Entrepreneurial intention modeling using hierarchical multiple regression
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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.
Hierarchical linear regression models for conditional quantiles
Institute of Scientific and Technical Information of China (English)
TIAN Maozai; CHEN Gemai
2006-01-01
The quantile regression has several useful features and therefore is gradually developing into a comprehensive approach to the statistical analysis of linear and nonlinear response models,but it cannot deal effectively with the data with a hierarchical structure.In practice,the existence of such data hierarchies is neither accidental nor ignorable,it is a common phenomenon.To ignore this hierarchical data structure risks overlooking the importance of group effects,and may also render many of the traditional statistical analysis techniques used for studying data relationships invalid.On the other hand,the hierarchical models take a hierarchical data structure into account and have also many applications in statistics,ranging from overdispersion to constructing min-max estimators.However,the hierarchical models are virtually the mean regression,therefore,they cannot be used to characterize the entire conditional distribution of a dependent variable given high-dimensional covariates.Furthermore,the estimated coefficient vector (marginal effects)is sensitive to an outlier observation on the dependent variable.In this article,a new approach,which is based on the Gauss-Seidel iteration and taking a full advantage of the quantile regression and hierarchical models,is developed.On the theoretical front,we also consider the asymptotic properties of the new method,obtaining the simple conditions for an n1/2-convergence and an asymptotic normality.We also illustrate the use of the technique with the real educational data which is hierarchical and how the results can be explained.
The Infinite Hierarchical Factor Regression Model
Rai, Piyush
2009-01-01
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.
Hierarchical Neural Regression Models for Customer Churn Prediction
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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.
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Jin-Jia Wang
2014-01-01
Full Text Available We present the hierarchical interactive lasso penalized logistic regression using the coordinate descent algorithm based on the hierarchy theory and variables interactions. We define the interaction model based on the geometric algebra and hierarchical constraint conditions and then use the coordinate descent algorithm to solve for the coefficients of the hierarchical interactive lasso model. We provide the results of some experiments based on UCI datasets, Madelon datasets from NIPS2003, and daily activities of the elder. The experimental results show that the variable interactions and hierarchy contribute significantly to the classification. The hierarchical interactive lasso has the advantages of the lasso and interactive lasso.
Hierarchical Matching and Regression with Application to Photometric Redshift Estimation
Murtagh, Fionn
2017-06-01
This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or `photo-z' problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.
Rocconi, Louis M.
2013-01-01
This study examined the differing conclusions one may come to depending upon the type of analysis chosen, hierarchical linear modeling or ordinary least squares (OLS) regression. To illustrate this point, this study examined the influences of seniors' self-reported critical thinking abilities three ways: (1) an OLS regression with the student…
Hierarchical Multiple Regression in Counseling Research: Common Problems and Possible Remedies.
Petrocelli, John V.
2003-01-01
A brief content analysis was conducted on the use of hierarchical regression in counseling research published in the "Journal of Counseling Psychology" and the "Journal of Counseling & Development" during the years 1997-2001. Common problems are cited and possible remedies are described. (Contains 43 references and 3 tables.) (Author)
DEFF Research Database (Denmark)
Ussery, David; Bohlin, Jon; Skjerve, Eystein
2009-01-01
Recently there has been an explosion in the availability of bacterial genomic sequences, making possible now an analysis of genomic signatures across more than 800 hundred different bacterial chromosomes, from a wide variety of environments. Using genomic signatures, we pair-wise compared 867...... different genomic DNA sequences, taken from chromosomes and plasmids more than 100,000 base-pairs in length. Hierarchical clustering was performed on the outcome of the comparisons before a multinomial regression model was fitted. The regression model included the cluster groups as the response variable...... AT content. Small improvements to the regression model, although significant, were also obtained by factors such as sequence size, habitat, growth temperature, selective pressure measured as oligonucleotide usage variance, and oxygen requirement.The statistics obtained using hierarchical clustering...
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.
Shetty, Rahul; Bigiel, Frank
2012-01-01
We develop a Bayesian linear regression method which rigorously treats measurement uncertainties, and accounts for hierarchical data structure for investigating the relationship between the star formation rate and gas surface density. The method simultaneously estimates the intercept, slope, and scatter about the regression line of each individual subject (e.g. a galaxy) and the population (e.g. an ensemble of galaxies). Using synthetic datasets, we demonstrate that the Bayesian method accurately recovers the parameters of both the individuals and the population, especially when compared to commonly employed least squares methods, such as the bisector. We apply the Bayesian method to estimate the Kennicutt-Schmidt (KS) parameters of a sample of spiral galaxies compiled by Bigiel et al. (2008). We find significant variation in the KS parameters, indicating that no single KS relationship holds for all galaxies. This suggests that the relationship between molecular gas and star formation differs between galaxies...
Wilderjans, Tom Frans; Vande Gaer, Eva; Kiers, Henk A L; Van Mechelen, Iven; Ceulemans, Eva
2017-03-01
In the behavioral sciences, many research questions pertain to a regression problem in that one wants to predict a criterion on the basis of a number of predictors. Although in many cases, ordinary least squares regression will suffice, sometimes the prediction problem is more challenging, for three reasons: first, multiple highly collinear predictors can be available, making it difficult to grasp their mutual relations as well as their relations to the criterion. In that case, it may be very useful to reduce the predictors to a few summary variables, on which one regresses the criterion and which at the same time yields insight into the predictor structure. Second, the population under study may consist of a few unknown subgroups that are characterized by different regression models. Third, the obtained data are often hierarchically structured, with for instance, observations being nested into persons or participants within groups or countries. Although some methods have been developed that partially meet these challenges (i.e., principal covariates regression (PCovR), clusterwise regression (CR), and structural equation models), none of these methods adequately deals with all of them simultaneously. To fill this gap, we propose the principal covariates clusterwise regression (PCCR) method, which combines the key idea's behind PCovR (de Jong & Kiers in Chemom Intell Lab Syst 14(1-3):155-164, 1992) and CR (Späth in Computing 22(4):367-373, 1979). The PCCR method is validated by means of a simulation study and by applying it to cross-cultural data regarding satisfaction with life.
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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.
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Cristina eGorrostieta
2013-11-01
Full Text Available Vector auto-regressive (VAR models typically form the basis for constructing directed graphical models for investigating connectivity in a brain network with brain regions of interest (ROIs as nodes. There are limitations in the standard VAR models. The number of parameters in the VAR model increases quadratically with the number of ROIs and linearly with the order of the model and thus due to the large number of parameters, the model could pose serious estimation problems. Moreover, when applied to imaging data, the standard VAR model does not account for variability in the connectivity structure across all subjects. In this paper, we develop a novel generalization of the VAR model that overcomes these limitations. To deal with the high dimensionality of the parameter space, we propose a Bayesian hierarchical framework for the VAR model that will account for both temporal correlation within a subject and between subject variation. Our approach uses prior distributions that give rise to estimates that correspond to penalized least squares criterion with the elastic net penalty. We apply the proposed model to investigate differences in effective connectivity during a hand grasp experiment between healthy controls and patients with residual motor deficit following a stroke.
Snyder, Carolyn W.
2016-09-01
Statistical challenges often preclude comparisons among different sea surface temperature (SST) reconstructions over the past million years. Inadequate consideration of uncertainty can result in misinterpretation, overconfidence, and biased conclusions. Here I apply Bayesian hierarchical regressions to analyze local SST responsiveness to climate changes for 54 SST reconstructions from across the globe over the past million years. I develop methods to account for multiple sources of uncertainty, including the quantification of uncertainty introduced from absolute dating into interrecord comparisons. The estimates of local SST responsiveness explain 64% (62% to 77%, 95% interval) of the total variation within each SST reconstruction with a single number. There is remarkable agreement between SST proxy methods, with the exception of Mg/Ca proxy methods estimating muted responses at high latitudes. The Indian Ocean exhibits a muted response in comparison to other oceans. I find a stable estimate of the proposed "universal curve" of change in local SST responsiveness to climate changes as a function of sin2(latitude) over the past 400,000 years: SST change at 45°N/S is larger than the average tropical response by a factor of 1.9 (1.5 to 2.6, 95% interval) and explains 50% (35% to 58%, 95% interval) of the total variation between each SST reconstruction. These uncertainty and statistical methods are well suited for application across paleoclimate and environmental data series intercomparisons.
Mandel, Kaisey S; Kirshner, Robert P
2014-01-01
We investigate the correlations between the peak intrinsic colors of Type Ia supernovae (SN Ia) and their expansion velocities at maximum light, measured from the Si II 6355 A spectral feature. We construct a new hierarchical Bayesian regression model and Gibbs sampler to estimate the dependence of the intrinsic colors of a SN Ia on its ejecta velocity, while accounting for the random effects of intrinsic scatter, measurement error, and reddening by host galaxy dust. The method is applied to the apparent color data from BVRI light curves and Si II velocity data for 79 nearby SN Ia. Comparison of the apparent color distributions of high velocity (HV) and normal velocity (NV) supernovae reveals significant discrepancies in B-V and B-R, but not other colors. Hence, they are likely due to intrinsic color differences originating in the B-band, rather than dust reddening. The mean intrinsic B-V and B-R color differences between HV and NV groups are 0.06 +/- 0.02 and 0.09 +/- 0.02 mag, respectively. Under a linear m...
Hierarchical design of a polymeric nanovehicle for efficient tumor regression and imaging
An, Jinxia; Guo, Qianqian; Zhang, Peng; Sinclair, Andrew; Zhao, Yu; Zhang, Xinge; Wu, Kan; Sun, Fang; Hung, Hsiang-Chieh; Li, Chaoxing; Jiang, Shaoyi
2016-04-01
Effective delivery of therapeutics to disease sites significantly contributes to drug efficacy, toxicity and clearance. Here we designed a hierarchical polymeric nanoparticle structure for anti-cancer chemotherapy delivery by utilizing state-of-the-art polymer chemistry and co-assembly techniques. This novel structural design combines the most desired merits for drug delivery in a single particle, including a long in vivo circulation time, inhibited non-specific cell uptake, enhanced tumor cell internalization, pH-controlled drug release and simultaneous imaging. This co-assembled nanoparticle showed exceptional stability in complex biological media. Benefiting from the synergistic effects of zwitterionic and multivalent galactose polymers, drug-loaded nanoparticles were selectively internalized by cancer cells rather than normal tissue cells. In addition, the pH-responsive core retained their cargo within their polymeric coating through hydrophobic interaction and released it under slightly acidic conditions. In vivo pharmacokinetic studies in mice showed minimal uptake of nanoparticles by the mononuclear phagocyte system and excellent blood circulation half-lives of 14.4 h. As a result, tumor growth was completely inhibited and no damage was observed for normal organ tissues. This newly developed drug nanovehicle has great potential in cancer therapy, and the hierarchical design principle should provide valuable information for the development of the next generation of drug delivery systems.Effective delivery of therapeutics to disease sites significantly contributes to drug efficacy, toxicity and clearance. Here we designed a hierarchical polymeric nanoparticle structure for anti-cancer chemotherapy delivery by utilizing state-of-the-art polymer chemistry and co-assembly techniques. This novel structural design combines the most desired merits for drug delivery in a single particle, including a long in vivo circulation time, inhibited non-specific cell uptake
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Mandel, Kaisey S.; Kirshner, Robert P. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Foley, Ryan J., E-mail: kmandel@cfa.harvard.edu [Astronomy Department, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801 (United States)
2014-12-20
We investigate the statistical dependence of the peak intrinsic colors of Type Ia supernovae (SNe Ia) on their expansion velocities at maximum light, measured from the Si II λ6355 spectral feature. We construct a new hierarchical Bayesian regression model, accounting for the random effects of intrinsic scatter, measurement error, and reddening by host galaxy dust, and implement a Gibbs sampler and deviance information criteria to estimate the correlation. The method is applied to the apparent colors from BVRI light curves and Si II velocity data for 79 nearby SNe Ia. The apparent color distributions of high-velocity (HV) and normal velocity (NV) supernovae exhibit significant discrepancies for B – V and B – R, but not other colors. Hence, they are likely due to intrinsic color differences originating in the B band, rather than dust reddening. The mean intrinsic B – V and B – R color differences between HV and NV groups are 0.06 ± 0.02 and 0.09 ± 0.02 mag, respectively. A linear model finds significant slopes of –0.021 ± 0.006 and –0.030 ± 0.009 mag (10{sup 3} km s{sup –1}){sup –1} for intrinsic B – V and B – R colors versus velocity, respectively. Because the ejecta velocity distribution is skewed toward high velocities, these effects imply non-Gaussian intrinsic color distributions with skewness up to +0.3. Accounting for the intrinsic-color-velocity correlation results in corrections to A{sub V} extinction estimates as large as –0.12 mag for HV SNe Ia and +0.06 mag for NV events. Velocity measurements from SN Ia spectra have the potential to diminish systematic errors from the confounding of intrinsic colors and dust reddening affecting supernova distances.
Buscot, Marie-Jeanne; Wotherspoon, Simon S; Magnussen, Costan G; Juonala, Markus; Sabin, Matthew A; Burgner, David P; Lehtimäki, Terho; Viikari, Jorma S A; Hutri-Kähönen, Nina; Raitakari, Olli T; Thomson, Russell J
2017-06-06
Bayesian hierarchical piecewise regression (BHPR) modeling has not been previously formulated to detect and characterise the mechanism of trajectory divergence between groups of participants that have longitudinal responses with distinct developmental phases. These models are useful when participants in a prospective cohort study are grouped according to a distal dichotomous health outcome. Indeed, a refined understanding of how deleterious risk factor profiles develop across the life-course may help inform early-life interventions. Previous techniques to determine between-group differences in risk factors at each age may result in biased estimate of the age at divergence. We demonstrate the use of Bayesian hierarchical piecewise regression (BHPR) to generate a point estimate and credible interval for the age at which trajectories diverge between groups for continuous outcome measures that exhibit non-linear within-person response profiles over time. We illustrate our approach by modeling the divergence in childhood-to-adulthood body mass index (BMI) trajectories between two groups of adults with/without type 2 diabetes mellitus (T2DM) in the Cardiovascular Risk in Young Finns Study (YFS). Using the proposed BHPR approach, we estimated the BMI profiles of participants with T2DM diverged from healthy participants at age 16 years for males (95% credible interval (CI):13.5-18 years) and 21 years for females (95% CI: 19.5-23 years). These data suggest that a critical window for weight management intervention in preventing T2DM might exist before the age when BMI growth rate is naturally expected to decrease. Simulation showed that when using pairwise comparison of least-square means from categorical mixed models, smaller sample sizes tended to conclude a later age of divergence. In contrast, the point estimate of the divergence time is not biased by sample size when using the proposed BHPR method. BHPR is a powerful analytic tool to model long-term non
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Chong Wei
2015-01-01
Full Text Available Logistic regression models have been widely used in previous studies to analyze public transport utilization. These studies have shown travel time to be an indispensable variable for such analysis and usually consider it to be a deterministic variable. This formulation does not allow us to capture travelers’ perception error regarding travel time, and recent studies have indicated that this error can have a significant effect on modal choice behavior. In this study, we propose a logistic regression model with a hierarchical random error term. The proposed model adds a new random error term for the travel time variable. This term structure enables us to investigate travelers’ perception error regarding travel time from a given choice behavior dataset. We also propose an extended model that allows constraining the sign of this error in the model. We develop two Gibbs samplers to estimate the basic hierarchical model and the extended model. The performance of the proposed models is examined using a well-known dataset.
Case of parotid tumor showing remarkable regression following hyperthermo-chemo-radiotherapy
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Fujimura, T.; Yonemura, Y.; Kamata, T.
1987-03-01
A 72-year-old woman developed adenocarcinoma of the left parotid gland. Because of the excessive size of her tumor and the fact that she suffered from severe liver dysfunction, she was treated by hyperthermo-chemo-radiotherapy (HCR therapy). After ten sessions of radiofrequency hyperthermia with HEH 500 (13.56 MHz radiofrequency wave), 50-Gy irradiation from a linac and administration of 33.0 g of tegafur in suppository form, the tumor mass showed remarkable regression decreasing in size by as much as 84 % on computed tomography. Histologically, the tumor which was resected under local anesthesia, showed almost total necrosis. The multidisciplinary HCR therapy was well tolerated and effective as a therapy for cancer in this case.
Estimating carbon and showing impacts of drought using satellite data in regression-tree models
Boyte, Stephen; Wylie, Bruce K.; Howard, Danny; Dahal, Devendra; Gilmanov, Tagir G.
2018-01-01
Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.
Hua, Anh N.; Keenan, Janice M.
2017-01-01
One of the most important findings to emerge from recent reading comprehension research is that there are large differences between tests in what they assess--specifically, the extent to which performance depends on word recognition versus listening comprehension skills. Because this research used ordinary least squares regression, it is not clear…
Keegan, John P.; Chan, Fong; Ditchman, Nicole; Chiu, Chung-Yi
2012-01-01
The main objective of this study was to validate Pender's Health Promotion Model (HPM) as a motivational model for exercise/physical activity self-management for people with spinal cord injuries (SCIs). Quantitative descriptive research design using hierarchical regression analysis (HRA) was used. A total of 126 individuals with SCI were recruited…
Maas, Iris L; Nolte, Sandra; Walter, Otto B; Berger, Thomas; Hautzinger, Martin; Hohagen, Fritz; Lutz, Wolfgang; Meyer, Björn; Schröder, Johanna; Späth, Christina; Klein, Jan Philipp; Moritz, Steffen; Rose, Matthias
2017-02-01
To compare treatment effect estimates obtained from a regression discontinuity (RD) design with results from an actual randomized controlled trial (RCT). Data from an RCT (EVIDENT), which studied the effect of an Internet intervention on depressive symptoms measured with the Patient Health Questionnaire (PHQ-9), were used to perform an RD analysis, in which treatment allocation was determined by a cutoff value at baseline (PHQ-9 = 10). A linear regression model was fitted to the data, selecting participants above the cutoff who had received the intervention (n = 317) and control participants below the cutoff (n = 187). Outcome was PHQ-9 sum score 12 weeks after baseline. Robustness of the effect estimate was studied; the estimate was compared with the RCT treatment effect. The final regression model showed a regression coefficient of -2.29 [95% confidence interval (CI): -3.72 to -.85] compared with a treatment effect found in the RCT of -1.57 (95% CI: -2.07 to -1.07). Although the estimates obtained from two designs are not equal, their confidence intervals overlap, suggesting that an RD design can be a valid alternative for RCTs. This finding is particularly important for situations where an RCT may not be feasible or ethical as is often the case in clinical research settings. Copyright © 2016 Elsevier Inc. All rights reserved.
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Fontez B.
2014-04-01
Full Text Available Back-calculation allows to increase available data on fish growth. The accuracy of back-calculation models is of paramount importance for growth analysis. Frequentist and Bayesian hierarchical approaches were used for regression between fish body size and scale size for the rare fish species Zingel asper. The Bayesian approach permits more reliable estimation of back-calculated size, taking into account biological information and cohort variability. This method greatly improves estimation of back-calculated length when sampling is uneven and/or small.
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Omholt Stig W
2011-06-01
Full Text Available Abstract Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs to variation in features of the trajectories of the state variables (outputs throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR, where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR and ordinary least squares (OLS regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback
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Gardênia Abbad
2002-01-01
Full Text Available Este artigo discute algumas aplicações das técnicas de análise de regressão múltipla stepwise e hierárquica, as quais são muito utilizadas em pesquisas da área de Psicologia Organizacional. São discutidas algumas estratégias de identificação e de solução de problemas relativos à ocorrência de erros do Tipo I e II e aos fenômenos de supressão, complementaridade e redundância nas equações de regressão múltipla. São apresentados alguns exemplos de pesquisas nas quais esses padrões de associação entre variáveis estiveram presentes e descritas as estratégias utilizadas pelos pesquisadores para interpretá-los. São discutidas as aplicações dessas análises no estudo de interação entre variáveis e na realização de testes para avaliação da linearidade do relacionamento entre variáveis. Finalmente, são apresentadas sugestões para lidar com as limitações das análises de regressão múltipla (stepwise e hierárquica.This article discusses applications of stepwise and hierarchical multiple regression analyses to research in organizational psychology. Strategies for identifying type I and II errors, and solutions to potential problems that may arise from such errors are proposed. In addition, phenomena such as suppression, complementarity, and redundancy are reviewed. The article presents examples of research where these phenomena occurred, and the manner in which they were explained by researchers. Some applications of multiple regression analyses to studies involving between-variable interactions are presented, along with tests used to analyze the presence of linearity among variables. Finally, some suggestions are provided for dealing with limitations implicit in multiple regression analyses (stepwise and hierarchical.
Villanueva, Lidón; Montoya-Castilla, Inmaculada; Prado-Gascó, Vicente
2017-07-01
The purpose of this study is to analyze the combined effects of trait emotional intelligence (EI) and feelings on healthy adolescents' stress. Identifying the extent to which adolescent stress varies with trait emotional differences and the feelings of adolescents is of considerable interest in the development of intervention programs for fostering youth well-being. To attain this goal, self-reported questionnaires (perceived stress, trait EI, and positive/negative feelings) and biological measures of stress (hair cortisol concentrations, HCC) were collected from 170 adolescents (12-14 years old). Two different methodologies were conducted, which included hierarchical regression models and a fuzzy-set qualitative comparative analysis (fsQCA). The results support trait EI as a protective factor against stress in healthy adolescents and suggest that feelings reinforce this relation. However, the debate continues regarding the possibility of optimal levels of trait EI for effective and adaptive emotional management, particularly in the emotional attention and clarity dimensions and for female adolescents.
Song, Hae-Ryoung; Lawson, Andrew; D'Agostino, Ralph B; Liese, Angela D
2011-03-01
Sparse count data violate assumptions of traditional Poisson models due to the excessive amount of zeros, and modeling sparse data becomes challenging. However, since aggregation to reduce sparseness may result in biased estimates of risk, solutions need to be found at the level of disaggregated data. We investigated different statistical approaches within a Bayesian hierarchical framework for modeling sparse data without aggregation of data. We compared our proposed models with the traditional Poisson model and the zero-inflated model based on simulated data. We applied statistical models to type 1 and type 2 diabetes in youth 10-19 years known as rare diseases, and compared models using the inference results and various model diagnostic tools. We showed that one of the models we proposed, a sparse Poisson convolution model, performed better than other models in the simulation and application based on the deviance information criterion (DIC) and the mean squared prediction error.
Antinomias do zoológico humano: sociabilidade selvagem, reality shows e regressão da consciência
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Francisco Rüdiger
2008-11-01
Full Text Available Estuda-se no artigo as articulações ideológicas e sentido histórico dos chamados reality shows na sociedade brasileira contemporânea. Em primeiro, situamos o gênero numa perspectiva histórica, sublinhado suas raízes religiosas e populares em conexão com a formação do sistema de poder próprio do Ocidente. Depois, expõem-se alguns aspectos do fenômeno, chamando atenção para sua estrutura interna e seu sentido concreto em nossa organização societária, com base nas suas versões brasileiras. Em terceiro, focamos os textos nas relações de poder que se articulam por meio desses programas, discutindo algumas das várias teorizações a seu respeito. Adiante e continuando a recorrer a matérias de imprensa, procede-se a um julgamento dessas últimas, visando propor uma interpretação histórica de seu significado. A conclusão retorna ao marco inicial e oferece uma visão geral em que talvez se possa pensar melhor o que está em jogo nos reality shows. Palavras-chave reality shows no Brasil, programas de televisão, sociabilidade Abstract This article analyses the ideological connections and historical meaning of the so-called reality shows in the contemporary Brazillian society. At first, we locate this genre in a historical perspective, stressing its religious and popular roots but also the connections between it and the power systems that have built Western World. Secondly, the text expose the main features of this kind of television show, calling attention to its inner structure but also to its meaning in our social organization, making critical remarks about their Brazillian versions. Focusing on the power relations that are articulated in it, we discuss some theories made about them. The historical meaning of these shows in our present circumstances is projected in the fourth stage of the article, that explores some materials extracted from the press. Finally, we return to the larger historical context
Nakamura, Ryota; Suhrcke, Marc; Jebb, Susan A; Pechey, Rachel; Almiron-Roig, Eva; Marteau, Theresa M
2015-04-01
There is a growing concern, but limited evidence, that price promotions contribute to a poor diet and the social patterning of diet-related disease. We examined the following questions: 1) Are less-healthy foods more likely to be promoted than healthier foods? 2) Are consumers more responsive to promotions on less-healthy products? 3) Are there socioeconomic differences in food purchases in response to price promotions? With the use of hierarchical regression, we analyzed data on purchases of 11,323 products within 135 food and beverage categories from 26,986 households in Great Britain during 2010. Major supermarkets operated the same price promotions in all branches. The number of stores that offered price promotions on each product for each week was used to measure the frequency of price promotions. We assessed the healthiness of each product by using a nutrient profiling (NP) model. A total of 6788 products (60%) were in healthier categories and 4535 products (40%) were in less-healthy categories. There was no significant gap in the frequency of promotion by the healthiness of products neither within nor between categories. However, after we controlled for the reference price, price discount rate, and brand-specific effects, the sales uplift arising from price promotions was larger in less-healthy than in healthier categories; a 1-SD point increase in the category mean NP score, implying the category becomes less healthy, was associated with an additional 7.7-percentage point increase in sales (from 27.3% to 35.0%; P sales uplift from promotions was larger for higher-socioeconomic status (SES) groups than for lower ones (34.6% for the high-SES group, 28.1% for the middle-SES group, and 23.1% for the low-SES group). Finally, there was no significant SES gap in the absolute volume of purchases of less-healthy foods made on promotion. Attempts to limit promotions on less-healthy foods could improve the population diet but would be unlikely to reduce health
Collaborative Hierarchical Sparse Modeling
Sprechmann, Pablo; Sapiro, Guillermo; Eldar, Yonina C
2010-01-01
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is done by solving an l_1-regularized linear regression problem, usually called Lasso. In this work we first combine the sparsity-inducing property of the Lasso model, at the individual feature level, with the block-sparsity property of the group Lasso model, where sparse groups of features are jointly encoded, obtaining a sparsity pattern hierarchically structured. This results in the hierarchical Lasso, which shows important practical modeling advantages. We then extend this approach to the collaborative case, where a set of simultaneously coded signals share the same sparsity pattern at the higher (group) level but not necessarily at the lower one. Signals then share the same active groups, or classes, but not necessarily the same active set. This is very well suited for applications such as source separation. An efficient optimization procedure, which guarantees convergence to the global opt...
Choi, Kilchan; Seltzer, Michael
2010-01-01
In studies of change in education and numerous other fields, interest often centers on how differences in the status of individuals at the start of a period of substantive interest relate to differences in subsequent change. In this article, the authors present a fully Bayesian approach to estimating three-level Hierarchical Models in which latent…
Hsu, C.; Cifelli, R.; Zamora, R. J.; Schneider, T.
2014-12-01
The PRISM monthly climatology has been widely used by various agencies for diverse purposes. In the River Forecast Centers (RFCs), the PRISM monthly climatology is used to support tasks such as QPE, or quality control of point precipitation observation, and fine tune QPFs. Validation studies by forecasters and researchers have shown that interpolation involving PRISM climatology can effectually reduce the estimation bias for the locations where moderate or little orographic phenomena occur. However, many studies have pointed out limitations in PRISM monthly climatology. These limitations are especially apparent in storm events with fast-moving wet air masses or with storm tracks that are different from climatology. In order to upgrade PRISM climatology so it possesses the capability to characterize the climatology of storm events, it is critical to integrate large-scale atmospheric conditions with the original PRISM predictor variables and to simulate them at a temporal resolution higher than monthly. To this end, a simple, flexible, and powerful framework for precipitation estimation modeling that can be applied to very large data sets is thus developed. In this project, a decision tree based estimation structure was developed to perform the aforementioned variable integration work. Three Atmospheric River events (ARs) were selected to explore the hierarchical relationships among these variables and how these relationships shape the event-based precipitation distribution pattern across California. Several atmospheric variables, including vertically Integrated Vapor Transport (IVT), temperature, zonal wind (u), meridional wind (v), and omega (ω), were added to enhance the sophistication of the tree-based structure in estimating precipitation. To develop a direction-based climatology, the directions the ARs moving over the Pacific Ocean were also calculated and parameterized within the tree estimation structure. The results show that the involvement of the
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
Sullivan, Paul
2017-01-01
Objectives Previous studies found that hospital and specialty have limited influence on patient experience scores, and patient level factors are more important. This could be due to heterogeneity of experience delivery across subunits within organisations. We aimed to determine whether organisation level factors have greater impact if scores for the same subspecialty microsystem are analysed in each hospital. Setting Acute medical admission units in all NHS Acute Trusts in England. Participants We analysed patient experience data from the English Adult Inpatient Survey which is administered to 850 patients annually in each acute NHS Trusts in England. We selected all 8753 patients who returned the survey and who were emergency medical admissions and stayed in their admission unit for 1–2 nights, so as to isolate the experience delivered during the acute admission process. Primary and secondary outcome measures We used multilevel logistic regression to determine the apportioned influence of host organisation and of organisation level factors (size and teaching status), and patient level factors (demographics, presence of long-term conditions and disabilities). We selected ‘being treated with respect and dignity’ and ‘pain control’ as primary outcome parameters. Other Picker Domain question scores were analysed as secondary parameters. Results The proportion of overall variance attributable at organisational level was small; 0.5% (NS) for respect and dignity, 0.4% (NS) for pain control. Long-standing conditions and consequent disabilities were associated with low scores. Other item scores also showed that most influence was from patient level factors. Conclusions When a single microsystem, the acute medical admission process, is isolated, variance in experience scores is mainly explainable by patient level factors with limited organisational level influence. This has implications for the use of generic patient experience surveys for comparison between
Institute of Scientific and Technical Information of China (English)
汤英汉
2015-01-01
By analyzing the features and status quo of China’s internet insurance development, this paper found that the main reason causing the weak growth in the insurance industry is the conflict between people’s increasing needs for insurance and the relatively backward insurance management approaches. Internet insurance is a supplement to traditional insurance to a certain degree. By using the hierarchical regression method, this paper analyzes the insurance premium and its relative data from 2003 to 2013. The result shows that the driving factors of the internet insurance are: tax, population, internet, etc. The study also indicates that internet insurance is not a replacement or a threat to the traditional insurance business, but a new form of it instead. Internet insurance can satisfy people’s various needs for insurance. Finally, the author proposes that internet insurance, as a new insurance business, its development facilitates changes in the thoughts and ideas of the insurance industry as a whole. Internet technology has pushed it forward, especially, in such areas as insurance channels, product and service innovations. Therefore, internet insurance also injects fresh blood to China’s insurance industry.%通过分析我国互联网保险的特点和发展现状，发现快速变化的市场环境引致的社会日益增长的保险需求同相对落后的保险经营管理方式之间的矛盾日益突出，造成当前保险业增长乏力。互联网保险的出现弥补了传统保险的不足，成为保险业新的增长动力。本文运用分层回归分析方法，对我国2003-2013年网销保费及相关数据进行研究，验证了我国互联网保险驱动因素主要取决于税收、人口、互联网等方面，保险业自身因素对互联网保险影响不显著。研究发现，互联网保险的发展不是对传统保险的替代和竞争，而是保险新需求的发现，互联网保险满足多层次的保险需求。提出互联
Hierarchical auxetic mechanical metamaterials.
Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I; Azzopardi, Keith M; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N
2015-02-11
Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts.
Hierarchical Auxetic Mechanical Metamaterials
Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I.; Azzopardi, Keith M.; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N.
2015-02-01
Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts.
Nakamura, Ryota; Suhrcke, Marc; Jebb, Susan A; Pechey, Rachel; Almiron-Roig, Eva; Marteau, Theresa M
2015-01-01
Background: There is a growing concern, but limited evidence, that price promotions contribute to a poor diet and the social patterning of diet-related disease. Objective: We examined the following questions: 1) Are less-healthy foods more likely to be promoted than healthier foods? 2) Are consumers more responsive to promotions on less-healthy products? 3) Are there socioeconomic differences in food purchases in response to price promotions? Design: With the use of hierarchical regression, we analyzed data on purchases of 11,323 products within 135 food and beverage categories from 26,986 households in Great Britain during 2010. Major supermarkets operated the same price promotions in all branches. The number of stores that offered price promotions on each product for each week was used to measure the frequency of price promotions. We assessed the healthiness of each product by using a nutrient profiling (NP) model. Results: A total of 6788 products (60%) were in healthier categories and 4535 products (40%) were in less-healthy categories. There was no significant gap in the frequency of promotion by the healthiness of products neither within nor between categories. However, after we controlled for the reference price, price discount rate, and brand-specific effects, the sales uplift arising from price promotions was larger in less-healthy than in healthier categories; a 1-SD point increase in the category mean NP score, implying the category becomes less healthy, was associated with an additional 7.7–percentage point increase in sales (from 27.3% to 35.0%; P sales uplift from promotions was larger for higher–socioeconomic status (SES) groups than for lower ones (34.6% for the high-SES group, 28.1% for the middle-SES group, and 23.1% for the low-SES group). Finally, there was no significant SES gap in the absolute volume of purchases of less-healthy foods made on promotion. Conclusion: Attempts to limit promotions on less-healthy foods could improve the
Gebreamlak, Bisratemariam; Dadi, Abel Fekadu; Atnafu, Azeb
2017-01-01
Background Iron deficiency during pregnancy is a risk factor for anemia, preterm delivery, and low birth weight. Iron/Folic Acid supplementation with optimal adherence can effectively prevent anemia in pregnancy. However, studies that address this area of adherence are very limited. Therefore, the current study was conducted to assess the adherence and to identify factors associated with a number of Iron/Folic Acid uptake during pregnancy time among mothers attending antenatal and postnatal care follow up in Akaki kality sub city. Methods Institutional based cross-sectional study was conducted on a sample of 557 pregnant women attending antenatal and postnatal care service. Systematic random sampling was used to select study subjects. The mothers were interviewed and the collected data was cleaned and entered into Epi Info 3.5.1 and analyzed by R version 3.2.0. Hierarchical Negative Binomial Poisson Regression Model was fitted to identify the factors associated with a number of Iron/Folic Acid uptake. Adjusted Incidence rate ratio (IRR) with 95% confidence interval (CI) was computed to assess the strength and significance of the association. Result More than 90% of the mothers were supplemented with at least one Iron/Folic Acid supplement from pill per week during their pregnancy time. Sixty percent of the mothers adhered (took four or more tablets per week) (95%CI, 56%—64.1%). Higher IRR of Iron/Folic Acid supplementation was observed among women: who received health education; which were privately employed; who achieved secondary education; and who believed that Iron/Folic Acid supplements increase blood, whereas mothers who reported a side effect, who were from families with relatively better monthly income, and who took the supplement when sick were more likely to adhere. Conclusion Adherence to Iron/Folic Acid supplement during their pregnancy time among mothers attending antenatal and postnatal care was found to be high. Activities that would address the
Li, Xin; Yu, Jiaguo; Jaroniec, Mietek
2016-05-01
As a green and sustainable technology, semiconductor-based heterogeneous photocatalysis has received much attention in the last few decades because it has potential to solve both energy and environmental problems. To achieve efficient photocatalysts, various hierarchical semiconductors have been designed and fabricated at the micro/nanometer scale in recent years. This review presents a critical appraisal of fabrication methods, growth mechanisms and applications of advanced hierarchical photocatalysts. Especially, the different synthesis strategies such as two-step templating, in situ template-sacrificial dissolution, self-templating method, in situ template-free assembly, chemically induced self-transformation and post-synthesis treatment are highlighted. Finally, some important applications including photocatalytic degradation of pollutants, photocatalytic H2 production and photocatalytic CO2 reduction are reviewed. A thorough assessment of the progress made in photocatalysis may open new opportunities in designing highly effective hierarchical photocatalysts for advanced applications ranging from thermal catalysis, separation and purification processes to solar cells.
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.
A Hierarchical Framework for Facial Age Estimation
Directory of Open Access Journals (Sweden)
Yuyu Liang
2014-01-01
Full Text Available Age estimation is a complex issue of multiclassification or regression. To address the problems of uneven distribution of age database and ignorance of ordinal information, this paper shows a hierarchic age estimation system, comprising age group and specific age estimation. In our system, two novel classifiers, sequence k-nearest neighbor (SKNN and ranking-KNN, are introduced to predict age group and value, respectively. Notably, ranking-KNN utilizes the ordinal information between samples in estimation process rather than regards samples as separate individuals. Tested on FG-NET database, our system achieves 4.97 evaluated by MAE (mean absolute error for age estimation.
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
How hierarchical is language use?
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
How hierarchical is language use?
Frank, Stefan L; Bod, Rens; Christiansen, Morten H
2012-11-22
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.
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
Semiparametric regression during 2003–2007
Ruppert, David
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Naghshpour, Shahdad
2012-01-01
Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Nick, Todd G; Campbell, Kathleen M
2007-01-01
The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable." Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin's lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Diniz, Daniel G.; Silva, Geane O.; Naves, Thaís B.; Fernandes, Taiany N.; Araújo, Sanderson C.; Diniz, José A. P.; de Farias, Luis H. S.; Sosthenes, Marcia C. K.; Diniz, Cristovam G.; Anthony, Daniel C.; da Costa Vasconcelos, Pedro F.; Picanço Diniz, Cristovam W.
2016-01-01
It is known that microglial morphology and function are related, but few studies have explored the subtleties of microglial morphological changes in response to specific pathogens. In the present report we quantitated microglia morphological changes in a monkey model of dengue disease with virus CNS invasion. To mimic multiple infections that usually occur in endemic areas, where higher dengue infection incidence and abundant mosquito vectors carrying different serotypes coexist, subjects received once a week subcutaneous injections of DENV3 (genotype III)-infected culture supernatant followed 24 h later by an injection of anti-DENV2 antibody. Control animals received either weekly anti-DENV2 antibodies, or no injections. Brain sections were immunolabeled for DENV3 antigens and IBA-1. Random and systematic microglial samples were taken from the polymorphic layer of dentate gyrus for 3-D reconstructions, where we found intense immunostaining for TNFα and DENV3 virus antigens. We submitted all bi- or multimodal morphological parameters of microglia to hierarchical cluster analysis and found two major morphological phenotypes designated types I and II. Compared to type I (stage 1), type II microglia were more complex; displaying higher number of nodes, processes and trees and larger surface area and volumes (stage 2). Type II microglia were found only in infected monkeys, whereas type I microglia was found in both control and infected subjects. Hierarchical cluster analysis of morphological parameters of 3-D reconstructions of random and systematic selected samples in control and ADE dengue infected monkeys suggests that microglia morphological changes from stage 1 to stage 2 may not be continuous. PMID:27047345
Institute of Scientific and Technical Information of China (English)
Guijun YANG; Lu LIN; Runchu ZHANG
2007-01-01
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
DEFF Research Database (Denmark)
Thomadsen, Tommy
2005-01-01
of different types of hierarchical networks. This is supplemented by a review of ring network design problems and a presentation of a model allowing for modeling most hierarchical networks. We use methods based on linear programming to design the hierarchical networks. Thus, a brief introduction to the various....... The thesis investigates models for hierarchical network design and methods used to design such networks. In addition, ring network design is considered, since ring networks commonly appear in the design of hierarchical networks. The thesis introduces hierarchical networks, including a classification scheme...... linear programming based methods is included. The thesis is thus suitable as a foundation for study of design of hierarchical networks. The major contribution of the thesis consists of seven papers which are included in the appendix. The papers address hierarchical network design and/or ring network...
Hierarchical Multiagent Reinforcement Learning
2004-01-25
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multiagent tasks. We...introduce a hierarchical multiagent reinforcement learning (RL) framework and propose a hierarchical multiagent RL algorithm called Cooperative HRL. In
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...
Hierarchical topic modeling with nested hierarchical Dirichlet process
Institute of Scientific and Technical Information of China (English)
Yi-qun DING; Shan-ping LI; Zhen ZHANG; Bin SHEN
2009-01-01
This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonparametric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as welt as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more free-grained topic relationships compared to the hierarchical latent Dirichlet allocation model.
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...... 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....... The thesis investigates models for hierarchical network design and methods used to design such networks. In addition, ring network design is considered, since ring networks commonly appear in the design of hierarchical networks. The thesis introduces hierarchical networks, including a classification scheme...
Robust Bayesian Regularized Estimation Based on t Regression Model
Directory of Open Access Journals (Sweden)
Zean Li
2015-01-01
Full Text Available The t distribution is a useful extension of the normal distribution, which can be used for statistical modeling of data sets with heavy tails, and provides robust estimation. In this paper, in view of the advantages of Bayesian analysis, we propose a new robust coefficient estimation and variable selection method based on Bayesian adaptive Lasso t regression. A Gibbs sampler is developed based on the Bayesian hierarchical model framework, where we treat the t distribution as a mixture of normal and gamma distributions and put different penalization parameters for different regression coefficients. We also consider the Bayesian t regression with adaptive group Lasso and obtain the Gibbs sampler from the posterior distributions. Both simulation studies and real data example show that our method performs well compared with other existing methods when the error distribution has heavy tails and/or outliers.
Hierarchical Affinity Propagation
Givoni, Inmar; Frey, Brendan J
2012-01-01
Affinity propagation is an exemplar-based clustering algorithm that finds a set of data-points that best exemplify the data, and associates each datapoint with one exemplar. We extend affinity propagation in a principled way to solve the hierarchical clustering problem, which arises in a variety of domains including biology, sensor networks and decision making in operational research. We derive an inference algorithm that operates by propagating information up and down the hierarchy, and is efficient despite the high-order potentials required for the graphical model formulation. We demonstrate that our method outperforms greedy techniques that cluster one layer at a time. We show that on an artificial dataset designed to mimic the HIV-strain mutation dynamics, our method outperforms related methods. For real HIV sequences, where the ground truth is not available, we show our method achieves better results, in terms of the underlying objective function, and show the results correspond meaningfully to geographi...
Structural integrity of hierarchical composites
Directory of Open Access Journals (Sweden)
Marco Paggi
2012-01-01
Full Text Available Interface mechanical problems are of paramount importance in engineering and materials science. Traditionally, due to the complexity of modelling their mechanical behaviour, interfaces are often treated as defects and their features are not explored. In this study, a different approach is illustrated, where the interfaces play an active role in the design of innovative hierarchical composites and are fundamental for their structural integrity. Numerical examples regarding cutting tools made of hierarchical cellular polycrystalline materials are proposed, showing that tailoring of interface properties at the different scales is the way to achieve superior mechanical responses that cannot be obtained using standard materials
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Biplots in Reduced-Rank Regression
Braak, ter C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal c
Memory Stacking in Hierarchical Networks.
Westö, Johan; May, Patrick J C; Tiitinen, Hannu
2016-02-01
Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns.
Kashuba, Roxolana; Cha, YoonKyung; Alameddine, Ibrahim; Lee, Boknam; Cuffney, Thomas F.
2010-01-01
Multilevel hierarchical modeling methodology has been developed for use in ecological data analysis. The effect of urbanization on stream macroinvertebrate communities was measured across a gradient of basins in each of nine metropolitan regions across the conterminous United States. The hierarchical nature of this dataset was harnessed in a multi-tiered model structure, predicting both invertebrate response at the basin scale and differences in invertebrate response at the region scale. Ordination site scores, total taxa richness, Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa richness, and richness-weighted mean tolerance of organisms at a site were used to describe invertebrate responses. Percentage of urban land cover was used as a basin-level predictor variable. Regional mean precipitation, air temperature, and antecedent agriculture were used as region-level predictor variables. Multilevel hierarchical models were fit to both levels of data simultaneously, borrowing statistical strength from the complete dataset to reduce uncertainty in regional coefficient estimates. Additionally, whereas non-hierarchical regressions were only able to show differing relations between invertebrate responses and urban intensity separately for each region, the multilevel hierarchical regressions were able to explain and quantify those differences within a single model. In this way, this modeling approach directly establishes the importance of antecedent agricultural conditions in masking the response of invertebrates to urbanization in metropolitan regions such as Milwaukee-Green Bay, Wisconsin; Denver, Colorado; and Dallas-Fort Worth, Texas. Also, these models show that regions with high precipitation, such as Atlanta, Georgia; Birmingham, Alabama; and Portland, Oregon, start out with better regional background conditions of invertebrates prior to urbanization but experience faster negative rates of change with urbanization. Ultimately, this urbanization
Associative Hierarchical Random Fields.
Ladický, L'ubor; Russell, Chris; Kohli, Pushmeet; Torr, Philip H S
2014-06-01
This paper makes two contributions: the first is the proposal of a new model-The associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the second is the application of this model to the problem of semantic segmentation. Most methods for semantic segmentation are formulated as a labeling problem for variables that might correspond to either pixels or segments such as super-pixels. It is well known that the generation of super pixel segmentations is not unique. This has motivated many researchers to use multiple super pixel segmentations for problems such as semantic segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the segmentations form a segmentation tree. Our new hierarchical random field model allows information from all of the multiple segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalizes much of the previous work based on pixels or segments, and the resulting labelings can be viewed both as a detailed segmentation at the pixel level, or at the other extreme, as a segment selector that pieces together a solution like a jigsaw, selecting the best segments from different segmentations as pieces. We evaluate its performance on some of the most challenging data sets for object class segmentation, and show that this ability to perform inference using multiple overlapping segmentations leads to state-of-the-art results.
Use of hierarchical models to analyze European trends in congenital anomaly prevalence.
Cavadino, Alana; Prieto-Merino, David; Addor, Marie-Claude; Arriola, Larraitz; Bianchi, Fabrizio; Draper, Elizabeth; Garne, Ester; Greenlees, Ruth; Haeusler, Martin; Khoshnood, Babak; Kurinczuk, Jenny; McDonnell, Bob; Nelen, Vera; O'Mahony, Mary; Randrianaivo, Hanitra; Rankin, Judith; Rissmann, Anke; Tucker, David; Verellen-Dumoulin, Christine; de Walle, Hermien; Wellesley, Diana; Morris, Joan K
2016-06-01
Surveillance of congenital anomalies is important to identify potential teratogens. Despite known associations between different anomalies, current surveillance methods examine trends within each subgroup separately. We aimed to evaluate whether hierarchical statistical methods that combine information from several subgroups simultaneously would enhance current surveillance methods using data collected by EUROCAT, a European network of population-based congenital anomaly registries. Ten-year trends (2003 to 2012) in 18 EUROCAT registries over 11 countries were analyzed for the following groups of anomalies: neural tube defects, congenital heart defects, digestive system, and chromosomal anomalies. Hierarchical Poisson regression models that combined related subgroups together according to EUROCAT's hierarchy of subgroup coding were applied. Results from hierarchical models were compared with those from Poisson models that consider each congenital anomaly separately. Hierarchical models gave similar results as those obtained when considering each anomaly subgroup in a separate analysis. Hierarchical models that included only around three subgroups showed poor convergence and were generally found to be over-parameterized. Larger sets of anomaly subgroups were found to be too heterogeneous to group together in this way. There were no substantial differences between independent analyses of each subgroup and hierarchical models when using the EUROCAT anomaly subgroups. Considering each anomaly separately, therefore, remains an appropriate method for the detection of potential changes in prevalence by surveillance systems. Hierarchical models do, however, remain an interesting alternative method of analysis when considering the risks of specific exposures in relation to the prevalence of congenital anomalies, which could be investigated in other studies. Birth Defects Research (Part A) 106:480-10, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Classifying hospitals as mortality outliers: logistic versus hierarchical logistic models.
Alexandrescu, Roxana; Bottle, Alex; Jarman, Brian; Aylin, Paul
2014-05-01
The use of hierarchical logistic regression for provider profiling has been recommended due to the clustering of patients within hospitals, but has some associated difficulties. We assess changes in hospital outlier status based on standard logistic versus hierarchical logistic modelling of mortality. The study population consisted of all patients admitted to acute, non-specialist hospitals in England between 2007 and 2011 with a primary diagnosis of acute myocardial infarction, acute cerebrovascular disease or fracture of neck of femur or a primary procedure of coronary artery bypass graft or repair of abdominal aortic aneurysm. We compared standardised mortality ratios (SMRs) from non-hierarchical models with SMRs from hierarchical models, without and with shrinkage estimates of the predicted probabilities (Model 1 and Model 2). The SMRs from standard logistic and hierarchical models were highly statistically significantly correlated (r > 0.91, p = 0.01). More outliers were recorded in the standard logistic regression than hierarchical modelling only when using shrinkage estimates (Model 2): 21 hospitals (out of a cumulative number of 565 pairs of hospitals under study) changed from a low outlier and 8 hospitals changed from a high outlier based on the logistic regression to a not-an-outlier based on shrinkage estimates. Both standard logistic and hierarchical modelling have identified nearly the same hospitals as mortality outliers. The choice of methodological approach should, however, also consider whether the modelling aim is judgment or improvement, as shrinkage may be more appropriate for the former than the latter.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
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...
Introduction into Hierarchical Matrices
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.
Applied Bayesian Hierarchical Methods
Congdon, Peter D
2010-01-01
Bayesian methods facilitate the analysis of complex models and data structures. Emphasizing data applications, alternative modeling specifications, and computer implementation, this book provides a practical overview of methods for Bayesian analysis of hierarchical models.
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....
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...
Semiparametric Quantile Modelling of Hierarchical Data
Institute of Scientific and Technical Information of China (English)
Mao Zai TIAN; Man Lai TANG; Ping Shing CHAN
2009-01-01
The classic hierarchical linear model formulation provides a considerable flexibility for modelling the random effects structure and a powerful tool for analyzing nested data that arise in various areas such as biology, economics and education. However, it assumes the within-group errors to be independently and identically distributed (i.i.d.) and models at all levels to be linear. Most importantly, traditional hierarchical models (just like other ordinary mean regression methods) cannot characterize the entire conditional distribution of a dependent variable given a set of covariates and fail to yield robust estimators. In this article, we relax the aforementioned and normality assumptions, and develop a so-called Hierarchical Semiparametric Quantile Regression Models in which the within-group errors could be heteroscedastic and models at some levels are allowed to be nonparametric. We present the ideas with a 2-level model. The level-l model is specified as a nonparametric model whereas level-2 model is set as a parametric model. Under the proposed semiparametric setting the vector of partial derivatives of the nonparametric function in level-1 becomes the response variable vector in level 2. The proposed method allows us to model the fixed effects in the innermost level (i.e., level 2) as a function of the covariates instead of a constant effect. We outline some mild regularity conditions required for convergence and asymptotic normality for our estimators. We illustrate our methodology with a real hierarchical data set from a laboratory study and some simulation studies.
Polynomial Regression on Riemannian Manifolds
Hinkle, Jacob; Fletcher, P Thomas; Joshi, Sarang
2012-01-01
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.
What are hierarchical models and how do we analyze them?
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)
An Automatic Hierarchical Delay Analysis Tool
Institute of Scientific and Technical Information of China (English)
FaridMheir－El－Saadi; BozenaKaminska
1994-01-01
The performance analysis of VLSI integrated circuits(ICs) with flat tools is slow and even sometimes impossible to complete.Some hierarchical tools have been developed to speed up the analysis of these large ICs.However,these hierarchical tools suffer from a poor interaction with the CAD database and poorly automatized operations.We introduce a general hierarchical framework for performance analysis to solve these problems.The circuit analysis is automatic under the proposed framework.Information that has been automatically abstracted in the hierarchy is kept in database properties along with the topological information.A limited software implementation of the framework,PREDICT,has also been developed to analyze the delay performance.Experimental results show that hierarchical analysis CPU time and memory requirements are low if heuristics are used during the abstraction process.
Hierarchical modularity in human brain functional networks
Meunier, D; Fornito, A; Ersche, K D; Bullmore, E T; 10.3389/neuro.11.037.2009
2010-01-01
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 ...
Institute of Scientific and Technical Information of China (English)
2004-01-01
<正> Story: Show Time!The whole class presents the story"Under the Sea".Everyone is so excited and happy.Both Leo and Kathy show their parentsthe characters of the play."Who’s he?"asks Kathy’s mom."He’s the prince."Kathy replies."Who’s she?"asks Leo’s dad."She’s the queen."Leo replieswith a smile.
Institute of Scientific and Technical Information of China (English)
YIN PUMIN
2010-01-01
@@ The State Administration of Radio,Film and Television (SARFT),China's media watchdog,issued a new set of mles on June 9 that strictly regulate TV match-making shows,which have been sweeping the country's primetime programming. "Improper social and love values such as money worship should not be presented in these shows.Humiliation,verbal attacks and sex-implied vulgar content are not allowed" the new roles said.
Parallel hierarchical radiosity rendering
Energy Technology Data Exchange (ETDEWEB)
Carter, M.
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.
Hierarchical bismuth phosphate microspheres with high photocatalytic performance
Energy Technology Data Exchange (ETDEWEB)
Pei, Lizhai; Wei, Tian; Lin, Nan; Yu, Haiyun [Anhui University of Technology, Ma' anshan (China). Key Laboratory of Materials Science and Processing of Anhui Province
2016-05-15
Hierarchical bismuth phosphate microspheres have been prepared by a simple hydrothermal process with polyvinyl pyrrolidone. Scanning electron microscopy observations show that the hierarchical bismuth phosphate microspheres consist of nanosheets with a thickness of about 30 nm. The diameter of the microspheres is about 1 - 3 μm. X-ray diffraction analysis shows that the microspheres are comprised of triclinic Bi{sub 23}P{sub 4}O{sub 44.5} phase. The formation of the hierarchical microspheres depends on polyvinyl pyrrolidone concentration, hydrothermal temperature and reaction time. Gentian violet acts as the pollutant model for investigating the photocatalytic activity of the hierarchical bismuth phosphate microspheres under ultraviolet-visible light irradiation. Irradiation time, dosage of the hierarchical microspheres and initial gentian violet concentration on the photocatalytic efficiency are also discussed. The hierarchical bismuth phosphate microspheres show good photocatalytic performance for gentian violet removal in aqueous solution.
Regression analysis by example
National Research Council Canada - National Science Library
Chatterjee, Samprit; Hadi, Ali S
2012-01-01
.... The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression...
Institute of Scientific and Technical Information of China (English)
2016-01-01
Visitors look at plane models of the Commercial Aircraft Corp. of China, developer of the count,s first homegrown large passenger jet C919, during the Singapore Airshow on February 16. The biennial event is the largest airshow in Asia and one of the most important aviation and defense shows worldwide. A number of Chinese companies took part in the event during which Okay Airways, the first privately owned aidine in China, signed a deal to acquire 12 Boeing 737 jets.
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...
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.
Hierarchical Classification of Chinese Documents Based on N-grams
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
We explore the techniques of utilizing N-gram informatio n tocategorize Chinese text documents hierarchically so that the classifier can shak e off the burden of large dictionaries and complex segmentation processing, and subsequently be domain and time independent. A hierarchical Chinese text classif ier is implemented. Experimental results show that hierarchically classifying Chinese text documents based N-grams can achieve satisfactory performance and outperforms the other traditional Chinese text classifiers.
Logistic regression: a brief primer.
Stoltzfus, Jill C
2011-10-01
Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model
Collaborative regression-based anatomical landmark detection
Gao, Yaozong; Shen, Dinggang
2015-12-01
Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head & neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods.
C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework
Sprechmann, Pablo; Sapiro, Guillermo; Eldar, Yonina
2010-01-01
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso model at the individual feature level, with the block-sparsity property of the Group Lasso model, where sparse groups of features are jointly encoded, obtaining a sparsity pattern hierarchically structured. This results in the Hierarchical Lasso (HiLasso), which shows important practical modeling advantages. We then extend this approach to the collaborative case, where a set of simultaneously coded signals share the same sparsity pattern at the higher (group) level, but not necessarily at the lower (inside the group) level, obtaining the collaborative HiLasso model (C-HiLasso). Such signals then share the same active groups, or classes, but not necessarily the same active set. This model is very well suited for ap...
Fast, Linear Time Hierarchical Clustering using the Baire Metric
Contreras, Pedro
2011-01-01
The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. In this work we evaluate empirically this new approach to hierarchical clustering. We compare hierarchical clustering based on the Baire metric with (i) agglomerative hierarchical clustering, in terms of algorithm properties; (ii) generalized ultrametrics, in terms of definition; and (iii) fast clustering through k-means partititioning, in terms of quality of results. For the latter, we carry out an in depth astronomical study. We apply the Baire distance to spectrometric and photometric redshifts from the Sloan Digital Sky Survey using, in this work, about half a million astronomical objects. We want to know how well the (more costly to determine) spectrometric redshifts can predict the (more easily obtained) photometric redshifts, i.e. we seek to regress the spectrometric on the photometric redshifts, and we use clusterwi...
Hierarchical manifold learning.
Bhatia, Kanwal K; Rao, Anil; Price, Anthony N; Wolz, Robin; Hajnal, Jo; Rueckert, Daniel
2012-01-01
We present a novel method of hierarchical manifold learning which aims to automatically discover regional variations within images. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate its utility in two very different settings: (1) to learn the regional correlations in motion within a sequence of time-resolved images of the thoracic cavity; (2) to find discriminative regions of 3D brain images in the classification of neurodegenerative disease,
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.
Pearce, Dave; Walter, Anton; Lupton, W. F.; Warren-Smith, Rodney F.; Lawden, Mike; McIlwrath, Brian; Peden, J. C. M.; Jenness, Tim; Draper, Peter W.
2015-02-01
The Hierarchical Data System (HDS) is a file-based hierarchical data system designed for the storage of a wide variety of information. It is particularly suited to the storage of large multi-dimensional arrays (with their ancillary data) where efficient access is needed. It is a key component of the Starlink software collection (ascl:1110.012) and is used by the Starlink N-Dimensional Data Format (NDF) library (ascl:1411.023). HDS organizes data into hierarchies, broadly similar to the directory structure of a hierarchical filing system, but contained within a single HDS container file. The structures stored in these files are self-describing and flexible; HDS supports modification and extension of structures previously created, as well as functions such as deletion, copying, and renaming. All information stored in HDS files is portable between the machines on which HDS is implemented. Thus, there are no format conversion problems when moving between machines. HDS can write files in a private binary format (version 4), or be layered on top of HDF5 (version 5).
Hierarchical video summarization
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.
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Scale of association: hierarchical linear models and the measurement of ecological systems
Sean M. McMahon; Jeffrey M. Diez
2007-01-01
A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...
Analysis hierarchical model for discrete event systems
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.
Unitary Response Regression Models
Lipovetsky, S.
2007-01-01
The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
Flexible survival regression modelling
DEFF Research Database (Denmark)
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....
Biased trapping issue on weighted hierarchical networks
Indian Academy of Sciences (India)
Meifeng Dai; Jie Liu; Feng Zhu
2014-10-01
In this paper, we present trapping issues of weight-dependent walks on weighted hierarchical networks which are based on the classic scale-free hierarchical networks. Assuming that edge’s weight is used as local information by a random walker, we introduce a biased walk. The biased walk is that a walker, at each step, chooses one of its neighbours with a probability proportional to the weight of the edge. We focus on a particular case with the immobile trap positioned at the hub node which has the largest degree in the weighted hierarchical networks. Using a method based on generating functions, we determine explicitly the mean first-passage time (MFPT) for the trapping issue. Let parameter (0 < < 1) be the weight factor. We show that the efficiency of the trapping process depends on the parameter a; the smaller the value of a, the more efficient is the trapping process.
Improving broadcast channel rate using hierarchical modulation
Meric, Hugo; Arnal, Fabrice; Lesthievent, Guy; Boucheret, Marie-Laure
2011-01-01
We investigate the design of a broadcast system where the aim is to maximise the throughput. This task is usually challenging due to the channel variability. Forty years ago, Cover introduced and compared two schemes: time sharing and superposition coding. The second scheme was proved to be optimal for some channels. Modern satellite communications systems such as DVB-SH and DVB-S2 mainly rely on time sharing strategy to optimize throughput. They consider hierarchical modulation, a practical implementation of superposition coding, but only for unequal error protection or backward compatibility purposes. We propose in this article to combine time sharing and hierarchical modulation together and show how this scheme can improve the performance in terms of available rate. We present the gain on a simple channel modeling the broadcasting area of a satellite. Our work is applied to the DVB-SH standard, which considers hierarchical modulation as an optional feature.
Incentive Mechanisms for Hierarchical Spectrum Markets
Iosifidis, George; Alpcan, Tansu; Koutsopoulos, Iordanis
2011-01-01
We study spectrum allocation mechanisms in hierarchical multi-layer markets which are expected to proliferate in the near future based on the current spectrum policy reform proposals. We consider a setting where a state agency sells spectrum to Primary Operators (POs) and in turn these resell it to Secondary Operators (SOs) through auctions. We show that these hierarchical markets do not result in a socially efficient spectrum allocation which is aimed by the agency, due to lack of coordination among the entities in different layers and the inherently selfish revenue-maximizing strategy of POs. In order to reconcile these opposing objectives, we propose an incentive mechanism which aligns the strategy and the actions of the POs with the objective of the agency, and thus it leads to system performance improvement in terms of social welfare. This pricing based mechanism constitutes a method for hierarchical market regulation and requires the feedback provision from SOs. A basic component of the proposed incenti...
Universal hierarchical behavior of citation networks
Mones, Enys; Vicsek, Tamás
2014-01-01
Many of the essential features of the evolution of scientific research are imprinted in the structure of citation networks. Connections in these networks imply information about the transfer of knowledge among papers, or in other words, edges describe the impact of papers on other publications. This inherent meaning of the edges infers that citation networks can exhibit hierarchical features, that is typical of networks based on decision-making. In this paper, we investigate the hierarchical structure of citation networks consisting of papers in the same field. We find that the majority of the networks follow a universal trend towards a highly hierarchical state, and i) the various fields display differences only concerning their phase in life (distance from the "birth" of a field) or ii) the characteristic time according to which they are approaching the stationary state. We also show by a simple argument that the alterations in the behavior are related to and can be understood by the degree of specializatio...
Genetic Algorithm for Hierarchical Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Sajid Hussain
2007-09-01
Full Text Available Large scale wireless sensor networks (WSNs can be used for various pervasive and ubiquitous applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments.
Hierarchical social networks and information flow
López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.
2002-12-01
Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.
Dynamic Organization of Hierarchical Memories.
Kurikawa, Tomoki; Kaneko, Kunihiko
2016-01-01
In the brain, external objects are categorized in a hierarchical way. Although it is widely accepted that objects are represented as static attractors in neural state space, this view does not take account interaction between intrinsic neural dynamics and external input, which is essential to understand how neural system responds to inputs. Indeed, structured spontaneous neural activity without external inputs is known to exist, and its relationship with evoked activities is discussed. Then, how categorical representation is embedded into the spontaneous and evoked activities has to be uncovered. To address this question, we studied bifurcation process with increasing input after hierarchically clustered associative memories are learned. We found a "dynamic categorization"; neural activity without input wanders globally over the state space including all memories. Then with the increase of input strength, diffuse representation of higher category exhibits transitions to focused ones specific to each object. The hierarchy of memories is embedded in the transition probability from one memory to another during the spontaneous dynamics. With increased input strength, neural activity wanders over a narrower state space including a smaller set of memories, showing more specific category or memory corresponding to the applied input. Moreover, such coarse-to-fine transitions are also observed temporally during transient process under constant input, which agrees with experimental findings in the temporal cortex. These results suggest the hierarchy emerging through interaction with an external input underlies hierarchy during transient process, as well as in the spontaneous activity.
A hierarchical linear model for tree height prediction.
Vicente J. Monleon
2003-01-01
Measuring tree height is a time-consuming process. Often, tree diameter is measured and height is estimated from a published regression model. Trees used to develop these models are clustered into stands, but this structure is ignored and independence is assumed. In this study, hierarchical linear models that account explicitly for the clustered structure of the data...
Directory of Open Access Journals (Sweden)
Marín Ignacio
2007-11-01
Full Text Available Abstract Background Classification procedures are widely used in phylogenetic inference, the analysis of expression profiles, the study of biological networks, etc. Many algorithms have been proposed to establish the similarity between two different classifications of the same elements. However, methods to determine significant coincidences between hierarchical and non-hierarchical partitions are still poorly developed, in spite of the fact that the search for such coincidences is implicit in many analyses of massive data. Results We describe a novel strategy to compare a hierarchical and a dichotomic non-hierarchical classification of elements, in order to find clusters in a hierarchical tree in which elements of a given "flat" partition are overrepresented. The key improvement of our strategy respect to previous methods is using permutation analyses of ranked clusters to determine whether regions of the dendrograms present a significant enrichment. We show that this method is more sensitive than previously developed strategies and how it can be applied to several real cases, including microarray and interactome data. Particularly, we use it to compare a hierarchical representation of the yeast mitochondrial interactome and a catalogue of known mitochondrial protein complexes, demonstrating a high level of congruence between those two classifications. We also discuss extensions of this method to other cases which are conceptually related. Conclusion Our method is highly sensitive and outperforms previously described strategies. A PERL script that implements it is available at http://www.uv.es/~genomica/treetracker.
Hierarchical structure of nanofibers by bubbfil spinning
Directory of Open Access Journals (Sweden)
Liu Chang
2015-01-01
Full Text Available A polymer bubble is easy to be broken under a small external force, various different fragments are formed, which can be produced to different morphologies of products including nanofibers and plate-like strip. Polyvinyl-alcohol/honey solution is used in the experiment to show hierarchical structure by the bubbfil spinning.
Detecting Hierarchical Structure in Networks
DEFF Research Database (Denmark)
Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard;
2012-01-01
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......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....... 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....
Context updates are hierarchical
Directory of Open Access Journals (Sweden)
Anton Karl Ingason
2016-10-01
Full Text Available This squib studies the order in which elements are added to the shared context of interlocutors in a conversation. It focuses on context updates within one hierarchical structure and argues that structurally higher elements are entered into the context before lower elements, even if the structurally higher elements are pronounced after the lower elements. The crucial data are drawn from a comparison of relative clauses in two head-initial languages, English and Icelandic, and two head-final languages, Korean and Japanese. The findings have consequences for any theory of a dynamic semantics.
Autistic epileptiform regression.
Canitano, Roberto; Zappella, Michele
2006-01-01
Autistic regression is a well known condition that occurs in one third of children with pervasive developmental disorders, who, after normal development in the first year of life, undergo a global regression during the second year that encompasses language, social skills and play. In a portion of these subjects, epileptiform abnormalities are present with or without seizures, resembling, in some respects, other epileptiform regressions of language and behaviour such as Landau-Kleffner syndrome. In these cases, for a more accurate definition of the clinical entity, the term autistic epileptifom regression has been suggested. As in other epileptic syndromes with regression, the relationships between EEG abnormalities, language and behaviour, in autism, are still unclear. We describe two cases of autistic epileptiform regression selected from a larger group of children with autistic spectrum disorders, with the aim of discussing the clinical features of the condition, the therapeutic approach and the outcome.
Scaled Sparse Linear Regression
Sun, Tingni
2011-01-01
Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold. For the scaled Lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the method yields simultaneously an estimator for the noise level and an estimated coefficient vector in the Lasso path satisfying certain oracle inequalities for the estimation of the noise level, prediction, and the estimation of regression coefficients. These oracle inequalities provide sufficient conditions for the consistency and asymptotic...
ORDINAL REGRESSION FOR INFORMATION RETRIEVAL
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.
Rolling Regressions with Stata
Kit Baum
2004-01-01
This talk will describe some work underway to add a "rolling regression" capability to Stata's suite of time series features. Although commands such as "statsby" permit analysis of non-overlapping subsamples in the time domain, they are not suited to the analysis of overlapping (e.g. "moving window") samples. Both moving-window and widening-window techniques are often used to judge the stability of time series regression relationships. We will present an implementation of a rolling regression...
Hierarchical Star Formation Across Galactic Disks
Gouliermis, Dimitrios
2016-09-01
Most stars form in clusters. This fact has emerged from the finding that "embedded clusters account for the 70 - 90% fraction of all stars formed in Giant Molecular Clouds (GMCs)." While this is the case at scales of few 10 parsecs, typical for GMCs, a look at star-forming galaxies in the Local Group (LG) shows significant populations of enormous loose complexes of early-type stars extending at scales from few 100 to few 1000 parsecs. The fact that these stellar complexes host extremely large numbers of loosely distributed massive blue stars implies either that stars form also in an unbound fashion or they are immediately dislocated from their original compact birthplaces or both. The Legacy Extra-Galactic UV Survey (LEGUS) has produced remarkable collections of resolved early-type stars in 50 star-forming LG galaxies, suited for testing ideas about recent star formation. I will present results from our ongoing project on star formation across LEGUS disk galaxies. We characterize the global clustering behavior of the massive young stars in order to understand the morphology of star formation over galactic scales. This morphology appears to be self-similar with fractal dimensions comparable to those of the molecular interstellar medium, apparently driven by large-scale turbulence. Our clustering analysis reveals compact stellar systems nested in larger looser concentrations, which themselves are the dense parts of unbound complexes and super-structures, giving evidence of hierarchical star formation up to galactic scales. We investigate the structural and star formation parameters demographics of the star-forming complexes revealed at various levels of compactness. I will discuss the outcome of our correlation and regression analyses on these parameters in an attempt to understand the link between galactic disk dynamics and morphological structure in spiral and ring galaxies of the local universe.
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Weisberg, Sanford
2005-01-01
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987
Groups possessing extensive hierarchical decompositions
Januszkiewicz, T; Leary, I J
2009-01-01
Kropholler's class of groups is the smallest class of groups which contains all finite groups and is closed under the following operator: whenever $G$ admits a finite-dimensional contractible $G$-CW-complex in which all stabilizer groups are in the class, then $G$ is itself in the class. Kropholler's class admits a hierarchical structure, i.e., a natural filtration indexed by the ordinals. For example, stage 0 of the hierarchy is the class of all finite groups, and stage 1 contains all groups of finite virtual cohomological dimension. We show that for each countable ordinal $\\alpha$, there is a countable group that is in Kropholler's class which does not appear until the $\\alpha+1$st stage of the hierarchy. Previously this was known only for $\\alpha= 0$, 1 and 2. The groups that we construct contain torsion. We also review the construction of a torsion-free group that lies in the third stage of the hierarchy.
Hierarchical networks of scientific journals
Palla, Gergely; Mones, Enys; Pollner, Péter; Vicsek, Tamás
2015-01-01
Scientific journals are the repositories of the gradually accumulating knowledge of mankind about the world surrounding us. Just as our knowledge is organised into classes ranging from major disciplines, subjects and fields to increasingly specific topics, journals can also be categorised into groups using various metrics. In addition to the set of topics characteristic for a journal, they can also be ranked regarding their relevance from the point of overall influence. One widespread measure is impact factor, but in the present paper we intend to reconstruct a much more detailed description by studying the hierarchical relations between the journals based on citation data. We use a measure related to the notion of m-reaching centrality and find a network which shows the level of influence of a journal from the point of the direction and efficiency with which information spreads through the network. We can also obtain an alternative network using a suitably modified nested hierarchy extraction method applied ...
Energy Technology Data Exchange (ETDEWEB)
Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
Study on Synthesis and Catalytic Performance of Hierarchical Zeolite
Institute of Scientific and Technical Information of China (English)
Zhang Lingling; Li Fengyan; ZhaoTianbo; Sun Guida
2007-01-01
A kind of hierarchical zeolite catalyst was synthesized by hydrothermal method.X-ray diffraction (XRD)and nitrogen adsorption-desorption method were used to study the phase and aperture structure of the prepared catalyst.Infrared(IR)spectra of pyridine adsorbed on the sample showed that the hierarchical zeolite really had much more Bronsted and Lewis acidic sites than the HZSM-5 zeolite.The catalytic cracking of large hydrocarbon molecules showed that the hierarchical zeolite had a higher catalytic activity than the HZSM-5 zeolite.
Hierarchical partial order ranking.
Carlsen, Lars
2008-09-01
Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritization of polluted sites is given.
Trees and Hierarchical Structures
Haeseler, Arndt
1990-01-01
The "raison d'etre" of hierarchical dustering theory stems from one basic phe nomenon: This is the notorious non-transitivity of similarity relations. In spite of the fact that very often two objects may be quite similar to a third without being that similar to each other, one still wants to dassify objects according to their similarity. This should be achieved by grouping them into a hierarchy of non-overlapping dusters such that any two objects in ~ne duster appear to be more related to each other than they are to objects outside this duster. In everyday life, as well as in essentially every field of scientific investigation, there is an urge to reduce complexity by recognizing and establishing reasonable das sification schemes. Unfortunately, this is counterbalanced by the experience of seemingly unavoidable deadlocks caused by the existence of sequences of objects, each comparatively similar to the next, but the last rather different from the first.
Optimisation by hierarchical search
Zintchenko, Ilia; Hastings, Matthew; Troyer, Matthias
2015-03-01
Finding optimal values for a set of variables relative to a cost function gives rise to some of the hardest problems in physics, computer science and applied mathematics. Although often very simple in their formulation, these problems have a complex cost function landscape which prevents currently known algorithms from efficiently finding the global optimum. Countless techniques have been proposed to partially circumvent this problem, but an efficient method is yet to be found. We present a heuristic, general purpose approach to potentially improve the performance of conventional algorithms or special purpose hardware devices by optimising groups of variables in a hierarchical way. We apply this approach to problems in combinatorial optimisation, machine learning and other fields.
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Hierarchical Context Modeling for Video Event Recognition.
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.
Strategic games on a hierarchical network model
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Among complex network models, the hierarchical network model is the one most close to such real networks as world trade web, metabolic network, WWW, actor network, and so on. It has not only the property of power-law degree distribution, but growth based on growth and preferential attachment, showing the scale-free degree distribution property. In this paper, we study the evolution of cooperation on a hierarchical network model, adopting the prisoner's dilemma (PD) game and snowdrift game (SG) as metaphors of the interplay between connected nodes. BA model provides a unifying framework for the emergence of cooperation. But interestingly, we found that on hierarchical model, there is no sign of cooperation for PD game, while the frequency of cooperation decreases as the common benefit decreases for SG. By comparing the scaling clustering coefficient properties of the hierarchical network model with that of BA model, we found that the former amplifies the effect of hubs. Considering different performances of PD game and SG on complex network, we also found that common benefit leads to cooperation in the evolution. Thus our study may shed light on the emergence of cooperation in both natural and social environments.
Hierarchical fuzzy identification of MR damper
Wang, Hao; Hu, Haiyan
2009-07-01
Magneto-rheological (MR) dampers, recently, have found many successful applications in civil engineering and numerous area of mechanical engineering. When an MR damper is to be used for vibration suppression, an inevitable problem is to determine the input voltage so as to gain the desired restoring force determined from the control law. This is the so-called inverse problem of MR dampers and is always an obstacle in the application of MR dampers to vibration control. It is extremely difficult to get the inverse model of MR damper because MR dampers are highly nonlinear and hysteretic. When identifying the inverse model of MR damper with simple fuzzy system, there maybe exists curse of dimensionality of fuzzy system. Therefore, it will take much more time, and even the inverse model may not be identifiable. The paper presents two-layer hierarchical fuzzy system, that is, two-layer hierarchical ANFIS to deal with the curse of dimensionality of the fuzzy identification of MR damper and to identify the inverse model of MR damper. Data used for training the model are generated from numerical simulation of nonlinear differential equations. The numerical simulation proves that the proposed hierarchical fuzzy system can model the inverse model of MR damper much more quickly than simple fuzzy system without any reduction of identification precision. Such hierarchical ANFIS shows the higher priority for the complicated system, and can also be used in system identification and system control for the complicated system.
Hierarchical organisation in perception of orientation.
Spinelli, D; Antonucci, G; Daini, R; Martelli, M L; Zoccolotti, P
1999-01-01
According to Rock [1990, in The Legacy of Solomon Asch (Hillsdale, NJ: Lawrence Erlbaum Associates)], hierarchical organisation of perception describes cases in which the orientation of an object is affected by the immediately surrounding elements in the visual field. Various experiments were performed to study the hierarchical organisation of orientation perception. In most of them the rod-and-frame-illusion (RFI: change of the apparent vertical measured on a central rod surrounded by a tilted frame) was measured in the presence/absence of a second inner frame. The first three experiments showed that, when the inner frame is vertical, the direction and size of the illusion are consistent with expectancies based on the hierarchical organisation hypothesis. An analysis of published and unpublished data collected on a large number of subjects showed that orientational hierarchical effects are independent from the absolute size of the RFI. In experiments 4 to 7 we examined the perceptual conditions of the inner stimulus (enclosure, orientation, and presence of luminance borders) critical for obtaining a hierarchical organisation effect. Although an inner vertical square was effective in reducing the illusion (experiment 3), an inner circle enclosing the rod was ineffective (experiment 4). This indicates that definite orientation is necessary to modulate the illusion. However, orientational information provided by a vertical or horizontal rectangle presented near the rod, but not enclosing it, did not modulate the RFI (experiment 5). This suggests that the presence of a figure with oriented contours enclosing the rod is critical. In experiments 6 and 7 we studied whether the presence of luminance borders is important or whether the inner upright square might be effective also if made of subjective contours. When the subjective contour figure was salient and the observers perceived it clearly, its effectiveness in modulating the RFI was comparable to that observed with
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Directory of Open Access Journals (Sweden)
Victor I Spoormaker
2012-05-01
Full Text Available Frontal and parietal regions are associated with some of the most complex cognitive functions, and several frontoparietal resting-state networks can be observed in wakefulness. We used functional magnetic resonance imaging (fMRI data acquired in polysomnographically validated wakefulness, light sleep and slow-wave sleep to examine the hierarchical structure of a low-frequency functional brain network, and to examine whether frontoparietal connectivity would disintegrate in sleep. Whole-brain analyses with hierarchical cluster analysis on predefined atlases were performed, as well as regression of inferior parietal lobules seeds against all voxels in the brain, and an evaluation of the integrity of voxel time-courses in subcortical regions-of-interest. We observed that frontoparietal functional connectivity disintegrated in sleep stage 1 and was absent in deeper sleep stages. Slow-wave sleep was characterized by strong hierarchical clustering of local submodules. Frontoparietal connectivity between inferior parietal lobules and superior medial and right frontal gyrus was lower in sleep stages than in wakefulness. Moreover, thalamus voxels showed maintained integrity in sleep stage 1, making intrathalamic desynchronization an unlikely source of reduced thalamocortical connectivity in this sleep stage. Our data suggest a transition from a globally integrated functional brain network in wakefulness to a disintegrated network consisting of local submodules in slow-wave sleep, in which frontoparietal inter-modular nodes may play a crucial role, possibly in combination with the thalamus.
Data with hierarchical structure: impact of intraclass correlation and sample size on type-I error.
Musca, Serban C; Kamiejski, Rodolphe; Nugier, Armelle; Méot, Alain; Er-Rafiy, Abdelatif; Brauer, Markus
2011-01-01
Least squares analyses (e.g., ANOVAs, linear regressions) of hierarchical data leads to Type-I error rates that depart severely from the nominal Type-I error rate assumed. Thus, when least squares methods are used to analyze hierarchical data coming from designs in which some groups are assigned to the treatment condition, and others to the control condition (i.e., the widely used "groups nested under treatment" experimental design), the Type-I error rate is seriously inflated, leading too often to the incorrect rejection of the null hypothesis (i.e., the incorrect conclusion of an effect of the treatment). To highlight the severity of the problem, we present simulations showing how the Type-I error rate is affected under different conditions of intraclass correlation and sample size. For all simulations the Type-I error rate after application of the popular Kish (1965) correction is also considered, and the limitations of this correction technique discussed. We conclude with suggestions on how one should collect and analyze data bearing a hierarchical structure.
Data with hierarchical structure: impact of intraclass correlation and sample size on Type-I error
Directory of Open Access Journals (Sweden)
Serban C Musca
2011-04-01
Full Text Available Least squares analyses (e.g., ANOVAs, linear regressions of hierarchical data leads to Type-I error rates that depart severely from the nominal Type-I error rate assumed. Thus, when least squares methods are used to analyze hierarchical data coming from designs in which some groups are assigned to the treatment condition, and others to the control condition (i.e., the widely used "groups nested under treatment" experimental design, the Type-I error rate is seriously inflated, leading too often to the incorrect rejection of the null hypothesis (i.e., the incorrect conclusion of an effect of the treatment. To highlight the severity of the problem, we present simulations showing how the Type-I error rate is affected under different conditions of intraclass correlation and sample size. For all simulations the Type-I error rate after application of the popular Kish (1965 correction is also considered, and the limitations of this correction technique discussed. We conclude with suggestions on how one should collect and analyze data bearing a hierarchical structure.
Field experiment on a robust hierarchical metropolitan quantum cryptography network
Institute of Scientific and Technical Information of China (English)
XU FangXing; CHEN Wei; WANG Shuang; YIN ZhenQiang; ZHANG Yang; LIU Yun; ZHOU Zheng; ZHAO YiBo; LI HongWei; LIU Dong; HAN ZhengFu; GUO GuangCan
2009-01-01
these bureaus.The whole implementation including the hierarchical quantum cryptographic communication network links and the corresponding application software shows a big step toward the practical user-oriented network with a high security level.
Modeling hierarchical structures - Hierarchical Linear Modeling using MPlus
Jelonek, M
2006-01-01
The aim of this paper is to present the technique (and its linkage with physics) of overcoming problems connected to modeling social structures, which are typically hierarchical. Hierarchical Linear Models provide a conceptual and statistical mechanism for drawing conclusions regarding the influence of phenomena at different levels of analysis. In the social sciences it is used to analyze many problems such as educational, organizational or market dilemma. This paper introduces the logic of modeling hierarchical linear equations and estimation based on MPlus software. I present my own model to illustrate the impact of different factors on school acceptation level.
Growth Regression and Economic Theory
Elbers, Chris; Gunning, Jan Willem
2002-01-01
In this note we show that the standard, loglinear growth regression specificationis consistent with one and only one model in the class of stochastic Ramsey models. Thismodel is highly restrictive: it requires a Cobb-Douglas technology and a 100% depreciationrate and it implies that risk does not af
Transductive Ordinal Regression
Seah, Chun-Wei; Ong, Yew-Soon
2011-01-01
Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, are often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive setting...
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
Directory of Open Access Journals (Sweden)
Zahra Sharafi
2017-01-01
Full Text Available Background. The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. Methods. The ordinal logistic regression (OLR and hierarchical ordinal logistic regression (HOLR were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™ 4.0 collected from 576 healthy school children were analyzed. Results. Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. Conclusions. The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.
Synthesis and Characterization of ZnTe Hierarchical Nanostructures
Directory of Open Access Journals (Sweden)
Baohua Zhang
2012-01-01
Full Text Available Single-crystalline ZnTe hierarchical nanostructures have been successfully synthesized by a simple thermal evaporation technology. The as-prepared products were characterized with X-ray diffraction (XRD, scanning electron microcopy (SEM, transmission electron microscope (TEM, and photoluminescence spectrum (PL. These results showed that the ZnTe hierarchical nanostructures consisted of nanowires and nanolumps. The room temperature PL spectrum exhibited a pure green luminescence centered at 545nm. The growth mechanism of hierarchical nanostructure was also discussed.
Modeling hierarchical structures - Hierarchical Linear Modeling using MPlus
Jelonek, Magdalena
2006-01-01
The aim of this paper is to present the technique (and its linkage with physics) of overcoming problems connected to modeling social structures, which are typically hierarchical. Hierarchical Linear Models provide a conceptual and statistical mechanism for drawing conclusions regarding the influence of phenomena at different levels of analysis. In the social sciences it is used to analyze many problems such as educational, organizational or market dilemma. This paper introduces the logic of m...
Regression with Sparse Approximations of Data
DEFF Research Database (Denmark)
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
Petrov, Romain G; Boskri, Abdelkarim; Folcher, Jean-Pierre; Lagarde, Stephane; Bresson, Yves; Benkhaldoum, Zouhair; Lazrek, Mohamed; Rakshit, Suvendu
2014-01-01
The limiting magnitude is a key issue for optical interferometry. Pairwise fringe trackers based on the integrated optics concepts used for example in GRAVITY seem limited to about K=10.5 with the 8m Unit Telescopes of the VLTI, and there is a general "common sense" statement that the efficiency of fringe tracking, and hence the sensitivity of optical interferometry, must decrease as the number of apertures increases, at least in the near infrared where we are still limited by detector readout noise. Here we present a Hierarchical Fringe Tracking (HFT) concept with sensitivity at least equal to this of a two apertures fringe trackers. HFT is based of the combination of the apertures in pairs, then in pairs of pairs then in pairs of groups. The key HFT module is a device that behaves like a spatial filter for two telescopes (2TSF) and transmits all or most of the flux of a cophased pair in a single mode beam. We give an example of such an achromatic 2TSF, based on very broadband dispersed fringes analyzed by g...
Tunesi, Luca; Armbruster, Philippe
2004-02-01
The objective of this paper is to demonstrate a suitable hierarchical networking solution to improve capabilities and performances of space systems, with significant recurrent costs saving and more efficient design & manufacturing flows. Classically, a satellite can be split in two functional sub-systems: the platform and the payload complement. The platform is in charge of providing power, attitude & orbit control and up/down-link services, whereas the payload represents the scientific and/or operational instruments/transponders and embodies the objectives of the mission. One major possibility to improve the performance of payloads, by limiting the data return to pertinent information, is to process data on board thanks to a proper implementation of the payload data system. In this way, it is possible to share non-recurring development costs by exploiting a system that can be adopted by the majority of space missions. It is believed that the Modular and Scalable Payload Data System, under development by ESA, provides a suitable solution to fulfil a large range of future mission requirements. The backbone of the system is the standardised high data rate SpaceWire network http://www.ecss.nl/. As complement, a lower speed command and control bus connecting peripherals is required. For instance, at instrument level, there is a need for a "local" low complexity bus, which gives the possibility to command and control sensors and actuators. Moreover, most of the connections at sub-system level are related to discrete signals management or simple telemetry acquisitions, which can easily and efficiently be handled by a local bus. An on-board hierarchical network can therefore be defined by interconnecting high-speed links and local buses. Additionally, it is worth stressing another important aspect of the design process: Agencies and ESA in particular are frequently confronted with a big consortium of geographically spread companies located in different countries, each one
Hierarchical Reverberation Mapping
Brewer, Brendon J
2013-01-01
Reverberation mapping (RM) is an important technique in studies of active galactic nuclei (AGN). The key idea of RM is to measure the time lag $\\tau$ between variations in the continuum emission from the accretion disc and subsequent response of the broad line region (BLR). The measurement of $\\tau$ is typically used to estimate the physical size of the BLR and is combined with other measurements to estimate the black hole mass $M_{\\rm BH}$. A major difficulty with RM campaigns is the large amount of data needed to measure $\\tau$. Recently, Fine et al (2012) introduced a new approach to RM where the BLR light curve is sparsely sampled, but this is counteracted by observing a large sample of AGN, rather than a single system. The results are combined to infer properties of the sample of AGN. In this letter we implement this method using a hierarchical Bayesian model and contrast this with the results from the previous stacked cross-correlation technique. We find that our inferences are more precise and allow fo...
[Understanding logistic regression].
El Sanharawi, M; Naudet, F
2013-10-01
Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.
Constrained Sparse Galerkin Regression
Loiseau, Jean-Christophe
2016-01-01
In this work, we demonstrate the use of sparse regression techniques from machine learning to identify nonlinear low-order models of a fluid system purely from measurement data. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, leading to energy conservation. The resulting models are closely related to Galerkin projection models, but the present method does not require the use of a full-order or high-fidelity Navier-Stokes solver to project onto basis modes. Instead, the most parsimonious nonlinear model is determined that is consistent with observed measurement data and satisfies necessary constraints. The constrained Galerkin regression algorithm is implemented on the fluid flow past a circular cylinder, demonstrating the ability to accurately construct models from data.
Hierarchical materials: Background and perspectives
DEFF Research Database (Denmark)
2016-01-01
Hierarchical design draws inspiration from analysis of biological materials and has opened new possibilities for enhancing performance and enabling new functionalities and extraordinary properties. With the development of nanotechnology, the necessary technological requirements for the manufactur...
Hierarchical clustering for graph visualization
Clémençon, Stéphan; Rossi, Fabrice; Tran, Viet Chi
2012-01-01
This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.
Direct hierarchical assembly of nanoparticles
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.
Practical Session: Logistic Regression
Clausel, M.; Grégoire, G.
2014-12-01
An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
Facile synthesis and photocatalytic activity of zinc oxide hierarchical microcrystals
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.
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
Hierarchical architecture of active knits
Abel, Julianna; Luntz, Jonathan; Brei, Diann
2013-12-01
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.
A Hierarchical Sensor Network Based on Voronoi Diagram
Institute of Scientific and Technical Information of China (English)
SHANG Rui-qiang; ZHAO Jian-li; SUN Qiu-xia; WANG Guang-xing
2006-01-01
A hierarchical sensor network is proposed which places the sensing and routing capacity at different layer nodes.It thus simplifies the hardware design and reduces cost. Adopting Voronoi diagram in the partition of backbone network,a mathematical model of data aggregation based on hierarchical architecture is given. Simulation shows that the number of transmission data packages is sharply cut down in the network, thus reducing the needs in the bandwidth and energy resources and is thus well adapted to sensor networks.
APPLICATION OF HIERARCHICAL REINFORCEMENT LEARNING IN ENGINEERING DOMAIN
Institute of Scientific and Technical Information of China (English)
WEI LI; Qingtai YE; Changming ZHU
2005-01-01
The slow convergence rate of reinforcement learning algorithms limits their wider application.In engineering domains, hierarchical reinforcement learning is developed to perform actions temporally according to prior knowledge. This system can converge fast due to reduced state space.There is a test of elevator group control to show the power of the new system. Two conventional group control algorithms are adopted as prior knowledge. Performance indicates that hierarchical reinforcement learning can reduce the learning time dramatically.
Advanced hierarchical distance sampling
Royle, Andy
2016-01-01
In this chapter, we cover a number of important extensions of the basic hierarchical distance-sampling (HDS) framework from Chapter 8. First, we discuss the inclusion of “individual covariates,” such as group size, in the HDS model. This is important in many surveys where animals form natural groups that are the primary observation unit, with the size of the group expected to have some influence on detectability. We also discuss HDS integrated with time-removal and double-observer or capture-recapture sampling. These “combined protocols” can be formulated as HDS models with individual covariates, and thus they have a commonality with HDS models involving group structure (group size being just another individual covariate). We cover several varieties of open-population HDS models that accommodate population dynamics. On one end of the spectrum, we cover models that allow replicate distance sampling surveys within a year, which estimate abundance relative to availability and temporary emigration through time. We consider a robust design version of that model. We then consider models with explicit dynamics based on the Dail and Madsen (2011) model and the work of Sollmann et al. (2015). The final major theme of this chapter is relatively newly developed spatial distance sampling models that accommodate explicit models describing the spatial distribution of individuals known as Point Process models. We provide novel formulations of spatial DS and HDS models in this chapter, including implementations of those models in the unmarked package using a hack of the pcount function for N-mixture models.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Software Regression Verification
2013-12-11
of recursive procedures. Acta Informatica , 45(6):403 – 439, 2008. [GS11] Benny Godlin and Ofer Strichman. Regression verifica- tion. Technical Report...functions. Therefore, we need to rede - fine m-term. – Mutual termination. If either function f or function f ′ (or both) is non- deterministic, then their
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Non-homogeneous fractal hierarchical weighted networks.
Dong, Yujuan; Dai, Meifeng; Ye, Dandan
2015-01-01
A model of fractal hierarchical structures that share the property of non-homogeneous weighted networks is introduced. These networks can be completely and analytically characterized in terms of the involved parameters, i.e., the size of the original graph Nk and the non-homogeneous weight scaling factors r1, r2, · · · rM. We also study the average weighted shortest path (AWSP), the average degree and the average node strength, taking place on the non-homogeneous hierarchical weighted networks. Moreover the AWSP is scrupulously calculated. We show that the AWSP depends on the number of copies and the sum of all non-homogeneous weight scaling factors in the infinite network order limit.
Hierarchical Self-organization of Complex Systems
Institute of Scientific and Technical Information of China (English)
CHAI Li-he; WEN Dong-sheng
2004-01-01
Researches on organization and structure in complex systems are academic and industrial fronts in modern sciences. Though many theories are tentatively proposed to analyze complex systems, we still lack a rigorous theory on them. Complex systems possess various degrees of freedom, which means that they should exhibit all kinds of structures. However, complex systems often show similar patterns and structures. Then the question arises why such similar structures appear in all kinds of complex systems. The paper outlines a theory on freedom degree compression and the existence of hierarchical self-organization for all complex systems is found. It is freedom degree compression and hierarchical self-organization that are responsible for the existence of these similar patterns or structures observed in the complex systems.
Hierarchical community structure in complex (social) networks
Massaro, Emanuele
2014-01-01
The investigation of community structure in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science where systems are often represented as graphs. One of the challenges is to find local communities from a local viewpoint in a graph without global information in order to reproduce the subjective hierarchical vision for each vertex. In this paper we present the improvement of an information dynamics algorithm in which the label propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints \\cite{Massaro2012}. In this framework we have introduced two more complex heuristics that allow the algorithm to detect the multi-resolution hierarchical community structure of networks from a source vertex or communities adopting fixed values of model's parameters. Experimental results show that the proposed methods are efficient and well-behaved in both real-world and synthetic networks.
On the geostatistical characterization of hierarchical media
Neuman, Shlomo P.; Riva, Monica; Guadagnini, Alberto
2008-02-01
; show that exponential and truncated power variograms are often difficult to distinguish from each other, which helps explain why hierarchical data may appear to fit the former; note that truncated power models are unique in their ability to represent multiscale random fields having either Gaussian or heavy-tailed symmetric Levy stable probability distributions; detail the way in which these models allow conditioning the spatial statistics of such fields on multiscale measurements via cokriging; and illustrate these capabilities on multiscale hydraulic data from an unconfined aquifer near Tübingen, Germany.
When to Use Hierarchical Linear Modeling
Directory of Open Access Journals (Sweden)
Veronika Huta
2014-04-01
Full Text Available Previous publications on hierarchical linear modeling (HLM have provided guidance on how to perform the analysis, yet there is relatively little information on two questions that arise even before analysis: Does HLM apply to ones data and research question? And if it does apply, how does one choose between HLM and other methods sometimes used in these circumstances, including multiple regression, repeated-measures or mixed ANOVA, and structural equation modeling or path analysis? The purpose of this tutorial is to briefly introduce HLM and then to review some of the considerations that are helpful in answering these questions, including the nature of the data, the model to be tested, and the information desired on the output. Some examples of how the same analysis could be performed in HLM, repeated-measures or mixed ANOVA, and structural equation modeling or path analysis are also provided. .
Image meshing via hierarchical optimization
Institute of Scientific and Technical Information of China (English)
Hao XIE; Ruo-feng TONG‡
2016-01-01
Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., defi nition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to fi nd a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it diﬃcult to fi nd a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to fi ner ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.
Image meshing via hierarchical optimization＊
Institute of Scientific and Technical Information of China (English)
Hao XIE; Ruo-feng TONGS
2016-01-01
Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.
Nasution, Inggrita Gusti Sari; Muchtar, Yasmin Chairunnisa
2013-01-01
This research is to study the factors which influence the business success of small business ‘processed rotan’. The data employed in the study are primary data within the period of July to August 2013, 30 research observations through census method. Method of analysis used in the study is multiple linear regressions. The results of analysis showed that the factors of labor, innovation and promotion have positive and significant influence on the business success of small busine...
Low rank Multivariate regression
Giraud, Christophe
2010-01-01
We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via...... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes....
Robust Nonstationary Regression
1993-01-01
This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed which allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities and they belong to the same ...
TWO REGRESSION CREDIBILITY MODELS
Directory of Open Access Journals (Sweden)
Constanţa-Nicoleta BODEA
2010-03-01
Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter θ. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. To illustrate the solution with the properties mentioned above, we shall need the well-known representation theorem for a special class of matrices, the properties of the trace for a square matrix, the scalar product of two vectors, the norm with respect to a positive definite matrix given in advance and the complicated mathematical properties of conditional expectations and of conditional covariances.
REGRESSION ANALYSIS OF PRODUCTIVITY USING MIXED EFFECT MODEL
Directory of Open Access Journals (Sweden)
Siana Halim
2007-01-01
Full Text Available Production plants of a company are located in several areas that spread across Middle and East Java. As the production process employs mostly manpower, we suspected that each location has different characteristics affecting the productivity. Thus, the production data may have a spatial and hierarchical structure. For fitting a linear regression using the ordinary techniques, we are required to make some assumptions about the nature of the residuals i.e. independent, identically and normally distributed. However, these assumptions were rarely fulfilled especially for data that have a spatial and hierarchical structure. We worked out the problem using mixed effect model. This paper discusses the model construction of productivity and several characteristics in the production line by taking location as a random effect. The simple model with high utility that satisfies the necessary regression assumptions was built using a free statistic software R version 2.6.1.
Metal hierarchical patterning by direct nanoimprint lithography.
Radha, Boya; Lim, Su Hui; Saifullah, Mohammad S M; Kulkarni, Giridhar U
2013-01-01
Three-dimensional hierarchical patterning of metals is of paramount importance in diverse fields involving photonics, controlling surface wettability and wearable electronics. Conventionally, this type of structuring is tedious and usually involves layer-by-layer lithographic patterning. Here, we describe a simple process of direct nanoimprint lithography using palladium benzylthiolate, a versatile metal-organic ink, which not only leads to the formation of hierarchical patterns but also is amenable to layer-by-layer stacking of the metal over large areas. The key to achieving such multi-faceted patterning is hysteretic melting of ink, enabling its shaping. It undergoes transformation to metallic palladium under gentle thermal conditions without affecting the integrity of the hierarchical patterns on micro- as well as nanoscale. A metallic rice leaf structure showing anisotropic wetting behavior and woodpile-like structures were thus fabricated. Furthermore, this method is extendable for transferring imprinted structures to a flexible substrate to make them robust enough to sustain numerous bending cycles.
Hierarchical unilamellar vesicles of controlled compositional heterogeneity.
Directory of Open Access Journals (Sweden)
Maik Hadorn
Full Text Available Eukaryotic life contains hierarchical vesicular architectures (i.e. organelles that are crucial for material production and trafficking, information storage and access, as well as energy production. In order to perform specific tasks, these compartments differ among each other in their membrane composition and their internal cargo and also differ from the cell membrane and the cytosol. Man-made structures that reproduce this nested architecture not only offer a deeper understanding of the functionalities and evolution of organelle-bearing eukaryotic life but also allow the engineering of novel biomimetic technologies. Here, we show the newly developed vesicle-in-water-in-oil emulsion transfer preparation technique to result in giant unilamellar vesicles internally compartmentalized by unilamellar vesicles of different membrane composition and internal cargo, i.e. hierarchical unilamellar vesicles of controlled compositional heterogeneity. The compartmentalized giant unilamellar vesicles were subsequently isolated by a separation step exploiting the heterogeneity of the membrane composition and the encapsulated cargo. Due to the controlled, efficient, and technically straightforward character of the new preparation technique, this study allows the hierarchical fabrication of compartmentalized giant unilamellar vesicles of controlled compositional heterogeneity and will ease the development of eukaryotic cell mimics that resemble their natural templates as well as the fabrication of novel multi-agent drug delivery systems for combination therapies and complex artificial microreactors.
A New Metrics for Hierarchical Clustering
Institute of Scientific and Technical Information of China (English)
YANGGuangwen; SHIShuming; WANGDingxing
2003-01-01
Hierarchical clustering is a popular method of performing unsupervised learning. Some metric must be used to determine the similarity between pairs of clusters in hierarchical clustering. Traditional similarity metrics either can deal with simple shapes (i.e. spherical shapes) only or are very sensitive to outliers (the chaining effect). The main contribution of this paper is to propose some potential-based similarity metrics (APES and AMAPES) between clusters in hierarchical clustering, inspired by the concepts of the electric potential and the gravitational potential in electromagnetics and astronomy. The main features of these metrics are: the first, they have strong antijamming capability; the second, they are capable of finding clusters of different shapes such as spherical, spiral, chain, circle, sigmoid, U shape or other complex irregular shapes; the third, existing algorithms and research fruits for classical metrics can be adopted to deal with these new potential-based metrics with no or little modification. Experiments showed that the new metrics are more superior to traditional ones. Different potential functions are compared, and the sensitivity to parameters is also analyzed in this paper.
Regression Verification Using Impact Summaries
Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana
2013-01-01
Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program
Deliberate change without hierarchical influence?
DEFF Research Database (Denmark)
Nørskov, Sladjana; Kesting, Peter; Ulhøi, John Parm
2017-01-01
Purpose This paper aims to present that deliberate change is strongly associated with formal structures and top-down influence. Hierarchical configurations have been used to structure processes, overcome resistance and get things done. But is deliberate change also possible without formal...... 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...
Static Correctness of Hierarchical Procedures
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff
1990-01-01
A system of hierarchical, fully recursive types in a truly imperative language allows program fragments written for small types to be reused for all larger types. To exploit this property to enable type-safe hierarchical procedures, it is necessary to impose a static requirement on procedure calls....... We introduce an example language and prove the existence of a sound requirement which preserves static correctness while allowing hierarchical procedures. This requirement is further shown to be optimal, in the sense that it imposes as few restrictions as possible. This establishes the theoretical...... basis for a general type hierarchy with static type checking, which enables first-order polymorphism combined with multiple inheritance and specialization in a language with assignments. We extend the results to include opaque types. An opaque version of a type is different from the original but has...
Electronic Properties in a Hierarchical Multilayer Structure
Institute of Scientific and Technical Information of China (English)
ZHU Chen-Ping; XIONG Shi-Jie
2001-01-01
We investigate electronic properties of a hierarchical multilayer structure consisting of stacking of barriers and wells. The structure is formed in a sequence of generations, each of which is constructed with the same pattern but with the previous generation as the basic building blocks. We calculate the transmission spectrum which shows the multifractal behavior for systems with large generation index. From the analysis of the average resistivity and the multifractal structure of the wavefunctions, we show that there exist different types of states exhibiting extended, localized and intermediate characteristics. The degree of localization is sensitive to the variation of the structural parameters.Suggestion of the possible experimental realization is discussed.
Enhanced piecewise regression based on deterministic annealing
Institute of Scientific and Technical Information of China (English)
ZHANG JiangShe; YANG YuQian; CHEN XiaoWen; ZHOU ChengHu
2008-01-01
Regression is one of the important problems in statistical learning theory. This paper proves the global convergence of the piecewise regression algorithm based on deterministic annealing and continuity of global minimum of free energy w.r.t temperature, and derives a new simplified formula to compute the initial critical temperature. A new enhanced piecewise regression algorithm by using "migration of prototypes" is proposed to eliminate "empty cell" in the annealing process. Numerical experiments on several benchmark datasets show that the new algo-rithm can remove redundancy and improve generalization of the piecewise regres-sion model.
Beta blockers & left ventricular hypertrophy regression.
George, Thomas; Ajit, Mullasari S; Abraham, Georgi
2010-01-01
Left ventricular hypertrophy (LVH) particularly in hypertensive patients is a strong predictor of adverse cardiovascular events. Identifying LVH not only helps in the prognostication but also in the choice of therapeutic drugs. The prevalence of LVH is age linked and has a direct correlation to the severity of hypertension. Adequate control of blood pressure, most importantly central aortic pressure and blocking the effects of cardiomyocyte stimulatory growth factors like Angiotensin II helps in regression of LVH. Among the various antihypertensives ACE-inhibitors and angiotensin receptor blockers are more potent than other drugs in regressing LVH. Beta blockers especially the newer cardio selective ones do still have a role in regressing LVH albeit a minor one. A meta-analysis of various studies on LVH regression shows many lacunae. There have been no consistent criteria for defining LVH and documenting LVH regression. This article reviews current evidence on the role of Beta Blockers in LVH regression.
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.
Three Ways to Link Merge with Hierarchical Concept-Combination
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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.
Novel algorithm for constructing support vector machine regression ensemble
Institute of Scientific and Technical Information of China (English)
Li Bo; Li Xinjun; Zhao Zhiyan
2006-01-01
A novel algorithm for constructing support vector machine regression ensemble is proposed. As to regression prediction, support vector machine regression(SVMR) ensemble is proposed by resampling from given training data sets repeatedly and aggregating several independent SVMRs, each of which is trained to use a replicated training set. After training, several independently trained SVMRs need to be aggregated in an appropriate combination manner. Generally, the linear weighting is usually used like expert weighting score in Boosting Regression and it is without optimization capacity. Three combination techniques are proposed, including simple arithmetic mean,linear least square error weighting and nonlinear hierarchical combining that uses another upper-layer SVMR to combine several lower-layer SVMRs. Finally, simulation experiments demonstrate the accuracy and validity of the presented algorithm.
HIERARCHICAL OPTIMIZATION MODEL ON GEONETWORK
Directory of Open Access Journals (Sweden)
Z. Zha
2012-07-01
Full Text Available In existing construction experience of Spatial Data Infrastructure (SDI, GeoNetwork, as the geographical information integrated solution, is an effective way of building SDI. During GeoNetwork serving as an internet application, several shortcomings are exposed. The first one is that the time consuming of data loading has been considerately increasing with the growth of metadata count. Consequently, the efficiency of query and search service becomes lower. Another problem is that stability and robustness are both ruined since huge amount of metadata. The final flaw is that the requirements of multi-user concurrent accessing based on massive data are not effectively satisfied on the internet. A novel approach, Hierarchical Optimization Model (HOM, is presented to solve the incapability of GeoNetwork working with massive data in this paper. HOM optimizes the GeoNetwork from these aspects: internal procedure, external deployment strategies, etc. This model builds an efficient index for accessing huge metadata and supporting concurrent processes. In this way, the services based on GeoNetwork can maintain stable while running massive metadata. As an experiment, we deployed more than 30 GeoNetwork nodes, and harvest nearly 1.1 million metadata. From the contrast between the HOM-improved software and the original one, the model makes indexing and retrieval processes more quickly and keeps the speed stable on metadata amount increasing. It also shows stable on multi-user concurrent accessing to system services, the experiment achieved good results and proved that our optimization model is efficient and reliable.
Conceptual hierarchical modeling to describe wetland plant community organization
Little, A.M.; Guntenspergen, G.R.; Allen, T.F.H.
2010-01-01
Using multivariate analysis, we created a hierarchical modeling process that describes how differently-scaled environmental factors interact to affect wetland-scale plant community organization in a system of small, isolated wetlands on Mount Desert Island, Maine. We followed the procedure: 1) delineate wetland groups using cluster analysis, 2) identify differently scaled environmental gradients using non-metric multidimensional scaling, 3) order gradient hierarchical levels according to spatiotem-poral scale of fluctuation, and 4) assemble hierarchical model using group relationships with ordination axes and post-hoc tests of environmental differences. Using this process, we determined 1) large wetland size and poor surface water chemistry led to the development of shrub fen wetland vegetation, 2) Sphagnum and water chemistry differences affected fen vs. marsh / sedge meadows status within small wetlands, and 3) small-scale hydrologic differences explained transitions between forested vs. non-forested and marsh vs. sedge meadow vegetation. This hierarchical modeling process can help explain how upper level contextual processes constrain biotic community response to lower-level environmental changes. It creates models with more nuanced spatiotemporal complexity than classification and regression tree procedures. Using this process, wetland scientists will be able to generate more generalizable theories of plant community organization, and useful management models. ?? Society of Wetland Scientists 2009.
Li, Dong; Yin, Jianhua; Dong, Liang; Lakes, Roderic S.
2017-02-01
Two-dimensional hierarchical re-entrant honeycomb structures were designed and the mechanical behaviors of the structures were studied using a finite element method. Hierarchical re-entrant structure of order n (n ≥ 1) was constructed by replacing each vertex of a lower order (n - 1) hierarchical re-entrant structure with a smaller re-entrant hexagon with identical strut aspect ratio. The Poisson’s ratio and energy absorption capacity of re-entrant structures of different hierarchical orders were studied under different compression velocities. The results showed that the Poisson’s ratio of the first and second order hierarchical structures can reach -1.36 and -1.33 with appropriate aspect ratio, 13.8% and 12.1% lower than that of the zeroth order hierarchical structure. The energy absorption capacity of the three models increased with an increasing compression velocity; the second order hierarchical structure exhibited the highest rate of increase in energy absorption capacity with an increasing compression velocity. The plateau stresses of the first and second order hierarchical structures were slightly lower than that of the zeroth order hierarchical structure; however the second order hierarchical structure exhibited the highest energy absorption capacity at high compression velocity (60 m s-1).
Sensory Hierarchical Organization and Reading.
Skapof, Jerome
The purpose of this study was to judge the viability of an operational approach aimed at assessing response styles in reading using the hypothesis of sensory hierarchical organization. A sample of 103 middle-class children from a New York City public school, between the ages of five and seven, took part in a three phase experiment. Phase one…
Directory of Open Access Journals (Sweden)
Karim Hardani*
2012-05-01
Full Text Available A 10-month-old baby presented with developmental delay. He had flaccid paralysis on physical examination.An MRI of the spine revealed malformation of the ninth and tenth thoracic vertebral bodies with complete agenesis of the rest of the spine down that level. The thoracic spinal cord ends at the level of the fifth thoracic vertebra with agenesis of the posterior arches of the eighth, ninth and tenth thoracic vertebral bodies. The roots of the cauda equina appear tightened down and backward and ended into a subdermal fibrous fatty tissue at the level of the ninth and tenth thoracic vertebral bodies (closed meningocele. These findings are consistent with caudal regression syndrome.
Synthesis strategies in the search for hierarchical zeolites.
Serrano, D P; Escola, J M; Pizarro, P
2013-05-07
Great interest has arisen in the past years in the development of hierarchical zeolites, having at least two levels of porosities. Hierarchical zeolites show an enhanced accessibility, leading to improved catalytic activity in reactions suffering from steric and/or diffusional limitations. Moreover, the secondary porosity offers an ideal space for the deposition of additional active phases and for functionalization with organic moieties. However, the secondary surface represents a discontinuity of the crystalline framework, with a low connectivity and a high concentration of silanols. Consequently, hierarchical zeolites exhibit a less "zeolitic behaviour" than conventional ones in terms of acidity, hydrophobic/hydrophilic character, confinement effects, shape-selectivity and hydrothermal stability. Nevertheless, this secondary surface is far from being amorphous, which provides hierarchical zeolites with a set of novel features. A wide variety of innovative strategies have been developed for generating a secondary porosity in zeolites. In the present review, the different synthetic routes leading to hierarchical zeolites have been classified into five categories: removal of framework atoms, surfactant-assisted procedures, hard-templating, zeolitization of preformed solids and organosilane-based methods. Significant advances have been achieved recently in several of these alternatives. These include desilication, due to its versatility, dual templating with polyquaternary ammonium surfactants and framework reorganization by treatment with surfactant-containing basic solutions. In the last two cases, the materials so prepared show both mesoscopic ordering and zeolitic lattice planes. Likewise, interesting results have been obtained with the incorporation of different types of organosilanes into the zeolite crystallization gels, taking advantage of their high affinity for silicate and aluminosilicate species. Crystallization of organofunctionalized species favours the
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R(2)) indicates the importance of independent variables in the outcome.
On Weighted Support Vector Regression
DEFF Research Database (Denmark)
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
Mechanics of hierarchical 3-D nanofoams
Chen, Q.; Pugno, N. M.
2012-01-01
In this paper, we study the mechanics of new three-dimensional hierarchical open-cell foams, and, in particular, its Young's modulus and plastic strength. We incorporate the effects of the surface elasticity and surface residual stress in the linear elastic and plastic analyses. The results show that, as the cross-sectional dimension decreases, the influences of the surface effect on Young's modulus and plastic strength increase, and the surface effect makes the solid stiffer and stronger; similarly, as level n increases, these quantities approach to those of the classical theory as lower bounds.
Indian Academy of Sciences (India)
Ling Xu; Limei Duan; Zongrui Liu; Jingqi Guan; Qiubin Kan
2013-12-01
A series of hierarchical porous aluminosilicate materials were prepared using hydrothermal treatment of the composite formed by polystyrene colloidal spheres and aluminosilicate gel. Influence of Al content on the textural properties, acidic properties and catalytic activity of the hierarchical porous aluminosilicate materials was studied. The results showed that textural and acidic properties of the hierarchical porous aluminosilicate materials were strongly related to Al content. As Al content is increased (Si/Al = 25), the hierarchical porous catalysts exhibited higher catalytic activity and major product selectivity for alkylation of phenol with tert-butanol than the catalysts with a lower Al content (Si/Al = 50).
Quantile regression modeling for Malaysian automobile insurance premium data
Fuzi, Mohd Fadzli Mohd; Ismail, Noriszura; Jemain, Abd Aziz
2015-09-01
Quantile regression is a robust regression to outliers compared to mean regression models. Traditional mean regression models like Generalized Linear Model (GLM) are not able to capture the entire distribution of premium data. In this paper we demonstrate how a quantile regression approach can be used to model net premium data to study the effects of change in the estimates of regression parameters (rating classes) on the magnitude of response variable (pure premium). We then compare the results of quantile regression model with Gamma regression model. The results from quantile regression show that some rating classes increase as quantile increases and some decrease with decreasing quantile. Further, we found that the confidence interval of median regression (τ = O.5) is always smaller than Gamma regression in all risk factors.
A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield
Kazama, Yoriko; Kujirai, Toshihiro
2014-10-01
A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.
Hierarchical Prisoner's Dilemma in Hierarchical Public-Goods Game
Fujimoto, Yuma; Kaneko, Kunihiko
2016-01-01
The dilemma in cooperation is one of the major concerns in game theory. In a public-goods game, each individual pays a cost for cooperation, or to prevent defection, and receives a reward from the collected cost in a group. Thus, defection is beneficial for each individual, while cooperation is beneficial for the group. Now, groups (say, countries) consisting of individual players also play games. To study such a multi-level game, we introduce a hierarchical public-goods (HPG) game in which two groups compete for finite resources by utilizing costs collected from individuals in each group. Analyzing this HPG game, we found a hierarchical prisoner's dilemma, in which groups choose the defection policy (say, armaments) as a Nash strategy to optimize each group's benefit, while cooperation optimizes the total benefit. On the other hand, for each individual within a group, refusing to pay the cost (say, tax) is a Nash strategy, which turns to be a cooperation policy for the group, thus leading to a hierarchical d...
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Hierarchical Parallelization of Gene Differential Association Analysis
Directory of Open Access Journals (Sweden)
Dwarkadas Sandhya
2011-09-01
Full Text Available Abstract Background Microarray gene differential expression analysis is a widely used technique that deals with high dimensional data and is computationally intensive for permutation-based procedures. Microarray gene differential association analysis is even more computationally demanding and must take advantage of multicore computing technology, which is the driving force behind increasing compute power in recent years. In this paper, we present a two-layer hierarchical parallel implementation of gene differential association analysis. It takes advantage of both fine- and coarse-grain (with granularity defined by the frequency of communication parallelism in order to effectively leverage the non-uniform nature of parallel processing available in the cutting-edge systems of today. Results Our results show that this hierarchical strategy matches data sharing behavior to the properties of the underlying hardware, thereby reducing the memory and bandwidth needs of the application. The resulting improved efficiency reduces computation time and allows the gene differential association analysis code to scale its execution with the number of processors. The code and biological data used in this study are downloadable from http://www.urmc.rochester.edu/biostat/people/faculty/hu.cfm. Conclusions The performance sweet spot occurs when using a number of threads per MPI process that allows the working sets of the corresponding MPI processes running on the multicore to fit within the machine cache. Hence, we suggest that practitioners follow this principle in selecting the appropriate number of MPI processes and threads within each MPI process for their cluster configurations. We believe that the principles of this hierarchical approach to parallelization can be utilized in the parallelization of other computationally demanding kernels.
Hierarchical structure of biological systems
Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M
2014-01-01
A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and functioning of the biological system. Our thermodynamical approach of hierarchical character can be applied to biological systems of varying sizes through some general principles, based on the exchange of energy information and/or mass from and within the systems. PMID:24145961
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.
Intuitionistic fuzzy hierarchical clustering algorithms
Institute of Scientific and Technical Information of China (English)
Xu Zeshui
2009-01-01
Intuitionistic fuzzy set (IFS) is a set of 2-tuple arguments, each of which is characterized by a mem-bership degree and a nonmembership degree. The generalized form of IFS is interval-valued intuitionistic fuzzy set (IVIFS), whose components are intervals rather than exact numbers. IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty. However, it seems that little attention has been focused on the clus-tering analysis of IFSs and IVIFSs. An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs, which is based on the traditional hierarchical clustering procedure, the intuitionistic fuzzy aggregation operator, and the basic distance measures between IFSs: the Hamming distance, normalized Hamming, weighted Hamming, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance. Subsequently, the algorithm is extended for clustering IVIFSs. Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.
Hierarchical Formation of Galactic Clusters
Elmegreen, B G
2006-01-01
Young stellar groupings and clusters have hierarchical patterns ranging from flocculent spiral arms and star complexes on the largest scale to OB associations, OB subgroups, small loose groups, clusters and cluster subclumps on the smallest scales. There is no obvious transition in morphology at the cluster boundary, suggesting that clusters are only the inner parts of the hierarchy where stars have had enough time to mix. The power-law cluster mass function follows from this hierarchical structure: n(M_cl) M_cl^-b for b~2. This value of b is independently required by the observation that the summed IMFs from many clusters in a galaxy equals approximately the IMF of each cluster.
Hierarchical matrices algorithms and analysis
Hackbusch, Wolfgang
2015-01-01
This self-contained monograph presents matrix algorithms and their analysis. The new technique enables not only the solution of linear systems but also the approximation of matrix functions, e.g., the matrix exponential. Other applications include the solution of matrix equations, e.g., the Lyapunov or Riccati equation. The required mathematical background can be found in the appendix. The numerical treatment of fully populated large-scale matrices is usually rather costly. However, the technique of hierarchical matrices makes it possible to store matrices and to perform matrix operations approximately with almost linear cost and a controllable degree of approximation error. For important classes of matrices, the computational cost increases only logarithmically with the approximation error. The operations provided include the matrix inversion and LU decomposition. Since large-scale linear algebra problems are standard in scientific computing, the subject of hierarchical matrices is of interest to scientists ...
Hierarchical Cont-Bouchaud model
Paluch, Robert; Holyst, Janusz A
2015-01-01
We extend the well-known Cont-Bouchaud model to include a hierarchical topology of agent's interactions. The influence of hierarchy on system dynamics is investigated by two models. The first one is based on a multi-level, nested Erdos-Renyi random graph and individual decisions by agents according to Potts dynamics. This approach does not lead to a broad return distribution outside a parameter regime close to the original Cont-Bouchaud model. In the second model we introduce a limited hierarchical Erdos-Renyi graph, where merging of clusters at a level h+1 involves only clusters that have merged at the previous level h and we use the original Cont-Bouchaud agent dynamics on resulting clusters. The second model leads to a heavy-tail distribution of cluster sizes and relative price changes in a wide range of connection densities, not only close to the percolation threshold.
Hierarchical Clustering and Active Galaxies
Hatziminaoglou, E; Manrique, A
2000-01-01
The growth of Super Massive Black Holes and the parallel development of activity in galactic nuclei are implemented in an analytic code of hierarchical clustering. The evolution of the luminosity function of quasars and AGN will be computed with special attention paid to the connection between quasars and Seyfert galaxies. One of the major interests of the model is the parallel study of quasar formation and evolution and the History of Star Formation.
Hybrid and hierarchical composite materials
Kim, Chang-Soo; Sano, Tomoko
2015-01-01
This book addresses a broad spectrum of areas in both hybrid materials and hierarchical composites, including recent development of processing technologies, structural designs, modern computer simulation techniques, and the relationships between the processing-structure-property-performance. Each topic is introduced at length with numerous and detailed examples and over 150 illustrations. In addition, the authors present a method of categorizing these materials, so that representative examples of all material classes are discussed.
Directory of Open Access Journals (Sweden)
Federico A. Sturzeneger
1992-03-01
Full Text Available Currency Substitution and the Regressivity of Inflationary Taxation The purpose of this paper is to show that in the presence of financial adaptation or currency substitution. the inflation tax is extremely regressive. This regressivity arises from the existence of a fixed cost of switching to inflation-proof transactions technologies. This fixed cost makes it optimal only for those agents with sufficiently high incomes to switch out of domestic currency. The effects are illustrated and quantified for a particular case.
Treatment Protocols as Hierarchical Structures
Ben-Bassat, Moshe; Carlson, Richard W.; Puri, Vinod K.; Weil, Max Harry
1978-01-01
We view a treatment protocol as a hierarchical structure of therapeutic modules. The lowest level of this structure consists of individual therapeutic actions. Combinations of individual actions define higher level modules, which we call routines. Routines are designed to manage limited clinical problems, such as the routine for fluid loading to correct hypovolemia. Combinations of routines and additional actions, together with comments, questions, or precautions organized in a branching logic, in turn, define the treatment protocol for a given disorder. Adoption of this modular approach may facilitate the formulation of treatment protocols, since the physician is not required to prepare complex flowcharts. This hierarchical approach also allows protocols to be updated and modified in a flexible manner. By use of such a standard format, individual components may be fitted together to create protocols for multiple disorders. The technique is suited for computer implementation. We believe that this hierarchical approach may facilitate standarization of patient care as well as aid in clinical teaching. A protocol for acute pancreatitis is used to illustrate this technique.
Institute of Scientific and Technical Information of China (English)
XU Jing; YANG Chi; ZHANG Guoping
2007-01-01
Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for modeling the probabilities of geological hazard occurrences, upon which hierarchical warnings for rainfall-induced geological hazards are produced. The forecasting and warning model takes numerical precipitation forecasts on grid points as its dynamic input, forecasts the probabilities of geological hazard occurrences on the same grid, and translates the results into likelihoods in the form of a 5-level hierarchy. Validation of the model with observational data for the year 2004 shows that 80% of the geological hazards of the year have been identified as "likely enough to release warning messages". The model can satisfy the requirements of an operational warning system, thus is an effective way to improve the meteorological warnings for geological hazards.
Combining Self-organizing Feature Map with Support Vector Regression Based on Expert System
Institute of Scientific and Technical Information of China (English)
WANGLing; MUZhi-Chun; GUOHui
2005-01-01
A new approach is proposed to model nonlinear dynamic systems by combining SOM(self-organizing feature map) with support vector regression (SVR) based on expert system. The whole system has a two-stage neural network architecture. In the first stage SOM is used as a clustering algorithm to partition the whole input space into several disjointed regions. A hierarchical architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVR, also called SVR experts, that best fit each partitioned region by the combination of different kernel function of SVR and promote the configuration and tuning of SVR. Finally, to apply this new approach to time-series prediction problems based on the Mackey-Glass differential equation and Santa Fe data, the results show that SVR experts has effective improvement in the generalization performance in comparison with the single SVR model.
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 PM2.5 is a promising way to fill the areas that are not covered by ground PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 estimates.
Free-Energy Bounds for Hierarchical Spin Models
Castellana, Michele; Barra, Adriano; Guerra, Francesco
2014-04-01
In this paper we study two non-mean-field (NMF) spin models built on a hierarchical lattice: the hierarchical Edward-Anderson model (HEA) of a spin glass, and Dyson's hierarchical model (DHM) of a ferromagnet. For the HEA, we prove the existence of the thermodynamic limit of the free energy and the replica-symmetry-breaking (RSB) free-energy bounds previously derived for the Sherrington-Kirkpatrick model of a spin glass. These RSB mean-field bounds are exact only if the order-parameter fluctuations (OPF) vanish: given that such fluctuations are not negligible in NMF models, we develop a novel strategy to tackle part of OPF in hierarchical models. The method is based on absorbing part of OPF of a block of spins into an effective Hamiltonian of the underlying spin blocks. We illustrate this method for DHM and show that, compared to the mean-field bound for the free energy, it provides a tighter NMF bound, with a critical temperature closer to the exact one. To extend this method to the HEA model, a suitable generalization of Griffith's correlation inequalities for Ising ferromagnets is needed: since correlation inequalities for spin glasses are still an open topic, we leave the extension of this method to hierarchical spin glasses as a future perspective.
Hierarchical Overlapping Clustering of Network Data Using Cut Metrics
Gama, Fernando; Ribeiro, Alejandro
2016-01-01
A novel method to obtain hierarchical and overlapping clusters from network data -i.e., a set of nodes endowed with pairwise dissimilarities- is presented. The introduced method is hierarchical in the sense that it outputs a nested collection of groupings of the node set depending on the resolution or degree of similarity desired, and it is overlapping since it allows nodes to belong to more than one group. Our construction is rooted on the facts that a hierarchical (non-overlapping) clustering of a network can be equivalently represented by a finite ultrametric space and that a convex combination of ultrametrics results in a cut metric. By applying a hierarchical (non-overlapping) clustering method to multiple dithered versions of a given network and then convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric. Furthermore, the so-called overlappi...
Renormalization of Hierarchically Interacting Isotropic Diffusions
den Hollander, F.; Swart, J. M.
1998-10-01
We study a renormalization transformation arising in an infinite system of interacting diffusions. The components of the system are labeled by the N-dimensional hierarchical lattice ( N≥2) and take values in the closure of a compact convex set bar D subset {R}^d (d ≥slant 1). Each component starts at some θ ∈ D and is subject to two motions: (1) an isotropic diffusion according to a local diffusion rate g: bar D to [0,infty ] chosen from an appropriate class; (2) a linear drift toward an average of the surrounding components weighted according to their hierarchical distance. In the local mean-field limit N→∞, block averages of diffusions within a hierarchical distance k, on an appropriate time scale, are expected to perform a diffusion with local diffusion rate F ( k) g, where F^{(k)} g = (F_{c_k } circ ... circ F_{c_1 } ) g is the kth iterate of renormalization transformations F c ( c>0) applied to g. Here the c k measure the strength of the interaction at hierarchical distance k. We identify F c and study its orbit ( F ( k) g) k≥0. We show that there exists a "fixed shape" g* such that lim k→∞ σk F ( k) g = g* for all g, where the σ k are normalizing constants. In terms of the infinite system, this property means that there is complete universal behavior on large space-time scales. Our results extend earlier work for d = 1 and bar D = [0,1], resp. [0, ∞). The renormalization transformation F c is defined in terms of the ergodic measure of a d-dimensional diffusion. In d = 1 this diffusion allows a Yamada-Watanabe-type coupling, its ergodic measure is reversible, and the renormalization transformation F c is given by an explicit formula. All this breaks down in d≥2, which complicates the analysis considerably and forces us to new methods. Part of our results depend on a certain martingale problem being well-posed.
Variable and subset selection in PLS regression
DEFF Research Database (Denmark)
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...
A Bayesian approach to linear regression in astronomy
Sereno, Mauro
2015-01-01
Linear regression is common in astronomical analyses. I discuss a Bayesian hierarchical modeling of data with heteroscedastic and possibly correlated measurement errors and intrinsic scatter. The method fully accounts for time evolution. The slope, the normalization, and the intrinsic scatter of the relation can evolve with the redshift. The intrinsic distribution of the independent variable is approximated using a mixture of Gaussian distributions whose means and standard deviations depend on time. The method can address scatter in the measured independent variable (a kind of Eddington bias), selection effects in the response variable (Malmquist bias), and departure from linearity in form of a knee. I tested the method with toy models and simulations and quantified the effect of biases and inefficient modeling. The R-package LIRA (LInear Regression in Astronomy) is made available to perform the regression.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Combining Alphas via Bounded Regression
Directory of Open Access Journals (Sweden)
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Quick Web Services Lookup Model Based on Hierarchical Registration
Institute of Scientific and Technical Information of China (English)
谢山; 朱国进; 陈家训
2003-01-01
Quick Web Services Lookup (Q-WSL) is a new model to registration and lookup of complex services in the Internet. The model is designed to quickly find complex Web services by using hierarchical registration method. The basic concepts of Web services system are introduced and presented, and then the method of hierarchical registration of services is described. In particular, service query document description and service lookup procedure are concentrated, and it addresses how to lookup these services which are registered in the Web services system. Furthermore, an example design and an evaluation of its performance are presented.Specifically, it shows that the using of attributionbased service query document design and contentbased hierarchical registration in Q-WSL allows service requesters to discover needed services more flexibly and rapidly. It is confirmed that Q-WSL is very suitable for Web services system.
Ways of looking ahead: hierarchical planning in language production.
Lee, Eun-Kyung; Brown-Schmidt, Sarah; Watson, Duane G
2013-12-01
It is generally assumed that language production proceeds incrementally, with chunks of linguistic structure planned ahead of speech. Extensive research has examined the scope of language production and suggests that the size of planned chunks varies across contexts (Ferreira & Swets, 2002; Wagner & Jescheniak, 2010). By contrast, relatively little is known about the structure of advance planning, specifically whether planning proceeds incrementally according to the surface structure of the utterance, or whether speakers plan according to the hierarchical relationships between utterance elements. In two experiments, we examine the structure and scope of lexical planning in language production using a picture description task. Analyses of speech onset times and word durations show that speakers engage in hierarchical planning such that structurally dependent lexical items are planned together and that hierarchical planning occurs for both direct and indirect dependencies. Copyright © 2013 Elsevier B.V. All rights reserved.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
PERFORMANCE OF SELECTED AGGLOMERATIVE HIERARCHICAL CLUSTERING METHODS
Directory of Open Access Journals (Sweden)
Nusa Erman
2015-01-01
Full Text Available A broad variety of different methods of agglomerative hierarchical clustering brings along problems how to choose the most appropriate method for the given data. It is well known that some methods outperform others if the analysed data have a specific structure. In the presented study we have observed the behaviour of the centroid, the median (Gower median method, and the average method (unweighted pair-group method with arithmetic mean – UPGMA; average linkage between groups. We have compared them with mostly used methods of hierarchical clustering: the minimum (single linkage clustering, the maximum (complete linkage clustering, the Ward, and the McQuitty (groups method average, weighted pair-group method using arithmetic averages - WPGMA methods. We have applied the comparison of these methods on spherical, ellipsoid, umbrella-like, “core-and-sphere”, ring-like and intertwined three-dimensional data structures. To generate the data and execute the analysis, we have used R statistical software. Results show that all seven methods are successful in finding compact, ball-shaped or ellipsoid structures when they are enough separated. Conversely, all methods except the minimum perform poor on non-homogenous, irregular and elongated ones. Especially challenging is a circular double helix structure; it is being correctly revealed only by the minimum method. We can also confirm formerly published results of other simulation studies, which usually favour average method (besides Ward method in cases when data is assumed to be fairly compact and well separated.
Quark flavor mixings from hierarchical mass matrices
Energy Technology Data Exchange (ETDEWEB)
Verma, Rohit [Chinese Academy of Sciences, Institute of High Energy Physics, Beijing (China); Rayat Institute of Engineering and Information Technology, Ropar (India); Zhou, Shun [Chinese Academy of Sciences, Institute of High Energy Physics, Beijing (China); Peking University, Center for High Energy Physics, Beijing (China)
2016-05-15
In this paper, we extend the Fritzsch ansatz of quark mass matrices while retaining their hierarchical structures and show that the main features of the Cabibbo-Kobayashi-Maskawa (CKM) matrix V, including vertical stroke V{sub us} vertical stroke ≅ vertical stroke V{sub cd} vertical stroke, vertical stroke V{sub cb} vertical stroke ≅ vertical stroke V{sub ts} vertical stroke and vertical stroke V{sub ub} vertical stroke / vertical stroke V{sub cb} vertical stroke < vertical stroke V{sub td} vertical stroke / vertical stroke V{sub ts} vertical stroke can be well understood. This agreement is observed especially when the mass matrices have non-vanishing (1, 3) and (3, 1) off-diagonal elements. The phenomenological consequences of these for the allowed texture content and gross structural features of 'hierarchical' quark mass matrices are addressed from a model-independent prospective under the assumption of factorizable phases in these. The approximate and analytical expressions of the CKM matrix elements are derived and a detailed analysis reveals that such structures are in good agreement with the observed quark flavor mixing angles and the CP-violating phase at the 1σ level and call upon a further investigation of the realization of these structures from a top-down prospective. (orig.)
Bimodal Color Distribution in Hierarchical Galaxy Formation
Menci, N; Giallongo, E; Salimbeni, S
2005-01-01
We show how the observed bimodality in the color distribution of galaxies can be explained in the framework of the hierarchical clustering picture in terms of the interplay between the properties of the merging histories and the feedback/star-formation processes in the progenitors of local galaxies. Using a semi-analytic model of hierarchical galaxy formation, we compute the color distributions of galaxies with different luminosities and compare them with the observations. Our fiducial model matches the fundamental properties of the observed distributions, namely: 1) the distribution of objects brighter than M_r = -18 is clearly bimodal, with a fraction of red objects increasing with luminosity; 2) for objects brighter than M_r = -21 the color distribution is dominated by red objects with color u-r = 2.2-2.4; 3) the spread on the distribution of the red population is smaller than that of the blue population; 4) the fraction of red galaxies is larger in denser environments, even for low-luminosity objects; 5) ...
Nonparametric Regression with Common Shocks
Directory of Open Access Journals (Sweden)
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Quark Masses and Mixings with Hierarchical Friedberg-Lee Symmetry
Araki, T
2010-01-01
We propose the hierarchically translating Friedberg-Lee (FL) symmetry for the quark sector and show that the symmetry closely relates to both quark masses and mixings angles. We also extend our scheme to the fourth generation quark model and find the relation $|V_{tb^{'}}| \\simeq |V_{t^{'}b}| \\simeq m_b/m_{b^{'}}199 \\gev$.
Polystyrene/octadecyltrichlorosilane superhydrophobic coatings with hierarchical morphology
Demirel, A. Levent; Latthe, Sanjay S.
2013-01-01
A simple, one pot dip-coating process for the fabrication of super-hydrophobic coatings using polystyrene (PS) and octadecyltrichlorosilane (OTS) is introduced. The hierarchical coating morphology and the resulting surface wettability were controlled by OTS concentration and by the number of dipping cycles. The coatings showed good durability for applications.
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.
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.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Hierarchical Control for Smart Grids
DEFF Research Database (Denmark)
Trangbæk, K; Bendtsen, Jan Dimon; Stoustrup, Jakob
2011-01-01
This paper deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high level MPC controller, a second level of so-called aggregators, which reduces the computational and communication-related load on the high-level control, and a lower level...... of autonomous consumers. The control system is tasked with balancing electric power production and consumption within the smart grid, and makes active use of the ﬂexibility of a large number of power producing and/or power consuming units. The objective is to accommodate the load variation on the grid, arising...
Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Business applications of multiple regression
Richardson, Ronny
2015-01-01
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta
A simulation study of sample size for multilevel logistic regression models
Directory of Open Access Journals (Sweden)
Moineddin Rahim
2007-07-01
Full Text Available Abstract Background Many studies conducted in health and social sciences collect individual level data as outcome measures. Usually, such data have a hierarchical structure, with patients clustered within physicians, and physicians clustered within practices. Large survey data, including national surveys, have a hierarchical or clustered structure; respondents are naturally clustered in geographical units (e.g., health regions and may be grouped into smaller units. Outcomes of interest in many fields not only reflect continuous measures, but also binary outcomes such as depression, presence or absence of a disease, and self-reported general health. In the framework of multilevel studies an important problem is calculating an adequate sample size that generates unbiased and accurate estimates. Methods In this paper simulation studies are used to assess the effect of varying sample size at both the individual and group level on the accuracy of the estimates of the parameters and variance components of multilevel logistic regression models. In addition, the influence of prevalence of the outcome and the intra-class correlation coefficient (ICC is examined. Results The results show that the estimates of the fixed effect parameters are unbiased for 100 groups with group size of 50 or higher. The estimates of the variance covariance components are slightly biased even with 100 groups and group size of 50. The biases for both fixed and random effects are severe for group size of 5. The standard errors for fixed effect parameters are unbiased while for variance covariance components are underestimated. Results suggest that low prevalent events require larger sample sizes with at least a minimum of 100 groups and 50 individuals per group. Conclusion We recommend using a minimum group size of 50 with at least 50 groups to produce valid estimates for multi-level logistic regression models. Group size should be adjusted under conditions where the prevalence
Hierarchical Structures in Hypertext Learning Environments
Bezdan, Eniko; Kester, Liesbeth; Kirschner, Paul A.
2011-01-01
Bezdan, E., Kester, L., & Kirschner, P. A. (2011, 9 September). Hierarchical Structures in Hypertext Learning Environments. Presentation for the visit of KU Leuven, Open University, Heerlen, The Netherlands.
Prediction of road accidents: A Bayesian hierarchical approach
DEFF Research Database (Denmark)
Deublein, Markus; Schubert, Matthias; Adey, Bryan T.;
2013-01-01
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...... in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models.Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis...
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Synchronization patterns: from network motifs to hierarchical networks
Krishnagopal, Sanjukta; Lehnert, Judith; Poel, Winnie; Zakharova, Anna; Schöll, Eckehard
2017-03-01
We investigate complex synchronization patterns such as cluster synchronization and partial amplitude death in networks of coupled Stuart-Landau oscillators with fractal connectivities. The study of fractal or self-similar topology is motivated by the network of neurons in the brain. This fractal property is well represented in hierarchical networks, for which we present three different models. In addition, we introduce an analytical eigensolution method and provide a comprehensive picture of the interplay of network topology and the corresponding network dynamics, thus allowing us to predict the dynamics of arbitrarily large hierarchical networks simply by analysing small network motifs. We also show that oscillation death can be induced in these networks, even if the coupling is symmetric, contrary to previous understanding of oscillation death. Our results show that there is a direct correlation between topology and dynamics: hierarchical networks exhibit the corresponding hierarchical dynamics. This helps bridge the gap between mesoscale motifs and macroscopic networks. This article is part of the themed issue 'Horizons of cybernetical physics'.
Efficient promotion strategies in hierarchical organizations
Pluchino, Alessandro; Rapisarda, Andrea; Garofalo, Cesare
2011-10-01
The Peter principle has recently been investigated by means of an agent-based simulation, and its validity has been numerically corroborated. It has been confirmed that, within certain conditions, it can really influence in a negative way the efficiency of a pyramidal organization adopting meritocratic promotions. It was also found that, in order to bypass these effects, alternative promotion strategies should be adopted, as for example a random selection choice. In this paper, within the same line of research, we study promotion strategies in a more realistic hierarchical and modular organization, and we show the robustness of our previous results, extending their validity to a more general context. We also discuss why the adoption of these strategies could be useful for real organizations.
Power Efficient Hierarchical Scheduling for DSP Transformations
Directory of Open Access Journals (Sweden)
P. K. Merakos
2002-01-01
Full Text Available In this paper, the problem of scheduling the computation of partial products in transformational Digital Signal Processing (DSP algorithms, aiming at the minimization of the switching activity in data and address buses, is addressed. The problem is stated as a hierarchical scheduling problem. Two different optimization algorithms, which are based on the Travelling Salesman Problem (TSP, are defined. The proposed optimization algorithms are independent on the target architecture and can be adapted to take into account it. Experimental results obtained from the application of the proposed algorithms in various widely used DSP transformations, like Discrete Cosine Transform (DCT and Discrete Fourier Transform (DFT, show that significant switching activity savings in data and address buses can be achieved, resulting in corresponding power savings. In addition, the differences between the two proposed methods are underlined, providing envisage for their suitable selection for implementation, in particular transformational algorithms and architectures.
Crack Propagation in Bamboo's Hierarchical Cellular Structure
Habibi, Meisam K.; Lu, Yang
2014-07-01
Bamboo, as a natural hierarchical cellular material, exhibits remarkable mechanical properties including excellent flexibility and fracture toughness. As far as bamboo as a functionally graded bio-composite is concerned, the interactions of different constituents (bamboo fibers; parenchyma cells; and vessels.) alongside their corresponding interfacial areas with a developed crack should be of high significance. Here, by using multi-scale mechanical characterizations coupled with advanced environmental electron microscopy (ESEM), we unambiguously show that fibers' interfacial areas along with parenchyma cells' boundaries were preferred routes for crack growth in both radial and longitudinal directions. Irrespective of the honeycomb structure of fibers along with cellular configuration of parenchyma ground, the hollow vessels within bamboo culm affected the crack propagation too, by crack deflection or crack-tip energy dissipation. It is expected that the tortuous crack propagation mode exhibited in the present study could be applicable to other cellular natural materials as well.
Secular Evolution of Hierarchical Triple Star Systems
Ford, E B; Kozinsky, B
1999-01-01
We derive octupole-level secular perturbation equations for hierarchical triple systems, using classical Hamiltonian perturbation techniques. Our equations describe the secular evolution of the orbital eccentricities and inclinations over timescales long compared to the orbital periods. By extending previous work done to leading (quadrupole) order to octupole level (i.e., including terms of order $\\alpha^3$, where $\\alpha\\equiv a_1/a_2<1$ is the ratio of semimajor axes) we obtain expressions that are applicable to a much wider range of parameters. For triple systems containing a close inner binary, we also discuss the possible interaction between the classical Newtonian perturbations and the general relativistic precession of the inner orbit. In some cases we show that this interaction can lead to resonances and a significant increase in the maximum amplitude of eccentricity perturbations. We establish the validity of our analytic expressions by providing detailed comparisons with the results of direct num...
Hierarchical Codebook Design for Massive MIMO
Directory of Open Access Journals (Sweden)
Xin Su
2015-02-01
Full Text Available The Research of Massive MIMO is an emerging area, since the more antennas the transmitters or receivers equipped with, the higher spectral efficiency and link reliability the system can provide. Due to the limited feedback channel, precoding and codebook design are important to exploit the performance of massive MIMO. To improve the precoding performance, we propose a novel hierarchical codebook with the Fourier-based perturbation matrices as the subcodebook and the Kerdock codebook as the main codebook, which could reduce storage and search complexity due to the finite a lphabet. Moreover, t o f urther r educe t he search complexity and feedback overhead without noticeable performance degradation, we use an adaptive selection algorithm to decide whether to use the subcodebook. Simulation results show that the proposed codebook has remarkable performance gain compared to the conventional Kerdock codebook, without significant increase in feedback overhead and search complexity.
[Iris movement mediates pupillary membrane regression].
Morizane, Yuki
2007-11-01
In the course of mammalian lens development, a transient capillary meshwork called as the pupillary membrane (PM) forms. It is located in the pupil area to nourish the anterior surface of the lens, and then regresses to clear the optical path. Although the involvement of the apoptotic process has been reported in PM regression, the initiating factor remains unknown. We initially found that regression of the PM coincided with the development of iris motility, and that iris movement caused cessation and resumption of blood flow within the PM. Therefore, we investigated whether the development of the capacity of the iris to constrict and dilate can function as an essential signal that induces apoptosis in the PM. Continuous inhibition of iris movement with mydriatic agents suppressed apoptosis of the PM and resulted in the persistence of PM in rats. The distribution of apoptotic cells in the regressing PM was diffuse and showed no apparent localization. These results indicated that iris movement induced regression of the PM by changing the blood flow within it. This study suggests the importance of the physiological interactions between tissues-in this case, the iris and the PM-as a signal to advance vascular regression during organ development.
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record
Registration Cost Performance Analysis of a Hierarchical Mobile Internet Protocol Network
Institute of Scientific and Technical Information of China (English)
XU Kai; JI Hong; YUE Guang-xin
2004-01-01
On the basis of introducing principles for hierarchical mobile Internet protocol networks, the registration cost performance in this network model is analyzed in detail. Furthermore, the functional relationship is also established in the paper among registration cost, hierarchical level number and the maximum handover time for gateway foreign agent regional registration. At last, the registration cost of the hierarchical mobile Internet protocol network is compared with that of the traditional mobile Internet protocol. Theoretic analysis and computer simulation results show that the hierarchical level number and the maximum handover times can both affect the registration cost importantly, when suitable values of which are chosen, the hierarchical network can significantly improve the registration performance compared with the traditional mobile IP.
A new approach for modeling generalization gradients: A case for Hierarchical Models
Directory of Open Access Journals (Sweden)
Koen eVanbrabant
2015-05-01
Full Text Available A case is made for the use of hierarchical models in the analysis of generalization gradients. Hierarchical models overcome several restrictions that are imposed by repeated measures analysis-of-variance (rANOVA, the default statistical method in current generalization research. More specifically, hierarchical models allow to include continuous independent variables and overcomes problematic assumptions such as sphericity. We focus on how generalization research can benefit from this added flexibility. In a simulation study we demonstrate the dominance of hierarchical models over rANOVA. In addition, we show the lack of efficiency of the Mauchly's sphericity test in sample sizes typical for generalization research, and confirm how violations of sphericity increase the probability of type I errors. A worked example of a hierarchical model is provided, with a specific emphasis on the interpretation of parameters relevant for generalization research.
A new approach for modeling generalization gradients: a case for hierarchical models.
Vanbrabant, Koen; Boddez, Yannick; Verduyn, Philippe; Mestdagh, Merijn; Hermans, Dirk; Raes, Filip
2015-01-01
A case is made for the use of hierarchical models in the analysis of generalization gradients. Hierarchical models overcome several restrictions that are imposed by repeated measures analysis-of-variance (rANOVA), the default statistical method in current generalization research. More specifically, hierarchical models allow to include continuous independent variables and overcomes problematic assumptions such as sphericity. We focus on how generalization research can benefit from this added flexibility. In a simulation study we demonstrate the dominance of hierarchical models over rANOVA. In addition, we show the lack of efficiency of the Mauchly's sphericity test in sample sizes typical for generalization research, and confirm how violations of sphericity increase the probability of type I errors. A worked example of a hierarchical model is provided, with a specific emphasis on the interpretation of parameters relevant for generalization research.
Regression Testing Cost Reduction Suite
Directory of Open Access Journals (Sweden)
Mohamed Alaa El-Din
2014-08-01
Full Text Available The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant when the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. This paper focuses on proposing an effective approach for reducing the cost of regression testing process. The proposed approach is applied on real-time case study. It was found that the reduction in cost of regression testing for each regression testing cycle is ranging highly improved in the case of programs containing high number of selected statements which in turn maximize the benefits of using it in regression testing of complex software systems. The reduction in the regression test suite size will reduce the effort and time required by the testing teams to execute the regression test suite. Since regression testing is done more frequently in software maintenance phase, the overall software maintenance cost can be reduced considerably by applying the proposed approach.
LINEAR REGRESSION WITH R AND HADOOP
Directory of Open Access Journals (Sweden)
Bogdan OANCEA
2015-07-01
Full Text Available In this paper we present a way to solve the linear regression model with R and Hadoop using the Rhadoop library. We show how the linear regression model can be solved even for very large models that require special technologies. For storing the data we used Hadoop and for computation we used R. The interface between R and Hadoop is the open source library RHadoop. We present the main features of the Hadoop and R software systems and the way of interconnecting them. We then show how the least squares solution for the linear regression problem could be expressed in terms of map-reduce programming paradigm and how could be implemented using the Rhadoop library.
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.
Discovering hierarchical structure in normal relational data
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Herlau, Tue; Mørup, Morten
2014-01-01
Hierarchical clustering is a widely used tool for structuring and visualizing complex data using similarity. Traditionally, hierarchical clustering is based on local heuristics that do not explicitly provide assessment of the statistical saliency of the extracted hierarchy. We propose a non-param...
Discursive Hierarchical Patterning in Economics Cases
Lung, Jane
2011-01-01
This paper attempts to apply Lung's (2008) model of the discursive hierarchical patterning of cases to a closer and more specific study of Economics cases and proposes a model of the distinct discursive hierarchical patterning of the same. It examines a corpus of 150 Economics cases with a view to uncovering the patterns of discourse construction.…
A Model of Hierarchical Key Assignment Scheme
Institute of Scientific and Technical Information of China (English)
ZHANG Zhigang; ZHAO Jing; XU Maozhi
2006-01-01
A model of the hierarchical key assignment scheme is approached in this paper, which can be used with any cryptography algorithm. Besides, the optimal dynamic control property of a hierarchical key assignment scheme will be defined in this paper. Also, our scheme model will meet this property.
Rank regression: an alternative regression approach for data with outliers.
Chen, Tian; Tang, Wan; Lu, Ying; Tu, Xin
2014-10-01
Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But semi-parametric models are quite sensitive to outlying observations, so the generated estimates are unreliable when study data includes outliers. In this situation, some researchers trim the extreme values prior to conducting the analysis, but the ad-hoc rules used for data trimming are based on subjective criteria so different methods of adjustment can yield different results. Rank regression provides a more objective approach to dealing with non-normal data that includes outliers. This paper uses simulated and real data to illustrate this useful regression approach for dealing with outliers and compares it to the results generated using classical regression models and semi-parametric regression models.
Galaxy formation through hierarchical clustering
White, Simon D. M.; Frenk, Carlos S.
1991-01-01
Analytic methods for studying the formation of galaxies by gas condensation within massive dark halos are presented. The present scheme applies to cosmogonies where structure grows through hierarchical clustering of a mixture of gas and dissipationless dark matter. The simplest models consistent with the current understanding of N-body work on dissipationless clustering, and that of numerical and analytic work on gas evolution and cooling are adopted. Standard models for the evolution of the stellar population are also employed, and new models for the way star formation heats and enriches the surrounding gas are constructed. Detailed results are presented for a cold dark matter universe with Omega = 1 and H(0) = 50 km/s/Mpc, but the present methods are applicable to other models. The present luminosity functions contain significantly more faint galaxies than are observed.
Quantum transport through hierarchical structures.
Boettcher, S; Varghese, C; Novotny, M A
2011-04-01
The transport of quantum electrons through hierarchical lattices is of interest because such lattices have some properties of both regular lattices and random systems. We calculate the electron transmission as a function of energy in the tight-binding approximation for two related Hanoi networks. HN3 is a Hanoi network with every site having three bonds. HN5 has additional bonds added to HN3 to make the average number of bonds per site equal to five. We present a renormalization group approach to solve the matrix equation involved in this quantum transport calculation. We observe band gaps in HN3, while no such band gaps are observed in linear networks or in HN5. We provide a detailed scaling analysis near the edges of these band gaps.
Adaptive Sampling in Hierarchical Simulation
Energy Technology Data Exchange (ETDEWEB)
Knap, J; Barton, N R; Hornung, R D; Arsenlis, A; Becker, R; Jefferson, D R
2007-07-09
We propose an adaptive sampling methodology for hierarchical multi-scale simulation. The method utilizes a moving kriging interpolation to significantly reduce the number of evaluations of finer-scale response functions to provide essential constitutive information to a coarser-scale simulation model. The underlying interpolation scheme is unstructured and adaptive to handle the transient nature of a simulation. To handle the dynamic construction and searching of a potentially large set of finer-scale response data, we employ a dynamic metric tree database. We study the performance of our adaptive sampling methodology for a two-level multi-scale model involving a coarse-scale finite element simulation and a finer-scale crystal plasticity based constitutive law.
Multicollinearity in hierarchical linear models.
Yu, Han; Jiang, Shanhe; Land, Kenneth C
2015-09-01
This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model.
Hierarchically Nanostructured Materials for Sustainable Environmental Applications
Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian
2013-11-01
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.
A neural signature of hierarchical reinforcement learning.
Ribas-Fernandes, José J F; Solway, Alec; Diuk, Carlos; McGuire, Joseph T; Barto, Andrew G; Niv, Yael; Botvinick, Matthew M
2011-07-28
Human behavior displays hierarchical structure: simple actions cohere into subtask sequences, which work together to accomplish overall task goals. Although the neural substrates of such hierarchy have been the target of increasing research, they remain poorly understood. We propose that the computations supporting hierarchical behavior may relate to those in hierarchical reinforcement learning (HRL), a machine-learning framework that extends reinforcement-learning mechanisms into hierarchical domains. To test this, we leveraged a distinctive prediction arising from HRL. In ordinary reinforcement learning, reward prediction errors are computed when there is an unanticipated change in the prospects for accomplishing overall task goals. HRL entails that prediction errors should also occur in relation to task subgoals. In three neuroimaging studies we observed neural responses consistent with such subgoal-related reward prediction errors, within structures previously implicated in reinforcement learning. The results reported support the relevance of HRL to the neural processes underlying hierarchical behavior.
Hierarchical Identity-Based Lossy Trapdoor Functions
Escala, Alex; Libert, Benoit; Rafols, Carla
2012-01-01
Lossy trapdoor functions, introduced by Peikert and Waters (STOC'08), have received a lot of attention in the last years, because of their wide range of applications in theoretical cryptography. The notion has been recently extended to the identity-based scenario by Bellare et al. (Eurocrypt'12). We provide one more step in this direction, by considering the notion of hierarchical identity-based lossy trapdoor functions (HIB-LTDFs). Hierarchical identity-based cryptography generalizes identitybased cryptography in the sense that identities are organized in a hierarchical way; a parent identity has more power than its descendants, because it can generate valid secret keys for them. Hierarchical identity-based cryptography has been proved very useful both for practical applications and to establish theoretical relations with other cryptographic primitives. In order to realize HIB-LTDFs, we first build a weakly secure hierarchical predicate encryption scheme. This scheme, which may be of independent interest, is...
Hierarchically nanostructured materials for sustainable environmental applications
Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian
2013-01-01
This review 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 toward water remediation, biosensing, 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. PMID:24790946
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.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu
2014-06-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.
Fabrication and analysis of gecko-inspired hierarchical polymer nanosetae.
Ho, Audrey Yoke Yee; Yeo, Lip Pin; Lam, Yee Cheong; Rodríguez, Isabel
2011-03-22
A gecko's superb ability to adhere to surfaces is widely credited to the large attachment area of the hierarchical and fibrillar structure on its feet. The combination of these two features provides the necessary compliance for the gecko toe-pad to effectively engage a high percentage of the spatulae at each step to any kind of surface topography. With the use of multi-tiered porous anodic alumina template and capillary force assisted nanoimprinting, we have successfully fabricated a gecko-inspired hierarchical topography of branched nanopillars on a stiff polymer. We also demonstrated that the hierarchical topography improved the shear adhesion force over a topography of linear structures by 150%. A systematic analysis to understand the phenomenon was performed. It was determined that the effective stiffness of the hierarchical branched structure was lower than that of the linear structure. The reduction in effective stiffness favored a more efficient bending of the branched topography and a better compliance to a test surface, hence resulting in a higher area of residual deformation. As the area of residual deformation increased, the shear adhesion force emulated. The branched pillar topography also showed a marked increase in hydrophobicity, which is an essential property in the practical applications of these structures for good self-cleaning in dry adhesion conditions.
Cellular interactions on hierarchical poly(ε-caprolactone) nanowire micropatterns.
Du, Ke; Gan, Zhihua
2012-09-26
A double template method to fabricate poly(ε-caprolactone) (PCL) hierarchical patterned nanowires with highly ordered nano- and microscaled topography was developed in this study. The topography of PCL film with a patterned nanowire surface can be easily and well controlled by changing the template and melting time of PCL film on the templates. The surface morphology, water contact angle, protein adsorption, and cell growth behavior on the PCL films with different surface structures were well studied. The results revealed that the PCL nanowire arrays and the hierarchical patterned nanowires showed higher capability of protein adsorption and better cell growth than the PCL film with smooth surface. Typically, the PCL surface with hierarchical nanowire patterns was most favorable for cell attachment and proliferation. The present study was innovative at fabrication of polymer substrates with hierarchical architecture of nanowires inside microscaled islands to gain insight into the cell response to this unique topography and to develop a new method of constructing the bionic surface for tissue engineering applications.
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...
Perotti, Juan Ignacio; Caldarelli, Guido
2015-01-01
The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the {\\it hierarchical mutual information}, which is a generalization of the traditional mutual information, and allows to compare hierarchical partitions and hierarchical community structures. The {\\it normalized} version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here, the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies, and on the hierarchical ...
Lee, Woojoo; Kim, Jeonghwan; Lee, Youngjo; Park, Taesung; Suh, Young Ju
2015-01-01
We explored a hierarchical generalized linear model (HGLM) in combination with dispersion modeling to improve the sib-pair linkage analysis based on the revised Haseman-Elston regression model for a quantitative trait. A dispersion modeling technique was investigated for sib-pair linkage analysis using simulation studies and real data applications. We considered 4 heterogeneous dispersion settings according to a signal-to-noise ratio (SNR) in the various statistical models based on the Haseman-Elston regression model. Our numerical studies demonstrated that susceptibility loci could be detected well by modeling the dispersion parameter appropriately. In particular, the HGLM had better performance than the linear regression model and the ordinary linear mixed model when the SNR is low, i.e., when substantial noise was present in the data. The study shows that the HGLM in combination with dispersion modeling can be utilized to identify multiple markers showing linkage to familial complex traits accurately. Appropriate dispersion modeling might be more powerful to identify markers closest to the major genes which determine a quantitative trait. © 2015 S. Karger AG, Basel.
Basak, Subhash C.; Mills, Denise; Hawkins, Douglas M.
2008-06-01
A hierarchical classification study was carried out based on a set of 70 chemicals—35 which produce allergic contact dermatitis (ACD) and 35 which do not. This approach was implemented using a regular ridge regression computer code, followed by conversion of regression output to binary data values. The hierarchical descriptor classes used in the modeling include topostructural (TS), topochemical (TC), and quantum chemical (QC), all of which are based solely on chemical structure. The concordance, sensitivity, and specificity are reported. The model based on the TC descriptors was found to be the best, while the TS model was extremely poor.
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain...
Selecting a Regression Saturated by Indicators
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren; Santos, Carlos
We consider selecting a regression model, using a variant of Gets, when there are more variables than observations, in the special case that the variables are impulse dummies (indicators) for every observation. We show that the setting is unproblematic if tackled appropriately, and obtain...
Structural Break Tests Robust to Regression Misspecification
Abi Morshed, Alaa; Andreou, E.; Boldea, Otilia
2016-01-01
Structural break tests developed in the literature for regression models are sensitive to model misspecification. We show - analytically and through simulations - that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe size distortions when the
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
National Research Council Canada - National Science Library
Allison A Vaughn; Matthew Bergman; Barry Fass-Holmes
2015-01-01
...) in the fall term of the five most recent academic years. Hierarchical linear modeling analyses showed that the predictors with the largest effect sizes were English writing programs and class level...
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Wrong Signs in Regression Coefficients
McGee, Holly
1999-01-01
When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.
From Rasch scores to regression
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Competing Risks Quantile Regression at Work
DEFF Research Database (Denmark)
Dlugosz, Stephan; Lo, Simon M. S.; Wilke, Ralf
2017-01-01
Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We us...... into the distribution of transitions out of maternity leave. It is found that cumulative incidences implied by the quantile regression model differ from those implied by a proportional hazards model. To foster the use of the model, we make an R-package (cmprskQR) available....... large-scale maternity duration data with multiple competing risks derived from German linked social security records to analyse how public policies are related to the length of economic inactivity of young mothers after giving birth. Our results show that the model delivers detailed insights...
KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS
Directory of Open Access Journals (Sweden)
HANY DEVITA
2015-02-01
Full Text Available Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR as an extension of Generalized Ridge Regression (GRR for solving multicollinearity. Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.
On Solving Lq-Penalized Regressions
Directory of Open Access Journals (Sweden)
Tracy Zhou Wu
2007-01-01
Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.
Regression Test Selection for C# Programs
Directory of Open Access Journals (Sweden)
Nashat Mansour
2009-01-01
Full Text Available We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Anti-hierarchical evolution of the AGN space density in a hierarchical universe
Enoki, Motohiro; Kobayashi, Masakazu A R; Nagashima, Masahiro
2014-01-01
Recent observations show that the space density of luminous active galactic nuclei (AGN) peaks at higher redshifts than that of faint AGN. 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 life time scaling with the dynamical time scale. 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 st...
Directory of Open Access Journals (Sweden)
Yuxue Zhou
2016-09-01
Full Text Available Highly hierarchical hollow bismuth tungstate (Bi2WO6 microspheres self-assembled by thin nanoplates have been fabricated via a facile template-free solvothermal route. The possible growth pattern and formation mechanism of hierarchical hollow Bi2WO6 microspheres was investigated. The excellent visible-light-driven photocatalytic performance on rhodamine B (RhB degradation was displayed by hierarchical hollow Bi2WO6 microspheres. Such well-organized hierarchical hollow Bi2WO6 microspheres show potential application in water treatment fields by full use of solar energy.
Polat, Esra; Gunay, Suleyman
2013-10-01
One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.
Hierarchically structured, nitrogen-doped carbon membranes
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.
A Model for Slicing JAVA Programs Hierarchically
Institute of Scientific and Technical Information of China (English)
Bi-Xin Li; Xiao-Cong Fan; Jun Pang; Jian-Jun Zhao
2004-01-01
Program slicing can be effectively used to debug, test, analyze, understand and maintain objectoriented software. In this paper, a new slicing model is proposed to slice Java programs based on their inherent hierarchical feature. The main idea of hierarchical slicing is to slice programs in a stepwise way, from package level, to class level, method level, and finally up to statement level. The stepwise slicing algorithm and the related graph reachability algorithms are presented, the architecture of the Java program Analyzing Tool (JATO) based on hierarchical slicing model is provided, the applications and a small case study are also discussed.
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.
Hierarchical modeling and analysis for spatial data
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 Semiconductor Oxide Photo catalyst:A Case of the SnO2 Microflower
Institute of Scientific and Technical Information of China (English)
Yang Liu; Yang Jiao; Bosi Yin; Siwen Zhang; Fengyu Qu; Xiang Wu∗
2013-01-01
Hierarchically assembled SnO2 microflowers were synthesized by a facile hydrothermal process. Field emission scanning electron microscope results showed these hierarchical nanostructures were built from two dimensional nanosheets with the thicknesses of about 50 nm. Photoluminescence spectrum of the as-obtained products demonstrated a strong visual emission peak at 564 nm. The photochemical measurement results indicated that the as-prepared sample exhibits excellent photocatalytic performance. These three di-mensional SnO2 hierarchical nanostructures may have potential applications in waste water purification.
Auger, P
2013-01-01
One of the most fundamental and efficient ways of conceptualizing complex systems is to organize them hierarchically. A hierarchically organized system is represented by a network of interconnected subsystems, each of which has its own network of subsystems, and so on, until some elementary subsystems are reached that are not further decomposed. This original and important book proposes a general mathematical theory of a hierarchical system and shows how it can be applied to very different topics such as physics (Hamiltonian systems), biology (coupling the molecular and the cellular levels), e
XRA image segmentation using regression
Jin, Jesse S.
1996-04-01
Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.
The structure of dust aggregates in hierarchical coagulation
Dominik, Carsten; Borel, Herman
2016-01-01
Dust coagulation in interstellar space and protoplanetary disks is usually treated as one of 2 extreme cases: Particle-Cluster Aggregation and Cluster-Cluster Aggregation. In this paper we study the process of hierarchical growth, where aggregates are built from significantly smaller aggregates (but not monomers). We show that this process can be understood as a modified, PCA-like process that produces porous, but non-fractal particles whose filling factor is chiefly determined by the porosity of the building blocks. We also show that in a coagulation environment where relative velocities are driven by turbulence, a logarithmically flat mass distribution (equal mass per mass decade) as it is typically found in environments where fragmentation replenishes small grains, leads to a situation where small particles and aggregates dominate the growth of large ones. Therefore, in such environments, hierarchical growth should be seen as the norm. Consequently, we predict that the aggregates in such environments are n...
Interpretation of Standardized Regression Coefficients in Multiple Regression.
Thayer, Jerome D.
The extent to which standardized regression coefficients (beta values) can be used to determine the importance of a variable in an equation was explored. The beta value and the part correlation coefficient--also called the semi-partial correlation coefficient and reported in squared form as the incremental "r squared"--were compared for…
CODE-CROSSING: HIERARCHICAL POLITENESS IN JAVANESE
Directory of Open Access Journals (Sweden)
Majid Wajdi
2015-02-01
Full Text Available Javanese is a well known for its speech levels called ngoko ‘low’ and krama ‘high’ which enable its speakers to show intimacy, deference, and hierarchy among the society members. This research applied critically Brown and Gilman (1960’s theory of terms of address to analyze the asymmetrical, factors which influence, and politeness of the use of speech levels in Javanese. Method of observation, in depth interview, and document study were applied to collect the data. Recorded conversation was then transcribed into written form, classified and codified according to the speech levels, and analyzed using politeness system (Scollon and Scollon, 2001 and status scale (Homes, 2001. The use of speech levels shows asymmetric communication: two speakers use two different codes, i.e. ngoko and krama because of power (+P and with/without distance (+/-D, and it is the reflection of hierarchical politeness. The asymmetrical use of ngoko and krama by God and His Angel, God and human beings strongly explicated the asymmetrical communication between superiors and inferiors. The finding of the research shows that the use of ngoko and krama could present the phenomena of code-switching, code-mixing, and the fundamental phenomenon is ‘code-crossing’. It is concluded that hierarchical politeness in Javanese is ‘social contract’ i.e. the acknowledgment of the existence of high class (superior and low class (inferior implemented in ‘communications contract’ using speech levels of the Javanese language in line with status scale. Asymmetrical use of ngoko and krama indexed inequality, hierarchy, and harmony
Hierarchical Bayes Ensemble Kalman Filtering
Tsyrulnikov, Michael
2015-01-01
Ensemble Kalman filtering (EnKF), when applied to high-dimensional systems, suffers from an inevitably small affordable ensemble size, which results in poor estimates of the background error covariance matrix ${\\bf B}$. The common remedy is a kind of regularization, usually an ad-hoc spatial covariance localization (tapering) combined with artificial covariance inflation. Instead of using an ad-hoc regularization, we adopt the idea by Myrseth and Omre (2010) and explicitly admit that the ${\\bf B}$ matrix is unknown and random and estimate it along with the state (${\\bf x}$) in an optimal hierarchical Bayes analysis scheme. We separate forecast errors into predictability errors (i.e. forecast errors due to uncertainties in the initial data) and model errors (forecast errors due to imperfections in the forecast model) and include the two respective components ${\\bf P}$ and ${\\bf Q}$ of the ${\\bf B}$ matrix into the extended control vector $({\\bf x},{\\bf P},{\\bf Q})$. Similarly, we break the traditional backgrou...
Inferential Models for Linear Regression
Directory of Open Access Journals (Sweden)
Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
Swann, David
2011-01-01
Dr David Swann interviewed on The Health Show, Series 1, Episode 5, 2011 for BBC World about the award-winning 21st Century Nursing Bag. BBC World News reaches 241million people every week, available in 296 million homes, 1.8 million hotel rooms and has the highest average viewership on a weekday of any international news channel. The Health Show is a new 26-part series for BBC World News covering the most important news stories from around the world.
[Is regression of atherosclerosis possible?].
Thomas, D; Richard, J L; Emmerich, J; Bruckert, E; Delahaye, F
1992-10-01
Experimental studies have shown the regression of atherosclerosis in animals given a cholesterol-rich diet and then given a normal diet or hypolipidemic therapy. Despite favourable results of clinical trials of primary prevention modifying the lipid profile, the concept of atherosclerosis regression in man remains very controversial. The methodological approach is difficult: this is based on angiographic data and requires strict standardisation of angiographic views and reliable quantitative techniques of analysis which are available with image processing. Several methodologically acceptable clinical coronary studies have shown not only stabilisation but also regression of atherosclerotic lesions with reductions of about 25% in total cholesterol levels and of about 40% in LDL cholesterol levels. These reductions were obtained either by drugs as in CLAS (Cholesterol Lowering Atherosclerosis Study), FATS (Familial Atherosclerosis Treatment Study) and SCOR (Specialized Center of Research Intervention Trial), by profound modifications in dietary habits as in the Lifestyle Heart Trial, or by surgery (ileo-caecal bypass) as in POSCH (Program On the Surgical Control of the Hyperlipidemias). On the other hand, trials with non-lipid lowering drugs such as the calcium antagonists (INTACT, MHIS) have not shown significant regression of existing atherosclerotic lesions but only a decrease on the number of new lesions. The clinical benefits of these regression studies are difficult to demonstrate given the limited period of observation, relatively small population numbers and the fact that in some cases the subjects were asymptomatic. The decrease in the number of cardiovascular events therefore seems relatively modest and concerns essentially subjects who were symptomatic initially. The clinical repercussion of studies of prevention involving a single lipid factor is probably partially due to the reduction in progression and anatomical regression of the atherosclerotic plaque
Hierarchical Control of Thermostatically Controller Loads for Primary Frequency Control
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Huang, Shaojun
2016-01-01
This paper proposes a hierarchical control of Thermostatically Controlled Loads (TCLs) to provide primary frequency control support. The control architecture is comprised of three levels. At the high level, an aggregator coordinates multiple distribution substations and dispatches the primary...... respond to the frequency event autonomously. Case studies show that the proposed controller can efficiently respond to frequency events and fulfill the requirement specified by the system operator. The users’ comforts are not compromised and the short cycling of TCLs is largely reduced. Due...
DVB-S2 Spectrum Efficiency Improvement with Hierarchical Modulation
Meric, Hugo; Piquer, Jose Miguel
2013-01-01
We study the design of a DVB-S2 system in order to maximise spectrum efficiency. This task is usually challenging due to channel variability. Modern satellite communications systems such as DVB-SH and DVB-S2 rely mainly on a time sharing strategy to optimise the spectrum efficiency. Recently, we showed that combining time sharing with hierarchical modulation can provide significant gains (in terms of spectrum efficiency) compared to the best time sharing strategy. However, our previous design...
Emergence of a 'visual number sense' in hierarchical generative models.
Stoianov, Ivilin; Zorzi, Marco
2012-01-08
Numerosity estimation is phylogenetically ancient and foundational to human mathematical learning, but its computational bases remain controversial. Here we show that visual numerosity emerges as a statistical property of images in 'deep networks' that learn a hierarchical generative model of the sensory input. Emergent numerosity detectors had response profiles resembling those of monkey parietal neurons and supported numerosity estimation with the same behavioral signature shown by humans and animals.
Self-organized Criticality in Hierarchical Brain Network
Institute of Scientific and Technical Information of China (English)
YANG Qiu-Ying; ZHANG Ying-Yue; CHEN Tian-Lun
2008-01-01
It is shown that the cortical brain network of the macaque displays a hierarchically clustered organization and the neuron network shows small-world properties. Now the two factors will be considered in our model and the dynamical behavior of the model will be studied. We study the characters of the model and find that the distribution of avalanche size of the model follows power-law behavior.
Hierarchical Bulk Synchronous Parallel Model and Performance Optimization
Institute of Scientific and Technical Information of China (English)
HUANG Linpeng; SUNYongqiang; YUAN Wei
1999-01-01
Based on the framework of BSP, aHierarchical Bulk Synchronous Parallel (HBSP) performance model isintroduced in this paper to capture the performance optimizationproblem for various stages in parallel program development and toaccurately predict the performance of a parallel program byconsidering factors causing variance at local computation and globalcommunication. The related methodology has been applied to several realapplications and the results show that HBSP is a suitable model foroptimizing parallel programs.
Update Legal Documents Using Hierarchical Ranking Models and Word Clustering
Pham, Minh Quang Nhat; Nguyen, Minh Le; Shimazu, Akira
2010-01-01
Our research addresses the task of updating legal documents when newinformation emerges. In this paper, we employ a hierarchical ranking model tothe task of updating legal documents. Word clustering features are incorporatedto the ranking models to exploit semantic relations between words. Experimentalresults on legal data built from the United States Code show that the hierarchicalranking model with word clustering outperforms baseline methods using VectorSpace Model, and word cluster-based ...
Logistic regression for circular data
Al-Daffaie, Kadhem; Khan, Shahjahan
2017-05-01
This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.
Quasi-least squares regression
Shults, Justine
2014-01-01
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitu
Scheibehenne, Benjamin; Pachur, Thorsten
2015-04-01
To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers' estimates for individuals within a group can mutually inform each other. Here, we examine Bayesian hierarchical approaches to evaluating model generalizability in the context of two prominent models of risky choice-cumulative prospect theory (Tversky & Kahneman, 1992) and the transfer-of-attention-exchange model (Birnbaum & Chavez, 1997). Using empirical data of risky choices collected for each individual at two time points, we compared the use of hierarchical versus independent, nonhierarchical Bayesian estimation techniques to assess two aspects of model generalizability: parameter stability (across time) and predictive accuracy. The relative performance of hierarchical versus independent estimation varied across the different measures of generalizability. The hierarchical approach improved parameter stability (in terms of a lower absolute discrepancy of parameter values across time) and predictive accuracy (in terms of deviance; i.e., likelihood). With respect to test-retest correlations and posterior predictive accuracy, however, the hierarchical approach did not outperform the independent approach. Further analyses suggested that this was due to strong correlations between some parameters within both models. Such intercorrelations make it difficult to identify and interpret single parameters and can induce high degrees of shrinkage in hierarchical models. Similar findings may also occur in the context of other cognitive models of choice.
Packaging glass with hierarchically nanostructured surface
He, Jr-Hau
2017-08-03
An optical device includes an active region and packaging glass located on top of the active region. A top surface of the packaging glass includes hierarchical nanostructures comprised of honeycombed nanowalls (HNWs) and nanorod (NR) structures extending from the HNWs.
Generation of hierarchically correlated multivariate symbolic sequences
Tumminello, Mi; Mantegna, R N
2008-01-01
We introduce an algorithm to generate multivariate series of symbols from a finite alphabet with a given hierarchical structure of similarities. The target hierarchical structure of similarities is arbitrary, for instance the one obtained by some hierarchical clustering procedure as applied to an empirical matrix of Hamming distances. The algorithm can be interpreted as the finite alphabet equivalent of the recently introduced hierarchically nested factor model (M. Tumminello et al. EPL 78 (3) 30006 (2007)). The algorithm is based on a generating mechanism that is different from the one used in the mutation rate approach. We apply the proposed methodology for investigating the relationship between the bootstrap value associated with a node of a phylogeny and the probability of finding that node in the true phylogeny.
HIERARCHICAL ORGANIZATION OF INFORMATION, IN RELATIONAL DATABASES
Directory of Open Access Journals (Sweden)
Demian Horia
2008-05-01
Full Text Available In this paper I will present different types of representation, of hierarchical information inside a relational database. I also will compare them to find the best organization for specific scenarios.
Hierarchical Network Design Using Simulated Annealing
DEFF Research Database (Denmark)
Thomadsen, Tommy; Clausen, Jens
2002-01-01
The hierarchical network problem is the problem of finding the least cost network, with nodes divided into groups, edges connecting nodes in each groups and groups ordered in a hierarchy. The idea of hierarchical networks comes from telecommunication networks where hierarchies exist. Hierarchical...... networks are described and a mathematical model is proposed for a two level version of the hierarchical network problem. The problem is to determine which edges should connect nodes, and how demand is routed in the network. The problem is solved heuristically using simulated annealing which as a sub......-algorithm uses a construction algorithm to determine edges and route the demand. Performance for different versions of the algorithm are reported in terms of runtime and quality of the solutions. The algorithm is able to find solutions of reasonable quality in approximately 1 hour for networks with 100 nodes....
When to Use Hierarchical Linear Modeling
National Research Council Canada - National Science Library
Veronika Huta
2014-01-01
Previous publications on hierarchical linear modeling (HLM) have provided guidance on how to perform the analysis, yet there is relatively little information on two questions that arise even before analysis...
An introduction to hierarchical linear modeling
National Research Council Canada - National Science Library
Woltman, Heather; Feldstain, Andrea; MacKay, J. Christine; Rocchi, Meredith
2012-01-01
This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is provided that describes when to use this statistical technique and identifies key factors to consider before conducting this analysis...
Conservation Laws in the Hierarchical Model
Beijeren, H. van; Gallavotti, G.; Knops, H.
1974-01-01
An exposition of the renormalization-group equations for the hierarchical model is given. Attention is drawn to some properties of the spin distribution functions which are conserved under the action of the renormalization group.
Hierarchical DSE for multi-ASIP platforms
DEFF Research Database (Denmark)
Micconi, Laura; Corvino, Rosilde; Gangadharan, Deepak;
2013-01-01
This work proposes a hierarchical Design Space Exploration (DSE) for the design of multi-processor platforms targeted to specific applications with strict timing and area constraints. In particular, it considers platforms integrating multiple Application Specific Instruction Set Processors (ASIPs...
Multiple dynamical time-scales in networks with hierarchically nested modular organization
Indian Academy of Sciences (India)
Sitabhra Sinha; Swarup Poria
2011-11-01
Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or modules. Often, such modularity is manifested at several different hierarchical levels, where the clusters deﬁned at one level appear as elementary entities at the next higher level. Using a simple model of a hierarchical modular network, we show that such a topological structure gives rise to characteristic time-scale separation between dynamics occurring at different levels of the hierarchy. This generalizes our earlier result for simple modular networks, where fast intramodular and slow intermodular processes were clearly distinguished. Investigating the process of synchronization of oscillators in a hierarchical modular network, we show the existence of as many distinct time-scales as there are hierarchical levels in the system. This suggests a possible functional role of such mesoscopic organization principle in natural systems, viz., in the dynamical separation of events occurring at different spatial scales.
Institute of Scientific and Technical Information of China (English)
2005-01-01
<正>Story: The yearly fashion show day.The children take turns to walk on the stage and show the class their favorite clothes.Now it’s Joe’s and Phoebe’s turn.Joe walks on the stage and says,“My shorts are blue.Do you like my blue shorts?”On the other side of the stage, Phoebe is wearing her favorite pink skirt.“My skirt is pink.Do you like my pink skirt?”asks
Hierarchical organization versus self-organization
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...
Hierarchical decision making for flood risk reduction
DEFF Research Database (Denmark)
Custer, Rocco; Nishijima, Kazuyoshi
2013-01-01
. In current practice, structures are often optimized individually without considering benefits of having a hierarchy of protection structures. It is here argued, that the joint consideration of hierarchically integrated protection structures is beneficial. A hierarchical decision model is utilized to analyze...... and compare the benefit of large upstream protection structures and local downstream protection structures in regard to epistemic uncertainty parameters. Results suggest that epistemic uncertainty influences the outcome of the decision model and that, depending on the magnitude of epistemic uncertainty...
Hierarchical self-organization of tectonic plates
2010-01-01
The Earth's surface is subdivided into eight large tectonic plates and many smaller ones. We reconstruct the plate tessellation history and demonstrate that both large and small plates display two distinct hierarchical patterns, described by different power-law size-relationships. While small plates display little organisational change through time, the structure of the large plates oscillate between minimum and maximum hierarchical tessellations. The organization of large plates rapidly chan...
Angelic Hierarchical Planning: Optimal and Online Algorithms
2008-12-06
restrict our attention to plans in I∗(Act, s0). Definition 2. ( Parr and Russell , 1998) A plan ah∗ is hierarchically optimal iff ah∗ = argmina∈I∗(Act,s0):T...Murdock, Dan Wu, and Fusun Yaman. SHOP2: An HTN planning system. JAIR, 20:379–404, 2003. Ronald Parr and Stuart Russell . Reinforcement Learning with...Angelic Hierarchical Planning: Optimal and Online Algorithms Bhaskara Marthi Stuart J. Russell Jason Wolfe Electrical Engineering and Computer
Hierarchical Needs, Income Comparisons and Happiness Levels
Drakopoulos, Stavros
2011-01-01
The cornerstone of the hierarchical approach is that there are some basic human needs which must be satisfied before non-basic needs come into the picture. The hierarchical structure of needs implies that the satisfaction of primary needs provides substantial increases to individual happiness compared to the subsequent satisfaction of secondary needs. This idea can be combined with the concept of comparison income which means that individuals compare rewards with individuals with similar char...
Fluorocarbon adsorption in hierarchical porous frameworks
Motkuri, Radha Kishan; Annapureddy, Harsha V. R.; Vijaykumar, M.; Schaef, H. Todd; Martin, Paul F.; McGrail, B. Peter; Dang, Liem X.; Krishna, Rajamani; Thallapally, Praveen K.
2014-07-01
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/Po) of 0.02. In contrast, the mesoporous framework shows an exceptionally high uptake capacity reaching >14 mmol g-1 at P/Po 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.
DEFF Research Database (Denmark)
Marselis, Randi Lorenz
2016-01-01
proposed by Janet Marstine, the editor of the Routledge Companion to Museum Ethics, I show how the museum succeeded in engaging users in questions of museum ethics. However, this specific debate on human remains in museums developed into an encounter between a global, museological discourse...
ÖZTÜRK, Yrd. Doç. Dr. Şinasi
2008-01-01
This study aims to discuss theories on theviolent effects of TV shows on viewers, especiallyon children. Therefore, this study includes a briefdiscussion of definitions of violence, discussionof violence theories, main results of researcheson televised violence, measuring TV violence,perception of televised violence, individualdifferences and reactions to TV violence,aggressiveness and preferences for TV violence.
Honored Teacher Shows Commitment.
Ratte, Kathy
1987-01-01
Part of the acceptance speech of the 1985 National Council for the Social Studies Teacher of the Year, this article describes the censorship experience of this honored social studies teacher. The incident involved the showing of a videotape version of the feature film entitled "The Seduction of Joe Tynan." (JDH)
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Seduction. Distinction. Relax. Pulsation. These are the "style universes" on display at Première Vision, heralded as "The World’s Premiere Fabric Show." Started more than 35 years ago by 15 French weavers, Première Vision has expanded beyond its
Honored Teacher Shows Commitment.
Ratte, Kathy
1987-01-01
Part of the acceptance speech of the 1985 National Council for the Social Studies Teacher of the Year, this article describes the censorship experience of this honored social studies teacher. The incident involved the showing of a videotape version of the feature film entitled "The Seduction of Joe Tynan." (JDH)
Regression of lumbar disk herniation
Directory of Open Access Journals (Sweden)
G. Yu Evzikov
2015-01-01
Full Text Available Compression of the spinal nerve root, giving rise to pain and sensory and motor disorders in the area of its innervation is the most vivid manifestation of herniated intervertebral disk. Different treatment modalities, including neurosurgery, for evolving these conditions are discussed. There has been recent evidence that spontaneous regression of disk herniation can regress. The paper describes a female patient with large lateralized disc extrusion that has caused compression of the nerve root S1, leading to obvious myotonic and radicular syndrome. Magnetic resonance imaging has shown that the clinical manifestations of discogenic radiculopathy, as well myotonic syndrome and morphological changes completely regressed 8 months later. The likely mechanism is inflammation-induced resorption of a large herniated disk fragment, which agrees with the data available in the literature. A decision to perform neurosurgery for which the patient had indications was made during her first consultation. After regression of discogenic radiculopathy, there was only moderate pain caused by musculoskeletal diseases (facet syndrome, piriformis syndrome that were successfully eliminated by minimally invasive techniques.
Heteroscedasticity checks for regression models
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Correlation Weights in Multiple Regression
Waller, Niels G.; Jones, Jeff A.
2010-01-01
A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…
Ridge Regression for Interactive Models.
Tate, Richard L.
1988-01-01
An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are favorable to…
Evaluating Hierarchical Structure in Music Annotations.
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.
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.
A Hierarchical Algorithm for Integrated Scheduling and Control With Applications to Power Systems
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Dinesen, Peter Juhler; Jørgensen, John Bagterp
2016-01-01
The contribution of this paper is a hierarchical algorithm for integrated scheduling and control via model predictive control of hybrid systems. The controlled system is a linear system composed of continuous control, state, and output variables. Binary variables occur as scheduling decisions...... portfolio case study show that the hierarchical algorithm reduces the computation to solve the OCP by several orders of magnitude. The improvement in computation time is achieved without a significant increase in the overall cost of operation....
Directory of Open Access Journals (Sweden)
Zhengyu Xie
2015-01-01
Full Text Available We consider the sensor networks hierarchical optimization problem in high-speed railway transport hub (HRTH. The sensor networks are optimized from three hierarchies which are key area sensors optimization, passenger line sensors optimization, and whole area sensors optimization. Case study on a specific HRTH in China showed that the hierarchical optimization method is effective to optimize the sensor networks for security monitoring in HRTH.
Institute of Scientific and Technical Information of China (English)
2007-01-01
The city known as China’s economic powerhouse showed a more caring face as host of the Special Olympic Games Between October 2 and 11,the Special Olympics Summer Games were hosted in Shanghai,the first time the 40-year-old athletic com- petition for people with intellectual disabilities came to a developing country. This Special Olympics was also larger than all previous games in temps of the number of athletes.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study,measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity office brown spot using hyperspectral reflectance data at the leaf level.
Selection of higher order regression models in the analysis of multi-factorial transcription data.
Directory of Open Access Journals (Sweden)
Olivia Prazeres da Costa
Full Text Available INTRODUCTION: Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control, and treatment/non-treatment with interferon-γ. RESULTS: We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction, alleviating (co-occurring effects are weaker than expected from the single effects, or aggravating (stronger than expected. We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. CONCLUSIONS: We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.
High fracture toughness in a hierarchical nanostructured zirconium
Energy Technology Data Exchange (ETDEWEB)
Li, Ming [State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, 066004 Qinhuangdao (China); College of Mechanical Engineering, Yanshan University, 066004 Qinhuangdao (China); Guo, Defeng; Ma, Tengyun; Zhang, Guosheng; Shi, Yindong [State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, 066004 Qinhuangdao (China); Zhang, Xiangyi, E-mail: xyzh66@ysu.edu.cn [State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, 066004 Qinhuangdao (China)
2014-06-01
Nanocrystalline metals usually exhibit a high strength but a disappointingly low ductility and toughness, which limit their practical utility. Here, we report a high fracture toughness (K{sub IC}∼117 MPa m{sup 1/2}) in a hierarchical nanostructured Zr that consists of nano-, sub-micrometer- and micrometer-sized grains, which is much larger than that K{sub IC}∼78 MPa m{sup 1/2} in coarse-grained Zr. This hierarchical nanostructured Zr shows a good combination of yield strength (σ{sub s}∼550 MPa) and fracture toughness as compared with its coarse-grained counterpart. We expect that these results will have implications in the enhancement of fracture toughness of nanocrystalline materials and in the design of high-performance structural materials.
Field Experiment on a Robust Hierarchical Metropolitan Quantum Cryptography Network
Xu, Fangxing; Wang, Shuang; Yin, Zhenqiang; Zhang, Yang; Liu, Yun; Zhou, Zheng; Zhao, Yibo; Li, Hongwei; Liu, Dong; Han, Zhengfu; Guo, Guangcan
2009-01-01
A hierarchical metropolitan quantum cryptography network upon the inner-city commercial telecom fiber cables is reported in this paper. The seven-user network contains a four-node backbone net with one node acting as the subnet gateway, a two-user subnet and a single-fiber access link, which is realized by the Faraday-Michelson Interferometer set-ups. The techniques of the quantum router, optical switch and trusted relay are assembled here to guarantee the feasibility and expandability of the quantum cryptography network. Five nodes of the network are located in the government departments and the secure keys generated by the quantum key distribution network are utilized to encrypt the instant video, sound, text messages and confidential files transmitting between these bureaus. The whole implementation including the hierarchical quantum cryptographic communication network links and corresponding application software shows a big step toward the practical user-oriented network with high security level.
Hierarchically porous carbon nanosheets from waste coffee grounds for supercapacitors.
Yun, Young Soo; Park, Min Hong; Hong, Sung Ju; Lee, Min Eui; Park, Yung Woo; Jin, Hyoung-Joon
2015-02-18
The nanostructure design of porous carbon-based electrode materials is key to improving the electrochemical performance of supercapacitors. In this study, hierarchically porous carbon nanosheets (HP-CNSs) were fabricated using waste coffee grounds by in situ carbonization and activation processes using KOH. Despite the simple synthesis process, the HP-CNSs had a high aspect ratio nanostructure (∼20 nm thickness to several micrometers in lateral size), a high specific surface area of 1945.7 m(2) g(-1), numerous heteroatoms, and good electrical transport properties, as well as hierarchically porous characteristics (0.5-10 nm in size). HP-CNS-based supercapacitors showed a specific energy of 35.4 Wh kg(-1) at 11250 W kg(-1) and of 23 Wh kg(-1) for a 3 s charge/discharge current rate corresponding to a specific power of 30000 W kg(-1). Additionally, the HP-CNS supercapacitors demonstrated good cyclic performance over 5000 cycles.
Understanding the unusual adsorption behavior in hierarchical zeolite nanosheets.
Bai, Peng; Olson, David H; Tsapatsis, Michael; Siepmann, J Ilja
2014-08-04
Hierarchical zeolites are advanced materials possessing the catalytic and adsorption properties of conventional zeolites while eliminating their transport limitations through the introduction of mesopores. Recent experiments comparing the adsorption in hierarchical self-pillared pentasils (SPP) and silicalite-1 (MFI) revealed an interesting crossover in sorbate loading for branched or long-chain alkanes but not for shorter linear alkanes, but an explanation for this behavior is not readily available through experimental probes due to the complications arising from the presence of multiple adsorption sites. Here we present a molecular simulation study on the adsorption of alkane isomers and show that a multi-step mechanism, found here for all molecules, is responsible for the observed phenomena. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Thin randomly aligned hierarchical carbon nanotube arrays as ultrablack metamaterials
De Nicola, Francesco; Hines, Peter; De Crescenzi, Maurizio; Motta, Nunzio
2017-07-01
Ultrablack metamaterials are artificial materials able to harvest all the incident light regardless of wavelength, angle, or polarization. Here, we show the ultrablack properties of randomly aligned hierarchical carbon nanotube arrays with thicknesses below 200 nm. The thin coatings are realized by solution processing and dry-transfer deposition on different substrates. The hierarchical surface morphology of the coatings is biomimetic and provides a large effective area that improves the film optical absorption. Also, such a morphology is responsible for the moth-eye effect, which leads to the omnidirectional and polarization-independent suppression of optical reflection. The films exhibit an emissivity up to 99.36% typical of an ideal black body, resulting in the thinnest ultrablack metamaterial ever reported. Such a material may be exploited for thermal, optical, and optoelectronic devices such as heat sinks, optical shields, solar cells, light and thermal sensors, and light-emitting diodes.
Hierarchical nanostructure and synergy of multimolecular signalling complexes
Sherman, Eilon; Barr, Valarie A.; Merrill, Robert K.; Regan, Carole K.; Sommers, Connie L.; Samelson, Lawrence E.
2016-01-01
Signalling complexes are dynamic, multimolecular structures and sites for intracellular signal transduction. Although they play a crucial role in cellular activation, current research techniques fail to resolve their structure in intact cells. Here we present a multicolour, photoactivated localization microscopy approach for imaging multiple types of single molecules in fixed and live cells and statistical tools to determine the nanoscale organization, topology and synergy of molecular interactions in signalling complexes downstream of the T-cell antigen receptor. We observe that signalling complexes nucleated at the key adapter LAT show a hierarchical topology. The critical enzymes PLCγ1 and VAV1 localize to the centre of LAT-based complexes, and the adapter SLP-76 and actin molecules localize to the periphery. Conditional second-order statistics reveal a hierarchical network of synergic interactions between these molecules. Our results extend our understanding of the nanostructure of signalling complexes and are relevant to studying a wide range of multimolecular complexes. PMID:27396911
Hierarchical nanostructure and synergy of multimolecular signalling complexes
Sherman, Eilon; Barr, Valarie A.; Merrill, Robert K.; Regan, Carole K.; Sommers, Connie L.; Samelson, Lawrence E.
2016-07-01
Signalling complexes are dynamic, multimolecular structures and sites for intracellular signal transduction. Although they play a crucial role in cellular activation, current research techniques fail to resolve their structure in intact cells. Here we present a multicolour, photoactivated localization microscopy approach for imaging multiple types of single molecules in fixed and live cells and statistical tools to determine the nanoscale organization, topology and synergy of molecular interactions in signalling complexes downstream of the T-cell antigen receptor. We observe that signalling complexes nucleated at the key adapter LAT show a hierarchical topology. The critical enzymes PLCγ1 and VAV1 localize to the centre of LAT-based complexes, and the adapter SLP-76 and actin molecules localize to the periphery. Conditional second-order statistics reveal a hierarchical network of synergic interactions between these molecules. Our results extend our understanding of the nanostructure of signalling complexes and are relevant to studying a wide range of multimolecular complexes.
Controlling attribute effect in linear regression
Calders, Toon
2013-12-01
In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.
Hierarchical, model-based risk management of critical infrastructures
Energy Technology Data Exchange (ETDEWEB)
Baiardi, F. [Polo G.Marconi La Spezia, Universita di Pisa, Pisa (Italy); Dipartimento di Informatica, Universita di Pisa, L.go B.Pontecorvo 3 56127, Pisa (Italy)], E-mail: f.baiardi@unipi.it; Telmon, C.; Sgandurra, D. [Dipartimento di Informatica, Universita di Pisa, L.go B.Pontecorvo 3 56127, Pisa (Italy)
2009-09-15
Risk management is a process that includes several steps, from vulnerability analysis to the formulation of a risk mitigation plan that selects countermeasures to be adopted. With reference to an information infrastructure, we present a risk management strategy that considers a sequence of hierarchical models, each describing dependencies among infrastructure components. A dependency exists anytime a security-related attribute of a component depends upon the attributes of other components. We discuss how this notion supports the formal definition of risk mitigation plan and the evaluation of the infrastructure robustness. A hierarchical relation exists among models that are analyzed because each model increases the level of details of some components in a previous one. Since components and dependencies are modeled through a hypergraph, to increase the model detail level, some hypergraph nodes are replaced by more and more detailed hypergraphs. We show how critical information for the assessment can be automatically deduced from the hypergraph and define conditions that determine cases where a hierarchical decomposition simplifies the assessment. In these cases, the assessment has to analyze the hypergraph that replaces the component rather than applying again all the analyses to a more detailed, and hence larger, hypergraph. We also show how the proposed framework supports the definition of a risk mitigation plan and discuss some indicators of the overall infrastructure robustness. Lastly, the development of tools to support the assessment is discussed.
Hao, Haijing
2013-01-01
Information technology adoption and diffusion is currently a significant challenge in the healthcare delivery setting. This thesis includes three papers that explore social influence on information technology adoption and sustained use in the healthcare delivery environment using conventional regression models and novel hierarchical Bayesian…
Antretter, Elfi; Dunkel, Dirk; Osvath, Peter; Voros, Viktor; Fekete, Sandor; Haring, Christian
2006-06-01
The prospective investigation of repetitive nonfatal suicidal behavior is associated with two methodological problems. Due to the commonly used definitions of nonfatal suicidal behavior, clinical samples usually consist of patients with a considerable between-person variability. Second, repeated nonfatal suicidal episodes of the same subjects are likely to be correlated. We examined three regression techniques to comparatively evaluate their efficiency in addressing the given methodological problems. Repeated episodes of nonfatal suicidal behavior were assessed in two independent patient samples during a 2-year follow-up period. The first regression design modeled repetitive nonfatal suicidal behavior as a summary measure. The second regression model treated repeated episodes of the same subject as independent events. The third regression model represented a hierarchical linear model. The estimated mean effects of the first model were likely to be nonrepresentative for a considerable part of the study subjects. The second regression design overemphasized the impact of the predictor variables. The hierarchical linear model most appropriately accounted for the heterogeneity of the samples and the correlated data structure. The nonhierarchical regression designs did not provide appropriate statistical models for the prospective investigation of repetitive nonfatal suicidal behavior. Multilevel modeling provides a convenient alternative.
Wrong, Terence; Baumgart, Erica
2013-01-01
The authors of the preceding articles raise legitimate questions about patient and staff rights and the unintended consequences of allowing ABC News to film inside teaching hospitals. We explain why we regard their fears as baseless and not supported by what we heard from individuals portrayed in the filming, our decade-long experience making medical documentaries, and the full un-aired context of the scenes shown in the broadcast. The authors don't and can't know what conversations we had, what documents we reviewed, and what protections we put in place in each televised scene. Finally, we hope to correct several misleading examples cited by the authors as well as their offhand mischaracterization of our program as a "reality" show.
GEODESIC RECONSTRUCTION, SADDLE ZONES & HIERARCHICAL SEGMENTATION
Directory of Open Access Journals (Sweden)
Serge Beucher
2011-05-01
Full Text Available The morphological reconstruction based on geodesic operators, is a powerful tool in mathematical morphology. The general definition of this reconstruction supposes the use of a marker function f which is not necessarily related to the function g to be built. However, this paper deals with operations where the marker function is defined from given characteristic regions of the initial function f, as it is the case, for instance, for the extrema (maxima or minima but also for the saddle zones. Firstly, we show that the intuitive definition of a saddle zone is not easy to handle, especially when digitised images are involved. However, some of these saddle zones (regional ones also called overflow zones can be defined, this definition providing a simple algorithm to extract them. The second part of the paper is devoted to the use of these overflow zones as markers in image reconstruction. This reconstruction provides a new function which exhibits a new hierarchy of extrema. This hierarchy is equivalent to the hierarchy produced by the so-called waterfall algorithm. We explain why the waterfall algorithm can be achieved by performing a watershed transform of the function reconstructed by its initial watershed lines. Finally, some examples of use of this hierarchical segmentation are described.
Electroanalysis using modified hierarchical nanoporous carbon materials.
Rodriguez, Rusbel Coneo; Moncada, Angelica Baena; Acevedo, Diego F; Planes, Gabriel A; Miras, Maria C; Barbero, Cesar A
2013-01-01
The role of the electrode nanoporosity in electroanalytical processes is discussed and specific phenomena (slow double layer charging, local pH effects) which can be present in porous electrode are described. Hierarchical porous carbon (HPC) materials are synthesized using a hard template method. The three dimensional carbon porosity is examined using scanning electron microscopy on flat surfaces cut using a focused ion beam (FIB-SEM). The electrochemical properties of the HPC are measured using cyclic voltammetry, AC impedance, chronoamperometry and Probe Beam Deflection (PBD) techniques. Chronoamperometry measurements of HPC seems to fit a transmission line model. PBD data show evidence of local pH changes inside the pores, during double layer charging. The HPC are modified by in situ (chemical or electrochemical) formation of metal (Pt/Ru) or metal oxide (CoOx, Fe3O4) nanoparticles. Additionally, HPC loaded with Pt decorated magnetite (Fe3O4) nanoparticles is produced by galvanic displacement. The modified HPC materials are used for the electroanalysis of different substances (CO, O2, AsO3(-3)). The role of the nanoporous carbon substrate in the electroanalytical data is evaluated.
The hierarchical brain network for face recognition.
Zhen, Zonglei; Fang, Huizhen; Liu, Jia
2013-01-01
Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.
Polynomial Regressions and Nonsense Inference
Directory of Open Access Journals (Sweden)
Daniel Ventosa-Santaulària
2013-11-01
Full Text Available Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340. by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
to be a committed artist, and how that translates into supporting al-Assad’s rule in Syria; the Ramadan programme Harrir Aqlak’s attempt to relaunch an intellectual renaissance and to promote religious pluralism; and finally, al-Mayadeen’s cooperation with the pan-Latin American TV station TeleSur and its ambitions...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...... coalition (Iran, Hizbollah, Syria), capitalises on a series of factors that bring them together in spite of their otherwise diverse worldviews and agendas. The New Regressive Left is united by resistance against the growing influence of Saudi Arabia in the religious, cultural, political, economic...
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Heteroscedasticity checks for regression models
Institute of Scientific and Technical Information of China (English)
ZHU; Lixing
2001-01-01
［1］Carroll, R. J., Ruppert, D., Transformation and Weighting in Regression, New York: Chapman and Hall, 1988.［2］Cook, R. D., Weisberg, S., Diagnostics for heteroscedasticity in regression, Biometrika, 1988, 70: 1—10.［3］Davidian, M., Carroll, R. J., Variance function estimation, J. Amer. Statist. Assoc., 1987, 82: 1079—1091.［4］Bickel, P., Using residuals robustly I: Tests for heteroscedasticity, Ann. Statist., 1978, 6: 266—291.［5］Carroll, R. J., Ruppert, D., On robust tests for heteroscedasticity, Ann. Statist., 1981, 9: 205—209.［6］Eubank, R. L., Thomas, W., Detecting heteroscedasticity in nonparametric regression, J. Roy. Statist. Soc., Ser. B, 1993, 55: 145—155.［7］Diblasi, A., Bowman, A., Testing for constant variance in a linear model, Statist. and Probab. Letters, 1997, 33: 95—103.［8］Dette, H., Munk, A., Testing heteoscedasticity in nonparametric regression, J. R. Statist. Soc. B, 1998, 60: 693—708.［9］Müller, H. G., Zhao, P. L., On a semi-parametric variance function model and a test for heteroscedasticity, Ann. Statist., 1995, 23: 946—967.［10］Stute, W., Manteiga, G., Quindimil, M. P., Bootstrap approximations in model checks for regression, J. Amer. Statist. Asso., 1998, 93: 141—149.［11］Stute, W., Thies, G., Zhu, L. X., Model checks for regression: An innovation approach, Ann. Statist., 1998, 26: 1916—1939.［12］Shorack, G. R., Wellner, J. A., Empirical Processes with Applications to Statistics, New York: Wiley, 1986.［13］Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Statist., 1979, 7: 1—26.［14］Wu, C. F. J., Jackknife, bootstrap and other re-sampling methods in regression analysis, Ann. Statist., 1986, 14: 1261—1295.［15］H rdle, W., Mammen, E., Comparing non-parametric versus parametric regression fits, Ann. Statist., 1993, 21: 1926—1947.［16］Liu, R. Y., Bootstrap procedures under some non-i.i.d. models, Ann. Statist., 1988, 16: 1696—1708.［17
Clustered regression with unknown clusters
Barman, Kishor
2011-01-01
We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization u...
Robust nonlinear regression in applications
Lim, Changwon; Sen, Pranab K.; Peddada, Shyamal D.
2013-01-01
Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose gr...
Hydrothermal Fabrication of WO3 Hierarchical Architectures: Structure, Growth and Response
Directory of Open Access Journals (Sweden)
Chuan-Sheng Wu
2015-07-01
Full Text Available Recently hierarchical architectures, consisting of two-dimensional (2D nanostructures, are of great interest for potential applications in energy and environmental. Here, novel rose-like WO3 hierarchical architectures were successfully synthesized via a facile hydrothermal method. The as-prepared WO3 hierarchical architectures were in fact assembled by numerous nanosheets with an average thickness of ~30 nm. We found that the oxalic acid played a significant role in governing morphologies of WO3 during hydrothermal process. Based on comparative studies, a possible formation mechanism was also proposed in detail. Furthermore, gas-sensing measurement showed that the well-defined 3D WO3 hierarchical architectures exhibited the excellent gas sensing properties towards CO.
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Truyen, Tran The; Bui, Hung H; Venkatesh, Svetha
2010-01-01
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we consider partiallysupervised learning and propose algorithms for generalised partially-supervised learning and constrained inference. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.
Guzman, J E Ortiz; Mitharwal, R; Beghein, Y; Eibert, T F; Cools, K; Andriulli, F P
2016-01-01
We present a hierarchical basis preconditioning strategy for the Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) integral equation considering both simply and multiply connected geometries.To this end, we first consider the direct application of hierarchical basis preconditioners, developed for the Electric Field Integral Equation (EFIE), to the PMCHWT. It is notably found that, whereas for the EFIE a diagonal preconditioner can be used for obtaining the hierarchical basis scaling factors, this strategy is catastrophic in the case of the PMCHWT since it leads to a severly ill-conditioned PMCHWT system in the case of multiply connected geometries. We then proceed to a theoretical analysis of the effect of hierarchical bases on the PMCHWT operator for which we obtain the correct scaling factors and a provably effective preconditioner for both low frequencies and mesh refinements. Numerical results will corroborate the theory and show the effectiveness of our approach.
A Distributed and Adaptive Location Management Scheme for Hierarchical Mobility Management
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Hierarchical mobility management is sensitive to the failure of gateway mobility agents and prone to degrade performance on heavy loads. This paper proposes a distributed and adaptive location management scheme based on Hierarchical Mobile IPv6. This scheme can balance the loads of mobility anchor points and increase the robustness of the hierarchical structure to certain extents. In this scheme, the optimized IP paging scheme is adopted to reduce the paging signaling cost and improve the scalability of the hierarchical mobility management. We implement the distributed and adaptive location management scheme in a simulation platform and compare its performance with that of two other location management schemes. Our simulation results show that our scheme is capable of balancing the signaling and traffic loads of mobility ancher points, decreasing the average handover latency, and increasing the throughout of the visited networks.
Ku-Herrera, J. J.; Pacheco-Salazar, O. F.; Ríos-Soberanis, C. R.; Domínguez-Rodríguez, G.; Avilés, F.
2016-01-01
The electrical sensitivity of glass fiber/multiwall carbon nanotube/vinyl ester hierarchical composites containing a tailored electrically-percolated network to self-sense accumulation of structural damage when subjected to cyclic tensile loading-unloading is investigated. The hierarchical composites were designed to contain two architectures differentiated by the location of the multiwall carbon nanotubes (MWCNTs), viz. MWCNTs deposited on the fibers and MWCNTs dispersed within the matrix. The changes in electrical resistance of the hierarchical composites are associated to their structural damage and correlated to acoustic emissions. The results show that such tailored hierarchical composites are able to self-sense damage onset and accumulation upon tensile loading-unloading cycles by means of their electrical response, and that the electrical response depends on the MWCNT location. PMID:26999158
Caticha, Nestor; Vicente, Renato
2016-01-01
Empirical evidence suggests that social structure may have changed from hierarchical to egalitarian and back along the evolutionary line of humans. We model a society subject to competing cognitive and social navigation constraints. The theory predicts that the degree of hierarchy decreases with encephalization and increases with group size. Hence hominin groups may have been driven from a phase with hierarchical order to a phase with egalitarian structures by the encephalization during the last two million years, and back to hierarchical due to fast demographical changes during the Neolithic. The dynamics in the perceived social network shows evidence in the egalitarian phase of the observed phenomenon of Reverse Dominance. The theory also predicts for modern hunter-gatherers in mild climates a trend towards an intermediate hierarchy degree and a phase transition for harder ecological conditions. In harsher climates societies would tend to bemore egalitarian if organized in small groups but more hierarchical...
ZnO hierarchical nanostructures and application on high-efficiency dye-sensitized solar cells
Energy Technology Data Exchange (ETDEWEB)
Fu Yingsong [Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300072 (China); MFAL-TJ, Motorola (China) Electronics Ltd, Tianjin 300457 (China); Sun Jing; Xie Yang [Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300072 (China); Liu, Jim [MFAL-TJ, Motorola (China) Electronics Ltd, Tianjin 300457 (China); Wang Hongli [School of Mechanical Engineering, Tianjin University, Tianjin 300072 (China); Du Xiwen, E-mail: xwdu@tju.edu.c [Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300072 (China)
2010-02-15
Uniform hierarchical ZnO nanostructures are synthesized on a large scale based on a solution approach at low temperature. The primary ZnO hexagonal prisms are firstly produced by the reaction of Zn(NO{sub 3}){sub 2} with hexamethylenetetramine, and then ZnO branches grow on the primary prisms by using ethylenediamine molecules as an evocator. The morphology of the hierarchical nanostructure can be controlled conveniently by adjusting the molar ratio of [EDA]/[Zn{sup 2+}]. The hierarchical structure provides an effective pathway for carrier transport as well as larger surface area for dye adsorption, when ZnO hierarchical nanostructures serve as photoanode materials, the solar cells show higher conversion efficiency than that of primary ZnO nanowires.
Directory of Open Access Journals (Sweden)
Denise Koufogiannakis
2009-06-01
Full Text Available When Su Cleyle and I first decided to start Evidence Based Library and Information Practice, one of the things we agreed upon immediately was that the journal be open access. We knew that a major obstacle to librarians using the research literature was that they did not have access to the research literature. Although Su and I are both academic librarians who can access a wide variety of library and information literature from our institutions, we belong to a profession where not everyone has equal access to the research in our field. Without such access to our own body of literature, how can we ever hope for practitioners to use research evidence in their decision making? It would have been contradictory to the principles of evidence based library and information practice to do otherwise.One of the specific groups we thought could use such an open access venue for discovering research literature was school librarians. School librarians are often isolated and lacking access to the research literature that may help them prove to stakeholders the importance of their libraries and their role within schools. Certainly, school libraries have been in decline and the use of evidence to show value is needed. As Ken Haycock noted in his 2003 report, The Crisis in Canada’s School Libraries: The Case for Reform and Reinvestment, “Across the country, teacher-librarians are losing their jobs or being reassigned. Collections are becoming depleted owing to budget cuts. Some principals believe that in the age of the Internet and the classroom workstation, the school library is an artifact” (9. Within this context, school librarians are looking to our research literature for evidence of the impact that school library programs have on learning outcomes and student success. They are integrating that evidence into their practice, and reflecting upon what can be improved locally. They are focusing on students and showing the impact of school libraries and
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Combining logistic regression and neural networks to create predictive models.
Spackman, K. A.
1992-01-01
Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building wit...
Walusinski, Olivier
2014-01-01
In the second half of the 19th century, Jean-Martin Charcot (1825-1893) became famous for the quality of his teaching and his innovative neurological discoveries, bringing many French and foreign students to Paris. A hunger for recognition, together with progressive and anticlerical ideals, led Charcot to invite writers, journalists, and politicians to his lessons, during which he presented the results of his work on hysteria. These events became public performances, for which physicians and patients were transformed into actors. Major newspapers ran accounts of these consultations, more like theatrical shows in some respects. The resultant enthusiasm prompted other physicians in Paris and throughout France to try and imitate them. We will compare the form and substance of Charcot's lessons with those given by Jules-Bernard Luys (1828-1897), Victor Dumontpallier (1826-1899), Ambroise-Auguste Liébault (1823-1904), Hippolyte Bernheim (1840-1919), Joseph Grasset (1849-1918), and Albert Pitres (1848-1928). We will also note their impact on contemporary cinema and theatre.
2007-01-01
its high spatial and spectral resolution, it was possible to zoom into the very heart of this very massive star. In this innermost region, the observations are dominated by the extremely dense stellar wind that totally obscures the underlying central star. The AMBER observations show that this dense stellar wind is not spherically symmetric, but exhibits a clearly elongated structure. Overall, the AMBER observations confirm that the extremely high mass loss of Eta Carinae's massive central star is non-spherical and much stronger along the poles than in the equatorial plane. This is in agreement with theoretical models that predict such an enhanced polar mass-loss in the case of rapidly rotating stars. ESO PR Photo 06c/07 ESO PR Photo 06c/07 RS Ophiuchi in Outburst Several papers from this special feature focus on the later stages in a star's life. One looks at the binary system Gamma 2 Velorum, which contains the closest example of a star known as a Wolf-Rayet. A single AMBER observation allowed the astronomers to separate the spectra of the two components, offering new insights in the modeling of Wolf-Rayet stars, but made it also possible to measure the separation between the two stars. This led to a new determination of the distance of the system, showing that previous estimates were incorrect. The observations also revealed information on the region where the winds from the two stars collide. The famous binary system RS Ophiuchi, an example of a recurrent nova, was observed just 5 days after it was discovered to be in outburst on 12 February 2006, an event that has been expected for 21 years. AMBER was able to detect the extension of the expanding nova emission. These observations show a complex geometry and kinematics, far from the simple interpretation of a spherical fireball in extension. AMBER has detected a high velocity jet probably perpendicular to the orbital plane of the binary system, and allowed a precise and careful study of the wind and the shockwave
Perception of hierarchical boundaries in music and its modulation by expertise.
Zhang, Jingjing; Jiang, Cunmei; Zhou, Linshu; Yang, Yufang
2016-10-01
Hierarchical structure with units of different timescales is a key feature of music. For the perception of such structures, the detection of each boundary is crucial. Here, using electroencephalography (EEG), we explore the perception of hierarchical boundaries in music, and test whether musical expertise modifies such processing. Musicians and non-musicians were presented with musical excerpts containing boundaries at three hierarchical levels, including section, phrase and period boundaries. Non-boundary was chosen as a baseline condition. Recordings from musicians showed CPS (closure positive shift) was evoked at all the three boundaries, and their amplitude increased as the hierarchical level became higher, which suggest that musicians could represent music events at different timescales in a hierarchical way. For non-musicians, the CPS was only elicited at the period boundary and undistinguishable negativities were induced at all the three boundaries. The results indicate that a different and less clear way was used by non-musicians in boundary perception. Our findings reveal, for the first time, an ERP correlate of perceiving hierarchical boundaries in music, and show that the phrasing ability could be enhanced by musical expertise. Copyright © 2016 Elsevier Ltd. All rights reserved.
Genetics Home Reference: caudal regression syndrome
... Twitter Home Health Conditions caudal regression syndrome caudal regression syndrome Enable Javascript to view the expand/collapse ... Download PDF Open All Close All Description Caudal regression syndrome is a disorder that impairs the development ...
Music emotion detection using hierarchical sparse kernel machines.
Chin, Yu-Hao; Lin, Chang-Hong; Siahaan, Ernestasia; Wang, Jia-Ching
2014-01-01
For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.
Osteoblast Behavior on Hierarchical Micro-/Nano-Structured Titanium Surface
Institute of Scientific and Technical Information of China (English)
Weiyan Meng; Yanmin Zhou; Yanjing Zhang; Qing Cai; Liming Yang; Jinghui Zhao; Chnnyan Li
2011-01-01
In the present work, osteoblast behavior on a hierarchical micro-/nano-structured titanium surface was investigated. A hierarchical hybrid micro-/nano-structured titanium surface topography was produced via Electrolytic Etching (EE). MG-63 cells were cultured on disks for 2 h to 7 days. The osteoblast response to the hierarchical hybrid micro-/nano-structured titanium surface was evaluated through the osteoblast cell morphology, attachment and proliferation. For comparison, MG-63 cells were also cultured on Sandblasted and Acid-etched (SLA) as well as Machined (M) surfaces respectively. The results show significant differences in the adhesion rates and proliferation levels of MG-63 cells on EE, SLA, and M surfaces. Both adhesion rate and proliferation level on EE surface are higher than those on SLA and M surfaces. Therefore, we may expect that, comparing with SLA and M surfaces, bone growth on EE surface could be accelerated and bone formation could be promoted at an early stage, which could be applied in the clinical practices for immediate and early-stage loadings.
P2MP MPLS-Based Hierarchical Service Management System
Kumaki, Kenji; Nakagawa, Ikuo; Nagami, Kenichi; Ogishi, Tomohiko; Ano, Shigehiro
This paper proposes a point-to-multipoint (P2MP) Multi-Protocol Label Switching (MPLS) based hierarchical service management system. Traditionally, general management systems deployed in some service providers control MPLS Label Switched Paths (LSPs) (e.g., RSVP-TE and LDP) and services (e.g., L2VPN, L3VPN and IP) separately. In order for dedicated management systems for MPLS LSPs and services to cooperate with each other automatically, a hierarchical service management system has been proposed with the main focus on point-to-point (P2P) TE LSPs in MPLS path management. In the case where P2MP TE LSPs and services are deployed in MPLS networks, the dedicated management systems for P2MP TE LSPs and services must work together automatically. Therefore, this paper proposes a new algorithm that uses a correlation between P2MP TE LSPs and multicast VPN services based on a P2MP MPLS-based hierarchical service management architecture. Also, the capacity and performance of the proposed algorithm are evaluated by simulations, which are actually based on certain real MPLS production networks, and are compared to that of the algorithm for P2P TE LSPs. Results show this system is very scalable within real MPLS production networks. This system, with the automatic correlation, appears to be deployable in real MPLS production networks.
A hierarchical instrumental decision theory of nicotine dependence.
Hogarth, Lee; Troisi, Joseph R
2015-01-01
It is important to characterize the learning processes governing tobacco-seeking in order to understand how best to treat this behavior. Most drug learning theories have adopted a Pavlovian framework wherein the conditioned response is the main motivational process. We favor instead a hierarchical instrumental decision account, wherein expectations about the instrumental contingency between voluntary tobacco-seeking and the receipt of nicotine reward determines the probability of executing this behavior. To support this view, we review titration and nicotine discrimination research showing that internal signals for deprivation/satiation modulate expectations about the current incentive value of smoking, thereby modulating the propensity of this behavior. We also review research on cue-reactivity which has shown that external smoking cues modulate expectations about the probability of the tobacco-seeking response being effective, thereby modulating the propensity of this behavior. Economic decision theory is then considered to elucidate how expectations about the value and probability of response-nicotine contingency are integrated to form an overall utility estimate for that option for comparison with qualitatively different, nonsubstitute reinforcers, to determine response selection. As an applied test for this hierarchical instrumental decision framework, we consider how well it accounts for individual liability to smoking uptake and perseveration, pharmacotherapy, cue-extinction therapies, and plain packaging. We conclude that the hierarchical instrumental account is successful in reconciling this broad range of phenomenon precisely because it accepts that multiple diverse sources of internal and external information must be integrated to shape the decision to smoke.
Hierarchical method of task assignment for multiple cooperating UAV teams
Institute of Scientific and Technical Information of China (English)
Xiaoxuan Hu; Huawei Ma; Qingsong Ye; He Luo
2015-01-01
The problem of task assignment for multiple cooperat-ing unmanned aerial vehicle (UAV) teams is considered. Multiple UAVs forming several smal teams are needed to perform attack tasks on a set of predetermined ground targets. A hierarchical task assignment method is presented to address the problem. It breaks the original problem down to three levels of sub-problems: tar-get clustering, cluster al ocation and target assignment. The first two sub-problems are central y solved by using clustering algo-rithms and integer linear programming, respectively, and the third sub-problem is solved in a distributed and paral el manner, using a mixed integer linear programming model and an improved ant colony algorithm. The proposed hierarchical method can reduce the computational complexity of the task assignment problem con-siderably, especial y when the number of tasks or the number of UAVs is large. Experimental results show that this method is feasi-ble and more efficient than non-hierarchical methods.
Bayesian hierarchical grouping: Perceptual grouping as mixture estimation.
Froyen, Vicky; Feldman, Jacob; Singh, Manish
2015-10-01
We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian hierarchical grouping (BHG). In BHG, we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters of the mixture components that generated the image, including estimating which image elements are "owned" by which objects. We present a tractable implementation of the framework, based on the hierarchical clustering approach of Heller and Ghahramani (2005). We illustrate it with examples drawn from a number of classical perceptual grouping problems, including dot clustering, contour integration, and part decomposition. Our approach yields an intuitive hierarchical representation of image elements, giving an explicit decomposition of the image into mixture components, along with estimates of the probability of various candidate decompositions. We show that BHG accounts well for a diverse range of empirical data drawn from the literature. Because BHG provides a principled quantification of the plausibility of grouping interpretations over a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz.
How Hierarchical Topics Evolve in Large Text Corpora.
Cui, Weiwei; Liu, Shixia; Wu, Zhuofeng; Wei, Hao
2014-12-01
Using a sequence of topic trees to organize documents is a popular way to represent hierarchical and evolving topics in text corpora. However, following evolving topics in the context of topic trees remains difficult for users. To address this issue, we present an interactive visual text analysis approach to allow users to progressively explore and analyze the complex evolutionary patterns of hierarchical topics. The key idea behind our approach is to exploit a tree cut to approximate each tree and allow users to interactively modify the tree cuts based on their interests. In particular, we propose an incremental evolutionary tree cut algorithm with the goal of balancing 1) the fitness of each tree cut and the smoothness between adjacent tree cuts; 2) the historical and new information related to user interests. A time-based visualization is designed to illustrate the evolving topics over time. To preserve the mental map, we develop a stable layout algorithm. As a result, our approach can quickly guide users to progressively gain profound insights into evolving hierarchical topics. We evaluate the effectiveness of the proposed method on Amazon's Mechanical Turk and real-world news data. The results show that users are able to successfully analyze evolving topics in text data.
Power margin improvement for OFDMA-PON using hierarchical modulation.
Cao, Pan; Hu, Xiaofeng; Zhuang, Zhiming; Zhang, Liang; Chang, Qingjiang; Yang, Qi; Hu, Rong; Su, Yikai
2013-04-08
We propose and experimentally demonstrate a hierarchical modulation scheme to improve power margin for orthogonal frequency division multiple access-passive optical networks (OFDMA-PONs). In a PON system, under the same launched optical power, optical network units (ONUs) have different power margins due to unequal distribution fiber lengths. The power margin of the PON system is determined by the ONU with the lowest power margin. In our proposed scheme, ONUs with long and short distribution fibers are grouped together, and downstream signals for the paired ONUs are mapped onto the same OFDM subcarriers using hierarchical modulation. In a pair of ONUs, part of the power margin of the ONU with short distribution fiber is re-allocated to the ONU with long distribution fiber. Therefore, the power margin of the ONU with the longest distribution fiber can be increased, leading to the power margin improvement of the PON system. Experimental results show that the hierarchical modulation scheme improves the power margin by 2.7 dB for an OFDMA-PON system, which can be used to support more users or extend transmission distance.
A nontransferring dry adhesive with hierarchical polymer nanohairs
Jeong, H. E.
2009-03-20
We present a simple yet robust method for fabricating angled, hierarchically patterned high-aspect-ratio polymer nanohairs to generate directionally sensitive dry adhesives. The slanted polymeric nanostructures were molded from an etched polySi substrate containing slanted nanoholes. An angled etching technique was developed to fabricate slanted nanoholes with flat tips by inserting an etch-stop layer of silicon dioxide. This unique etching method was equipped with a Faraday cage system to control the ion-incident angles in the conventional plasma etching system. The polymeric nanohairs were fabricated with tailored leaning angles, sizes, tip shapes, and hierarchical structures. As a result of controlled leaning angle and bulged flat top of the nanohairs, the replicated, slanted nanohairs showed excellent directional adhesion, exhibiting strong shear attachment (approximately 26 N/cm(2) in maximum) in the angled direction and easy detachment (approximately 2.2 N/cm(2)) in the opposite direction, with a hysteresis value of approximately 10. In addition to single scale nanohairs, monolithic, micro-nanoscale combined hierarchical hairs were also fabricated by using a 2-step UV-assisted molding technique. These hierarchical nanoscale patterns maintained their adhesive force even on a rough surface (roughness <20 microm) because of an increase in the contact area by the enhanced height of hierarchy, whereas simple nanohairs lost their adhesion strength. To demonstrate the potential applications of the adhesive patch, the dry adhesive was used to transport a large-area glass (47.5 x 37.5 cm(2), second-generation TFT-LCD glass), which could replace the current electrostatic transport/holding system with further optimization.
Clinical time series prediction: Toward a hierarchical dynamical system framework.
Liu, Zitao; Hauskrecht, Milos
2015-09-01
Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.
Clinical time series prediction: towards a hierarchical dynamical system framework
Liu, Zitao; Hauskrecht, Milos
2014-01-01
Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive
Self-assembled biomimetic superhydrophobic hierarchical arrays.
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.
Hierarchical models and chaotic spin glasses
Berker, A. Nihat; McKay, Susan R.
1984-09-01
Renormalization-group studies in position space have led to the discovery of hierarchical models which are exactly solvable, exhibiting nonclassical critical behavior at finite temperature. Position-space renormalization-group approximations that had been widely and successfully used are in fact alternatively applicable as exact solutions of hierarchical models, this realizability guaranteeing important physical requirements. For example, a hierarchized version of the Sierpiriski gasket is presented, corresponding to a renormalization-group approximation which has quantitatively yielded the multicritical phase diagrams of submonolayers on graphite. Hierarchical models are now being studied directly as a testing ground for new concepts. For example, with the introduction of frustration, chaotic renormalization-group trajectories were obtained for the first time. Thus, strong and weak correlations are randomly intermingled at successive length scales, and a new microscopic picture and mechanism for a spin glass emerges. An upper critical dimension occurs via a boundary crisis mechanism in cluster-hierarchical variants developed to have well-behaved susceptibilities.
Kim, J.; Kwon, H. H.
2014-12-01
The existing regional frequency analysis has disadvantages in that it is difficult to consider geographical characteristics in estimating areal rainfall. In this regard, This study aims to develop a hierarchical Bayesian model based regional frequency analysis in that spatial patterns of the design rainfall with geographical information are explicitly incorporated. This study assumes that the parameters of Gumbel distribution are a function of geographical characteristics (e.g. altitude, latitude and longitude) within a general linear regression framework. Posterior distributions of the regression parameters are estimated by Bayesian Markov Chain Monte Calro (MCMC) method, and the identified functional relationship is used to spatially interpolate the parameters of the Gumbel distribution by using digital elevation models (DEM) as inputs. The proposed model is applied to derive design rainfalls over the entire Han-river watershed. It was found that the proposed Bayesian regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis. In addition, the model showed an advantage in terms of quantifying uncertainty of the design rainfall and estimating the area rainfall considering geographical information. Acknowledgement: This research was supported by a grant (14AWMP-B079364-01) from Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.
Are increases in cigarette taxation regressive?
Borren, P; Sutton, M
1992-12-01
Using the latest published data from Tobacco Advisory Council surveys, this paper re-evaluates the question of whether or not increases in cigarette taxation are regressive in the United Kingdom. The extended data set shows no evidence of increasing price-elasticity by social class as found in a major previous study. To the contrary, there appears to be no clear pattern in the price responsiveness of smoking behaviour across different social classes. Increases in cigarette taxation, while reducing smoking levels in all groups, fall most heavily on men and women in the lowest social class. Men and women in social class five can expect to pay eight and eleven times more of a tax increase respectively, than their social class one counterparts. Taken as a proportion of relative incomes, the regressive nature of increases in cigarette taxation is even more pronounced.
Regression-kriging for characterizing soils with remotesensing data
Institute of Scientific and Technical Information of China (English)
Yufeng GE; J.Alex THOMASSON; Ruixiu SUI; James WOOTEN
2011-01-01
In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables.However,spatial correlation existing in soil samples usually violates a basic assumption of regression:sample independence.In this study,a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance,Mississippi,USA.The regressionkriging model was developed and tested by using 273 soil samples collected from the field.The result showed that by properly incorporating the spatial correlation information of regression residuals,the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model.Most strikingly,a 50％ increase in prediction accuracy was shown in soil sodium concentration.Potential usages of regressionkriging in future precision agriculture applications include real-time soil sensor development and digital soil mapping.
Secular Evolution of Hierarchical Triple Star Systems
Ford, Eric B.; Kozinsky, Boris; Rasio, Frederic A.
2000-05-01
We derive octupole-level secular perturbation equations for hierarchical triple systems, using classical Hamiltonian perturbation techniques. Our equations describe the secular evolution of the orbital eccentricities and inclinations over timescales that are long compared to the orbital periods. By extending previous work done to leading (quadrupole) order to octupole level (i.e., including terms of order α3, where α≡a1/a2quadrupole-level theory of Kozai gives a vanishing result in the limit of zero relative inclination. The classical planetary perturbation theory, while valid to all orders in α, applies only to orbits of low-mass objects orbiting a common central mass, with low eccentricities and low relative inclinations. For triple systems containing a close inner binary, we also discuss the possible interaction between the classical Newtonian perturbations and the general relativistic precession of the inner orbit. In some cases we show that this interaction can lead to resonances and a significant increase in the maximum amplitude of eccentricity perturbations. We establish the validity of our analytic expressions by providing detailed comparisons with the results of direct numerical integrations of the three-body problem obtained for a large number of representative cases. In addition, we show that our expressions reduce correctly to previously published analytic results obtained in various limiting regimes. We also discuss applications of the theory in the context of several observed triple systems of current interest, including the millisecond pulsar PSR B1620-26 in M4, the giant planet in 16 Cygni, and the protostellar binary TMR-1.
Hierarchical self-organization of tectonic plates
Morra, Gabriele; Müller, R Dietmar
2010-01-01
The Earth's surface is subdivided into eight large tectonic plates and many smaller ones. We reconstruct the plate tessellation history and demonstrate that both large and small plates display two distinct hierarchical patterns, described by different power-law size-relationships. While small plates display little organisational change through time, the structure of the large plates oscillate between minimum and maximum hierarchical tessellations. The organization of large plates rapidly changes from a weak hierarchy at 120-100 million years ago (Ma) towards a strong hierarchy, which peaked at 65-50, Ma subsequently relaxing back towards a minimum hierarchical structure. We suggest that this fluctuation reflects an alternation between top and bottom driven plate tectonics, revealing a previously undiscovered tectonic cyclicity at a timescale of 100 million years.
Towards a sustainable manufacture of hierarchical zeolites.
Verboekend, Danny; Pérez-Ramírez, Javier
2014-03-01
Hierarchical zeolites have been established as a superior type of aluminosilicate catalysts compared to their conventional (purely microporous) counterparts. An impressive array of bottom-up and top-down approaches has been developed during the last decade to design and subsequently exploit these exciting materials catalytically. However, the sustainability of the developed synthetic methods has rarely been addressed. This paper highlights important criteria to ensure the ecological and economic viability of the manufacture of hierarchical zeolites. Moreover, by using base leaching as a promising case study, we verify a variety of approaches to increase reactor productivity, recycle waste streams, prevent the combustion of organic compounds, and minimize separation efforts. By reducing their synthetic footprint, hierarchical zeolites are positioned as an integral part of sustainable chemistry. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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...... an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to “information double-counting” and interaction omission. In this paper we focus on a relatively new set of models......, 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...
Hierarchical Neural Network Structures for Phoneme Recognition
Vasquez, Daniel; Minker, Wolfgang
2013-01-01
In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.
Static and dynamic friction of hierarchical surfaces
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.
Interpreting Bivariate Regression Coefficients: Going beyond the Average
Halcoussis, Dennis; Phillips, G. Michael
2010-01-01
Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…
Neither fixed nor random: weighted least squares meta-regression.
Stanley, T D; Doucouliagos, Hristos
2016-06-20
Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Directory of Open Access Journals (Sweden)
M. Guns
2012-06-01
Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.
Sleep in children with autism with and without autistic regression.
Giannotti, Flavia; Cortesi, Flavia; Cerquiglini, Antonella; Vagnoni, Cristina; Valente, Donatella
2011-06-01
The purpose of the present investigation was to characterize and compare traditional sleep architecture and non-rapid eye movement (NREM) sleep microstructure in a well-defined cohort of children with regressive and non-regressive autism, and in typically developing children (TD). We hypothesized that children with regressive autism would demonstrate a greater degree of sleep disruption either at a macrostructural or microstructural level and a more problematic sleep as reported by parents. Twenty-two children with non-regressive autism, 18 with regressive autism without comorbid pathologies and 12 with TD, aged 5-10years, underwent standard overnight multi-channel polysomnographic evaluation. Parents completed a structured questionnaire (Childrens' Sleep Habits Questionnaire-CSHQ). The initial hypothesis, that regressed children have more disrupted sleep, was supported by our findings that they scored significantly higher on CSHQ, particularly on bedtime resistance, sleep onset delay, sleep duration and night wakings CSHQ subdomains than non-regressed peers, and both scored more than typically developing controls. Regressive subjects had significantly less efficient sleep, less total sleep time, prolonged sleep latency, prolonged REM latency and more time awake after sleep onset than non-regressive children and the TD group. Regressive children showed lower cyclic alternating pattern (CAP) rates and A1 index in light sleep than non-regressive and TD children. Our findings suggest that, even though no particular differences in sleep architecture were found between the two groups of children with autism, those who experienced regression showed more sleep disorders and a disruption of sleep either from a macro- or from a microstructural viewpoint. © 2010 European Sleep Research Society.
Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.
Nitta, Tohru
2016-06-30
We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).
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...
DC Hierarchical Control System for Microgrid Applications
Lu, Xiaonan; Sun, Kai; Guerrero, Josep M.; Huang, Lipei
2013-01-01
In order to enhance the DC side performance of AC-DC hybrid microgrid,a DC hierarchical control system is proposed in this paper.To meet the requirement of DC load sharing between the parallel power interfaces,droop method is adopted.Meanwhile,DC voltage secondary control is employed to restore the deviation in the DC bus voltage.The hierarchical control system is composed of two levels.DC voltage and AC current controllers are achieved in the primary control level.
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...
Hierarchic Models of Turbulence, Superfluidity and Superconductivity
Kaivarainen, A
2000-01-01
New models of Turbulence, Superfluidity and Superconductivity, based on new Hierarchic theory, general for liquids and solids (physics/0102086), have been proposed. CONTENTS: 1 Turbulence. General description; 2 Mesoscopic mechanism of turbulence; 3 Superfluidity. General description; 4 Mesoscopic scenario of fluidity; 5 Superfluidity as a hierarchic self-organization process; 6 Superfluidity in 3He; 7 Superconductivity: General properties of metals and semiconductors; Plasma oscillations; Cyclotron resonance; Electroconductivity; 8. Microscopic theory of superconductivity (BCS); 9. Mesoscopic scenario of superconductivity: Interpretation of experimental data in the framework of mesoscopic model of superconductivity.
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.
Leukemia prediction using sparse logistic regression.
Directory of Open Access Journals (Sweden)
Tapio Manninen
Full Text Available We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Formula: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical.
General regression and representation model for classification.
Directory of Open Access Journals (Sweden)
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
A Dirty Model for Multiple Sparse Regression
Jalali, Ali; Sanghavi, Sujay
2011-01-01
Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sparse linear regression problem, where several related vectors -- with partially shared support sets -- have to be recovered. A natural question in this setting is whether one can use the sharing to further decrease the overall number of samples required. A line of recent research has studied the use of \\ell_1/\\ell_q norm block-regularizations with q>1 for such problems; however these could actually perform worse in sample complexity -- vis a vis solving each problem separately ignoring sharing -- depending on the level of sharing. We present a new method for multiple sparse linear regression that can leverage support and parameter overlap when it exists, but not pay a penalty when it does not. A very simple idea: we decompose the parameters into two components and regularize these differently. We show both theore...
Regression Cloud Models and Their Applications in Energy Consumption of Data Center
Directory of Open Access Journals (Sweden)
Yanshuang Zhou
2015-01-01
Full Text Available As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.
Werner, Benjamin; Scott, Jacob G; Sottoriva, Andrea; Anderson, Alexander R A; Traulsen, Arne; Altrock, Philipp M
2016-04-01
Many tumors are hierarchically organized and driven by a subpopulation of tumor-initiating cells (TIC), or cancer stem cells. TICs are uniquely capable of recapitulating the tumor and are thought to be highly resistant to radio- and chemotherapy. Macroscopic patterns of tumor expansion before treatment and tumor regression during treatment are tied to the dynamics of TICs. Until now, the quantitative information about the fraction of TICs from macroscopic tumor burden trajectories could not be inferred. In this study, we generated a quantitative method based on a mathematical model that describes hierarchically organized tumor dynamics and patient-derived tumor burden information. The method identifies two characteristic equilibrium TIC regimes during expansion and regression. We show that tumor expansion and regression curves can be leveraged to infer estimates of the TIC fraction in individual patients at detection and after continued therapy. Furthermore, our method is parameter-free; it solely requires the knowledge of a patient's tumor burden over multiple time points to reveal microscopic properties of the malignancy. We demonstrate proof of concept in the case of chronic myeloid leukemia (CML), wherein our model recapitulated the clinical history of the disease in two independent patient cohorts. On the basis of patient-specific treatment responses in CML, we predict that after one year of targeted treatment, the fraction of TICs increases 100-fold and continues to increase up to 1,000-fold after 5 years of treatment. Our novel framework may significantly influence the implementation of personalized treatment strategies and has the potential for rapid translation into the clinic. Cancer Res; 76(7); 1705-13. ©2016 AACR.
Stress generation and hierarchical fracturing in reactive systems
Jamtveit, B.; Iyer, K.; Royne, A.; Malthe-Sorenssen, A.; Mathiesen, J.; Feder, J.
2007-12-01
Hierarchical fracture patterns are the result of a slowly driven fracturing process that successively divides the rocks into smaller domains. In quasi-2D systems, such fracture patterns are characterized by four sided domains, and T-junctions where new fractures stop at right angles to pre-existing fractures. We describe fracturing of mm to dm thick enstatite layers in a dunite matrix from the Leka ophiolite complex in Norway. The fracturing process is driven by expansion of the dunite matrix during serpentinization. The cumulative distributions of fracture lengths show a scaling behavior that lies between a log - normal and power law (fractal) distribution. This is consistent with a simple fragmentation model in which domains are divided according to a 'top hat' distribution of new fracture positions within unfractured domains. Reaction-assisted hierarchical fracturing is also likely to be responsible for other (3-D) structures commonly observed in serpentinized ultramafic rocks, including the mesh-textures observed in individual olivine grains, and the high abundance of rectangular domains at a wide range of scales. Spectacular examples of 3-D hierarchical fracture patterns also form during the weathering of basaltic intrusions (dolerites). Incipient chemical weathering of dolerites in the Karoo Basin in South Africa occurs around water- filled fractures, originally produced by thermal contraction or by externally imposed stresses. This chemical weathering causes local expansion of the rock matrix and generates elastic stresses. On a mm to cm scale, these stresses lead to mechanical layer-by-layer spalling, producing the characteristic spheroidal weathering patterns. However, our field observations and computer simulations demonstrate that in confined environments, the spalling process alone is unable to relieve the elastic stresses. In such cases, chemical weathering drives a much larger scale hierarchical fracturing process in which fresh dolerite undergoes a
About wave field modeling in hierarchic medium with fractal inclusions
Hachay, Olga; Khachay, Andrey
2014-05-01
The processes of oil gaseous deposits outworking are linked with moving of polyphase multicomponent media, which are characterized by no equilibrium and nonlinear rheological features. The real behavior of layered systems is defined as complicated rheology moving liquids and structural morphology of porous media. It is eargently needed to account those factors for substantial description of the filtration processes. Additionally we must account also the synergetic effects. That allows suggesting new methods of control and managing of complicated natural systems, which can research these effects. Thus our research is directed to the layered system, from which we have to outwork oil and which is a complicated hierarchic dynamical system with fractal inclusions. In that paper we suggest the algorithm of modeling of 2-d seismic field distribution in the heterogeneous medium with hierarchic inclusions. Also we can compare the integral 2-D for seismic field in a frame of local hierarchic heterogeneity with a porous inclusion and pure elastic inclusion for the case when the parameter Lame is equal to zero for the inclusions and the layered structure. For that case we can regard the problem for the latitude and longitudinal waves independently. Here we shall analyze the first case. The received results can be used for choosing criterions of joined seismic methods for high complicated media research.If the boundaries of the inclusion of the k rank are fractals, the surface and contour integrals in the integral equations must be changed to repeated fractional integrals of Riman-Liuvill type .Using the developed earlier 3-d method of induction electromagnetic frequency geometric monitoring we showed the opportunity of defining of physical and structural features of hierarchic oil layer structure and estimating of water saturating by crack inclusions. For visualization we had elaborated some algorithms and programs for constructing cross sections for two hierarchic structural
Alligator osteoderms: Mechanical behavior and hierarchical structure
Energy Technology Data Exchange (ETDEWEB)
Chen, Irene H. [Materials Science and Engineering Program, University of California, San Diego, La Jolla, CA 92093 (United States); Yang, Wen, E-mail: wey005@eng.ucsd.edu [Materials Science and Engineering Program, University of California, San Diego, La Jolla, CA 92093 (United States); Meyers, Marc A. [Materials Science and Engineering Program, University of California, San Diego, La Jolla, CA 92093 (United States); Departments of Mechanical and Aerospace Engineering and Nanoengineering, University of California, San Diego, La Jolla, CA 92093 (United States)
2014-02-01
Osteoderms are bony scutes embedded underneath the dermal layers of the skin acting as a protection of the alligator (Archosauria: Crocodylia) internal organs and tissues. Additionally, these scutes function as an aid in temperature regulation. The scutes are inter-linked by fibrous connective tissue. They have properties similar to bone and thus have the necessary toughness to provide protection against predators. The scutes consist of hydroxyapatite and have a porosity of approximately 12%. They have a disc-like morphology with a ridge along the middle of the plate, called the keel; the outer perimeter of the disc has depressions, grooves, and jagged edges which anchor the collagen and act as sutures. Computerized tomography reveals the pattern of elongated pores, which emanate from the keel in a radial pattern. Micro-indentation measurements along the cross-section show a zigzag behavior due to the porosity. Compression results indicate that the axial direction is the strongest (UTS ∼ 67 MPa) and toughest (11 MJ/m{sup 3}); this is the orientation in which they undergo the largest external compression forces from predator teeth. Toughening mechanisms are identified through observation of the damage progression and interpreted in mechanistic terms. They are: flattening of pores, microcrack opening, and microcrack growth and coalescence. Collagen plays an essential role in toughening and plasticity by providing bridges that impede the opening of the cracks and prevent their growth. - Highlights: • We characterized the hierarchical structure of alligator scute. • The anisotropic mechanical behavior of alligator scute was studied. • Toughening mechanisms were identified at the micro- and nano-levels.
Hierarchical Representation Learning for Kinship Verification.
Kohli, Naman; Vatsa, Mayank; Singh, Richa; Noore, Afzel; Majumdar, Angshul
2017-01-01
Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender and age and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d' , and perceptual information entropy. Utilizing the information obtained from the human study, a hierarchical kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU kinship database is created, which consists of multiple images per subject to facilitate kinship verification. The results show that the proposed deep learning framework (KVRL-fcDBN) yields the state-of-the-art kinship verification accuracy on the WVU kinship database and on four existing benchmark data sets. Furthermore, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification.
Regression away from the mean: Theory and examples.
Schwarz, Wolf; Reike, Dennis
2017-06-30
Using a standard repeated measures model with arbitrary true score distribution and normal error variables, we present some fundamental closed-form results which explicitly indicate the conditions under which regression effects towards (RTM) and away from the mean are expected. Specifically, we show that for skewed and bimodal distributions many or even most cases will show a regression effect that is in expectation away from the mean, or that is not just towards but actually beyond the mean. We illustrate our results in quantitative detail with typical examples from experimental and biometric applications, which exhibit a clear regression away from the mean ('egression from the mean') signature. We aim not to repeal cautionary advice against potential RTM effects, but to present a balanced view of regression effects, based on a clear identification of the conditions governing the form that regression effects take in repeated measures designs. © 2017 The British Psychological Society.
Prediction, Regression and Critical Realism
DEFF Research Database (Denmark)
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... of prediction necessary and possible in spatial planning of urban development. Finally, the political implications of positions within theory of science rejecting the possibility of predictions about social phenomena are addressed....... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...
Practical Session: Multiple Linear Regression
Clausel, M.; Grégoire, G.
2014-12-01
Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).
Vathsangam, Harshvardhan; Emken, B Adar; Schroeder, E Todd; Spruijt-Metz, Donna; Sukhatme, Gaurav S
2013-12-01
Walking is a commonly available activity to maintain a healthy lifestyle. Accurately tracking and measuring calories expended during walking can improve user feedback and intervention measures. Inertial sensors are a promising measurement tool to achieve this purpose. An important aspect in mapping inertial sensor data to energy expenditure is the question of normalizing across physiological parameters. Common approaches such as weight scaling require validation for each new population. An alternative is to use a hierarchical approach to model subject-specific parameters at one level and cross-subject parameters connected by physiological variables at a higher level. In this paper, we evaluate an inertial sensor-based hierarchical model to measure energy expenditure across a target population. We first determine the optimal movement and physiological features set to represent data. Periodicity based features are more accurate (phierarchical model with a subject-specific regression model and weight exponent scaled models. Subject-specific models perform significantly better (pmodels at all exponent scales whereas the hierarchical model performed worse than both. However, using an informed prior from the hierarchical model produces similar errors to using a subject-specific model with large amounts of training data (phierarchical modeling is a promising technique for generalized prediction energy expenditure prediction across a target population in a clinical setting.
Lumbar herniated disc: spontaneous regression
Yüksel, Kasım Zafer
2017-01-01
Background Low back pain is a frequent condition that results in substantial disability and causes admission of patients to neurosurgery clinics. To evaluate and present the therapeutic outcomes in lumbar disc hernia (LDH) patients treated by means of a conservative approach, consisting of bed rest and medical therapy. Methods This retrospective cohort was carried out in the neurosurgery departments of hospitals in Kahramanmaraş city and 23 patients diagnosed with LDH at the levels of L3−L4, L4−L5 or L5−S1 were enrolled. Results The average age was 38.4 ± 8.0 and the chief complaint was low back pain and sciatica radiating to one or both lower extremities. Conservative treatment was administered. Neurological examination findings, durations of treatment and intervals until symptomatic recovery were recorded. Laségue tests and neurosensory examination revealed that mild neurological deficits existed in 16 of our patients. Previously, 5 patients had received physiotherapy and 7 patients had been on medical treatment. The number of patients with LDH at the level of L3−L4, L4−L5, and L5−S1 were 1, 13, and 9, respectively. All patients reported that they had benefit from medical treatment and bed rest, and radiologic improvement was observed simultaneously on MRI scans. The average duration until symptomatic recovery and/or regression of LDH symptoms was 13.6 ± 5.4 months (range: 5−22). Conclusions It should be kept in mind that lumbar disc hernias could regress with medical treatment and rest without surgery, and there should be an awareness that these patients could recover radiologically. This condition must be taken into account during decision making for surgical intervention in LDH patients devoid of indications for emergent surgery. PMID:28119770
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Cyclodextrin promotes atherosclerosis regression via macrophage reprogramming
DEFF Research Database (Denmark)
2016-01-01
Atherosclerosis is an inflammatory disease linked to elevated blood cholesterol concentrations. Despite ongoing advances in the prevention and treatment of atherosclerosis, cardiovascular disease remains the leading cause of death worldwide. Continuous retention of apolipoprotein B...... that increases cholesterol solubility in preventing and reversing atherosclerosis. We showed that CD treatment of murine atherosclerosis reduced atherosclerotic plaque size and CC load and promoted plaque regression even with a continued cholesterol-rich diet. Mechanistically, CD increased oxysterol production...... of CD as well as for augmented reverse cholesterol transport. Because CD treatment in humans is safe and CD beneficially affects key mechanisms of atherogenesis, it may therefore be used clinically to prevent or treat human atherosclerosis....
Hierarchical machining materials and their performance
DEFF Research Database (Denmark)
Sidorenko, Daria; Loginov, Pavel; Levashov, Evgeny
2016-01-01
as nanoparticles in the binder, or polycrystalline, aggregate-like reinforcements, also at several scale levels). Such materials can ensure better productivity, efficiency, and lower costs of drilling, cutting, grinding, and other technological processes. This article reviews the main groups of hierarchical...
Hierarchical Optimization of Material and Structure
DEFF Research Database (Denmark)
Rodrigues, Helder C.; Guedes, Jose M.; Bendsøe, Martin P.
2002-01-01
This paper describes a hierarchical computational procedure for optimizing material distribution as well as the local material properties of mechanical elements. The local properties are designed using a topology design approach, leading to single scale microstructures, which may be restricted...... in various ways, based on design and manufacturing criteria. Implementation issues are also discussed and computational results illustrate the nature of the procedure....
Sharing the proceeds from a hierarchical venture
DEFF Research Database (Denmark)
Hougaard, Jens Leth; Moreno-Ternero, Juan D.; Tvede, Mich;
2017-01-01
We consider the problem of distributing the proceeds generated from a joint venture in which the participating agents are hierarchically organized. We introduce and characterize a family of allocation rules where revenue ‘bubbles up’ in the hierarchy. The family is flexible enough to accommodate...
Metal oxide nanostructures with hierarchical morphology
Ren, Zhifeng; Lao, Jing Yu; Banerjee, Debasish
2007-11-13
The present invention relates generally to metal oxide materials with varied symmetrical nanostructure morphologies. In particular, the present invention provides metal oxide materials comprising one or more metallic oxides with three-dimensionally ordered nanostructural morphologies, including hierarchical morphologies. The present invention also provides methods for producing such metal oxide materials.
Hierarchical Scaling in Systems of Natural Cities
Chen, Yanguang
2016-01-01
Hierarchies can be modeled by a set of exponential functions, from which we can derive a set of power laws indicative of scaling. These scaling laws are followed by many natural and social phenomena such as cities, earthquakes, and rivers. This paper is devoted to revealing the scaling patterns in systems of natural cities by reconstructing the hierarchy with cascade structure. The cities of America, Britain, France, and Germany are taken as examples to make empirical analyses. The hierarchical scaling relations can be well fitted to the data points within the scaling ranges of the size and area of the natural cities. The size-number and area-number scaling exponents are close to 1, and the allometric scaling exponent is slightly less than 1. The results suggest that natural cities follow hierarchical scaling laws and hierarchical conservation law. Zipf's law proved to be one of the indications of the hierarchical scaling, and the primate law of city-size distribution represents a local pattern and can be mer...
Managing Clustered Data Using Hierarchical Linear Modeling
Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.
2012-01-01
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…
Endogenous Effort Norms in Hierarchical Firms
J. Tichem (Jan)
2013-01-01
markdownabstract__Abstract__ This paper studies how a three-layer hierarchical firm (principal-supervisor-agent) optimally creates effort norms for its employees. The key assumption is that effort norms are affected by the example of superiors. In equilibrium, norms are eroded as one moves down
Complex Evaluation of Hierarchically-Network Systems
Polishchuk, Dmytro; Yadzhak, Mykhailo
2016-01-01
Methods of complex evaluation based on local, forecasting, aggregated, and interactive evaluation of the state, function quality, and interaction of complex system's objects on the all hierarchical levels is proposed. Examples of analysis of the structural elements of railway transport system are used for illustration of efficiency of proposed approach.
A Hierarchical Grouping of Great Educators
Barker, Donald G.
1977-01-01
Great educators of history were categorized on the basis of their: aims of education, fundamental ideas, and educational theories. They were classed by Ward's method of hierarchical analysis into six groupings: Socrates, Ausonius, Jerome, Abelard; Quintilian, Origen, Melanchthon, Ascham, Loyola; Alciun, Comenius; Vittorino, Basedow, Pestalozzi,…
Ultrafast Hierarchical OTDM/WDM Network
Directory of Open Access Journals (Sweden)
Hideyuki Sotobayashi
2003-12-01
Full Text Available Ultrafast hierarchical OTDM/WDM network is proposed for the future core-network. We review its enabling technologies: C- and L-wavelength-band generation, OTDM-WDM mutual multiplexing format conversions, and ultrafast OTDM wavelengthband conversions.
Statistical theory of hierarchical avalanche ensemble
Olemskoi, Alexander I.
1999-01-01
The statistical ensemble of avalanche intensities is considered to investigate diffusion in ultrametric space of hierarchically subordinated avalanches. The stationary intensity distribution and the steady-state current are obtained. The critical avalanche intensity needed to initiate the global avalanche formation is calculated depending on noise intensity. The large time asymptotic for the probability of the global avalanche appearance is derived.
Managing Clustered Data Using Hierarchical Linear Modeling
Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.
2012-01-01
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…
Equivalence Checking of Hierarchical Combinational Circuits
DEFF Research Database (Denmark)
Williams, Poul Frederick; Hulgaard, Henrik; Andersen, Henrik Reif
1999-01-01
This paper presents a method for verifying that two hierarchical combinational circuits implement the same Boolean functions. The key new feature of the method is its ability to exploit the modularity of circuits to reuse results obtained from one part of the circuits in other parts. We demonstrate...... our method on large adder and multiplier circuits....
A hierarchical bayesian model to quantify uncertainty of stream water temperature forecasts.
Directory of Open Access Journals (Sweden)
Guillaume Bal
Full Text Available Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i an emotive simulated example, ii application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife.
B Gadžurić, Slobodan; O Podunavac Kuzmanović, Sanja; B Vraneš, Milan; Petrin, Marija; Bugarski, Tatjana; Kovačević, Strahinja Z
2016-01-01
The purpose of this work is to promote and facilitate forensic profiling and chemical analysis of illicit drug samples in order to determine their origin, methods of production and transfer through the country. The article is based on the gas chromatography analysis of heroin samples seized from three different locations in Serbia. Chemometric approach with appropriate statistical tools (multiple-linear regression (MLR), hierarchical cluster analysis (HCA) and Wald-Wolfowitz run (WWR) test) were applied on chromatographic data of heroin samples in order to correlate and examine the geographic origin of seized heroin samples. The best MLR models were further validated by leave-one-out technique as well as by the calculation of basic statistical parameters for the established models. To confirm the predictive power of the models, external set of heroin samples was used. High agreement between experimental and predicted values of acetyl thebaol and diacetyl morphine peak ratio, obtained in the validation procedure, indicated the good quality of derived MLR models. WWR test showed which examined heroin samples come from the same population, and HCA was applied in order to overview the similarities among the studied heroine samples.
Generic hierarchical engine for mask data preparation
Kalus, Christian K.; Roessl, Wolfgang; Schnitker, Uwe; Simecek, Michal
2002-07-01
Electronic layouts are usually flattened on their path from the hierarchical source downstream to the wafer. Mask data preparation has certainly been identified as a severe bottleneck since long. Data volumes are not only doubling every year along the ITRS roadmap. With the advent of optical proximity correction and phase-shifting masks data volumes are escalating up to non-manageable heights. Hierarchical treatment is one of the most powerful means to keep memory and CPU consumption in reasonable ranges. Only recently, however, has this technique acquired more public attention. Mask data preparation is the most critical area calling for a sound infrastructure to reduce the handling problem. Gaining more and more attention though, are other applications such as large area simulation and manufacturing rule checking (MRC). They all would profit from a generic engine capable to efficiently treat hierarchical data. In this paper we will present a generic engine for hierarchical treatment which solves the major problem, steady transitions along cell borders. Several alternatives exist how to walk through the hierarchy tree. They have, to date, not been thoroughly investigated. One is a bottom-up attempt to treat cells starting with the most elementary cells. The other one is a top-down approach which lends itself to creating a new hierarchy tree. In addition, since the variety, degree of hierarchy and quality of layouts extends over a wide range a generic engine has to take intelligent decisions when exploding the hierarchy tree. Several applications will be shown, in particular how far the limits can be pushed with the current hierarchical engine.
Dumrongrojthanath, Phattharanit; Thongtem, Titipun; Phuruangrat, Anukorn; Thongtem, Somchai
2013-02-01
Bi2WO6 hierarchical multi-layered flower-like assemblies were synthesized by a hydrothermal method at 180 °C for 24 h. XRD patterns were specified as pure orthorhombic well-crystallized Bi2WO6 phase. Their FTIR spectra show main absorption bands at 400-1000 cm-1, assigned as the stretching modes of the Bi-O and W-O, and W-O-W bridging stretching modes. SEM analysis shows that the product was 3D hierarchical flower-like assemblies, constructed by orderly arranged 2D layers of nanoplates. The UV-visible absorption shows an absorbance in the ultraviolet region with 3.4 eV band gap. Photocatalytic activity of Bi2WO6 hierarchical flowers was determined from the degradation of rhodamine-B by Xe light at 88% for 360 min irradiation.
Optimal DVB-S2 spectral efficiency with hierarchical modulation
Meric, Hugo
2014-01-01
We study the design of a DVB-S2 system in order to maximise spectral efficiency. This task is usually challenging due to channel variability. The solution adopted in modern satellite communications systems such as DVB-SH and DVB-S2 relies mainly on a time sharing strategy. Recently, we proposed to combine time sharing with hierarchical modulation to increase the transmission rate of broadcast systems. However, the optimal spectral efficiency remained an open question. In this paper, we show t...
Frozen impacted drop: From fragmentation to hierarchical crack patterns
Ghabache, Elisabeth; Séon, Thomas
2016-01-01
We investigate experimentally the quenching of a liquid pancake, obtained through the impact of a water drop on a cold solid substrate ($0$ to $-60^\\circ$C). We show that, below a certain substrate temperature, fractures appear on the frozen pancake and the crack patterns change from a 2D fragmentation regime to a hierarchical fracture regime as the thermal shock is stronger. The different regimes are discussed and the transition temperatures are estimated through classical fracture scaling arguments. Finally, a phase diagram presents how these regimes can be controlled by the drop impact parameters.
Hierarchical control based on Hopfield network for nonseparable optimization problems
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
The nonseparable optimization control problem is considered, where the overall objective function is not of an additive form with respect to subsystems. Since there exists the problem that computation is very slow when using iterative algorithms in multiobjective optimization, Hopfield optimization hierarchical network based on IPM is presented to overcome such slow computation difficulty. Asymptotic stability of this Hopfield network is proved and its equilibrium point is the optimal point of the original problem. The simulation shows that the net is effective to deal with the optimization control problem for large-scale nonseparable steady state systems.
Obstacle Avoidance of a Mobile Robot with Hierarchical Structure
Energy Technology Data Exchange (ETDEWEB)
Park, Chan Gyu [Yeungnam College of Science and Technolgy, Taegu (Korea)
2001-06-01
This paper proposed a new hierarchical fuzzy-neural network algorithm for navigation of a mobile robot within unknown dynamic environment. Proposed navigation algorithm used the learning ability of the neural network and the feasibility of control highly nonlinear system of fuzzy theory. The proposed navigation algorithm used fuzzy algorithm for goal approach and fuzzy-network for effective collision avoidance. Some computer simulation results for a mobile robot equipped with ultrasonic range sensors show that the suggested navigation algorithm is very effective to escape in stationary and moving obstacles environment. (author). 11 refs., 14 figs., 2 tabs.
Hierarchical Gompertzian growth maps with application in astrophysics
De Martino, S
2010-01-01
The Gompertz model describes the growth in time of the size of significant quantities associated to a large number of systems, taking into account nonlinearity features by a linear equation satisfied by a nonlinear function of the size. Following this scheme, we introduce a class of hierarchical maps which describe discrete sequences of intermediate characteristic scales. We find the general solutions of the maps, which account for a rich set of possible phenomena. Eventually, we provide an important application, by showing that a map belonging to the class so introduced generates all the observed astrophysical length and mass scales.
Ensemble renormalization group for the random-field hierarchical model.
Decelle, Aurélien; Parisi, Giorgio; Rocchi, Jacopo
2014-03-01
The renormalization group (RG) methods are still far from being completely understood in quenched disordered systems. In order to gain insight into the nature of the phase transition of these systems, it is common to investigate simple models. In this work we study a real-space RG transformation on the Dyson hierarchical lattice with a random field, which leads to a reconstruction of the RG flow and to an evaluation of the critical exponents of the model at T=0. We show that this method gives very accurate estimations of the critical exponents by comparing our results with those obtained by some of us using an independent method.
Wavelet based hierarchical coding scheme for radar image compression
Sheng, Wen; Jiao, Xiaoli; He, Jifeng
2007-12-01
This paper presents a wavelet based hierarchical coding scheme for radar image compression. Radar signal is firstly quantized to digital signal, and reorganized as raster-scanned image according to radar's repeated period frequency. After reorganization, the reformed image is decomposed to image blocks with different frequency band by 2-D wavelet transformation, each block is quantized and coded by the Huffman coding scheme. A demonstrating system is developed, showing that under the requirement of real time processing, the compression ratio can be very high, while with no significant loss of target signal in restored radar image.
Hierarchical clustering techniques for image database organization and summarization
Vellaikal, Asha; Kuo, C.-C. Jay
1998-10-01
This paper investigates clustering techniques as a method of organizing image databases to support popular visual management functions such as searching, browsing and navigation. Different types of hierarchical agglomerative clustering techniques are studied as a method of organizing features space as well as summarizing image groups by the selection of a few appropriate representatives. Retrieval performance using both single and multiple level hierarchies are experimented with and the algorithms show an interesting relationship between the top k correct retrievals and the number of comparisons required. Some arguments are given to support the use of such cluster-based techniques for managing distributed image databases.
Dynamical Properties of Potassium Ion Channels with a Hierarchical Model
Institute of Scientific and Technical Information of China (English)
ZHAN Yong; AN Hai-Long; YU Hui; ZHANG Su-Hua; HAN Ying-Rong
2006-01-01
@@ It is well known that potassium ion channels have higher permeability than K ions, and the permeable rate of a single K ion channel is about 108 ions per second. We develop a hierarchical model of potassium ion channel permeation involving ab initio quantum calculations and Brownian dynamics simulations, which can consistently explain a range of channel dynamics. The results show that the average velocity of K ions, the mean permeable time of K ions and the permeable rate of single channel are about 0.92nm/ns, 4.35ns and 2.30×108 ions/s,respectively.
A Hierarchical Security Architecture for Cyber-Physical Systems
Energy Technology Data Exchange (ETDEWEB)
Quanyan Zhu; Tamer Basar
2011-08-01
Security of control systems is becoming a pivotal concern in critical national infrastructures such as the power grid and nuclear plants. In this paper, we adopt a hierarchical viewpoint to these security issues, addressing security concerns at each level and emphasizing a holistic cross-layer philosophy for developing security solutions. We propose a bottom-up framework that establishes a model from the physical and control levels to the supervisory level, incorporating concerns from network and communication levels. We show that the game-theoretical approach can yield cross-layer security strategy solutions to the cyber-physical systems.
Increased Hierarchical Wrinklons on Stiff Metal Thin Film on a Liquid Meniscus.
Deng, Shikai; Berry, Vikas
2016-09-21
Wrinklons-the hierarchical merging of wrinkles-are observed on several surfaces including thin films, curtains, graphene sheets, and skin. Wrinklons are a consequence of the interplay between bending, stretching, and gravitational energies and generally exhibit 1 to 2 hierarchical transitions (λn+1 = 2λn). Here we show that parallel and self-similar wrinklons on ultrathin cobalt/chromium film atop a contracting silicone oil meniscus can produce up to 5 hierarchical wrinklon transitions near the fluid-solid boundary. Further, these wrinklons do not follow the standard von-Kármán wrinklon scaling near the edge, attributed to the added surface energy (L/λ ∝ (A/t)(0.31)). A model developed via scale analysis shows (a) the relationship between wavelength and length of the wrinkles and (b) a linear relation between the amplitude and the length of wrinkles at all observed hierarchic levels (L ∝ A), fitted well with previous literature results. This work provides a mechanism for thin-film metal wrinkling on liquids and shows that surface stretching effects can allow increased hierarchical levels in wrinklons.
FBH1 Catalyzes Regression of Stalled Replication Forks
DEFF Research Database (Denmark)
Fugger, Kasper; Mistrik, Martin; Neelsen, Kai J
2015-01-01
DNA replication fork perturbation is a major challenge to the maintenance of genome integrity. It has been suggested that processing of stalled forks might involve fork regression, in which the fork reverses and the two nascent DNA strands anneal. Here, we show that FBH1 catalyzes regression...... a model whereby FBH1 promotes early checkpoint signaling by remodeling of stalled DNA replication forks....... of a model replication fork in vitro and promotes fork regression in vivo in response to replication perturbation. Cells respond to fork stalling by activating checkpoint responses requiring signaling through stress-activated protein kinases. Importantly, we show that FBH1, through its helicase activity...
Poor smokers, poor quitters, and cigarette tax regressivity.
Remler, Dahlia K
2004-02-01
The traditional view that excise taxes are regressive has been challenged. I document the history of the term regressive tax, show that traditional definitions have always found cigarette taxes to be regressive, and illustrate the implications of the greater price responsiveness observed among the poor. I explain the different definitions of tax burden: accounting, welfare-based willingness to pay, and welfare-based time inconsistent. Progressivity (equity across income groups) is sensitive to the way in which tax burden is assessed. Analysis of horizontal equity (fairness within a given income group) shows that cigarette taxes heavily burden poor smokers who do not quit, no matter how tax burden is assessed.
Varying-coefficient functional linear regression
Wu, Yichao; Müller, Hans-Georg; 10.3150/09-BEJ231
2011-01-01
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our methodology combines smoothing methods with regularization by truncation at a finite numb...
Constructive Epistemic Modeling: A Hierarchical Bayesian Model Averaging Method
Tsai, F. T. C.; Elshall, A. S.
2014-12-01
Constructive epistemic modeling is the idea that our understanding of a natural system through a scientific model is a mental construct that continually develops through learning about and from the model. Using the hierarchical Bayesian model averaging (HBMA) method [1], this study shows that segregating different uncertain model components through a BMA tree of posterior model probabilities, model prediction, within-model variance, between-model variance and total model variance serves as a learning tool [2]. First, the BMA tree of posterior model probabilities permits the comparative evaluation of the candidate propositions of each uncertain model component. Second, systemic model dissection is imperative for understanding the individual contribution of each uncertain model component to the model prediction and variance. Third, the hierarchical representation of the between-model variance facilitates the prioritization of the contribution of each uncertain model component to the overall model uncertainty. We illustrate these concepts using the groundwater modeling of a siliciclastic aquifer-fault system. The sources of uncertainty considered are from geological architecture, formation dip, boundary conditions and model parameters. The study shows that the HBMA analysis helps in advancing knowledge about the model rather than forcing the model to fit a particularly understanding or merely averaging several candidate models. [1] Tsai, F. T.-C., and A. S. Elshall (2013), Hierarchical Bayesian model averaging for hydrostratigraphic modeling: Uncertainty segregation and comparative evaluation. Water Resources Research, 49, 5520-5536, doi:10.1002/wrcr.20428. [2] Elshall, A.S., and F. T.-C. Tsai (2014). Constructive epistemic modeling of groundwater flow with geological architecture and boundary condition uncertainty under Bayesian paradigm, Journal of Hydrology, 517, 105-119, doi: 10.1016/j.jhydrol.2014.05.027.
Broca's area processes the hierarchical organization of observed action.
Wakita, Masumi
2013-01-01
Broca's area has been suggested as the area responsible for the domain-general hierarchical processing of language and music. Although meaningful action shares a common hierarchical structure with language and music, the role of Broca's area in this domain remains controversial. To address the involvement of Broca's area in the processing action hierarchy, the activation of Broca's area was measured using near-infrared spectroscopy. Measurements were taken while participants watched silent movies that featured hand movements playing familiar and unfamiliar melodies. The unfamiliar melodies were reversed versions of the familiar melodies. Additionally, to investigate the effect of a motor experience on the activation of Broca's area, the participants were divided into well-trained and less-trained groups. The results showed that Broca's area in the well-trained participants demonstrated a significantly larger activation in response to the hand motion when an unfamiliar melody was played than when a familiar melody was played. However, Broca's area in the less-trained participants did not show a contrast between conditions despite identical abilities of the two participant groups to identify the melodies by watching key pressing actions. These results are consistent with previous findings that Broca's area exhibits increased activation in response to grammatically violated sentences and musically deviated chord progressions as well as the finding that this region does not represent the processing of grammatical structure in less-proficient foreign language speakers. Thus, the current study suggests that Broca's area represents action hierarchy and that sufficiently long motor training is necessary for it to become sensitive to motor syntax. Therefore, the notion that hierarchical processing in Broca's area is a common function shared between language and music may help to explain the role of Broca's area in action perception.
Functional Regression for Quasar Spectra
Ciollaro, Mattia; Freeman, Peter; Genovese, Christopher; Lei, Jing; O'Connell, Ross; Wasserman, Larry
2014-01-01
The Lyman-alpha forest is a portion of the observed light spectrum of distant galactic nuclei which allows us to probe remote regions of the Universe that are otherwise inaccessible. The observed Lyman-alpha forest of a quasar light spectrum can be modeled as a noisy realization of a smooth curve that is affected by a `damping effect' which occurs whenever the light emitted by the quasar travels through regions of the Universe with higher matter concentration. To decode the information conveyed by the Lyman-alpha forest about the matter distribution, we must be able to separate the smooth `continuum' from the noise and the contribution of the damping effect in the quasar light spectra. To predict the continuum in the Lyman-alpha forest, we use a nonparametric functional regression model in which both the response and the predictor variable (the smooth part of the damping-free portion of the spectrum) are function-valued random variables. We demonstrate that the proposed method accurately predicts the unobserv...
Knowledge and Awareness: Linear Regression
Directory of Open Access Journals (Sweden)
Monika Raghuvanshi
2016-12-01
Full Text Available Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task. Economically driven state of development in younger generations is an impediment to the correct manner of development. As youths are at the learning phase, they can be molded to follow a correct lifestyle. Awareness and knowledge are important components of any formal or informal environmental education. The purpose of this study is to evaluate the relationship of these components among students of secondary/ senior secondary schools who have undergone a formal study of environment in their curricula. A suitable instrument is developed in order to measure the elements of Awareness and Knowledge among the participants of the study. Data was collected from various secondary and senior secondary school students in the age group 14 to 20 years using cluster sampling technique from the city of Bikaner, India. Linear regression analysis was performed using IBM SPSS 23 statistical tool. There exists a weak relation between knowledge and awareness about environmental issues, caused due to routine practices mishandling; hence one component can be complemented by other for improvement in both. Knowledge and awareness are crucial factors and can provide huge opportunities in any field. Resource utilization for economic solutions may pave the way for eco-friendly products and practices. If green practices are inculcated at the learning phase, they may become normal routine. This will also help in repletion of the environment.
Streamflow forecasting using functional regression
Masselot, Pierre; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.
2016-07-01
Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Directory of Open Access Journals (Sweden)
Qiutong Jin
2016-06-01
Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Spontaneous regression together with increased calcification of incidental meningioma
Directory of Open Access Journals (Sweden)
Kengo Hirota
2014-01-01
Full Text Available Background: Regression of meningioma has been reported after hemorrhage or hormonal withdrawal. However, meningioma regression is rarely observed spontaneously. Case Description: A right falx meningioma was incidentally diagnosed and was followed at every one-year by magnetic resonance imaging (MRI for over 7 years. The tumor, with a maximum diameter of 4 cm, showed a slightly high density and was enhanced on computed tomography (CT, and a high intensity with a low-intensity core on T2 MRI, with significant edema. The meningioma gradually shrank together with a decrease of edema and increase of calcification. The initial volume, 25.5 cm 3 , regressed linearly to less than half, 9.9 cm 3 . Conclusion: Here, we report a case of an incidentally diagnosed meningioma that regressed spontaneously. The pattern of the regression was similar to that following gamma knife radiosurgery.
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor
2012-06-29
In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.
Inferring gene regression networks with model trees
Directory of Open Access Journals (Sweden)
Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Leung, K M; Elashoff, R M; Rees, K S; Hasan, M M; Legorreta, A P
1998-03-01
The purpose of this study was to identify factors related to pregnancy and childbirth that might be predictive of a patient's length of stay after delivery and to model variations in length of stay. California hospital discharge data on maternity patients (n = 499,912) were analyzed. Hierarchical linear modeling was used to adjust for patient case mix and hospital characteristics and to account for the dependence of outcome variables within hospitals. Substantial variation in length of stay among patients was observed. The variation was mainly attributed to delivery type (vaginal or cesarean section), the patient's clinical risk factors, and severity of complications (if any). Furthermore, hospitals differed significantly in maternity lengths of stay even after adjustment for patient case mix. Developing risk-adjusted models for length of stay is a complex process but is essential for understanding variation. The hierarchical linear model approach described here represents a more efficient and appropriate way of studying interhospital variations than the traditional regression approach.
HD 181068: A Red Giant in a Triply Eclipsing Compact Hierarchical Triple System
DEFF Research Database (Denmark)
Derekas, A.; Kiss, Lazlo L.; Borkovits, T.
2011-01-01
by ground-based spectroscopy and interferometry, which show it to be a hierarchical triple with two types of mutual eclipses. The primary is a red giant that is in a 45-day orbit with a pair of red dwarfs in a close 0.9-day orbit. The red giant shows evidence for tidally induced oscillations that are driven...
Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan
2015-06-01
Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.
Mixed Frequency Data Sampling Regression Models: The R Package midasr
Directory of Open Access Journals (Sweden)
Eric Ghysels
2016-08-01
Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.
Eriksson, Brian; Singh, Aarti; Nowak, Robert
2011-01-01
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small subset of pairwise similarities, significantly less than the complete set of N(N-1)/2 similarities. First, we show that if the intracluster similarities exceed intercluster similarities, then it is possible to correctly determine the hierarchical clustering from as few as 3N log N similarities. We demonstrate this order of magnitude savings in the number of pairwise similarities necessitates sequentially selecting which similarities to obtain in an adaptive fashion, rather than picking them at random. We then propose an active clustering method that is robust to a limited fraction of anomalous similarities, and show how even in the presence of these noisy similarity values we can resolve the hierar...
First-born siblings show better second language skills than later born siblings.
Keller, Karin; Troesch, Larissa M; Grob, Alexander
2015-01-01
We examined the extent to which three sibling structure variables number of siblings, birth order, and presence of an older sibling at school age are linked to the second language skills of bilingual children. The research questions were tested using an ethnically heterogeneous sample of 1209 bilingual children with German as a second language. Controlling for children's age, sex, nationality, number of children's books at home, family language and parental German language skills, hierarchical regression analyses showed an inverse relationship between the number of siblings and second language skills: the more siblings a child had, the lower was his/her second language proficiency. This relationship was mediated by attendance in early education institutions. Moreover, first-born siblings showed better second language skills than later born siblings. The current study revealed that the resource dilution model, i.e., the decrease in resources for every additional sibling, holds for second language acquisition. Moreover, the results indicate that bilingual children from families with several children benefit from access to early education institutions.
First-born siblings show better second language skills than later born siblings
Directory of Open Access Journals (Sweden)
Karin eKeller
2015-06-01
Full Text Available We examined the extent to which three sibling structure variables number of siblings, birth order and presence of an older sibling at school age are linked to the second language skills of bilingual children. The research questions were tested using an ethnically heterogeneous sample of 1209 bilingual children with German as a second language. Controlling for children’s age, sex, nationality, number of children’s books at home, family language and parental German language skills, hierarchical regression analyses showed an inverse relationship between the number of siblings and second language skills: The more siblings a child had, the lower was his/her second language proficiency. This relationship was mediated by attendance in early education institutions. Moreover, first-born siblings showed better second language skills than later born siblings.The current study revealed that the resource dilution model, i.e., the decrease in resources for every additional sibling, holds for second language acquisition. Moreover, the results indicate that bilingual children from families with several children benefit from access to early education institutions.
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Spontaneous Regression of an Incidental Spinal Meningioma
National Research Council Canada - National Science Library
Yilmaz, Ali; Kizilay, Zahir; Sair, Ahmet; Avcil, Mucahit; Ozkul, Ayca
2015-01-01
AIM: The regression of meningioma has been reported in literature before. In spite of the fact that the regression may be involved by hemorrhage, calcification or some drugs withdrawal, it is rarely observed spontaneously. CASE REPORT...