

Wang, D. H.; Yang, X. J.; Hao, F. J.

2017-07-01

This paper used SPSS and ARCGIS to measure the urban integration degree and well-being index, spatial features, and their correlation. This results show: (1) The space differentiation of migrant workers’ urban integration degree in Xi’an distinct: The northern great site protection zone area is low, eastern military area is peak and the western electronic district and southwest high-tech zone are second peak areas. (2) Migrant workers’ well-being index has differentiation spatial distribution: eastern military area is significantly higher than other regions, northern economic zone shows low-lying shape, southern cultural and educational area is higher than northern economic development zone, and central business district is higher than the surrounding. (3) As the result of correlation analysis in SPSS 19.0, it is shown that there is certain positive correlation between urban integration degree and well-being index of migrant workers in main urban districts of Xi’an. Economic integration and social integration have positive prediction to well-being.
ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

Science.gov (United States)

Bittig, Arne T; Uhrmacher, Adelinde M

2017-01-01

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.
Discovering hierarchical structure in normal relational data

DEFF Research Database (Denmark)

Schmidt, Mikkel Nørgaard; Herlau, Tue; Mørup, Morten

2014-01-01

-parametric generative model for hierarchical clustering of similarity based on multifurcating Gibbs fragmentation trees. This allows us to infer and display the posterior distribution of hierarchical structures that comply with the data. We demonstrate the utility of our method on synthetic data and data of functional...
Spatially correlated two-dimensional arrays of semiconductor and metal quantum dots in GaAs-based heterostructures

International Nuclear Information System (INIS)

Nevedomskiy, V. N.; Bert, N. A.; Chaldyshev, V. V.; Preobrazhernskiy, V. V.; Putyato, M. A.; Semyagin, B. R.

2015-01-01

A single molecular-beam epitaxy process is used to produce GaAs-based heterostructures containing two-dimensional arrays of InAs semiconductor quantum dots and AsSb metal quantum dots. The twodimensional array of AsSb metal quantum dots is formed by low-temperature epitaxy which provides a large excess of arsenic in the epitaxial GaAs layer. During the growth of subsequent layers at a higher temperature, excess arsenic forms nanoinclusions, i.e., metal quantum dots in the GaAs matrix. The two-dimensional array of such metal quantum dots is created by the δ doping of a low-temperature GaAs layer with antimony which serves as a precursor for the heterogeneous nucleation of metal quantum dots and accumulates in them with the formation of AsSb metal alloy. The two-dimensional array of InAs semiconductor quantum dots is formed via the Stranski–Krastanov mechanism at the GaAs surface. Between the arrays of metal and semiconductor quantum dots, a 3-nm-thick AlAs barrier layer is grown. The total spacing between the arrays of metal and semiconductor quantum dots is 10 nm. Electron microscopy of the structure shows that the arrangement of metal quantum dots and semiconductor quantum dots in the two-dimensional arrays is spatially correlated. The spatial correlation is apparently caused by elastic strain and stress fields produced by both AsSb metal and InAs semiconductor quantum dots in the GaAs matrix
Hierarchical Models for Type Ia Supernova Light Curves in the Optical and Near Infrared

Science.gov (United States)

Mandel, Kaisey; Narayan, G.; Kirshner, R. P.

2011-01-01

I have constructed a comprehensive statistical model for Type Ia supernova optical and near infrared light curves. Since the near infrared light curves are excellent standard candles and are less sensitive to dust extinction and reddening, the combination of near infrared and optical data better constrains the host galaxy extinction and improves the precision of distance predictions to SN Ia. A hierarchical probabilistic model coherently accounts for multiple random and uncertain effects, including photometric error, intrinsic supernova light curve variations and correlations across phase and wavelength, dust extinction and reddening, peculiar velocity dispersion and distances. An improved BayeSN MCMC code is implemented for computing probabilistic inferences for individual supernovae and the SN Ia and host galaxy dust populations. I use this hierarchical model to analyze nearby Type Ia supernovae with optical and near infared data from the PAIRITEL, CfA3, and CSP samples and the literature. Using cross-validation to test the robustness of the model predictions, I find that the rms Hubble diagram scatter of predicted distance moduli is 0.11 mag for SN with optical and near infrared data versus 0.15 mag for SN with only optical data. Accounting for the dispersion expected from random peculiar velocities, the rms intrinsic prediction error is 0.08-0.10 mag for SN with both optical and near infrared light curves. I discuss results for the inferred intrinsic correlation structures of the optical-NIR SN Ia light curves and the host galaxy dust distribution captured by the hierarchical model. The continued observation and analysis of Type Ia SN in the optical and near infrared is important for improving their utility as precise and accurate cosmological distance indicators.
Road Network Selection Based on Road Hierarchical Structure Control

Directory of Open Access Journals (Sweden)

HE Haiwei

2015-04-01

Full Text Available A new road network selection method based on hierarchical structure is studied. Firstly, road network is built as strokes which are then classified into hierarchical collections according to the criteria of betweenness centrality value (BC value. Secondly, the hierarchical structure of the strokes is enhanced using structural characteristic identification technique. Thirdly, the importance calculation model was established according to the relationships among the hierarchical structure of the strokes. Finally, the importance values of strokes are got supported with the model's hierarchical calculation, and with which the road network is selected. Tests are done to verify the advantage of this method by comparing it with other common stroke-oriented methods using three kinds of typical road network data. Comparision of the results show that this method had few need to semantic data, and could eliminate the negative influence of edge strokes caused by the criteria of BC value well. So, it is better to maintain the global hierarchical structure of road network, and suitable to meet with the selection of various kinds of road network at the same time.
Hierarchically Structured Electrospun Fibers

Directory of Open Access Journals (Sweden)

Nicole E. Zander

2013-01-01

Full Text Available Traditional electrospun nanofibers have a myriad of applications ranging from scaffolds for tissue engineering to components of biosensors and energy harvesting devices. The generally smooth one-dimensional structure of the fibers has stood as a limitation to several interesting novel applications. Control of fiber diameter, porosity and collector geometry will be briefly discussed, as will more traditional methods for controlling fiber morphology and fiber mat architecture. The remainder of the review will focus on new techniques to prepare hierarchically structured fibers. Fibers with hierarchical primary structures—including helical, buckled, and beads-on-a-string fibers, as well as fibers with secondary structures, such as nanopores, nanopillars, nanorods, and internally structured fibers and their applications—will be discussed. These new materials with helical/buckled morphology are expected to possess unique optical and mechanical properties with possible applications for negative refractive index materials, highly stretchable/high-tensile-strength materials, and components in microelectromechanical devices. Core-shell type fibers enable a much wider variety of materials to be electrospun and are expected to be widely applied in the sensing, drug delivery/controlled release fields, and in the encapsulation of live cells for biological applications. Materials with a hierarchical secondary structure are expected to provide new superhydrophobic and self-cleaning materials.
Hierarchical control of electron-transfer

DEFF Research Database (Denmark)

Westerhoff, Hans V.; Jensen, Peter Ruhdal; Egger, Louis

1997-01-01

In this chapter the role of electron transfer in determining the behaviour of the ATP synthesising enzyme in E. coli is analysed. It is concluded that the latter enzyme lacks control because of special properties of the electron transfer components. These properties range from absence of a strong...... back pressure by the protonmotive force on the rate of electron transfer to hierarchical regulation of the expression of the gens that encode the electron transfer proteins as a response to changes in the bioenergetic properties of the cell.The discussion uses Hierarchical Control Analysis...
Fluorocarbon Adsorption in Hierarchical Porous Frameworks

Energy Technology Data Exchange (ETDEWEB)

Motkuri, Radha K.; Annapureddy, Harsha V.; Vijayakumar, M.; Schaef, Herbert T.; Martin, P F.; McGrail, B. Peter; Dang, Liem X.; Krishna, Rajamani; Thallapally, Praveen K.

2014-07-09

The adsorption behavior of a series of fluorocarbon derivatives was examined on a set of microporous metal organic framework (MOF) sorbents and another set of hierarchical mesoporous MOFs. The microporous M-DOBDC (M = Ni, Co) showed a saturation uptake capacity for R12 of over 4 mmol/g at a very low relative saturation pressure (P/Po) of 0.02. In contrast, the mesoporous MOF MIL-101 showed an exceptionally high uptake capacity reaching over 14 mmol/g at P/Po of 0.4. Adsorption affinity in terms of mass loading and isosteric heats of adsorption were found to generally correlate with the polarizability of the refrigerant with R12 > R22 > R13 > R14 > methane. These results suggest the possibility of exploiting MOFs for separation of azeotropic mixtures of fluorocarbons and use in eco-friendly fluorocarbon-based adsorption cooling and refrigeration applications.
Organotypic culture of normal, dysplastic and squamous cell carcinoma-derived oral cell lines reveals loss of spatial regulation of CD44 and p75 NTR in malignancy.

Science.gov (United States)

Dalley, Andrew J; AbdulMajeed, Ahmad A; Upton, Zee; Farah, Camile S

2013-01-01

Oral squamous cell carcinomas (OSCC) often arise from dysplastic lesions. The role of cancer stem cells in tumour initiation is widely accepted, yet the potential existence of pre-cancerous stem cells in dysplastic tissue has received little attention. Cell lines from oral diseases ranging in severity from dysplasia to malignancy provide opportunity to investigate the involvement of stem cells in malignant progression from dysplasia. Stem cells are functionally defined by their ability to generate hierarchical tissue structures in consortium with spatial regulation. Organotypic cultures readily display tissue hierarchy in vitro; hence, in this study, we compared hierarchical expression of stem cell-associated markers in dermis-based organotypic cultures of oral epithelial cells from normal tissue (OKF6-TERT2), mild dysplasia (DOK), severe dysplasia (POE-9n) and OSCC (PE/CA P J15). Expression of CD44, p75(NTR), CD24 and ALDH was studied in monolayers by flow cytometry and in organotypic cultures by immunohistochemistry. Spatial regulation of CD44 and p75(NTR) was evident for organotypic cultures of normal (OKF6-TERT2) and dysplasia (DOK and POE-9n) but was lacking for OSCC (PE/CA PJ15)-derived cells. Spatial regulation of CD24 was not evident. All monolayer cultures exhibited CD44, p75(NTR), CD24 antigens and ALDH activity (ALDEFLUOR(®) assay), with a trend towards loss of population heterogeneity that mirrored disease severity. In monolayer, increased FOXA1 and decreased FOXA2 expression correlated with disease severity, but OCT3/4, Sox2 and NANOG did not. We conclude that dermis-based organotypic cultures give opportunity to investigate the mechanisms that underlie loss of spatial regulation of stem cell markers seen with OSCC-derived cells. © 2012 John Wiley & Sons A/S.
Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Energy Technology Data Exchange (ETDEWEB)

Kumar, Ranjan [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: ranjan.k@ks3.ecs.kyoto-u.ac.jp; Izui, Kazuhiro [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: izui@prec.kyoto-u.ac.jp; Yoshimura, Masataka [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: yoshimura@prec.kyoto-u.ac.jp; Nishiwaki, Shinji [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: shinji@prec.kyoto-u.ac.jp

2009-04-15

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.
Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

International Nuclear Information System (INIS)

Kumar, Ranjan; Izui, Kazuhiro; Yoshimura, Masataka; Nishiwaki, Shinji

2009-01-01

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets
Hierarchical Neural Regression Models for Customer Churn Prediction

Directory of Open Access Journals (Sweden)

Golshan Mohammadi

2013-01-01

Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.
Exogenous spatial attention influences figure-ground assignment.

Science.gov (United States)

Vecera, Shaun P; Flevaris, Anastasia V; Filapek, Joseph C

2004-01-01

In a hierarchical stage account of vision, figure-ground assignment is thought to be completed before the operation of focal spatial attention. Results of previous studies have supported this account by showing that unpredictive, exogenous spatial precues do not influence figure-ground assignment, although voluntary attention can influence figure-ground assignment. However, in these studies, attention was not summoned directly to a region in a figure-ground display. In three experiments, we addressed the relationship between figure-ground assignment and visuospatial attention. In Experiment 1, we replicated the finding that exogenous precues do not influence figure-ground assignment when they direct attention outside of a figure-ground stimulus. In Experiment 2, we demonstrated that exogenous attention can influence figure-ground assignment if it is directed to one of the regions in a figure-ground stimulus. In Experiment 3, we demonstrated that exogenous attention can influence figure-ground assignment in displays that contain a Gestalt figure-ground cue; this result suggests that figure-ground processes are not entirely completed prior to the operation of focal spatial attention. Exogenous spatial attention acts as a cue for figure-ground assignment and can affect the outcome of figure-ground processes.
Geometrical optics in correlated imaging systems

International Nuclear Information System (INIS)

Cao Dezhong; Xiong Jun; Wang Kaige

2005-01-01

We discuss the geometrical optics of correlated imaging for two kinds of spatial correlations corresponding, respectively, to a classical thermal light source and a quantum two-photon entangled source. Due to the different features in the second-order spatial correlation, the two sources obey different imaging equations. The quantum entangled source behaves as a mirror, whereas the classical thermal source looks like a phase-conjugate mirror in the correlated imaging
Hierarchical Traces for Reduced NSM Memory Requirements

Science.gov (United States)

Dahl, Torbjørn S.

This paper presents work on using hierarchical long term memory to reduce the memory requirements of nearest sequence memory (NSM) learning, a previously published, instance-based reinforcement learning algorithm. A hierarchical memory representation reduces the memory requirements by allowing traces to share common sub-sequences. We present moderated mechanisms for estimating discounted future rewards and for dealing with hidden state using hierarchical memory. We also present an experimental analysis of how the sub-sequence length affects the memory compression achieved and show that the reduced memory requirements do not effect the speed of learning. Finally, we analyse and discuss the persistence of the sub-sequences independent of specific trace instances.
Generating spatial precipitation ensembles: impact of temporal correlation structure

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O. Rakovec

2012-09-01

Full Text Available Sound spatially distributed rainfall fields including a proper spatial and temporal error structure are of key interest for hydrologists to force hydrological models and to identify uncertainties in the simulated and forecasted catchment response. The current paper presents a temporally coherent error identification method based on time-dependent multivariate spatial conditional simulations, which are conditioned on preceding simulations. A sensitivity analysis and real-world experiment are carried out within the hilly region of the Belgian Ardennes. Precipitation fields are simulated for pixels of 10 km × 10 km resolution. Uncertainty analyses in the simulated fields focus on (1 the number of previous simulation hours on which the new simulation is conditioned, (2 the advection speed of the rainfall event, (3 the size of the catchment considered, and (4 the rain gauge density within the catchment. The results for a sensitivity analysis show for typical advection speeds >20 km h⁻¹, no uncertainty is added in terms of across ensemble spread when conditioned on more than one or two previous hourly simulations. However, for the real-world experiment, additional uncertainty can still be added when conditioning on a larger number of previous simulations. This is because for actual precipitation fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by thinning the observation network with 50%, the added uncertainty increases only slightly and the cross-validation shows that the simulations at the unobserved locations are unbiased. Finally, the first-order autocorrelation coefficients show clear temporal coherence in the time series of the areal precipitation using the time-dependent multivariate conditional simulations, which was not the case using the time-independent univariate conditional simulations. The presented work can be easily implemented within a hydrological calibration and data assimilation

Hierarchical subtask discovery with non-negative matrix factorization

CSIR Research Space (South Africa)

Earle, AC

2018-04-01

Full Text Available Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We...
Hierarchical subtask discovery with non-negative matrix factorization

CSIR Research Space (South Africa)

Earle, AC

2017-08-01

Full Text Available Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We...
Concurrent and convergent validity of the mobility- and multidimensional-hierarchical disability categorization models with physical performance in community older adults.

Science.gov (United States)

Hu, Ming-Hsia; Yeh, Chih-Jun; Chen, Tou-Rong; Wang, Ching-Yi

2014-01-01

A valid, time-efficient and easy-to-use instrument is important for busy clinical settings, large scale surveys, or community screening use. The purpose of this study was to validate the mobility hierarchical disability categorization model (an abbreviated model) by investigating its concurrent validity with the multidimensional hierarchical disability categorization model (a comprehensive model) and triangulating both models with physical performance measures in older adults. 604 community-dwelling older adults of at least 60 years in age volunteered to participate. Self-reported function on mobility, instrumental activities of daily living (IADL) and activities of daily living (ADL) domains were recorded and then the disability status determined based on both the multidimensional hierarchical categorization model and the mobility hierarchical categorization model. The physical performance measures, consisting of grip strength and usual and fastest gait speeds (UGS, FGS), were collected on the same day. Both categorization models showed high correlation (γs = 0.92, p categorization models. The results of multiple regression analysis indicated that both models individually explain similar amount of variance on all physical performances, with adjustments for age, sex, and number of comorbidities. Our results found that the mobility hierarchical disability categorization model is a valid and time efficient tool for large survey or screening use.
Statistical Significance for Hierarchical Clustering

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Kimes, Patrick K.; Liu, Yufeng; Hayes, D. Neil; Marron, J. S.

2017-01-01

Summary Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this paper, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets. PMID:28099990
Creating biomaterials with spatially organized functionality.

Science.gov (United States)

Chow, Lesley W; Fischer, Jacob F

2016-05-01

Biomaterials for tissue engineering provide scaffolds to support cells and guide tissue regeneration. Despite significant advances in biomaterials design and fabrication techniques, engineered tissue constructs remain functionally inferior to native tissues. This is largely due to the inability to recreate the complex and dynamic hierarchical organization of the extracellular matrix components, which is intimately linked to a tissue's biological function. This review discusses current state-of-the-art strategies to control the spatial presentation of physical and biochemical cues within a biomaterial to recapitulate native tissue organization and function. © 2016 by the Society for Experimental Biology and Medicine.
A Bayesian Hierarchical Model for Glacial Dynamics Based on the Shallow Ice Approximation and its Evaluation Using Analytical Solutions

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Gopalan, Giri; Hrafnkelsson, Birgir; Aðalgeirsdóttir, Guðfinna; Jarosch, Alexander H.; Pálsson, Finnur

2018-03-01

Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatio-temporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier.
Multiscale mining of fMRI data with hierarchical structured sparsity

International Nuclear Information System (INIS)

Jenatton, R.; Obozinski, G.; Bach, F.; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Eger, Evelyne

2012-01-01

Reverse inference, or 'brain reading', is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Reverse inference takes into account the multivariate information between voxels and is currently the only way to assess how precisely some cognitive information is encoded by the activity of neural populations within the whole brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, since there are far more features than samples, i.e., more voxels than fMRI volumes. To address this problem, different methods have been proposed, such as, among others, univariate feature selection, feature agglomeration and regularization techniques. In this paper, we consider a sparse hierarchical structured regularization. Specifically, the penalization we use is constructed from a tree that is obtained by spatially-constrained agglomerative clustering. This approach encodes the spatial structure of the data at different scales into the regularization, which makes the overall prediction procedure more robust to inter-subject variability. The regularization used induces the selection of spatially coherent predictive brain regions simultaneously at different scales. We test our algorithm on real data acquired to study the mental representation of objects, and we show that the proposed algorithm not only delineates meaningful brain regions but yields as well better prediction accuracy than reference methods. (authors)
Hierarchically Nanoporous Bioactive Glasses for High Efficiency Immobilization of Enzymes

DEFF Research Database (Denmark)

He, W.; Min, D.D.; Zhang, X.D.

2014-01-01

Bioactive glasses with hierarchical nanoporosity and structures have been heavily involved in immobilization of enzymes. Because of meticulous design and ingenious hierarchical nanostructuration of porosities from yeast cell biotemplates, hierarchically nanostructured porous bioactive glasses can...... and products of catalytic reactions can freely diffuse through open mesopores (2–40 nm). The formation mechanism of hierarchically structured porous bioactive glasses, the immobilization mechanism of enzyme and the catalysis mechanism of immobilized enzyme are then discussed. The novel nanostructure...
On Information Metrics for Spatial Coding.

Science.gov (United States)

Souza, Bryan C; Pavão, Rodrigo; Belchior, Hindiael; Tort, Adriano B L

2018-04-01

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.
Three Ways to Link Merge with Hierarchical Concept-Combination

Directory of Open Access Journals (Sweden)

Chris Thornton

2016-11-01

Full Text Available In the Minimalist Program, language competence is seen to stem from a fundamental ability to construct hierarchical structure, an operation dubbed ‘Merge’. This raises the problem of how to view hierarchical concept-combination. This is a conceptual operation which also builds hierarchical structure. We can conceive of a garden that consists of a lawn and a flower-bed, for example, or a salad consisting of lettuce, fennel and rocket, or a crew consisting of a pilot and engineer. In such cases, concepts are put together in a way that makes one the accommodating element with respect to the others taken in combination. The accommodating element becomes the root of a hierarchical unit. Since this unit is itself a concept, the operation is inherently recursive. Does this mean the mind has two independent systems of hierarchical construction? Or is some form of integration more likely? Following a detailed examination of the operations involved, this paper shows there are three main ways in which Merge might be linked to hierarchical concept-combination. Also examined are the architectural implications that arise in each case.
Hierarchical modeling and its numerical implementation for layered thin elastic structures

Energy Technology Data Exchange (ETDEWEB)

Cho, Jin-Rae [Hongik University, Sejong (Korea, Republic of)

2017-05-15

Thin elastic structures such as beam- and plate-like structures and laminates are characterized by the small thickness, which lead to classical plate and laminate theories in which the displacement fields through the thickness are assumed linear or higher-order polynomials. These classical theories are either insufficient to represent the complex stress variation through the thickness or may encounter the accuracy-computational cost dilemma. In order to overcome the inherent problem of classical theories, the concept of hierarchical modeling has been emerged. In the hierarchical modeling, the hierarchical models with different model levels are selected and combined within a structure domain, in order to make the modeling error be distributed as uniformly as possible throughout the problem domain. The purpose of current study is to explore the potential of hierarchical modeling for the effective numerical analysis of layered structures such as laminated composite. For this goal, the hierarchical models are constructed and the hierarchical modeling is implemented by selectively adjusting the level of hierarchical models. As well, the major characteristics of hierarchical models are investigated through the numerical experiments.
Temporal and spatial responses of Chironomidae (Diptera) and other benthic invertebrates to urban stormwater runoff

Science.gov (United States)

Susan E. Gresens; Kenneth T. Belt; Jamie A. Tang; Daniel C. Gwinn; Patricia A. Banks

2007-01-01

In a longitudinal study of two streams whose lower reaches received unattenuated urban stormwater runoff, physical disturbance by stormflow was less important than the persistant unidentified chemical impacts of urban stormwater in limiting the distribution of Chironomidae, and Ephemeroptera, Trichoptera and Plecoptera (EPT). A hierarchical spatial analysis showed that...
Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning

Directory of Open Access Journals (Sweden)

Eric Chalmers

2016-12-01

Full Text Available The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL to guide goal-directed behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals’ ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change. We investigate this idea by implementing a computational MBRL framework that incorporates features inspired by computational properties of the hippocampus: a hierarchical representation of space, forward sweeps through future spatial trajectories, and context-driven remapping of place cells. We find that a hierarchical abstraction of space greatly reduces the computational load (mental effort required for adaptation to changing environmental conditions, and allows efficient scaling to large problems. It also allows abstract knowledge gained at high levels to guide adaptation to new obstacles. Moreover, a context-driven remapping mechanism allows learning and memory of multiple tasks. Simulating dorsal or ventral hippocampal lesions in our computational framework qualitatively reproduces behavioral deficits observed in rodents with analogous lesions. The framework may thus embody key features of how the brain organizes model-based RL to efficiently solve navigation and other difficult tasks.
Hierarchical surfaces for enhanced self-cleaning applications

Science.gov (United States)

Fernández, Ariadna; Francone, Achille; Thamdrup, Lasse H.; Johansson, Alicia; Bilenberg, Brian; Nielsen, Theodor; Guttmann, Markus; Sotomayor Torres, Clivia M.; Kehagias, Nikolaos

2017-04-01

In this study we present a flexible and adaptable fabrication method to create complex hierarchical structures over inherently hydrophobic resist materials. We have tested these surfaces for their superhydrophobic behaviour and successfully verified their self-cleaning properties. The followed approach allow us to design and produce superhydrophobic surfaces in a reproducible manner. We have analysed different combination of hierarchical micro-nanostructures for their application to self-cleaning surfaces. A static contact angle value of 170° with a hysteresis of 4° was achieved without the need of any additional chemical treatment on the fabricated hierarchical structures. Dynamic effects were analysed on these surfaces, obtaining a remarkable self-cleaning effect as well as a good robustness over impacting droplets.
Correlation filtering in financial time series (Invited Paper)

Science.gov (United States)

Aste, T.; Di Matteo, Tiziana; Tumminello, M.; Mantegna, R. N.

2005-05-01

We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al.,1 we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have found that triangular loops and 4 element cliques have important and significant relations with the market structure and properties. Here we apply this filtering procedure to the analysis of correlation in two different kind of interest rate time series (16 Eurodollars and 34 US interest rates).
Repeated measures from FIA data facilitates analysis across spatial scales of tree growth responses to nitrogen deposition from individual trees to whole ecoregions

Science.gov (United States)

Charles H. (Hobie) Perry; Kevin J. Horn; R. Quinn Thomas; Linda H. Pardo; Erica A.H. Smithwick; Doug Baldwin; Gregory B. Lawrence; Scott W. Bailey; Sabine Braun; Christopher M. Clark; Mark Fenn; Annika Nordin; Jennifer N. Phelan; Paul G. Schaberg; Sam St. Clair; Richard Warby; Shaun Watmough; Steven S. Perakis

2015-01-01

The abundance of temporally and spatially consistent Forest Inventory and Analysis data facilitates hierarchical/multilevel analysis to investigate factors affecting tree growth, scaling from plot-level to continental scales. Herein we use FIA tree and soil inventories in conjunction with various spatial climate and soils data to estimate species-specific responses of...
Privacy-Preserving Task Assignment in Spatial Crowdsourcing

KAUST Repository

Liu, An

2017-09-20

With the progress of mobile devices and wireless networks, spatial crowdsourcing (SC) is emerging as a promising approach for problem solving. In SC, spatial tasks are assigned to and performed by a set of human workers. To enable effective task assignment, however, both workers and task requesters are required to disclose their locations to untrusted SC systems. In this paper, we study the problem of assigning workers to tasks in a way that location privacy for both workers and task requesters is preserved. We first combine the Paillier cryptosystem with Yao’s garbled circuits to construct a secure protocol that assigns the nearest worker to a task. Considering that this protocol cannot scale to a large number of workers, we then make use of Geohash, a hierarchical spatial index to design a more efficient protocol that can securely find approximate nearest workers. We theoretically show that these two protocols are secure against semi-honest adversaries. Through extensive experiments on two real-world datasets, we demonstrate the efficiency and effectiveness of our protocols.
Hierarchical Micro-Nano Coatings by Painting

Science.gov (United States)

Kirveslahti, Anna; Korhonen, Tuulia; Suvanto, Mika; Pakkanen, Tapani A.

2016-03-01

In this paper, the wettability properties of coatings with hierarchical surface structures and low surface energy were studied. Hierarchically structured coatings were produced by using hydrophobic fumed silica nanoparticles and polytetrafluoroethylene (PTFE) microparticles as additives in polyester (PES) and polyvinyldifluoride (PVDF). These particles created hierarchical micro-nano structures on the paint surfaces and lowered or supported the already low surface energy of the paint. Two standard application techniques for paint application were employed and the presented coatings are suitable for mass production and use in large surface areas. By regulating the particle concentrations, it was possible to modify wettability properties gradually. Highly hydrophobic surfaces were achieved with the highest contact angle of 165∘. Dynamic contact angle measurements were carried out for a set of selected samples and low hysteresis was obtained. Produced coatings possessed long lasting durability in the air and in underwater conditions.
Hierarchical virtual screening approaches in small molecule drug discovery.

Science.gov (United States)

Kumar, Ashutosh; Zhang, Kam Y J

2015-01-01

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.
Spatio-temporal structure, path characteristics and perceptual grouping in immediate serial spatial recall

Directory of Open Access Journals (Sweden)

Carlo De Lillo

2016-11-01

Full Text Available Immediate serial spatial recall measures the ability to retain sequences of locations in short-term memory and is considered the spatial equivalent of digit span. It is tested by requiring participants to reproduce sequences of movements performed by an experimenter or displayed on a monitor. Different organizational factors dramatically affect serial spatial recall but they are often confounded or underspecified. Untangling them is crucial for the characterization of working-memory models and for establishing the contribution of structure and memory capacity to spatial span. We report five experiments assessing the relative role and independence of factors that have been reported in the literature. Experiment 1 disentangled the effects of spatial clustering and path-length by manipulating the distance of items displayed on a touchscreen monitor. Long-path sequences segregated by spatial clusters were compared with short-path sequences not segregated by clusters. Recall was more accurate for sequences segregated by clusters independently from path-length. Experiment 2 featured conditions where temporal pauses were introduced between or within cluster boundaries during the presentation of sequences with the same paths. Thus, the temporal structure of the sequences was either consistent or inconsistent with a hierarchical representation based on segmentation by spatial clusters but the effect of structure could not be confounded with effects of path-characteristics. Pauses at cluster boundaries yielded more accurate recall, as predicted by a hierarchical model. In Experiment 3, the systematic manipulation of sequence structure, path-length and presence of path-crossings of sequences showed that structure explained most of the variance, followed by the presence/absence of path-crossings, and path-length. Experiments 4 and 5 replicated the results of the previous experiments in immersive virtual reality navigation tasks where the viewpoint of the

Spatial correlations in compressible granular flows

NARCIS (Netherlands)

van Noije, T.P.C.; Ernst, M.H.; Brito, R.

The clustering instability in freely evolving granular fluids manifests itself in the density-density correlation function and structure factor. These functions are calculated from fluctuating hydrodynamics. As time increases, the structure factor of density fluctuations develops a maximum, which
Spatial distribution of enzyme driven reactions at micro-scales

Science.gov (United States)

Kandeler, Ellen; Boeddinghaus, Runa; Nassal, Dinah; Preusser, Sebastian; Marhan, Sven; Poll, Christian

2017-04-01

Studies of microbial biogeography can often provide key insights into the physiologies, environmental tolerances, and ecological strategies of soil microorganisms that dominate in natural environments. In comparison with aquatic systems, soils are particularly heterogeneous. Soil heterogeneity results from the interaction of a hierarchical series of interrelated variables that fluctuate at many different spatial and temporal scales. Whereas spatial dependence of chemical and physical soil properties is well known at scales ranging from decimetres to several hundred metres, the spatial structure of soil enzymes is less clear. Previous work has primarily focused on spatial heterogeneity at a single analytical scale using the distribution of individual cells, specific types of organisms or collective parameters such as bacterial abundance or total microbial biomass. There are fewer studies that have considered variations in community function and soil enzyme activities. This presentation will give an overview about recent studies focusing on spatial pattern of different soil enzymes in the terrestrial environment. Whereas zymography allows the visualization of enzyme pattern in the close vicinity of roots, micro-sampling strategies followed by MUF analyses clarify micro-scale pattern of enzymes associated to specific microhabitats (micro-aggregates, organo-mineral complexes, subsoil compartments).
Fluorocarbon adsorption in hierarchical porous frameworks

Energy Technology Data Exchange (ETDEWEB)

Motkuri, RK; Annapureddy, HVR; Vijaykumar, M; Schaef, HT; Martin, PF; McGrail, BP; Dang, LX; Krishna, R; Thallapally, PK

2014-07-09

Metal-organic frameworks comprise an important class of solid-state materials and have potential for many emerging applications such as energy storage, separation, catalysis and bio-medical. Here we report the adsorption behaviour of a series of fluorocarbon derivatives on a set of microporous and hierarchical mesoporous frameworks. The microporous frameworks show a saturation uptake capacity for dichlorodifluoromethane of >4 mmol g(-1) at a very low relative saturation pressure (P/P-o) of 0.02. In contrast, the mesoporous framework shows an exceptionally high uptake capacity reaching >14 mmol g(-1) at P/P-o of 0.4. Adsorption affinity in terms of mass loading and isosteric heats of adsorption is found to generally correlate with the polarizability and boiling point of the refrigerant, with dichlorodifluoromethane >chlorodifluoromethane >chlorotrifluoromethane >tetrafluoromethane >methane. These results suggest the possibility of exploiting these sorbents for separation of azeotropic mixtures of fluorocarbons and use in eco-friendly fluorocarbon-based adsorption cooling.
Extension of mixture-of-experts networks for binary classification of hierarchical data.

Science.gov (United States)

Ng, Shu-Kay; McLachlan, Geoffrey J

2007-09-01

For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be
Keeping in Touch With the Visual System: Spatial Alignment and Multisensory Integration of Visual-Somatosensory Inputs

Directory of Open Access Journals (Sweden)

Jeannette Rose Mahoney

2015-08-01

Full Text Available Correlated sensory inputs coursing along the individual sensory processing hierarchies arrive at multisensory convergence zones in cortex where inputs are processed in an integrative manner. The exact hierarchical level of multisensory convergence zones and the timing of their inputs are still under debate, although increasingly, evidence points to multisensory integration at very early sensory processing levels. The objective of the current study was to determine, both psychophysically and electrophysiologically, whether differential visual-somatosensory integration patterns exist for stimuli presented to the same versus opposite hemifields. Using high-density electrical mapping and complementary psychophysical data, we examined multisensory integrative processing for combinations of visual and somatosensory inputs presented to both left and right spatial locations. We assessed how early during sensory processing visual-somatosensory (VS interactions were seen in the event-related potential and whether spatial alignment of the visual and somatosensory elements resulted in differential integration effects. Reaction times to all VS pairings were significantly faster than those to the unisensory conditions, regardless of spatial alignment, pointing to engagement of integrative multisensory processing in all conditions. In support, electrophysiological results revealed significant differences between multisensory simultaneous VS and summed V+S responses, regardless of the spatial alignment of the constituent inputs. Nonetheless, multisensory effects were earlier in the aligned conditions, and were found to be particularly robust in the case of right-sided inputs (beginning at just 55ms. In contrast to previous work on audio-visual and audio-somatosensory inputs, the current work suggests a degree of spatial specificity to the earliest detectable multisensory integrative effects in response to visual-somatosensory pairings.
Introduction into Hierarchical Matrices

KAUST Repository

Litvinenko, Alexander

2013-01-01

Hierarchical matrices allow us to reduce computational storage and cost from cubic to almost linear. This technique can be applied for solving PDEs, integral equations, matrix equations and approximation of large covariance and precision matrices.
Introduction into Hierarchical Matrices

KAUST Repository

Litvinenko, Alexander

2013-12-05

Hierarchical matrices allow us to reduce computational storage and cost from cubic to almost linear. This technique can be applied for solving PDEs, integral equations, matrix equations and approximation of large covariance and precision matrices.
Mapping brucellosis increases relative to elk density using hierarchical Bayesian models

Science.gov (United States)

Cross, Paul C.; Heisey, Dennis M.; Scurlock, Brandon M.; Edwards, William H.; Brennan, Angela; Ebinger, Michael R.

2010-01-01

The relationship between host density and parasite transmission is central to the effectiveness of many disease management strategies. Few studies, however, have empirically estimated this relationship particularly in large mammals. We applied hierarchical Bayesian methods to a 19-year dataset of over 6400 brucellosis tests of adult female elk (Cervus elaphus) in northwestern Wyoming. Management captures that occurred from January to March were over two times more likely to be seropositive than hunted elk that were killed in September to December, while accounting for site and year effects. Areas with supplemental feeding grounds for elk had higher seroprevalence in 1991 than other regions, but by 2009 many areas distant from the feeding grounds were of comparable seroprevalence. The increases in brucellosis seroprevalence were correlated with elk densities at the elk management unit, or hunt area, scale (mean 2070 km2; range = [95–10237]). The data, however, could not differentiate among linear and non-linear effects of host density. Therefore, control efforts that focus on reducing elk densities at a broad spatial scale were only weakly supported. Additional research on how a few, large groups within a region may be driving disease dynamics is needed for more targeted and effective management interventions. Brucellosis appears to be expanding its range into new regions and elk populations, which is likely to further complicate the United States brucellosis eradication program. This study is an example of how the dynamics of host populations can affect their ability to serve as disease reservoirs.
Mapping brucellosis increases relative to elk density using hierarchical Bayesian models.

Directory of Open Access Journals (Sweden)

Paul C Cross

Full Text Available The relationship between host density and parasite transmission is central to the effectiveness of many disease management strategies. Few studies, however, have empirically estimated this relationship particularly in large mammals. We applied hierarchical Bayesian methods to a 19-year dataset of over 6400 brucellosis tests of adult female elk (Cervus elaphus in northwestern Wyoming. Management captures that occurred from January to March were over two times more likely to be seropositive than hunted elk that were killed in September to December, while accounting for site and year effects. Areas with supplemental feeding grounds for elk had higher seroprevalence in 1991 than other regions, but by 2009 many areas distant from the feeding grounds were of comparable seroprevalence. The increases in brucellosis seroprevalence were correlated with elk densities at the elk management unit, or hunt area, scale (mean 2070 km(2; range = [95-10237]. The data, however, could not differentiate among linear and non-linear effects of host density. Therefore, control efforts that focus on reducing elk densities at a broad spatial scale were only weakly supported. Additional research on how a few, large groups within a region may be driving disease dynamics is needed for more targeted and effective management interventions. Brucellosis appears to be expanding its range into new regions and elk populations, which is likely to further complicate the United States brucellosis eradication program. This study is an example of how the dynamics of host populations can affect their ability to serve as disease reservoirs.
Hierarchical surfaces for enhanced self-cleaning applications

International Nuclear Information System (INIS)

Fernández, Ariadna; Francone, Achille; Sotomayor Torres, Clivia M; Kehagias, Nikolaos; Thamdrup, Lasse H; Johansson, Alicia; Bilenberg, Brian; Nielsen, Theodor; Guttmann, Markus

2017-01-01

In this study we present a flexible and adaptable fabrication method to create complex hierarchical structures over inherently hydrophobic resist materials. We have tested these surfaces for their superhydrophobic behaviour and successfully verified their self-cleaning properties. The followed approach allow us to design and produce superhydrophobic surfaces in a reproducible manner. We have analysed different combination of hierarchical micro-nanostructures for their application to self-cleaning surfaces. A static contact angle value of 170° with a hysteresis of 4° was achieved without the need of any additional chemical treatment on the fabricated hierarchical structures. Dynamic effects were analysed on these surfaces, obtaining a remarkable self-cleaning effect as well as a good robustness over impacting droplets. (paper)
Toward combining thematic information with hierarchical multiscale segmentations using tree Markov random field model

Science.gov (United States)

Zhang, Xueliang; Xiao, Pengfeng; Feng, Xuezhi

2017-09-01

It has been a common idea to produce multiscale segmentations to represent the various geographic objects in high-spatial resolution remote sensing (HR) images. However, it remains a great challenge to automatically select the proper segmentation scale(s) just according to the image information. In this study, we propose a novel way of information fusion at object level by combining hierarchical multiscale segmentations with existed thematic information produced by classification or recognition. The tree Markov random field (T-MRF) model is designed for the multiscale combination framework, through which the object type is determined as close as the existed thematic information. At the same time, the object boundary is jointly determined by the thematic labels and the multiscale segments through the minimization of the energy function. The benefits of the proposed T-MRF combination model include: (1) reducing the dependence of segmentation scale selection when utilizing multiscale segmentations; (2) exploring the hierarchical context naturally imbedded in the multiscale segmentations. The HR images in both urban and rural areas are used in the experiments to show the effectiveness of the proposed combination framework on these two aspects.
Superadditive correlation

International Nuclear Information System (INIS)

Giraud, B.G.; Heumann, J.M.; Lapedes, A.S.

1999-01-01

The fact that correlation does not imply causation is well known. Correlation between variables at two sites does not imply that the two sites directly interact, because, e.g., correlation between distant sites may be induced by chaining of correlation between a set of intervening, directly interacting sites. Such 'noncausal correlation' is well understood in statistical physics: an example is long-range order in spin systems, where spins which have only short-range direct interactions, e.g., the Ising model, display correlation at a distance. It is less well recognized that such long-range 'noncausal' correlations can in fact be stronger than the magnitude of any causal correlation induced by direct interactions. We call this phenomenon superadditive correlation (SAC). We demonstrate this counterintuitive phenomenon by explicit examples in (i) a model spin system and (ii) a model continuous variable system, where both models are such that two variables have multiple intervening pathways of indirect interaction. We apply the technique known as decimation to explain SAC as an additive, constructive interference phenomenon between the multiple pathways of indirect interaction. We also explain the effect using a definition of the collective mode describing the intervening spin variables. Finally, we show that the SAC effect is mirrored in information theory, and is true for mutual information measures in addition to correlation measures. Generic complex systems typically exhibit multiple pathways of indirect interaction, making SAC a potentially widespread phenomenon. This affects, e.g., attempts to deduce interactions by examination of correlations, as well as, e.g., hierarchical approximation methods for multivariate probability distributions, which introduce parameters based on successive orders of correlation. copyright 1999 The American Physical Society
Hierarchical Ag mesostructures for single particle SERS substrate

Energy Technology Data Exchange (ETDEWEB)

Xu, Minwei, E-mail: xuminwei@xjtu.edu.cn; Zhang, Yin

2017-01-30

Highlights: • Hierarchical Ag mesostructures with the size of 250, 360 and 500 nm are synthesized via a seed-mediated approach. • The Ag mesostructures present the tailorable size and highly roughened surfaces. • The average enhancement factors for individual Ag mesostructures were estimated to be as high as 10{sup 6}. - Abstract: Hierarchical Ag mesostructures with highly rough surface morphology have been synthesized at room temperature through a simple seed-mediated approach. Electron microscopy characterizations indicate that the obtained Ag mesostructures exhibit a textured surface morphology with the flower-like architecture. Moreover, the particle size can be tailored easily in the range of 250–500 nm. For the growth process of the hierarchical Ag mesostructures, it is believed that the self-assembly mechanism is more reasonable rather than the epitaxial overgrowth of Ag seed. The oriented attachment of nanoparticles is revealed during the formation of Ag mesostructures. Single particle surface enhanced Raman spectra (sp-SERS) of crystal violet adsorbed on the hierarchical Ag mesostructures were measured. Results reveal that the hierarchical Ag mesostructures can be highly sensitive sp-SERS substrates with good reproducibility. The average enhancement factors for individual Ag mesostructures are estimated to be about 10{sup 6}.
Job boredom and its correlates in 87 Finnish organizations

NARCIS (Netherlands)

Harju, Lotta; Hakanen, Jari J.; Schaufeli, Wilmar B.

2014-01-01

OBJECTIVE: To investigate the correlates of job boredom in 87 Finnish workplaces (N = 11,468) and to examine the associations between job boredom, health outcomes, and job attitudes. METHODS: We applied the Dutch Boredom Scale to measure job boredom. Hierarchical logistic regression analysis and
Is a matrix exponential specification suitable for the modeling of spatial correlation structures?

Science.gov (United States)

Strauß, Magdalena E; Mezzetti, Maura; Leorato, Samantha

2017-05-01

This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the performances of the different model specifications in applications to a real data set and by running simulations. Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms.
Spatial correlations and probability density function of the phase difference in a developed speckle-field: numerical and natural experiments

International Nuclear Information System (INIS)

Mysina, N Yu; Maksimova, L A; Ryabukho, V P; Gorbatenko, B B

2015-01-01

Investigated are statistical properties of the phase difference of oscillations in speckle-fields at two points in the far-field diffraction region, with different shapes of the scatterer aperture. Statistical and spatial nonuniformity of the probability density function of the field phase difference is established. Numerical experiments show that, for the speckle-fields with an oscillating alternating-sign transverse correlation function, a significant nonuniformity of the probability density function of the phase difference in the correlation region of the field complex amplitude, with the most probable values 0 and p, is observed. A natural statistical interference experiment using Young diagrams has confirmed the results of numerical experiments. (laser applications and other topics in quantum electronics)
Robust Unit Commitment Considering the Temporal and Spatial Correlations of Wind Farms Using a Data-Adaptive Approach

DEFF Research Database (Denmark)

Zhang, Yipu; Ai, Xiaomeng; Wen, Jinyu

2018-01-01

. In this paper, a novel data-adaptive robust optimization method for the unit commitment is proposed for the power system with wind farms integrated. The extreme scenario extraction and the two stage robust optimization are combined in the proposed method. The data-adaptive set consisting of a few extreme...... scenarios is derived to reduce the conservativeness by considering the temporal and spatial correlations of multiple wind farms. Numerical results demonstrate that the proposed data-adaptive robust optimization algorithm is less conservative than the current two-stage optimization approaches while maintains...
A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

Directory of Open Access Journals (Sweden)

Jie Hu

2018-01-01

Full Text Available Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM, and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.
Effects of water salinity on the correlation scale of Root density and Evapotranspiration fluxes

Science.gov (United States)

Ajeel, Ali; Saeed, Ali; Dragonetti, Giovanna; Comegna, Alessandro; Lamaddalena, Nicola; Coppola, Antonio

2015-04-01

Spatial pattern and the correlation of different soil and plant parameters were examined in a green bean field experiment carried out at the Mediterranean Agronomic Institute of Bari, Italy. The experiment aimed to evaluate the role of local processes of salt accumulation and transport which mainly influences the evapotranspiration (and thus the root uptake) processes under different water salinity levels. The experiment consisted of three transects of 30m length and 4.2 m width, irrigated with three different salinity levels (1dSm-1, 3dSm-1, 6dSm-1). Soil measurements (electrical conductivity and soil water content) were monitored along transects in 24 sites, 1 m apart by using TDR probes and Diviner 2000. Water storage measured by TDR and Diviner sensor were coupled for calculating directly the evapotranspiration fluxes along the whole soil profile under the different salinity levels imposed during the experiment. In the same sites, crop monitoring involved measurements of Leaf Area Index (LAI), Osmotic Potential (OP), Leaf Water Potential (LWP), and Root length Density (RlD). Soil and plant properties were analyzed by classical statistics, geostatistics methods and spectral analysis. Results indicated moderate to large spatial variability across the field for soil and plant parameters under all salinity treatments. Furthermore, cross-semivariograms exhibited a strong positive spatial interdependence between electrical conductivity of soil solution ECw with ET and RlD in transect treated with 3dSm-1 as well as with LAI in transect treated with 6dSm-1 at all 24 monitoring sites. Spectral analysis enabled to identify the observation window to sample the soil salinity information responsible for a given plant response (ET, OP, RlD). It is also allowed a clear identification of the spatial scale at which the soil water salinity level and distribution and the crop response in terms of actual evapotranspiration ET, RlD and OP, are actually correlated. Additionally
Analysis hierarchical model for discrete event systems

Science.gov (United States)

Ciortea, E. M.

2015-11-01

The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

Hierarchical effects on target detection and conflict monitoring

Science.gov (United States)

Cao, Bihua; Gao, Feng; Ren, Maofang; Li, Fuhong

2016-01-01

Previous neuroimaging studies have demonstrated a hierarchical functional structure of the frontal cortices of the human brain, but the temporal course and the electrophysiological signature of the hierarchical representation remains unaddressed. In the present study, twenty-one volunteers were asked to perform a nested cue-target task, while their scalp potentials were recorded. The results showed that: (1) in comparison with the lower-level hierarchical targets, the higher-level targets elicited a larger N2 component (220–350 ms) at the frontal sites, and a smaller P3 component (350–500 ms) across the frontal and parietal sites; (2) conflict-related negativity (non-target minus target) was greater for the lower-level hierarchy than the higher-level, reflecting a more intensive process of conflict monitoring at the final step of target detection. These results imply that decision making, context updating, and conflict monitoring differ among different hierarchical levels of abstraction. PMID:27561989
Prediction of road accidents: A Bayesian hierarchical approach

DEFF Research Database (Denmark)

Deublein, Markus; Schubert, Matthias; Adey, Bryan T.

2013-01-01

the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link......In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson......-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks...
Programming with Hierarchical Maps

DEFF Research Database (Denmark)

Ørbæk, Peter

This report desribes the hierarchical maps used as a central data structure in the Corundum framework. We describe its most prominent features, ague for its usefulness and briefly describe some of the software prototypes implemented using the technology....
Decentralization as a Cause of Spatial Segregation

Directory of Open Access Journals (Sweden)

Jasarovic Ema Alihodzic

2016-01-01

Full Text Available City represents an incomplete dynamic process prone to the expansion with a causal link between urban expansion and socio-spatial segregation. The socio-spatial distribution in the city is mostly related to the increased social polarization and inequality. There is a clear connection between divided society and divided city: if society is divided, urban space must be divided. It is the question of the relations between the social inequalities on one hand, and spatial segregation on the other. In the last 10 years, Podgorica is the city that shows alarming statistic values when it comes to demographic trends and the influx of the residents from the northern municipalities, which necessarily causes the city sprawl. Past experiences show that city is unevenly expanding, creating new functions and zones expressed by socio-spatial differences. The beginning of this process lies in modernist conception of the city, by which city was mostly developed, while the current functional organization is based on the same concept. With the first urban plans, which carried similarproblems mentioned in previous section, Podgorica was divided into three clearly differentiated zones: Stara Varoš, Nova Varoš and Novi grad, which became a platform for hierarchical divisions within the space, reflecting them in the society.
Correlation Factors Describing Primary and Spatial Sensations of Sound Fields

Science.gov (United States)

ANDO, Y.

2002-11-01

The theory of subjective preference of the sound field in a concert hall is established based on the model of human auditory-brain system. The model consists of the autocorrelation function (ACF) mechanism and the interaural crosscorrelation function (IACF) mechanism for signals arriving at two ear entrances, and the specialization of human cerebral hemispheres. This theory can be developed to describe primary sensations such as pitch or missing fundamental, loudness, timbre and, in addition, duration sensation which is introduced here as a fourth. These four primary sensations may be formulated by the temporal factors extracted from the ACF associated with the left hemisphere and, spatial sensations such as localization in the horizontal plane, apparent source width and subjective diffuseness are described by the spatial factors extracted from the IACF associated with the right hemisphere. Any important subjective responses of sound fields may be described by both temporal and spatial factors.
Smoothing effect for spatially distributed renewable resources and its impact on power grid robustness.

Science.gov (United States)

Nagata, Motoki; Hirata, Yoshito; Fujiwara, Naoya; Tanaka, Gouhei; Suzuki, Hideyuki; Aihara, Kazuyuki

2017-03-01

In this paper, we show that spatial correlation of renewable energy outputs greatly influences the robustness of the power grids against large fluctuations of the effective power. First, we evaluate the spatial correlation among renewable energy outputs. We find that the spatial correlation of renewable energy outputs depends on the locations, while the influence of the spatial correlation of renewable energy outputs on power grids is not well known. Thus, second, by employing the topology of the power grid in eastern Japan, we analyze the robustness of the power grid with spatial correlation of renewable energy outputs. The analysis is performed by using a realistic differential-algebraic equations model. The results show that the spatial correlation of the energy resources strongly degrades the robustness of the power grid. Our results suggest that we should consider the spatial correlation of the renewable energy outputs when estimating the stability of power grids.
Hierarchical Porous Structures

Energy Technology Data Exchange (ETDEWEB)

Grote, Christopher John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-06-07

Materials Design is often at the forefront of technological innovation. While there has always been a push to generate increasingly low density materials, such as aero or hydrogels, more recently the idea of bicontinuous structures has gone more into play. This review will cover some of the methods and applications for generating both porous, and hierarchically porous structures.
Bovine spongiform encephalopathy and spatial analysis of the feed industry.

Science.gov (United States)

Paul, Mathilde; Abrial, David; Jarrige, Nathalie; Rican, Stéphane; Garrido, Myriam; Calavas, Didier; Ducrot, Christian

2007-06-01

In France, despite the ban of meat-and-bone meal (MBM) in cattle feed, bovine spongiform encephalopathy (BSE) was detected in hundreds of cattle born after the ban. To study the role of MBM, animal fat, and dicalcium phosphate on the risk for BSE after the feed ban, we conducted a spatial analysis of the feed industry. We used data from 629 BSE cases as well as data on use of each byproduct and market area of the feed factories. We mapped risk for BSE in 951 areas supplied by the same factories and connection with use of byproducts. A disease map of BSE with covariates was built with the hierarchical Bayesian modeling methods, based on Poisson distribution with spatial smoothing. Only use of MBM was spatially linked to risk for BSE, which highlights cross-contamination as the most probable source of infection after the feed ban.
Global Crop Monitoring: A Satellite-Based Hierarchical Approach

Directory of Open Access Journals (Sweden)

Bingfang Wu

2015-04-01

Full Text Available Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The approach adopts a hierarchical system covering four spatial levels of detail: global, regional, national (thirty-one key countries including China and “sub-countries” (for the nine largest countries. The thirty-one countries encompass more that 80% of both production and exports of maize, rice, soybean and wheat. The methodology resorts to climatic and remote sensing indicators at different scales. The global patterns of crop environmental growing conditions are first analyzed with indicators for rainfall, temperature, photosynthetically active radiation (PAR as well as potential biomass. At the regional scale, the indicators pay more attention to crops and include Vegetation Health Index (VHI, Vegetation Condition Index (VCI, Cropped Arable Land Fraction (CALF as well as Cropping Intensity (CI. Together, they characterize crop situation, farming intensity and stress. CropWatch carries out detailed crop condition analyses at the national scale with a comprehensive array of variables and indicators. The Normalized Difference Vegetation Index (NDVI, cropped areas and crop conditions are integrated to derive food production estimates. For the nine largest countries, CropWatch zooms into the sub-national units to acquire detailed information on crop condition and production by including new indicators (e.g., Crop type proportion. Based on trend analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. The hierarchical approach adopted by CropWatch is the basis of the analyses of climatic and crop conditions assessments published in the quarterly “CropWatch bulletin” which
Investigating the effects of climate variations on bacillary dysentery incidence in northeast China using ridge regression and hierarchical cluster analysis

Directory of Open Access Journals (Sweden)

Guo Junqiao

2008-09-01

Full Text Available Abstract Background The effects of climate variations on bacillary dysentery incidence have gained more recent concern. However, the multi-collinearity among meteorological factors affects the accuracy of correlation with bacillary dysentery incidence. Methods As a remedy, a modified method to combine ridge regression and hierarchical cluster analysis was proposed for investigating the effects of climate variations on bacillary dysentery incidence in northeast China. Results All weather indicators, temperatures, precipitation, evaporation and relative humidity have shown positive correlation with the monthly incidence of bacillary dysentery, while air pressure had a negative correlation with the incidence. Ridge regression and hierarchical cluster analysis showed that during 1987–1996, relative humidity, temperatures and air pressure affected the transmission of the bacillary dysentery. During this period, all meteorological factors were divided into three categories. Relative humidity and precipitation belonged to one class, temperature indexes and evaporation belonged to another class, and air pressure was the third class. Conclusion Meteorological factors have affected the transmission of bacillary dysentery in northeast China. Bacillary dysentery prevention and control would benefit from by giving more consideration to local climate variations.
Analyzing security protocols in hierarchical networks

DEFF Research Database (Denmark)

Zhang, Ye; Nielson, Hanne Riis

2006-01-01

Validating security protocols is a well-known hard problem even in a simple setting of a single global network. But a real network often consists of, besides the public-accessed part, several sub-networks and thereby forms a hierarchical structure. In this paper we first present a process calculus...... capturing the characteristics of hierarchical networks and describe the behavior of protocols on such networks. We then develop a static analysis to automate the validation. Finally we demonstrate how the technique can benefit the protocol development and the design of network systems by presenting a series...
Hierarchical Analysis of the Omega Ontology

Energy Technology Data Exchange (ETDEWEB)

Joslyn, Cliff A.; Paulson, Patrick R.

2009-12-01

Initial delivery for mathematical analysis of the Omega Ontology. We provide an analysis of the hierarchical structure of a version of the Omega Ontology currently in use within the US Government. After providing an initial statistical analysis of the distribution of all link types in the ontology, we then provide a detailed order theoretical analysis of each of the four main hierarchical links present. This order theoretical analysis includes the distribution of components and their properties, their parent/child and multiple inheritance structure, and the distribution of their vertical ranks.
Modelling Spatial Compositional Data: Reconstructions of past land cover and uncertainties

DEFF Research Database (Denmark)

Pirzamanbein, Behnaz; Lindström, Johan; Poska, Anneli

2018-01-01

In this paper, we construct a hierarchical model for spatial compositional data, which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past $6\\,000$ years over Europe. The model...... to a fast MCMC algorithm. Reconstructions are obtained by combining pollen-based estimates of vegetation cover at a limited number of locations with scenarios of past deforestation and output from a dynamic vegetation model. To evaluate uncertainties in the predictions a novel way of constructing joint...... confidence regions for the entire composition at each prediction location is proposed. The hierarchical model's ability to reconstruct past land cover is evaluated through cross validation for all time periods, and by comparing reconstructions for the recent past to a present day European forest map...
Hierarchical composites: Analysis of damage evolution based on fiber bundle model

DEFF Research Database (Denmark)

Mishnaevsky, Leon

2011-01-01

A computational model of multiscale composites is developed on the basis of the fiber bundle model with the hierarchical load sharing rule, and employed to study the effect of the microstructures of hierarchical composites on their damage resistance. Two types of hierarchical materials were consi...
Hierarchical cellular designs for load-bearing biocomposite beams and plates

International Nuclear Information System (INIS)

Burgueno, Rigoberto; Quagliata, Mario J.; Mohanty, Amar K.; Mehta, Geeta; Drzal, Lawrence T.; Misra, Manjusri

2005-01-01

Scrutiny into the composition of natural, or biological materials convincingly reveals that high material and structural efficiency can be attained, even with moderate-quality constituents, by hierarchical topologies, i.e., successively organized material levels or layers. The present study demonstrates that biologically inspired hierarchical designs can help improve the moderate properties of natural fiber polymer composites or biocomposites and allow them to compete with conventional materials for load-bearing applications. An overview of the mechanics concepts that allow hierarchical designs to achieve higher performance is presented, followed by observation and results from flexural tests on periodic and hierarchical cellular beams and plates made from industrial hemp fibers and unsaturated polyester resin biocomposites. The experimental data is shown to agree well with performance indices predicted by mechanics models. A procedure for the multi-scale integrated material/structural analysis of hierarchical cellular biocomposite components is presented and its advantages and limitations are discussed
Hierarchical phase space structure of dark matter haloes: Tidal debris, caustics, and dark matter annihilation

International Nuclear Information System (INIS)

Afshordi, Niayesh; Mohayaee, Roya; Bertschinger, Edmund

2009-01-01

Most of the mass content of dark matter haloes is expected to be in the form of tidal debris. The density of debris is not constant, but rather can grow due to formation of caustics at the apocenters and pericenters of the orbit, or decay as a result of phase mixing. In the phase space, the debris assemble in a hierarchy that is truncated by the primordial temperature of dark matter. Understanding this phase structure can be of significant importance for the interpretation of many astrophysical observations and, in particular, dark matter detection experiments. With this purpose in mind, we develop a general theoretical framework to describe the hierarchical structure of the phase space of cold dark matter haloes. We do not make any assumption of spherical symmetry and/or smooth and continuous accretion. Instead, working with correlation functions in the action-angle space, we can fully account for the hierarchical structure (predicting a two-point correlation function ∝ΔJ -1.6 in the action space), as well as the primordial discreteness of the phase space. As an application, we estimate the boost to the dark matter annihilation signal due to the structure of the phase space within virial radius: the boost due to the hierarchical tidal debris is of order unity, whereas the primordial discreteness of the phase structure can boost the total annihilation signal by up to an order of magnitude. The latter is dominated by the regions beyond 20% of the virial radius, and is largest for the recently formed haloes with the least degree of phase mixing. Nevertheless, as we argue in a companion paper, the boost due to small gravitationally-bound substructure can dominate this effect at low redshifts.
Hierarchical phase space structure of dark matter haloes: Tidal debris, caustics, and dark matter annihilation

Science.gov (United States)

Afshordi, Niayesh; Mohayaee, Roya; Bertschinger, Edmund

2009-04-01

Most of the mass content of dark matter haloes is expected to be in the form of tidal debris. The density of debris is not constant, but rather can grow due to formation of caustics at the apocenters and pericenters of the orbit, or decay as a result of phase mixing. In the phase space, the debris assemble in a hierarchy that is truncated by the primordial temperature of dark matter. Understanding this phase structure can be of significant importance for the interpretation of many astrophysical observations and, in particular, dark matter detection experiments. With this purpose in mind, we develop a general theoretical framework to describe the hierarchical structure of the phase space of cold dark matter haloes. We do not make any assumption of spherical symmetry and/or smooth and continuous accretion. Instead, working with correlation functions in the action-angle space, we can fully account for the hierarchical structure (predicting a two-point correlation function ∝ΔJ-1.6 in the action space), as well as the primordial discreteness of the phase space. As an application, we estimate the boost to the dark matter annihilation signal due to the structure of the phase space within virial radius: the boost due to the hierarchical tidal debris is of order unity, whereas the primordial discreteness of the phase structure can boost the total annihilation signal by up to an order of magnitude. The latter is dominated by the regions beyond 20% of the virial radius, and is largest for the recently formed haloes with the least degree of phase mixing. Nevertheless, as we argue in a companion paper, the boost due to small gravitationally-bound substructure can dominate this effect at low redshifts.
Fabrication of Superhydrophobic Surface with Controlled Wetting Property by Hierarchical Particles.

Science.gov (United States)

Xu, Jianxiong; Liu, Weiwei; Du, Jingjing; Tang, Zengmin; Xu, Lijian; Li, Na

2015-04-01

Hierarchical particles were prepared by synthetically joining appropriately functionalized polystyrene spheres of poly[styrene-co-(3-(4-vinylphenyl)pentane-2,4-dione)] (PS-co-PVPD) nanoparticles and poly(styrene-co-chloromethylstyrene) (PS-co-PCMS) microparticles. The coupling reaction of nucleophilic substitution of pendent β-diketone groups with benzyl chloride was used to form the hierarchical particles. Since the polymeric nanoparticles and microparticles were synthesized by dispersion polymerization and emulsion polymerization, respectively, both the core microparticles and the surface nanoparticles can be different size and chemical composition. By means of changing the size of the PS-co-PVPD surface nanoparticles, a series of hierarchical particles with different scale ratio of the micro/nano surface structure were successfully prepared. Moreover, by employing the PS-co-PVPD microparticles and PS-co-PCMS nanoparticles as building blocks, hierarchical particles with surface nanoaprticles of different composition were made. These as-prepared hierarchical particles were subsequently assembled on glass substrates to form particulate films. Contact angle measurement shows that superhydrophobic surfaces can be obtained and the contact angle of water on the hierarchically structured surface can be adjusted by the scale ratio of the micro/nano surface structure and surface chemical component of hierarchical particles.
Teacher spatial skills are linked to differences in geometry instruction.

Science.gov (United States)

Otumfuor, Beryl Ann; Carr, Martha

2017-12-01

Spatial skills have been linked to better performance in mathematics. The purpose of this study was to examine the relationship between teacher spatial skills and their instruction, including teacher content and pedagogical knowledge, use of pictorial representations, and use of gestures during geometry instruction. Fifty-six middle school teachers participated in the study. The teachers were administered spatial measures of mental rotations and spatial visualization. Next, a single geometry class was videotaped. Correlational analyses revealed that spatial skills significantly correlate with teacher's use of representational gestures and content and pedagogical knowledge during instruction of geometry. Spatial skills did not independently correlate with the use of pointing gestures or the use of pictorial representations. However, an interaction term between spatial skills and content and pedagogical knowledge did correlate significantly with the use of pictorial representations. Teacher experience as measured by the number of years of teaching and highest degree did not appear to affect the relationships among the variables with the exception of the relationship between spatial skills and teacher content and pedagogical knowledge. Teachers with better spatial skills are also likely to use representational gestures and to show better content and pedagogical knowledge during instruction. Spatial skills predict pictorial representation use only as a function of content and pedagogical knowledge. © 2017 The British Psychological Society.
Discursive Hierarchical Patterning in Law and Management Cases

Science.gov (United States)

Lung, Jane

2008-01-01

This paper investigates the differences in the discursive patterning of cases in Law and Management. It examines a corpus of 271 Law and Management cases and discusses the kind of information that these two disciplines call for and how discourses are constructed in discursive hierarchical patterns. A discursive hierarchical pattern is a model…

Evaluating the effect of sampling and spatial correlation on ground-water travel time uncertainty coupling geostatistical, stochastic, and first order, second moment methods

International Nuclear Information System (INIS)

Andrews, R.W.; LaVenue, A.M.; McNeish, J.A.

1989-01-01

Ground-water travel time predictions at potential high-level waste repositories are subject to a degree of uncertainty due to the scale of averaging incorporated in conceptual models of the ground-water flow regime as well as the lack of data on the spatial variability of the hydrogeologic parameters. The present study describes the effect of limited observations of a spatially correlated permeability field on the predicted ground-water travel time uncertainty. Varying permeability correlation lengths have been used to investigate the importance of this geostatistical property on the tails of the travel time distribution. This study uses both geostatistical and differential analysis techniques. Following the generation of a spatially correlated permeability field which is considered reality, semivariogram analyses are performed upon small random subsets of the generated field to determine the geostatistical properties of the field represented by the observations. Kriging is then employed to generate a kriged permeability field and the corresponding standard deviation of the estimated field conditioned by the limited observations. Using both the real and kriged fields, the ground-water flow regime is simulated and ground-water travel paths and travel times are determined for various starting points. These results are used to define the ground-water travel time uncertainty due to path variability. The variance of the ground-water travel time along particular paths due to the variance of the permeability field estimated using kriging is then calculated using the first order, second moment method. The uncertainties in predicted travel time due to path and parameter uncertainties are then combined into a single distribution
Hierarchical modularity in human brain functional networks

Directory of Open Access Journals (Sweden)

David Meunier

2009-10-01

Full Text Available The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules” decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.
Hierarchical Context Modeling for Video Event Recognition.

Science.gov (United States)

Wang, Xiaoyang; Ji, Qiang

2016-10-11

Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.
Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants.

Science.gov (United States)

Werchan, Denise M; Collins, Anne G E; Frank, Michael J; Amso, Dima

2016-10-05

Recent research indicates that adults and infants spontaneously create and generalize hierarchical rule sets during incidental learning. Computational models and empirical data suggest that, in adults, this process is supported by circuits linking prefrontal cortex (PFC) with striatum and their modulation by dopamine, but the neural circuits supporting this form of learning in infants are largely unknown. We used near-infrared spectroscopy to record PFC activity in 8-month-old human infants during a simple audiovisual hierarchical-rule-learning task. Behavioral results confirmed that infants adopted hierarchical rule sets to learn and generalize spoken object-label mappings across different speaker contexts. Infants had increased activity over right dorsal lateral PFC when rule sets switched from one trial to the next, a neural marker related to updating rule sets into working memory in the adult literature. Infants' eye blink rate, a possible physiological correlate of striatal dopamine activity, also increased when rule sets switched from one trial to the next. Moreover, the increase in right dorsolateral PFC activity in conjunction with eye blink rate also predicted infants' generalization ability, providing exploratory evidence for frontostriatal involvement during learning. These findings provide evidence that PFC is involved in rudimentary hierarchical rule learning in 8-month-old infants, an ability that was previously thought to emerge later in life in concert with PFC maturation. Hierarchical rule learning is a powerful learning mechanism that allows rules to be selected in a context-appropriate fashion and transferred or reused in novel contexts. Data from computational models and adults suggests that this learning mechanism is supported by dopamine-innervated interactions between prefrontal cortex (PFC) and striatum. Here, we provide evidence that PFC also supports hierarchical rule learning during infancy, challenging the current dogma that PFC is an
Redshift distortions of galaxy correlation functions

International Nuclear Information System (INIS)

Fry, J.N.; Florida Univ., Gainesville, FL; Gaztanaga, E.; Oxford Univ.

1993-01-01

To examine how peculiar velocities can affect the 2-, 3-, and 4-point correlation functions, we evaluate volume-average correlations for configurations that emphasize and minimize distortions for four different volume-limited samples from each of the CfA, SSRS, and IRAS redshift catalogs. We present the results as the correlation length r 0 and power index γ of the 2-point correlation, anti Ξ 2 = (r 0 /r) γ , and as the hierarchical amplitudes of the 3- and 4-point functions, S 3 = anti Ξ 3 /anti Ξ 2 2 and S 4 = anti Ξ/anti Ξ 2 3 . We find a characteristic distortion for anti Ξ 2 : The slope γ is flatter and the correlation length is larger in redshift space than in real space; that is, redshift distortions ''move'' correlations from small to large scales. At the largest scales, extra power in the redshift distribution is compatible with Ω 4/7 /b ∼ 1; we find 0.53 ± 0.15, 1.10 ± 0.16 and 0.84 ± 0.45 for the CfA, SSRS and IRAS catalogs. Higher order correlations anti Ξ 3 and anti Ξ 4 suffer similar redshift distortions, but in such a way that, within the accuracy of our analysis, the normalized amplitudes S 3 and S 4 are insensitive to this effect. The hierarchical amplitudes S 3 and S 4 are constant as a function of scale between 1-12 h -1 Mpc and have similar values in all samples and catalogues, S 3 ∼ 2 and S 4 ∼ 6, despite the fact that anti Ξ 2 , anti Ξ 3 , and anti Ξ 4 differ from one sample to another by large factors. The agreement between the independent estimations of S 3 and S 4 is remarkable given the different criteria in the selection of galaxies and also the difference in the resulting range of densities, luminosities and locations between samples
Robust feature estimation by non-rigid hierarchical image registration and its application in disparity measurement

Science.gov (United States)

Badshah, Amir; Choudhry, Aadil Jaleel; Ullah, Shan

2017-03-01

Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality inspection of products. Majority of these systems are equipped with cameras and rely on diverse image processing algorithms. Information is lost in 2D image, therefore acquiring accurate 3D data from 2D images is an open issue. FAST, SURF and SIFT are well-known spatial domain techniques for features extraction and henceforth image registration to find correspondence between images. The efficiency of these methods is measured in terms of the number of perfect matches found. A novel fast and robust technique for stereo-image processing is proposed. It is based on non-rigid registration using modified normalized phase correlation. The proposed method registers two images in hierarchical fashion using quad-tree structure. The registration process works through global to local level resulting in robust matches even in presence of blur and noise. The computed matches can further be utilized to determine disparity and depth for industrial product inspection. The same can be used in driver assistance systems. The preliminary tests on Middlebury dataset produced satisfactory results. The execution time for a 413 x 370 stereo-pair is 500ms approximately on a low cost DSP.
Bayesian nonparametric hierarchical modeling.

Science.gov (United States)

Dunson, David B

2009-04-01

In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.
Hierarchical silica particles by dynamic multicomponent assembly

DEFF Research Database (Denmark)

Wu, Z. W.; Hu, Q. Y.; Pang, J. B.

2005-01-01

Abstract: Aerosol-assisted assembly of mesoporous silica particles with hierarchically controllable pore structure has been prepared using cetyltrimethylammonium bromide (CTAB) and poly(propylene oxide) (PPO, H[OCH(CH3)CH2],OH) as co-templates. Addition of the hydrophobic PPO significantly...... influences the delicate hydrophilic-hydrophobic balance in the well-studied CTAB-silicate co-assembling system, resulting in various mesostructures (such as hexagonal, lamellar, and hierarchical structure). The co-assembly of CTAB, silicate clusters, and a low-molecular-weight PPO (average M-n 425) results...... in a uniform lamellar structure, while the use of a high-molecular-weight PPO (average M-n 2000), which is more hydrophobic, leads to the formation of hierarchical pore structure that contains meso-meso or meso-macro pore structure. The role of PPO additives on the mesostructure evolution in the CTAB...
Overcoming artificial spatial correlations in simulations of superstructure domain growth with parallel Monte Carlo algorithms

International Nuclear Information System (INIS)

Schleier, W.; Besold, G.; Heinz, K.

1992-01-01

The authors study the applicability of parallelized/vectorized Monte Carlo (MC) algorithms to the simulation of domain growth in two-dimensional lattice gas models undergoing an ordering process after a rapid quench below an order-disorder transition temperature. As examples they consider models with 2 x 1 and c(2 x 2) equilibrium superstructures on the square and rectangular lattices, respectively. They also study the case of phase separation ('1 x 1' islands) on the square lattice. A generalized parallel checkerboard algorithm for Kawasaki dynamics is shown to give rise to artificial spatial correlations in all three models. However, only if superstructure domains evolve do these correlations modify the kinetics by influencing the nucleation process and result in a reduced growth exponent compared to the value from the conventional heat bath algorithm with random single-site updates. In order to overcome these artificial modifications, two MC algorithms with a reduced degree of parallelism ('hybrid' and 'mask' algorithms, respectively) are presented and applied. As the results indicate, these algorithms are suitable for the simulation of superstructure domain growth on parallel/vector computers. 60 refs., 10 figs., 1 tab
On the performance of dual-hop systems with multiple antennas: Effects of spatial correlation, keyhole, and co-channel interference

KAUST Repository

Yang, Liang

2012-12-01

In this paper, taking into account realistic propagation conditions, namely, spatial correlation, keyhole channels, and unequal-power co-channel interference, we investigate the performance of a wireless relay network where all the nodes are equipped with multiple antennas. Considering channel state information assisted amplify-and-forward protocol, we present analytical expressions for the symbol error rate (SER) and outage probability. More specifically, we first derive the SER expressions of a relay system with orthogonal space-time block coding (OSTBC) over correlated/keyhole fading channels. We also analyze the outage probability of interference corrupted relay systems with maximal ratio combing (MRC) at the receiver as well as multiple-input multiple-output MRC (MIMO MRC). Numerical results are given to illustrate and verify the analytical results. © 2012 IEEE.
On the performance of dual-hop systems with multiple antennas: Effects of spatial correlation, keyhole, and co-channel interference

KAUST Repository

Yang, Liang; Alouini, Mohamed-Slim; Qaraqe, Khalid A.; Liu, Weiping

2012-01-01

In this paper, taking into account realistic propagation conditions, namely, spatial correlation, keyhole channels, and unequal-power co-channel interference, we investigate the performance of a wireless relay network where all the nodes are equipped with multiple antennas. Considering channel state information assisted amplify-and-forward protocol, we present analytical expressions for the symbol error rate (SER) and outage probability. More specifically, we first derive the SER expressions of a relay system with orthogonal space-time block coding (OSTBC) over correlated/keyhole fading channels. We also analyze the outage probability of interference corrupted relay systems with maximal ratio combing (MRC) at the receiver as well as multiple-input multiple-output MRC (MIMO MRC). Numerical results are given to illustrate and verify the analytical results. © 2012 IEEE.
Comparing Spatial Predictions

KAUST Repository

Hering, Amanda S.

2011-11-01

Under a general loss function, we develop a hypothesis test to determine whether a significant difference in the spatial predictions produced by two competing models exists on average across the entire spatial domain of interest. The null hypothesis is that of no difference, and a spatial loss differential is created based on the observed data, the two sets of predictions, and the loss function chosen by the researcher. The test assumes only isotropy and short-range spatial dependence of the loss differential but does allow it to be non-Gaussian, non-zero-mean, and spatially correlated. Constant and nonconstant spatial trends in the loss differential are treated in two separate cases. Monte Carlo simulations illustrate the size and power properties of this test, and an example based on daily average wind speeds in Oklahoma is used for illustration. Supplemental results are available online. © 2011 American Statistical Association and the American Society for Qualitys.
Integrative Spatial Data Analytics for Public Health Studies of New York State.

Science.gov (United States)

Chen, Xin; Wang, Fusheng

2016-01-01

Increased accessibility of health data made available by the government provides unique opportunity for spatial analytics with much higher resolution to discover patterns of diseases, and their correlation with spatial impact indicators. This paper demonstrated our vision of integrative spatial analytics for public health by linking the New York Cancer Mapping Dataset with datasets containing potential spatial impact indicators. We performed spatial based discovery of disease patterns and variations across New York State, and identify potential correlations between diseases and demographic, socio-economic and environmental indicators. Our methods were validated by three correlation studies: the correlation between stomach cancer and Asian race, the correlation between breast cancer and high education population, and the correlation between lung cancer and air toxics. Our work will allow public health researchers, government officials or other practitioners to adequately identify, analyze, and monitor health problems at the community or neighborhood level for New York State.
Hierarchical tailoring of strut architecture to control permeability of additive manufactured titanium implants

Energy Technology Data Exchange (ETDEWEB)

Zhang, Z. [Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ (United Kingdom); Jones, D. [School of Engineering, University of Liverpool, Brownlow Hill, Liverpool, L69 3GH (United Kingdom); Yue, S. [Manchester X-ray Imaging Facility, School of Materials, The University of Manchester, Oxford Road, M13 9PL (United Kingdom); Lee, P.D., E-mail: peter.lee@manchester.ac.uk [Manchester X-ray Imaging Facility, School of Materials, The University of Manchester, Oxford Road, M13 9PL (United Kingdom); Jones, J.R. [Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ (United Kingdom); Sutcliffe, C.J. [School of Engineering, University of Liverpool, Brownlow Hill, Liverpool, L69 3GH (United Kingdom); Jones, E. [Department of Advanced Technology, Stryker Orthopaedics, Raheen Business Park, Limerick (Ireland)

2013-10-15

Porous titanium implants are a common choice for bone augmentation. Implants for spinal fusion and repair of non-union fractures must encourage blood flow after implantation so that there is sufficient cell migration, nutrient and growth factor transport to stimulate bone ingrowth. Additive manufacturing techniques allow a large number of pore network designs. This study investigates how the design factors offered by selective laser melting technique can be used to alter the implant architecture on multiple length scales to control and even tailor the flow. Permeability is a convenient parameter that characterises flow, correlating to structure openness (interconnectivity and pore window size), tortuosity and hence flow shear rates. Using experimentally validated computational simulations, we demonstrate how additive manufacturing can be used to tailor implant properties by controlling surface roughness at a microstructual level (microns), and by altering the strut ordering and density at a mesoscopic level (millimetre). Highlights: • Experimentally validated permeability prediction tools for hierarchical implants. • Randomised structures form preferential flow channels with stronger shear flows. • Hierarchical strut structures allow independent tailoring of flow and pore size.
Hierarchical tailoring of strut architecture to control permeability of additive manufactured titanium implants

International Nuclear Information System (INIS)

Zhang, Z.; Jones, D.; Yue, S.; Lee, P.D.; Jones, J.R.; Sutcliffe, C.J.; Jones, E.

2013-01-01

Porous titanium implants are a common choice for bone augmentation. Implants for spinal fusion and repair of non-union fractures must encourage blood flow after implantation so that there is sufficient cell migration, nutrient and growth factor transport to stimulate bone ingrowth. Additive manufacturing techniques allow a large number of pore network designs. This study investigates how the design factors offered by selective laser melting technique can be used to alter the implant architecture on multiple length scales to control and even tailor the flow. Permeability is a convenient parameter that characterises flow, correlating to structure openness (interconnectivity and pore window size), tortuosity and hence flow shear rates. Using experimentally validated computational simulations, we demonstrate how additive manufacturing can be used to tailor implant properties by controlling surface roughness at a microstructual level (microns), and by altering the strut ordering and density at a mesoscopic level (millimetre). Highlights: • Experimentally validated permeability prediction tools for hierarchical implants. • Randomised structures form preferential flow channels with stronger shear flows. • Hierarchical strut structures allow independent tailoring of flow and pore size
Hierarchical classification with a competitive evolutionary neural tree.

Science.gov (United States)

Adams, R G.; Butchart, K; Davey, N

1999-04-01

A new, dynamic, tree structured network, the Competitive Evolutionary Neural Tree (CENT) is introduced. The network is able to provide a hierarchical classification of unlabelled data sets. The main advantage that the CENT offers over other hierarchical competitive networks is its ability to self determine the number, and structure, of the competitive nodes in the network, without the need for externally set parameters. The network produces stable classificatory structures by halting its growth using locally calculated heuristics. The results of network simulations are presented over a range of data sets, including Anderson's IRIS data set. The CENT network demonstrates its ability to produce a representative hierarchical structure to classify a broad range of data sets.
Socio-economic factors of bacillary dysentery based on spatial correlation analysis in Guangxi Province, China.

Directory of Open Access Journals (Sweden)

Chengjing Nie

Full Text Available BACKGROUND: In the past decade, bacillary dysentery was still a big public health problem in China, especially in Guangxi Province, where thousands of severe diarrhea cases occur every year. METHODS: Reported bacillary dysentery cases in Guangxi Province were obtained from local Centers for Diseases Prevention and Control. The 14 socio-economic indexes were selected as potential explanatory variables for the study. The spatial correlation analysis was used to explore the associations between the selected factors and bacillary dysentery incidence at county level, which was based on the software of ArcGIS10.2 and GeoDA 0.9.5i. RESULTS: The proportion of primary industry, the proportion of younger than 5-year-old children in total population, the number of hospitals per thousand persons and the rates of bacillary dysentery incidence show statistically significant positive correlation. But the proportion of secondary industry, per capital GDP, per capital government revenue, rural population proportion, popularization rate of tap water in rural area, access rate to the sanitation toilets in rural, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons and the rate of bacillary dysentery incidence show statistically significant negative correlation. The socio-economic factors can be divided into four aspects, including economic development, health development, medical development and human own condition. The four aspects were not isolated from each other, but interacted with each other.
Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells.

Directory of Open Access Journals (Sweden)

Praveen K Pilly

Full Text Available Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous
Determination and correlation of spatial distribution of trace elements in normal and neoplastic breast tissues evaluated by μ-XRF

International Nuclear Information System (INIS)

Silva, M.P.; Oliveira, M.A.; Poletti, M.E.

2012-01-01

Full text: Some trace elements, naturally present in breast tissues, participate in a large number of biological processes, which include among others, activation or inhibition of enzymatic reactions and changes on cell membranes permeability, suggesting that these elements may influence carcinogenic processes. Thus, knowledge of the amounts of these elements and their spatial distribution in normal and neoplastic tissues may help in understanding the role of these elements in the carcinogenic process and tumor progression of breast cancers. Concentrations of trace elements like Ca, Fe, Cu and Zn, previously studied at LNLS using TXRF and conventional XRF, were elevated in neoplastic breast tissues compared to normal tissues. In this study we determined the spatial distribution of these elements in normal and neoplastic breast tissues using μ-XRF technique. We analyzed 22 samples of normal and neoplastic breast tissues (malignant and benign) obtained from paraffin blocks available for study at the Department of Pathology HC-FMRP/USP. From the blocks, a small fraction of material was removed and subjected to histological sections of 60 μm thick made with a microtome. The slices where placed in holder samples and covered with ultralen film. Tissue samples were irradiated with a white beam of synchrotron radiation. The samples were positioned at 45 degrees with respect to the incident beam on a table with 3 freedom degrees (x, y and z), allowing independent positioning of the sample in these directions. The white beam was collimated by a 20 μm microcapillary and samples were fully scanned. At each step, a spectrum was detected for 10 s. The fluorescence emitted by elements present in the sample was detected by a Si (Li) detector with 165 eV at 5.9 keV energy resolution, placed at 90 deg with respect to the incident beam. Results reveal that trace elements Ca-Zn and Fe-Cu could to be correlated in malignant breast tissues. Quantitative results, achieved by Spearman
Pore Network Modeling: Alternative Methods to Account for Trapping and Spatial Correlation

KAUST Repository

De La Garza Martinez, Pablo

2016-05-01

Pore network models have served as a predictive tool for soil and rock properties with a broad range of applications, particularly in oil recovery, geothermal energy from underground reservoirs, and pollutant transport in soils and aquifers [39]. They rely on the representation of the void space within porous materials as a network of interconnected pores with idealised geometries. Typically, a two-phase flow simulation of a drainage (or imbibition) process is employed, and by averaging the physical properties at the pore scale, macroscopic parameters such as capillary pressure and relative permeability can be estimated. One of the most demanding tasks in these models is to include the possibility of fluids to remain trapped inside the pore space. In this work I proposed a trapping rule which uses the information of neighboring pores instead of a search algorithm. This approximation reduces the simulation time significantly and does not perturb the accuracy of results. Additionally, I included spatial correlation to generate the pore sizes using a matrix decomposition method. Results show higher relative permeabilities and smaller values for irreducible saturation, which emphasizes the effects of ignoring the intrinsic correlation seen in pore sizes from actual porous media. Finally, I implemented the algorithm from Raoof et al. (2010) [38] to generate the topology of a Fontainebleau sandstone by solving an optimization problem using the steepest descent algorithm with a stochastic approximation for the gradient. A drainage simulation is performed on this representative network and relative permeability is compared with published results. The limitations of this algorithm are discussed and other methods are suggested to create a more faithful representation of the pore space.

Pore Network Modeling: Alternative Methods to Account for Trapping and Spatial Correlation

KAUST Repository

De La Garza Martinez, Pablo

2016-01-01

Pore network models have served as a predictive tool for soil and rock properties with a broad range of applications, particularly in oil recovery, geothermal energy from underground reservoirs, and pollutant transport in soils and aquifers [39]. They rely on the representation of the void space within porous materials as a network of interconnected pores with idealised geometries. Typically, a two-phase flow simulation of a drainage (or imbibition) process is employed, and by averaging the physical properties at the pore scale, macroscopic parameters such as capillary pressure and relative permeability can be estimated. One of the most demanding tasks in these models is to include the possibility of fluids to remain trapped inside the pore space. In this work I proposed a trapping rule which uses the information of neighboring pores instead of a search algorithm. This approximation reduces the simulation time significantly and does not perturb the accuracy of results. Additionally, I included spatial correlation to generate the pore sizes using a matrix decomposition method. Results show higher relative permeabilities and smaller values for irreducible saturation, which emphasizes the effects of ignoring the intrinsic correlation seen in pore sizes from actual porous media. Finally, I implemented the algorithm from Raoof et al. (2010) [38] to generate the topology of a Fontainebleau sandstone by solving an optimization problem using the steepest descent algorithm with a stochastic approximation for the gradient. A drainage simulation is performed on this representative network and relative permeability is compared with published results. The limitations of this algorithm are discussed and other methods are suggested to create a more faithful representation of the pore space.
Direct hierarchical assembly of nanoparticles

Science.gov (United States)

Xu, Ting; Zhao, Yue; Thorkelsson, Kari

2014-07-22

The present invention provides hierarchical assemblies of a block copolymer, a bifunctional linking compound and a nanoparticle. The block copolymers form one micro-domain and the nanoparticles another micro-domain.
Band structures of two dimensional solid/air hierarchical phononic crystals

Energy Technology Data Exchange (ETDEWEB)

Xu, Y.L.; Tian, X.G. [State Key Laboratory for Mechanical Structure Strength and Vibration, Xi' an Jiaotong University, Xi' an 710049 (China); Chen, C.Q., E-mail: chencq@tsinghua.edu.cn [Department of Engineering Mechanics, AML and CNMM, Tsinghua University, Beijing 100084 (China)

2012-06-15

The hierarchical phononic crystals to be considered show a two-order 'hierarchical' feature, which consists of square array arranged macroscopic periodic unit cells with each unit cell itself including four sub-units. Propagation of acoustic wave in such two dimensional solid/air phononic crystals is investigated by the finite element method (FEM) with the Bloch theory. Their band structure, wave filtering property, and the physical mechanism responsible for the broadened band gap are explored. The corresponding ordinary phononic crystal without hierarchical feature is used for comparison. Obtained results show that the solid/air hierarchical phononic crystals possess tunable outstanding band gap features, which are favorable for applications such as sound insulation and vibration attenuation.
Nearly Cyclic Pursuit and its Hierarchical variant for Multi-agent Systems

DEFF Research Database (Denmark)

Iqbal, Muhammad; Leth, John-Josef; Ngo, Trung Dung

2015-01-01

The rendezvous problem for multiple agents under nearly cyclic pursuit and hierarchical nearly cyclic pursuit is discussed in this paper. The control law designed under nearly cyclic pursuit strategy enables the agents to converge at a point dictated by a beacon. A hierarchical version of the nea......The rendezvous problem for multiple agents under nearly cyclic pursuit and hierarchical nearly cyclic pursuit is discussed in this paper. The control law designed under nearly cyclic pursuit strategy enables the agents to converge at a point dictated by a beacon. A hierarchical version...
Trajectories of Brownian particles with space-correlated noise

Indian Academy of Sciences (India)

Spatial correlations of the noise are usually ruled out, and the paths traced by the random walkers are statistically independent. In this study, I consider instead noise which is white in time and has a Gaussian correlation in space, and by means of numerical simulation, I show how the spatial correlation determines the time ...
Automatic Hierarchical Color Image Classification

Directory of Open Access Journals (Sweden)

Jing Huang

2003-02-01

Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.
Spatial organization and correlation properties quantify structural changes on mesoscale of parenchymatous plant tissue

Energy Technology Data Exchange (ETDEWEB)

Valous, N. A.; Delgado, A.; Sun, D.-W., E-mail: dawen.sun@ucd.ie [School of Biosystems Engineering, University College Dublin, National University of Ireland, Belfield, Dublin 4, Dublin (Ireland); Drakakis, K. [Complex and Adaptive Systems Laboratory, University College Dublin, National University of Ireland, Belfield, Dublin 4, Dublin (Ireland)

2014-02-14

The study of plant tissue parenchyma's intercellular air spaces contributes to the understanding of anatomy and physiology. This is challenging due to difficulty in making direct measurements of the pore space and the complex mosaic of parenchymatous tissue. The architectural complexity of pore space has shown that single geometrical measurements are not sufficient for characterization. The inhomogeneity of distribution depends not only on the percentage content of phase, but also on how the phase fills the space. The lacunarity morphometric, as multiscale measure, provides information about the distribution of gaps that correspond to degree of spatial organization in parenchyma. Additionally, modern theories have suggested strategies, where the focus has shifted from the study of averages and histograms to the study of patterns in data fluctuations. Detrended fluctuation analysis provides information on the correlation properties of the parenchyma at different spatial scales. The aim is to quantify (with the aid of the aforementioned metrics), the mesostructural changes—that occur from one cycle of freezing and thawing—in the void phase of pome fruit parenchymatous tissue, acquired with X-ray microcomputed tomography. Complex systems methods provide numerical indices and detailed insights regarding the freezing-induced modifications upon the arrangement of cells and voids. These structural changes have the potential to lead to physiological disorders. The work can further stimulate interest for the analysis of internal plant tissue structures coupled with other physico-chemical processes or phenomena.
Spatial organization and correlation properties quantify structural changes on mesoscale of parenchymatous plant tissue

International Nuclear Information System (INIS)

Valous, N. A.; Delgado, A.; Sun, D.-W.; Drakakis, K.

2014-01-01

The study of plant tissue parenchyma's intercellular air spaces contributes to the understanding of anatomy and physiology. This is challenging due to difficulty in making direct measurements of the pore space and the complex mosaic of parenchymatous tissue. The architectural complexity of pore space has shown that single geometrical measurements are not sufficient for characterization. The inhomogeneity of distribution depends not only on the percentage content of phase, but also on how the phase fills the space. The lacunarity morphometric, as multiscale measure, provides information about the distribution of gaps that correspond to degree of spatial organization in parenchyma. Additionally, modern theories have suggested strategies, where the focus has shifted from the study of averages and histograms to the study of patterns in data fluctuations. Detrended fluctuation analysis provides information on the correlation properties of the parenchyma at different spatial scales. The aim is to quantify (with the aid of the aforementioned metrics), the mesostructural changes—that occur from one cycle of freezing and thawing—in the void phase of pome fruit parenchymatous tissue, acquired with X-ray microcomputed tomography. Complex systems methods provide numerical indices and detailed insights regarding the freezing-induced modifications upon the arrangement of cells and voids. These structural changes have the potential to lead to physiological disorders. The work can further stimulate interest for the analysis of internal plant tissue structures coupled with other physico-chemical processes or phenomena
Sub-seasonal-to-seasonal Reservoir Inflow Forecast using Bayesian Hierarchical Hidden Markov Model

Science.gov (United States)

Mukhopadhyay, S.; Arumugam, S.

2017-12-01

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

International Nuclear Information System (INIS)

Vaulina, O. S.

2012-01-01

A new semiempirical model is proposed for describing the spatial correlation between interacting particles in nonideal systems. The developed model describes the main features in the behavior of the pair correlation function for crystalline structures and can also be used for qualitative and quantitative description of the spatial correlation of particles in strongly nonideal liquid systems. The proposed model is compared with the results of simulation of the pair correlation function.
Static and dynamic friction of hierarchical surfaces.

Science.gov (United States)

Costagliola, Gianluca; Bosia, Federico; Pugno, Nicola M

2016-12-01

Hierarchical structures are very common in nature, but only recently have they been systematically studied in materials science, in order to understand the specific effects they can have on the mechanical properties of various systems. Structural hierarchy provides a way to tune and optimize macroscopic mechanical properties starting from simple base constituents and new materials are nowadays designed exploiting this possibility. This can be true also in the field of tribology. In this paper we study the effect of hierarchical patterned surfaces on the static and dynamic friction coefficients of an elastic material. Our results are obtained by means of numerical simulations using a one-dimensional spring-block model, which has previously been used to investigate various aspects of friction. Despite the simplicity of the model, we highlight some possible mechanisms that explain how hierarchical structures can significantly modify the friction coefficients of a material, providing a means to achieve tunability.
Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

Science.gov (United States)

Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.

2017-11-01

In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.
Deep hierarchical attention network for video description

Science.gov (United States)

Li, Shuohao; Tang, Min; Zhang, Jun

2018-03-01

Pairing video to natural language description remains a challenge in computer vision and machine translation. Inspired by image description, which uses an encoder-decoder model for reducing visual scene into a single sentence, we propose a deep hierarchical attention network for video description. The proposed model uses convolutional neural network (CNN) and bidirectional LSTM network as encoders while a hierarchical attention network is used as the decoder. Compared to encoder-decoder models used in video description, the bidirectional LSTM network can capture the temporal structure among video frames. Moreover, the hierarchical attention network has an advantage over single-layer attention network on global context modeling. To make a fair comparison with other methods, we evaluate the proposed architecture with different types of CNN structures and decoders. Experimental results on the standard datasets show that our model has a more superior performance than the state-of-the-art techniques.
Entrepreneurial intention modeling using hierarchical multiple regression

Directory of Open Access Journals (Sweden)

Marina Jeger

2014-12-01

Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
Spatial proximity and the risk of psychopathology after a terrorist attack

OpenAIRE

DiMaggio, Charles; Galea, Sandro; Emch, Michael

2010-01-01

Previous studies concerned with the relation of proximity to the September 11, 2001 terrorist attacks and subsequent psychopathology have produced conflicting results. The goals of this analysis are to assess the appropriateness of using Bayesian hierarchical spatial techniques to answer the question of the role of proximity to a mass trauma as a risk factor for psychopathology. Using a set of individual-level Medicaid data for New York State, and controlling for age, gender, median household...
[On the principle of substance stability and thermodynamic feedback in hierarchic systems of the bio-world].

Science.gov (United States)

Gladyshev, G P

2002-01-01

The creation of structural hierarchies in open natural biosystems within the framework of quasi-closed systems is investigated by the methods of hierarchic thermodynamics (thermostatics). During the evolution of natural open systems, every higher hierarchic level j appears as a consequence of thermodynamic self-organization (self-assembly) of the structures of the lower (j-1)-th level. Such a self-assembly proceeds as a result of stabilization of the j-th level. This is related to the Gibbs' (Helmholtz') specific function of formation of the structure of the j-th level tending to a minimum. As a result of action of the principle of substance (matter) stability, the structures of the j-th level are enriched with less stable structures of the (j-1)-th level in the course of evolution. This provides a thermodynamic feedback between the structures of the higher j-th level and lower (j-1)-th level, thus preventing full structural stabilization of the j-th level and causing "thermodynamic rejuvenation" of biosystems. The latter enhances "thermodynamic" deceleration of evolution and practically unlimited maintenance of life. Examples of quantitative correlations are provided that call for further application of the substance stability principle to living and nonliving hierarchic structures.
Hierarchical Sets: Analyzing Pangenome Structure through Scalable Set Visualizations

DEFF Research Database (Denmark)

Pedersen, Thomas Lin

2017-01-01

of hierarchical sets by applying it to a pangenome based on 113 Escherichia and Shigella genomes and find it provides a powerful addition to pangenome analysis. The described clustering algorithm and visualizations are implemented in the hierarchicalSets R package available from CRAN (https...
Group-level self-definition and self-investment: a hierarchical (multicomponent) model of in-group identification.

Science.gov (United States)

Leach, Colin Wayne; van Zomeren, Martijn; Zebel, Sven; Vliek, Michael L W; Pennekamp, Sjoerd F; Doosje, Bertjan; Ouwerkerk, Jaap W; Spears, Russell

2008-07-01

Recent research shows individuals' identification with in-groups to be psychologically important and socially consequential. However, there is little agreement about how identification should be conceptualized or measured. On the basis of previous work, the authors identified 5 specific components of in-group identification and offered a hierarchical 2-dimensional model within which these components are organized. Studies 1 and 2 used confirmatory factor analysis to validate the proposed model of self-definition (individual self-stereotyping, in-group homogeneity) and self-investment (solidarity, satisfaction, and centrality) dimensions, across 3 different group identities. Studies 3 and 4 demonstrated the construct validity of the 5 components by examining their (concurrent) correlations with established measures of in-group identification. Studies 5-7 demonstrated the predictive and discriminant validity of the 5 components by examining their (prospective) prediction of individuals' orientation to, and emotions about, real intergroup relations. Together, these studies illustrate the conceptual and empirical value of a hierarchical multicomponent model of in-group identification.
What are hierarchical models and how do we analyze them?

Science.gov (United States)

Royle, Andy

2016-01-01

In this chapter we provide a basic definition of hierarchical models and introduce the two canonical hierarchical models in this book: site occupancy and N-mixture models. The former is a hierarchical extension of logistic regression and the latter is a hierarchical extension of Poisson regression. We introduce basic concepts of probability modeling and statistical inference including likelihood and Bayesian perspectives. We go through the mechanics of maximizing the likelihood and characterizing the posterior distribution by Markov chain Monte Carlo (MCMC) methods. We give a general perspective on topics such as model selection and assessment of model fit, although we demonstrate these topics in practice in later chapters (especially Chapters 5, 6, 7, and 10 Chapter 5 Chapter 6 Chapter 7 Chapter 10)
Facile synthesis and photocatalytic activity of zinc oxide hierarchical microcrystals

KAUST Repository

Xu, Xinjiang

2013-04-04

ZnO microcrystals with hierarchical structure have been synthesized by a simple solvothermal approach. The microcrystals were studied by means of X-ray diffraction, transmission electron microscopy, and scanning electron microscopy. Research on the formation mechanism of the hierarchical microstructure shows that the coordination solvent and precursor concentration have considerable influence on the size and morphology of the microstructures. A possible formation mechanism of the hierarchical structure was suggested. Furthermore, the catalytic activity of the ZnO microcrystals was studied by treating low concentration Rhodamine B (RhB) solution under UV light, and research results show the hierarchical microstructures of ZnO display high catalytic activity in photocatalysis, the catalysis process follows first-order reaction kinetics, and the apparent rate constant k = 0.03195 min-1.

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