Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
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 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.
Stepwise versus Hierarchical Regression: Pros and Cons
Lewis, Mitzi
2007-01-01
Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…
Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression.
Houpt, Joseph W; Bittner, Jennifer L
2018-05-10
Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences. Copyright © 2018 Elsevier Ltd. All rights reserved.
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.
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright
Choi, Kilchan
2011-01-01
This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…
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.
Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo
2018-05-10
Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.
International Nuclear Information System (INIS)
Mandel, Kaisey S.; Kirshner, Robert P.; Foley, Ryan J.
2014-01-01
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 3 km s –1 ) –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 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
Wilderjans, Tom F.; Van de Gaer, E.; Kiers, H.A.L.; Van Mechelen, Iven; Ceulemans, Eva
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
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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.
A case of parotid tumor showing remarkable regression following hyperthermo-chemo-radiotherapy
International Nuclear Information System (INIS)
Fujimura, Takashi; Yonemura, Yutaka; Kamata, Toru
1987-01-01
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. (author)
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.
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…
Erdheim-Chester disease in a child with MR imaging showing regression of marrow changes
International Nuclear Information System (INIS)
Joo, Chan Uhng; Go, Yang Sim; Kim, In Hwan; Kim, Chul Seong; Lee, Sang Yong
2005-01-01
Erdheim-Chester disease is a disseminated xanthogranulomatous infiltrative disease of unknown origin that generally presents in adulthood. A review of the English-language literature demonstrated that pediatric cases were extremely rare, and to our knowledge, only two cases, a 7- and 14-year-old, have been published. We report a case of Erdheim-Chester disease in a 10-year-old girl evaluated with MR imaging. Radiographs revealed typical bilateral, symmetric osteosclerosis of the metaphyseal regions of long bones of the upper and lower extremities. A histologic examination demonstrated foamy histiocytes in bone marrow smears. Bilateral symmetric low signal intensities of both proximal tibiae and distal femurs were demonstrated on T1-weighted MR images. After oral steroid therapy for 8 months, follow-up MR imaging showed remarkable restoration of normal high signal intensity in both the tibial and femoral metaphyses. To our knowledge, this may be the first case of Erdheim-Chester disease that showed normal restoration of the abnormal signal intensities in the metaphyses of long bones after steroid therapy. (orig.)
How to show that unicorn milk is a chronobiotic: the regression-to-the-mean statistical artifact.
Atkinson, G; Waterhouse, J; Reilly, T; Edwards, B
2001-11-01
Few chronobiologists may be aware of the regression-to-the-mean (RTM) statistical artifact, even though it may have far-reaching influences on chronobiological data. With the aid of simulated measurements of the circadian rhythm phase of body temperature and a completely bogus stimulus (unicorn milk), we explain what RTM is and provide examples relevant to chronobiology. We show how RTM may lead to erroneous conclusions regarding individual differences in phase responses to rhythm disturbances and how it may appear as though unicorn milk has phase-shifting effects and can successfully treat some circadian rhythm disorders. Guidelines are provided to ensure RTM effects are minimized in chronobiological investigations.
Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald
2011-06-01
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. 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 loops. HC-PLSR is a promising approach for
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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.
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
Shaffer, Kelly M; Jacobs, Jamie M; Nipp, Ryan D; Carr, Alaina; Jackson, Vicki A; Park, Elyse R; Pirl, William F; El-Jawahri, Areej; Gallagher, Emily R; Greer, Joseph A; Temel, Jennifer S
2017-03-01
Caregiver, relational, and patient factors have been associated with the health of family members and friends providing care to patients with early-stage cancer. Little research has examined whether findings extend to family caregivers of patients with incurable cancer, who experience unique and substantial caregiving burdens. We examined correlates of mental and physical health among caregivers of patients with newly-diagnosed incurable lung or non-colorectal gastrointestinal cancer. At baseline for a trial of early palliative care, caregivers of participating patients (N = 275) reported their mental and physical health (Medical Outcome Survey-Short Form-36); patients reported their quality of life (Functional Assessment of Cancer Therapy-General). Analyses used hierarchical linear regression with two-tailed significance tests. Caregivers' mental health was worse than the U.S. national population (M = 44.31, p caregiver, relational, and patient factors simultaneously revealed that younger (B = 0.31, p = .001), spousal caregivers (B = -8.70, p = .003), who cared for patients reporting low emotional well-being (B = 0.51, p = .01) reported worse mental health; older (B = -0.17, p = .01) caregivers with low educational attainment (B = 4.36, p family caregivers of patients with incurable cancer, caregiver demographics, relational factors, and patient-specific factors were all related to caregiver mental health, while caregiver demographics were primarily associated with caregiver physical health. These findings help identify characteristics of family caregivers at highest risk of poor mental and physical health who may benefit from greater supportive care.
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
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.
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
Spady, Richard; Stouli, Sami
2012-01-01
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...
Hierarchical species distribution models
Hefley, Trevor J.; Hooten, Mevin B.
2016-01-01
Determining the distribution pattern of a species is important to increase scientific knowledge, inform management decisions, and conserve biodiversity. To infer spatial and temporal patterns, species distribution models have been developed for use with many sampling designs and types of data. Recently, it has been shown that count, presence-absence, and presence-only data can be conceptualized as arising from a point process distribution. Therefore, it is important to understand properties of the point process distribution. We examine how the hierarchical species distribution modeling framework has been used to incorporate a wide array of regression and theory-based components while accounting for the data collection process and making use of auxiliary information. The hierarchical modeling framework allows us to demonstrate how several commonly used species distribution models can be derived from the point process distribution, highlight areas of potential overlap between different models, and suggest areas where further research is needed.
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.
Differentiating regressed melanoma from regressed lichenoid keratosis.
Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A
2017-04-01
Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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
Directory of Open Access Journals (Sweden)
Daniel Sofron
2015-05-01
Full Text Available This paper is focused on the hierarchical perspective, one of the methods for representing space that was used before the discovery of the Renaissance linear perspective. The hierarchical perspective has a more or less pronounced scientific character and its study offers us a clear image of the way the representatives of the cultures that developed it used to perceive the sensitive reality. This type of perspective is an original method of representing three-dimensional space on a flat surface, which characterises the art of Ancient Egypt and much of the art of the Middle Ages, being identified in the Eastern European Byzantine art, as well as in the Western European Pre-Romanesque and Romanesque art. At the same time, the hierarchical perspective is also present in naive painting and infantile drawing. Reminiscences of this method can be recognised also in the works of some precursors of the Italian Renaissance. The hierarchical perspective can be viewed as a subjective ranking criterion, according to which the elements are visually represented by taking into account their relevance within the image while perception is ignored. This paper aims to show how the main objective of the artists of those times was not to faithfully represent the objective reality, but rather to emphasize the essence of the world and its perennial aspects. This may represent a possible explanation for the refusal of perspective in the Egyptian, Romanesque and Byzantine painting, characterised by a marked two-dimensionality.
Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.
2017-01-01
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...
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
Semiparametric regression during 2003–2007
Ruppert, David; Wand, M.P.; Carroll, Raymond J.
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.
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…
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...
Modular networks with hierarchical organization
Indian Academy of Sciences (India)
Several networks occurring in real life have modular structures that are arranged in a hierarchical fashion. In this paper, we have proposed a model for such networks, using a stochastic generation method. Using this model we show that, the scaling relation between the clustering and degree of the nodes is not a necessary ...
Hierarchical Rhetorical Sentence Categorization for Scientific Papers
Rachman, G. H.; Khodra, M. L.; Widyantoro, D. H.
2018-03-01
Important information in scientific papers can be composed of rhetorical sentences that is structured from certain categories. To get this information, text categorization should be conducted. Actually, some works in this task have been completed by employing word frequency, semantic similarity words, hierarchical classification, and the others. Therefore, this paper aims to present the rhetorical sentence categorization from scientific paper by employing TF-IDF and Word2Vec to capture word frequency and semantic similarity words and employing hierarchical classification. Every experiment is tested in two classifiers, namely Naïve Bayes and SVM Linear. This paper shows that hierarchical classifier is better than flat classifier employing either TF-IDF or Word2Vec, although it increases only almost 2% from 27.82% when using flat classifier until 29.61% when using hierarchical classifier. It shows also different learning model for child-category can be built by hierarchical classifier.
Transmutations across hierarchical levels
International Nuclear Information System (INIS)
O'Neill, R.V.
1977-01-01
The development of large-scale ecological models depends implicitly on a concept known as hierarchy theory which views biological systems in a series of hierarchical levels (i.e., organism, population, trophic level, ecosystem). The theory states that an explanation of a biological phenomenon is provided when it is shown to be the consequence of the activities of the system's components, which are themselves systems in the next lower level of the hierarchy. Thus, the behavior of a population is explained by the behavior of the organisms in the population. The initial step in any modeling project is, therefore, to identify the system components and the interactions between them. A series of examples of transmutations in aquatic and terrestrial ecosystems are presented to show how and why changes occur. The types of changes are summarized and possible implications of transmutation for hierarchy theory, for the modeler, and for the ecological theoretician are discussed
Directory of Open Access Journals (Sweden)
Woosang Lim
Full Text Available Hierarchical organizations of information processing in the brain networks have been known to exist and widely studied. To find proper hierarchical structures in the macaque brain, the traditional methods need the entire pairwise hierarchical relationships between cortical areas. In this paper, we present a new method that discovers hierarchical structures of macaque brain networks by using partial information of pairwise hierarchical relationships. Our method uses a graph-based manifold learning to exploit inherent relationship, and computes pseudo distances of hierarchical levels for every pair of cortical areas. Then, we compute hierarchy levels of all cortical areas by minimizing the sum of squared hierarchical distance errors with the hierarchical information of few cortical areas. We evaluate our method on the macaque brain data sets whose true hierarchical levels are known as the FV91 model. The experimental results show that hierarchy levels computed by our method are similar to the FV91 model, and its errors are much smaller than the errors of hierarchical clustering approaches.
Processing of hierarchical syntactic structure in music.
Koelsch, Stefan; Rohrmeier, Martin; Torrecuso, Renzo; Jentschke, Sebastian
2013-09-17
Hierarchical structure with nested nonlocal dependencies is a key feature of human language and can be identified theoretically in most pieces of tonal music. However, previous studies have argued against the perception of such structures in music. Here, we show processing of nonlocal dependencies in music. We presented chorales by J. S. Bach and modified versions in which the hierarchical structure was rendered irregular whereas the local structure was kept intact. Brain electric responses differed between regular and irregular hierarchical structures, in both musicians and nonmusicians. This finding indicates that, when listening to music, humans apply cognitive processes that are capable of dealing with long-distance dependencies resulting from hierarchically organized syntactic structures. Our results reveal that a brain mechanism fundamental for syntactic processing is engaged during the perception of music, indicating that processing of hierarchical structure with nested nonlocal dependencies is not just a key component of human language, but a multidomain capacity of human cognition.
Energy Technology Data Exchange (ETDEWEB)
Erbacher, Robert; Frincke, Deb
2007-07-02
Coordinated views have proven critical to the development of effective visualization environments. This results from the fact that a single view or representation of the data cannot show all of the intricacies of a given data set. Additionally, users will often need to correlate more data parameters than can effectively be integrated into a single visual display. Typically, development of multiple-linked views results in an adhoc configuration of views and associated interactions. The hierarchical model we are proposing is geared towards more effective organization of such environments and the views they encompass. At the same time, this model can effectively integrate much of the prior work on interactive and visual frameworks. Additionally, we expand the concept of views to incorporate perceptual views. This is related to the fact that visual displays can have information encoded at various levels of focus. Thus, a global view of the display provides overall trends of the data while focusing in on individual elements provides detailed specifics. By integrating interaction and perception into a single model, we show how one impacts the other. Typically, interaction and perception are considered separately, however, when interaction is being considered at a fundamental level and allowed to direct/modify the visualization directly we must consider them simultaneously and how they impact one another.
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...
DEFF Research Database (Denmark)
Thomadsen, Tommy
2005-01-01
Communication networks are immensely important today, since both companies and individuals use numerous services that rely on them. This thesis considers the design of hierarchical (communication) networks. Hierarchical networks consist of layers of networks and are well-suited for coping...... with changing and increasing demands. Two-layer networks consist of one backbone network, which interconnects cluster networks. The clusters consist of nodes and links, which connect the nodes. One node in each cluster is a hub node, and the backbone interconnects the hub nodes of each cluster and thus...... the clusters. The design of hierarchical networks involves clustering of nodes, hub selection, and network design, i.e. selection of links and routing of ows. Hierarchical networks have been in use for decades, but integrated design of these networks has only been considered for very special types of networks...
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...
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....
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.
Introduction into Hierarchical Matrices
Litvinenko, Alexander
2013-01-01
Hierarchical matrices allow us to reduce computational storage and cost from cubic to almost linear. This technique can be applied for solving PDEs, integral equations, matrix equations and approximation of large covariance and precision matrices.
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).
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
Parallel hierarchical radiosity rendering
Energy Technology Data Exchange (ETDEWEB)
Carter, Michael [Iowa State Univ., Ames, IA (United States)
1993-07-01
In this dissertation, the step-by-step development of a scalable parallel hierarchical radiosity renderer is documented. First, a new look is taken at the traditional radiosity equation, and a new form is presented in which the matrix of linear system coefficients is transformed into a symmetric matrix, thereby simplifying the problem and enabling a new solution technique to be applied. Next, the state-of-the-art hierarchical radiosity methods are examined for their suitability to parallel implementation, and scalability. Significant enhancements are also discovered which both improve their theoretical foundations and improve the images they generate. The resultant hierarchical radiosity algorithm is then examined for sources of parallelism, and for an architectural mapping. Several architectural mappings are discussed. A few key algorithmic changes are suggested during the process of making the algorithm parallel. Next, the performance, efficiency, and scalability of the algorithm are analyzed. The dissertation closes with a discussion of several ideas which have the potential to further enhance the hierarchical radiosity method, or provide an entirely new forum for the application of hierarchical methods.
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)
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.…
Hierarchical wave functions revisited
International Nuclear Information System (INIS)
Li Dingping.
1997-11-01
We study the hierarchical wave functions on a sphere and on a torus. We simplify some wave functions on a sphere or a torus using the analytic properties of wave functions. The open question, the construction of the wave function for quasi electron excitation on a torus, is also solved in this paper. (author)
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.
Proposing a Hierarchical Utility Package with Reference to Mobile Advertising
Shalini N. Tripathi; Masood H. Siddiqui
2011-01-01
Mobile advertising is a powerful tool for direct and interactive marketing. However effective marketing requires examining consumersâ€™ psyche. This study proposes a hierarchical utility package (in the consumersâ€™ perception) with reference to mobile advertising, thus enhancing its acceptance. Confirmatory factor analysis revealed four consolidated utility dimensions (with reference to mobile advertising). Binary logistic regression was used to create a hierarchical utility package with res...
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bayesian nonparametric hierarchical modeling.
Dunson, David B
2009-04-01
In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.
Learning with hierarchical-deep models.
Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio
2013-08-01
We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.
Hierarchically Structured Electrospun Fibers
2013-01-07
in the natural lotus and silver ragwort leaves. Figure 4. Examples of electrospun bio-mimics of natural hierarchical structures. (A) Lotus leaf...B) pillared poly(methyl methacrylate) (PMMA) electrospun fiber mimic; (C) silver ragwort leaf; (D) electrospun fiber mimic made from nylon 6 and...domains containing the protein in the surrounding EVA fibers [115]. A wide variety of core-shell fibers have been generated, including PCL/ gelatin
Better Autologistic Regression
Directory of Open Access Journals (Sweden)
Mark A. Wolters
2017-11-01
Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.
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.
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.
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.
Hierarchically nested river landform sequences
Pasternack, G. B.; Weber, M. D.; Brown, R. A.; Baig, D.
2017-12-01
River corridors exhibit landforms nested within landforms repeatedly down spatial scales. In this study we developed, tested, and implemented a new way to create river classifications by mapping domains of fluvial processes with respect to the hierarchical organization of topographic complexity that drives fluvial dynamism. We tested this approach on flow convergence routing, a morphodynamic mechanism with different states depending on the structure of nondimensional topographic variability. Five nondimensional landform types with unique functionality (nozzle, wide bar, normal channel, constricted pool, and oversized) represent this process at any flow. When this typology is nested at base flow, bankfull, and floodprone scales it creates a system with up to 125 functional types. This shows how a single mechanism produces complex dynamism via nesting. Given the classification, we answered nine specific scientific questions to investigate the abundance, sequencing, and hierarchical nesting of these new landform types using a 35-km gravel/cobble river segment of the Yuba River in California. The nested structure of flow convergence routing landforms found in this study revealed that bankfull landforms are nested within specific floodprone valley landform types, and these types control bankfull morphodynamics during moderate to large floods. As a result, this study calls into question the prevailing theory that the bankfull channel of a gravel/cobble river is controlled by in-channel, bankfull, and/or small flood flows. Such flows are too small to initiate widespread sediment transport in a gravel/cobble river with topographic complexity.
Deep hierarchical attention network for video description
Li, Shuohao; Tang, Min; Zhang, Jun
2018-03-01
Pairing video to natural language description remains a challenge in computer vision and machine translation. Inspired by image description, which uses an encoder-decoder model for reducing visual scene into a single sentence, we propose a deep hierarchical attention network for video description. The proposed model uses convolutional neural network (CNN) and bidirectional LSTM network as encoders while a hierarchical attention network is used as the decoder. Compared to encoder-decoder models used in video description, the bidirectional LSTM network can capture the temporal structure among video frames. Moreover, the hierarchical attention network has an advantage over single-layer attention network on global context modeling. To make a fair comparison with other methods, we evaluate the proposed architecture with different types of CNN structures and decoders. Experimental results on the standard datasets show that our model has a more superior performance than the state-of-the-art techniques.
Hierarchical Traces for Reduced NSM Memory Requirements
Dahl, Torbjørn S.
This paper presents work on using hierarchical long term memory to reduce the memory requirements of nearest sequence memory (NSM) learning, a previously published, instance-based reinforcement learning algorithm. A hierarchical memory representation reduces the memory requirements by allowing traces to share common sub-sequences. We present moderated mechanisms for estimating discounted future rewards and for dealing with hidden state using hierarchical memory. We also present an experimental analysis of how the sub-sequence length affects the memory compression achieved and show that the reduced memory requirements do not effect the speed of learning. Finally, we analyse and discuss the persistence of the sub-sequences independent of specific trace instances.
Steganalysis using logistic regression
Lubenko, Ivans; Ker, Andrew D.
2011-02-01
We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.
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...
Hierarchically organized layout for visualization of biochemical pathways.
Tsay, Jyh-Jong; Wu, Bo-Liang; Jeng, Yu-Sen
2010-01-01
Many complex pathways are described as hierarchical structures in which a pathway is recursively partitioned into several sub-pathways, and organized hierarchically as a tree. The hierarchical structure provides a natural way to visualize the global structure of a complex pathway. However, none of the previous research on pathway visualization explores the hierarchical structures provided by many complex pathways. In this paper, we aim to develop algorithms that can take advantages of hierarchical structures, and give layouts that explore the global structures as well as local structures of pathways. We present a new hierarchically organized layout algorithm to produce layouts for hierarchically organized pathways. Our algorithm first decomposes a complex pathway into sub-pathway groups along the hierarchical organization, and then partition each sub-pathway group into basic components. It then applies conventional layout algorithms, such as hierarchical layout and force-directed layout, to compute the layout of each basic component. Finally, component layouts are joined to form a final layout of the pathway. Our main contribution is the development of algorithms for decomposing pathways and joining layouts. Experiment shows that our algorithm is able to give comprehensible visualization for pathways with hierarchies, cycles as well as complex structures. It clearly renders the global component structures as well as the local structure in each component. In addition, it runs very fast, and gives better visualization for many examples from previous related research. 2009 Elsevier B.V. All rights reserved.
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.
Tumor regression patterns in retinoblastoma
International Nuclear Information System (INIS)
Zafar, S.N.; Siddique, S.N.; Zaheer, N.
2016-01-01
To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)
Detecting Hierarchical Structure in Networks
DEFF Research Database (Denmark)
Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard
2012-01-01
Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose...... a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....
Collaborative regression-based anatomical landmark detection
International Nuclear Information System (INIS)
Gao, Yaozong; Shen, Dinggang
2015-01-01
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 and 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. (paper)
Hierarchical quark mass matrices
International Nuclear Information System (INIS)
Rasin, A.
1998-02-01
I define a set of conditions that the most general hierarchical Yukawa mass matrices have to satisfy so that the leading rotations in the diagonalization matrix are a pair of (2,3) and (1,2) rotations. In addition to Fritzsch structures, examples of such hierarchical structures include also matrices with (1,3) elements of the same order or even much larger than the (1,2) elements. Such matrices can be obtained in the framework of a flavor theory. To leading order, the values of the angle in the (2,3) plane (s 23 ) and the angle in the (1,2) plane (s 12 ) do not depend on the order in which they are taken when diagonalizing. We find that any of the Cabbibo-Kobayashi-Maskawa matrix parametrizations that consist of at least one (1,2) and one (2,3) rotation may be suitable. In the particular case when the s 13 diagonalization angles are sufficiently small compared to the product s 12 s 23 , two special CKM parametrizations emerge: the R 12 R 23 R 12 parametrization follows with s 23 taken before the s 12 rotation, and vice versa for the R 23 R 12 R 23 parametrization. (author)
Hierarchical partial order ranking
International Nuclear Information System (INIS)
Carlsen, Lars
2008-01-01
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 prioritisation of polluted sites is given. - Hierarchical partial order ranking of polluted sites has been developed for prioritization based on a large number of parameters
Nested and Hierarchical Archimax copulas
Hofert, Marius; Huser, Raphaë l; Prasad, Avinash
2017-01-01
The class of Archimax copulas is generalized to nested and hierarchical Archimax copulas in several ways. First, nested extreme-value copulas or nested stable tail dependence functions are introduced to construct nested Archimax copulas based on a single frailty variable. Second, a hierarchical construction of d-norm generators is presented to construct hierarchical stable tail dependence functions and thus hierarchical extreme-value copulas. Moreover, one can, by itself or additionally, introduce nested frailties to extend Archimax copulas to nested Archimax copulas in a similar way as nested Archimedean copulas extend Archimedean copulas. Further results include a general formula for the density of Archimax copulas.
Nested and Hierarchical Archimax copulas
Hofert, Marius
2017-07-03
The class of Archimax copulas is generalized to nested and hierarchical Archimax copulas in several ways. First, nested extreme-value copulas or nested stable tail dependence functions are introduced to construct nested Archimax copulas based on a single frailty variable. Second, a hierarchical construction of d-norm generators is presented to construct hierarchical stable tail dependence functions and thus hierarchical extreme-value copulas. Moreover, one can, by itself or additionally, introduce nested frailties to extend Archimax copulas to nested Archimax copulas in a similar way as nested Archimedean copulas extend Archimedean copulas. Further results include a general formula for the density of Archimax copulas.
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.
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...
Silver Films with Hierarchical Chirality.
Ma, Liguo; Cao, Yuanyuan; Duan, Yingying; Han, Lu; Che, Shunai
2017-07-17
Physical fabrication of chiral metallic films usually results in singular or large-sized chirality, restricting the optical asymmetric responses to long electromagnetic wavelengths. The chiral molecule-induced formation of silver films prepared chemically on a copper substrate through a redox reaction is presented. Three levels of chirality were identified: primary twisted nanoflakes with atomic crystal lattices, secondary helical stacking of these nanoflakes to form nanoplates, and tertiary micrometer-sized circinates consisting of chiral arranged nanoplates. The chiral Ag films exhibited multiple plasmonic absorption- and scattering-based optical activities at UV/Vis wavelengths based on their hierarchical chirality. The Ag films showed chiral selectivity for amino acids in catalytic electrochemical reactions, which originated from their primary atomic crystal lattices. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Applied Bayesian hierarchical methods
National Research Council Canada - National Science Library
Congdon, P
2010-01-01
.... It also incorporates BayesX code, which is particularly useful in nonlinear regression. To demonstrate MCMC sampling from first principles, the author includes worked examples using the R package...
Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis
Luo, Wen; Azen, Razia
2013-01-01
Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…
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
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.
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......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 modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...
Leadership styles across hierarchical levels in nursing departments.
Stordeur, S; Vandenberghe, C; D'hoore, W
2000-01-01
Some researchers have reported on the cascading effect of transformational leadership across hierarchical levels. One study examined this effect in nursing, but it was limited to a single hospital. To examine the cascading effect of leadership styles across hierarchical levels in a sample of nursing departments and to investigate the effect of hierarchical level on the relationships between leadership styles and various work outcomes. Based on a sample of eight hospitals, the cascading effect was tested using correlation analysis. The main sources of variation among leadership scores were determined with analyses of variance (ANOVA), and the interaction effect of hierarchical level and leadership styles on criterion variables was tested with moderated regression analysis. No support was found for a cascading effect of leadership across hierarchical levels. Rather, the variation of leadership scores was explained primarily by the organizational context. Transformational leadership had a stronger impact on criterion variables than transactional leadership. Interaction effects between leadership styles and hierarchical level were observed only for perceived unit effectiveness. The hospital's structure and culture are major determinants of leadership styles.
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.
Regression to Causality : Regression-style presentation influences causal attribution
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
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...... 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...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Multiple dynamical time-scales in networks with hierarchically
Indian Academy of Sciences (India)
Modular networks; hierarchical organization; synchronization. ... we show that such a topological structure gives rise to characteristic time-scale separation ... This suggests a possible functional role of such mesoscopic organization principle in ...
Understanding logistic regression analysis
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...
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
Alternative Methods of Regression
Birkes, David
2011-01-01
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s
Hierarchical modularity in human brain functional networks
Directory of Open Access Journals (Sweden)
David Meunier
2009-10-01
Full Text Available The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules” decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.
Hierarchical Discriminant Analysis
Directory of Open Access Journals (Sweden)
Di Lu
2018-01-01
Full Text Available The Internet of Things (IoT generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA. It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.
Regression analysis with categorized regression calibrated exposure: some interesting findings
Directory of Open Access Journals (Sweden)
Hjartåker Anette
2006-07-01
Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a
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.
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...
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
Luo, Chongliang; Liu, Jin; Dey, Dipak K; Chen, Kun
2016-07-01
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Hierarchical architecture of active knits
International Nuclear Information System (INIS)
Abel, Julianna; Luntz, Jonathan; Brei, Diann
2013-01-01
Nature eloquently utilizes hierarchical structures to form the world around us. Applying the hierarchical architecture paradigm to smart materials can provide a basis for a new genre of actuators which produce complex actuation motions. One promising example of cellular architecture—active knits—provides complex three-dimensional distributed actuation motions with expanded operational performance through a hierarchically organized structure. The hierarchical structure arranges a single fiber of active material, such as shape memory alloys (SMAs), into a cellular network of interlacing adjacent loops according to a knitting grid. This paper defines a four-level hierarchical classification of knit structures: the basic knit loop, knit patterns, grid patterns, and restructured grids. Each level of the hierarchy provides increased architectural complexity, resulting in expanded kinematic actuation motions of active knits. The range of kinematic actuation motions are displayed through experimental examples of different SMA active knits. The results from this paper illustrate and classify the ways in which each level of the hierarchical knit architecture leverages the performance of the base smart material to generate unique actuation motions, providing necessary insight to best exploit this new actuation paradigm. (paper)
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.
Hu, Ming-Hsia; Yeh, Chih-Jun; Chen, Tou-Rong; Wang, Ching-Yi
2014-01-01
A valid, time-efficient and easy-to-use instrument is important for busy clinical settings, large scale surveys, or community screening use. The purpose of this study was to validate the mobility hierarchical disability categorization model (an abbreviated model) by investigating its concurrent validity with the multidimensional hierarchical disability categorization model (a comprehensive model) and triangulating both models with physical performance measures in older adults. 604 community-dwelling older adults of at least 60 years in age volunteered to participate. Self-reported function on mobility, instrumental activities of daily living (IADL) and activities of daily living (ADL) domains were recorded and then the disability status determined based on both the multidimensional hierarchical categorization model and the mobility hierarchical categorization model. The physical performance measures, consisting of grip strength and usual and fastest gait speeds (UGS, FGS), were collected on the same day. Both categorization models showed high correlation (γs = 0.92, p categorization models. The results of multiple regression analysis indicated that both models individually explain similar amount of variance on all physical performances, with adjustments for age, sex, and number of comorbidities. Our results found that the mobility hierarchical disability categorization model is a valid and time efficient tool for large survey or screening use.
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.
Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
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
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-
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.
Cluster Based Hierarchical Routing Protocol for Wireless Sensor Network
Rashed, Md. Golam; Kabir, M. Hasnat; Rahim, Muhammad Sajjadur; Ullah, Shaikh Enayet
2012-01-01
The efficient use of energy source in a sensor node is most desirable criteria for prolong the life time of wireless sensor network. In this paper, we propose a two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP). We introduce a new concept called head-set, consists of one active cluster head and some other associate cluster heads within a cluster. The head-set members are responsible for control and management of the network. Results show that t...
Understanding poisson regression.
Hayat, Matthew J; Higgins, Melinda
2014-04-01
Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.
Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models
Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.
2017-12-01
Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Directory of Open Access Journals (Sweden)
Mok Tik
2014-06-01
Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.
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.
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...
Multicollinearity and Regression Analysis
Daoud, Jamal I.
2017-12-01
In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.
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....
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....
Deliberate change without hierarchical influence?
DEFF Research Database (Denmark)
Nørskov, Sladjana; Kesting, Peter; Ulhøi, John Parm
2017-01-01
reveals that deliberate change is indeed achievable in a non-hierarchical collaborative OSS community context. However, it presupposes the presence and active involvement of informal change agents. The paper identifies and specifies four key drivers for change agents’ influence. Originality....../value The findings contribute to organisational analysis by providing a deeper understanding of the importance of leadership in making deliberate change possible in non-hierarchical settings. It points to the importance of “change-by-conviction”, essentially based on voluntary behaviour. This can open the door...
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.
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
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.
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.
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
and Multinomial Logistic Regression
African Journals Online (AJOL)
This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for classifying students based on their academic performance. The predictive accuracy for each model was measured by their average Classification Correct Rate (CCR).
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
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...
Spontaneous regression of a congenital melanocytic nevus
Directory of Open Access Journals (Sweden)
Amiya Kumar Nath
2011-01-01
Full Text Available Congenital melanocytic nevus (CMN may rarely regress which may also be associated with a halo or vitiligo. We describe a 10-year-old girl who presented with CMN on the left leg since birth, which recently started to regress spontaneously with associated depigmentation in the lesion and at a distant site. Dermoscopy performed at different sites of the regressing lesion demonstrated loss of epidermal pigments first followed by loss of dermal pigments. Histopathology and Masson-Fontana stain demonstrated lymphocytic infiltration and loss of pigment production in the regressing area. Immunohistochemistry staining (S100 and HMB-45, however, showed that nevus cells were present in the regressing areas.
Three Ways to Link Merge with Hierarchical Concept-Combination
Directory of Open Access Journals (Sweden)
Chris Thornton
2016-11-01
Full Text Available In the Minimalist Program, language competence is seen to stem from a fundamental ability to construct hierarchical structure, an operation dubbed ‘Merge’. This raises the problem of how to view hierarchical concept-combination. This is a conceptual operation which also builds hierarchical structure. We can conceive of a garden that consists of a lawn and a flower-bed, for example, or a salad consisting of lettuce, fennel and rocket, or a crew consisting of a pilot and engineer. In such cases, concepts are put together in a way that makes one the accommodating element with respect to the others taken in combination. The accommodating element becomes the root of a hierarchical unit. Since this unit is itself a concept, the operation is inherently recursive. Does this mean the mind has two independent systems of hierarchical construction? Or is some form of integration more likely? Following a detailed examination of the operations involved, this paper shows there are three main ways in which Merge might be linked to hierarchical concept-combination. Also examined are the architectural implications that arise in each case.
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.
Hierarchical Microaggressions in Higher Education
Young, Kathryn; Anderson, Myron; Stewart, Saran
2015-01-01
Although there has been substantial research examining the effects of microaggressions in the public sphere, there has been little research that examines microaggressions in the workplace. This study explores the types of microaggressions that affect employees at universities. We coin the term "hierarchical microaggression" to represent…
Ridge Regression Signal Processing
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
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...
Regression in organizational leadership.
Kernberg, O F
1979-02-01
The choice of good leaders is a major task for all organizations. Inforamtion regarding the prospective administrator's personality should complement questions regarding his previous experience, his general conceptual skills, his technical knowledge, and the specific skills in the area for which he is being selected. The growing psychoanalytic knowledge about the crucial importance of internal, in contrast to external, object relations, and about the mutual relationships of regression in individuals and in groups, constitutes an important practical tool for the selection of leaders.
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.
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Hierarchically structured, nitrogen-doped carbon membranes
Wang, Hong; Wu, Tao
2017-01-01
The present invention is a structure, method of making and method of use for a novel macroscopic hierarchically structured, nitrogen-doped, nano-porous carbon membrane (HNDCMs) with asymmetric and hierarchical pore architecture that can be produced
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.
SEPARATION PHENOMENA LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Ikaro Daniel de Carvalho Barreto
2014-03-01
Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.
DEFF Research Database (Denmark)
Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas
2017-01-01
In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface......-product we obtain fast access to the baseline hazards (compared to survival::basehaz()) and predictions of survival probabilities, their confidence intervals and confidence bands. Confidence intervals and confidence bands are based on point-wise asymptotic expansions of the corresponding statistical...
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... 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...
Adaptive Metric Kernel Regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one 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 the standard...
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.
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 Semantic Model of Geovideo
Directory of Open Access Journals (Sweden)
XIE Xiao
2015-05-01
Full Text Available The public security incidents were getting increasingly challenging with regard to their new features, including multi-scale mobility, multistage dynamic evolution, as well as spatiotemporal concurrency and uncertainty in the complex urban environment. However, the existing video models, which were used/designed for independent archive or local analysis of surveillance video, have seriously inhibited emergency response to the urgent requirements.Aiming at the explicit representation of change mechanism in video, the paper proposed a novel hierarchical geovideo semantic model using UML. This model was characterized by the hierarchical representation of both data structure and semantics based on the change-oriented three domains (feature domain, process domain and event domain instead of overall semantic description of video streaming; combining both geographical semantics and video content semantics, in support of global semantic association between multiple geovideo data. The public security incidents by video surveillance are inspected as an example to illustrate the validity of this model.
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.
Hierarchical analysis of urban space
Kataeva, Y.
2014-01-01
Multi-level structure of urban space, multitude of subjects of its transformation, which follow asymmetric interests, multilevel system of institutions which regulate interaction in the "population business government -public organizations" system, determine the use of hierarchic approach to the analysis of urban space. The article observes theoretical justification of using this approach to study correlations and peculiarities of interaction in urban space as in an intricately organized syst...
Statistical Significance for Hierarchical Clustering
Kimes, Patrick K.; Liu, Yufeng; Hayes, D. Neil; Marron, J. S.
2017-01-01
Summary Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this paper, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets. PMID:28099990
Polylinear regression analysis in radiochemistry
International Nuclear Information System (INIS)
Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.
1995-01-01
A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy....... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes.......This paper examines the relationship between foreign aid and growth in real GDP per capita as it emerges from simple augmentations of popular cross country growth specifications. It is shown that aid in all likelihood increases the growth rate, and this result is not conditional on ‘good’ policy...
Anti-hierarchical evolution of the active galactic nucleus space density in a hierarchical universe
International Nuclear Information System (INIS)
Enoki, Motohiro; Ishiyama, Tomoaki; Kobayashi, Masakazu A. R.; Nagashima, Masahiro
2014-01-01
Recent observations show that the space density of luminous active galactic nuclei (AGNs) peaks at higher redshifts than that of faint AGNs. This downsizing trend in the AGN evolution seems to be contradictory to the hierarchical structure formation scenario. In this study, we present the AGN space density evolution predicted by a semi-analytic model of galaxy and AGN formation based on the hierarchical structure formation scenario. We demonstrate that our model can reproduce the downsizing trend of the AGN space density evolution. The reason for the downsizing trend in our model is a combination of the cold gas depletion as a consequence of star formation, the gas cooling suppression in massive halos, and the AGN lifetime scaling with the dynamical timescale. We assume that a major merger of galaxies causes a starburst, spheroid formation, and cold gas accretion onto a supermassive black hole (SMBH). We also assume that this cold gas accretion triggers AGN activity. Since the cold gas is mainly depleted by star formation and gas cooling is suppressed in massive dark halos, the amount of cold gas accreted onto SMBHs decreases with cosmic time. Moreover, AGN lifetime increases with cosmic time. Thus, at low redshifts, major mergers do not always lead to luminous AGNs. Because the luminosity of AGNs is correlated with the mass of accreted gas onto SMBHs, the space density of luminous AGNs decreases more quickly than that of faint AGNs. We conclude that the anti-hierarchical evolution of the AGN space density is not contradictory to the hierarchical structure formation scenario.
Anti-hierarchical evolution of the active galactic nucleus space density in a hierarchical universe
Energy Technology Data Exchange (ETDEWEB)
Enoki, Motohiro [Faculty of Business Administration, Tokyo Keizai University, Kokubunji, Tokyo 185-8502 (Japan); Ishiyama, Tomoaki [Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577 (Japan); Kobayashi, Masakazu A. R. [Research Center for Space and Cosmic Evolution, Ehime University, Matsuyama, Ehime 790-8577 (Japan); Nagashima, Masahiro, E-mail: enokimt@tku.ac.jp [Faculty of Education, Nagasaki University, Nagasaki, Nagasaki 852-8521 (Japan)
2014-10-10
Recent observations show that the space density of luminous active galactic nuclei (AGNs) peaks at higher redshifts than that of faint AGNs. This downsizing trend in the AGN evolution seems to be contradictory to the hierarchical structure formation scenario. In this study, we present the AGN space density evolution predicted by a semi-analytic model of galaxy and AGN formation based on the hierarchical structure formation scenario. We demonstrate that our model can reproduce the downsizing trend of the AGN space density evolution. The reason for the downsizing trend in our model is a combination of the cold gas depletion as a consequence of star formation, the gas cooling suppression in massive halos, and the AGN lifetime scaling with the dynamical timescale. We assume that a major merger of galaxies causes a starburst, spheroid formation, and cold gas accretion onto a supermassive black hole (SMBH). We also assume that this cold gas accretion triggers AGN activity. Since the cold gas is mainly depleted by star formation and gas cooling is suppressed in massive dark halos, the amount of cold gas accreted onto SMBHs decreases with cosmic time. Moreover, AGN lifetime increases with cosmic time. Thus, at low redshifts, major mergers do not always lead to luminous AGNs. Because the luminosity of AGNs is correlated with the mass of accreted gas onto SMBHs, the space density of luminous AGNs decreases more quickly than that of faint AGNs. We conclude that the anti-hierarchical evolution of the AGN space density is not contradictory to the hierarchical structure formation scenario.
Ross, Michelle; Wakefield, Jon
2015-10-01
Two-phase study designs are appealing since they allow for the oversampling of rare sub-populations which improves efficiency. In this paper we describe a Bayesian hierarchical model for the analysis of two-phase data. Such a model is particularly appealing in a spatial setting in which random effects are introduced to model between-area variability. In such a situation, one may be interested in estimating regression coefficients or, in the context of small area estimation, in reconstructing the population totals by strata. The efficiency gains of the two-phase sampling scheme are compared to standard approaches using 2011 birth data from the research triangle area of North Carolina. We show that the proposed method can overcome small sample difficulties and improve on existing techniques. We conclude that the two-phase design is an attractive approach for small area estimation.
Detecting the overlapping and hierarchical community structure in complex networks
International Nuclear Information System (INIS)
Lancichinetti, Andrea; Fortunato, Santo; Kertesz, Janos
2009-01-01
Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.
Organization of excitable dynamics in hierarchical biological networks.
Directory of Open Access Journals (Sweden)
Mark Müller-Linow
Full Text Available This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.
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.
Radhika, N P; Selvin, Rosilda; Kakkar, Rita; Roselin, L Selva
2018-08-01
In this paper, authors report the synthesis of nanocrystalline hierarchical zeolite ZSM-5 and its application as a heterogeneous catalyst in the alkylation of phenol with cyclohexene. The catalyst was synthesized by vacuum-concentration coupled hydrothermal technique in the presence of two templates. This synthetic route could successfully introduce pores of higher hierarchy in the zeolite ZSM-5 structure. Hierarchical ZSM-5 could catalyse effectively the industrially important reaction of cyclohexene with phenol. We ascribe the high efficiency of the catalyst to its conducive structural features such as nanoscale size, high surface area, presence of hierarchy of pores and existence of Lewis sites along with Brønsted acid sites. The effect of various reaction parameters like duration, catalyst amount, reactant mole ratio and temperature were assessed. Under optimum reaction conditions, the catalyst showed up to 65% selectivity towards the major product, cyclohexyl phenyl ether. There was no discernible decline in percent conversion or selectivity even when the catalyst was re-used for up to four runs. Kinetic studies were done through regression analysis and a mechanistic route based on LHHW model was suggested.
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.
[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.
Facile synthesis and photocatalytic activity of zinc oxide hierarchical microcrystals
Xu, Xinjiang; Kuang, Fangcheng; Xu, Jiangping
2013-01-01
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
Hierarchal scalar and vector tetrahedra
International Nuclear Information System (INIS)
Webb, J.P.; Forghani, B.
1993-01-01
A new set of scalar and vector tetrahedral finite elements are presented. The elements are hierarchal, allowing mixing of polynomial orders; scalar orders up to 3 and vector orders up to 2 are defined. The vector elements impose tangential continuity on the field but not normal continuity, making them suitable for representing the vector electric or magnetic field. Further, the scalar and vector elements are such that they can easily be used in the same mesh, a requirement of many quasi-static formulations. Results are presented for two 50 Hz problems: the Bath Cube, and TEAM Problem 7
Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun
2017-08-01
Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2 = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.
Loops in hierarchical channel networks
Katifori, Eleni; Magnasco, Marcelo
2012-02-01
Nature provides us with many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture. Although a number of methods have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated and natural graphs extracted from digitized images of dicotyledonous leaves and animal vasculature. We calculate various metrics on the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.
Stability of glassy hierarchical networks
Zamani, M.; Camargo-Forero, L.; Vicsek, T.
2018-02-01
The structure of interactions in most animal and human societies can be best represented by complex hierarchical networks. In order to maintain close-to-optimal function both stability and adaptability are necessary. Here we investigate the stability of hierarchical networks that emerge from the simulations of an organization type with an efficiency function reminiscent of the Hamiltonian of spin glasses. Using this quantitative approach we find a number of expected (from everyday observations) and highly non-trivial results for the obtained locally optimal networks, including, for example: (i) stability increases with growing efficiency and level of hierarchy; (ii) the same perturbation results in a larger change for more efficient states; (iii) networks with a lower level of hierarchy become more efficient after perturbation; (iv) due to the huge number of possible optimal states only a small fraction of them exhibit resilience and, finally, (v) ‘attacks’ targeting the nodes selectively (regarding their position in the hierarchy) can result in paradoxical outcomes.
Hierarchical modeling of active materials
International Nuclear Information System (INIS)
Taya, Minoru
2003-01-01
Intelligent (or smart) materials are increasingly becoming key materials for use in actuators and sensors. If an intelligent material is used as a sensor, it can be embedded in a variety of structure functioning as a health monitoring system to make their life longer with high reliability. If an intelligent material is used as an active material in an actuator, it plays a key role of making dynamic movement of the actuator under a set of stimuli. This talk intends to cover two different active materials in actuators, (1) piezoelectric laminate with FGM microstructure, (2) ferromagnetic shape memory alloy (FSMA). The advantage of using the FGM piezo laminate is to enhance its fatigue life while maintaining large bending displacement, while that of use in FSMA is its fast actuation while providing a large force and stroke capability. Use of hierarchical modeling of the above active materials is a key design step in optimizing its microstructure for enhancement of their performance. I will discuss briefly hierarchical modeling of the above two active materials. For FGM piezo laminate, we will use both micromechanical model and laminate theory, while for FSMA, the modeling interfacing nano-structure, microstructure and macro-behavior is discussed. (author)
Hierarchical organisation of causal graphs
International Nuclear Information System (INIS)
Dziopa, P.
1993-01-01
This paper deals with the design of a supervision system using a hierarchy of models formed by graphs, in which the variables are the nodes and the causal relations between the variables of the arcs. To obtain a representation of the variables evolutions which contains only the relevant features of their real evolutions, the causal relations are completed with qualitative transfer functions (QTFs) which produce roughly the behaviour of the classical transfer functions. Major improvements have been made in the building of the hierarchical organization. First, the basic variables of the uppermost level and the causal relations between them are chosen. The next graph is built by adding intermediary variables to the upper graph. When the undermost graph has been built, the transfer functions parameters corresponding to its causal relations are identified. The second task consists in the upwelling of the information from the undermost graph to the uppermost one. A fusion procedure of the causal relations has been designed to compute the QFTs relevant for each level. This procedure aims to reduce the number of parameters needed to represent an evolution at a high level of abstraction. These techniques have been applied to the hierarchical modelling of nuclear process. (authors). 8 refs., 12 figs
Polynomial regression analysis and significance test of the regression function
International Nuclear Information System (INIS)
Gao Zhengming; Zhao Juan; He Shengping
2012-01-01
In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)
Hierarchical graphs for rule-based modeling of biochemical systems
Directory of Open Access Journals (Sweden)
Hu Bin
2011-02-01
Full Text Available Abstract Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal of an edge represents a class of association (dissociation reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for
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.
DEFF Research Database (Denmark)
2014-01-01
The anti-reenactment 'Show-Bix &' consists of 5 dias projectors, a dial phone, quintophonic sound, and interactive elements. A responsive interface will enable the Dias projectors to show copies of original dias slides from the Show-Bix piece ”March på Stedet”, 265 images in total. The copies are...
Spontaneous regression of pulmonary bullae
International Nuclear Information System (INIS)
Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.
2002-01-01
The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd
Quantum algorithm for linear regression
Wang, Guoming
2017-07-01
We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.
Hierarchical reorganization of dimensions in OLAP visualizations.
Lafon, Sébastien; Bouali, Fatma; Guinot, Christiane; Venturini, Gilles
2013-11-01
In this paper, we propose a new method for the visual reorganization of online analytical processing (OLAP) cubes that aims at improving their visualization. Our method addresses dimensions with hierarchically organized members. It uses a genetic algorithm that reorganizes k-ary trees. Genetic operators perform permutations of subtrees to optimize a visual homogeneity function. We propose several ways to reorganize an OLAP cube depending on which set of members is selected for the reorganization: all of the members, only the displayed members, or the members at a given level (level by level approach). The results that are evaluated by using optimization criteria show that our algorithm has a reliable performance even when it is limited to 1 minute runs. Our algorithm was integrated in an interactive 3D interface for OLAP. A user study was conducted to evaluate our approach with users. The results highlight the usefulness of reorganization in two OLAP tasks.
Lu, Junfei; Woo, Hongryun
2017-01-01
In this study, 74 master's-level counseling students from various programs completed a questionnaire inquiring about their perceived program environment in relation to the topics of spirituality and religion (S/R), program emphasis on nine specific S/R competencies, as well as their outcome expectations toward being S/R competent through training.…
Ker, H. W.
2014-01-01
Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…
Chad Babcock; Andrew O. Finley; John B. Bradford; Randy Kolka; Richard Birdsey; Michael G. Ryan
2015-01-01
Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both...
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.
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.
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...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... 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...
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.
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...
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.
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. Copyright © 2015 Elsevier Inc. All rights reserved.
Distributed hierarchical radiation monitoring system
International Nuclear Information System (INIS)
Barak, D.
1985-01-01
A solution to the problem of monitoring the radiation levels in and around a nuclear facility is presented in this paper. This is a private case of a large scale general purpose data acqisition system with high reliability, availability and short maintenance time. The physical layout of the detectors in the plant, and the strict control demands dictated a distributed and hierarchical system. The system is comprised of three levels, each level contains modules. Level one contains the Control modules which collects data from groups of detectors and executes emergency local control tasks. In level two are the Group controllers which concentrate data from the Control modules, and enable local display and communication. The system computer is in level three, enabling the plant operator to receive information from the detectors and execute control tasks. The described system was built and is operating successfully for about two years. (author)
Hierarchical Control for Smart Grids
DEFF Research Database (Denmark)
Trangbæk, K; Bendtsen, Jan Dimon; Stoustrup, Jakob
2011-01-01
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......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...... on one hand from varying consumption, and on the other hand by natural variations in power production e.g. from wind turbines. The high-level MPC problem is solved using quadratic optimisation, while the aggregator level can either involve quadratic optimisation or simple sorting-based min-max solutions...
Hierarchical coarse-graining transform.
Pancaldi, Vera; King, Peter R; Christensen, Kim
2009-03-01
We present a hierarchical transform that can be applied to Laplace-like differential equations such as Darcy's equation for single-phase flow in a porous medium. A finite-difference discretization scheme is used to set the equation in the form of an eigenvalue problem. Within the formalism suggested, the pressure field is decomposed into an average value and fluctuations of different kinds and at different scales. The application of the transform to the equation allows us to calculate the unknown pressure with a varying level of detail. A procedure is suggested to localize important features in the pressure field based only on the fine-scale permeability, and hence we develop a form of adaptive coarse graining. The formalism and method are described and demonstrated using two synthetic toy problems.
Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
Two Paradoxes in Linear Regression Analysis
FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong
2016-01-01
Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214
Adaptive hierarchical multi-agent organizations
Ghijsen, M.; Jansweijer, W.N.H.; Wielinga, B.J.; Babuška, R.; Groen, F.C.A.
2010-01-01
In this chapter, we discuss the design of adaptive hierarchical organizations for multi-agent systems (MAS). Hierarchical organizations have a number of advantages such as their ability to handle complex problems and their scalability to large organizations. By introducing adaptivity in the
The Case for a Hierarchical Cosmology
Vaucouleurs, G. de
1970-01-01
The development of modern theoretical cosmology is presented and some questionable assumptions of orthodox cosmology are pointed out. Suggests that recent observations indicate that hierarchical clustering is a basic factor in cosmology. The implications of hierarchical models of the universe are considered. Bibliography. (LC)
Discovering hierarchical structure in normal relational data
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Herlau, Tue; Mørup, Morten
2014-01-01
-parametric generative model for hierarchical clustering of similarity based on multifurcating Gibbs fragmentation trees. This allows us to infer and display the posterior distribution of hierarchical structures that comply with the data. We demonstrate the utility of our method on synthetic data and data of functional...
Biased trapping issue on weighted hierarchical networks
Indian Academy of Sciences (India)
archical networks which are based on the classic scale-free hierarchical networks. ... Weighted hierarchical networks; weight-dependent walks; mean first passage ..... The weighted networks can mimic some real-world natural and social systems to ... the Priority Academic Program Development of Jiangsu Higher Education ...
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.
Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization
Energy Technology Data Exchange (ETDEWEB)
Kumar, Ranjan [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: ranjan.k@ks3.ecs.kyoto-u.ac.jp; Izui, Kazuhiro [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: izui@prec.kyoto-u.ac.jp; Yoshimura, Masataka [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: yoshimura@prec.kyoto-u.ac.jp; Nishiwaki, Shinji [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: shinji@prec.kyoto-u.ac.jp
2009-04-15
Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.
Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization
International Nuclear Information System (INIS)
Kumar, Ranjan; Izui, Kazuhiro; Yoshimura, Masataka; Nishiwaki, Shinji
2009-01-01
Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets
Hierarchical effects on target detection and conflict monitoring
Cao, Bihua; Gao, Feng; Ren, Maofang; Li, Fuhong
2016-01-01
Previous neuroimaging studies have demonstrated a hierarchical functional structure of the frontal cortices of the human brain, but the temporal course and the electrophysiological signature of the hierarchical representation remains unaddressed. In the present study, twenty-one volunteers were asked to perform a nested cue-target task, while their scalp potentials were recorded. The results showed that: (1) in comparison with the lower-level hierarchical targets, the higher-level targets elicited a larger N2 component (220–350 ms) at the frontal sites, and a smaller P3 component (350–500 ms) across the frontal and parietal sites; (2) conflict-related negativity (non-target minus target) was greater for the lower-level hierarchy than the higher-level, reflecting a more intensive process of conflict monitoring at the final step of target detection. These results imply that decision making, context updating, and conflict monitoring differ among different hierarchical levels of abstraction. PMID:27561989
Hierarchical sets: analyzing pangenome structure through scalable set visualizations
2017-01-01
Abstract Motivation: The increase in available microbial genome sequences has resulted in an increase in the size of the pangenomes being analyzed. Current pangenome visualizations are not intended for the pangenome sizes possible today and new approaches are necessary in order to convert the increase in available information to increase in knowledge. As the pangenome data structure is essentially a collection of sets we explore the potential for scalable set visualization as a tool for pangenome analysis. Results: We present a new hierarchical clustering algorithm based on set arithmetics that optimizes the intersection sizes along the branches. The intersection and union sizes along the hierarchy are visualized using a composite dendrogram and icicle plot, which, in pangenome context, shows the evolution of pangenome and core size along the evolutionary hierarchy. Outlying elements, i.e. elements whose presence pattern do not correspond with the hierarchy, can be visualized using hierarchical edge bundles. When applied to pangenome data this plot shows putative horizontal gene transfers between the genomes and can highlight relationships between genomes that is not represented by the hierarchy. We illustrate the utility of hierarchical sets by applying it to a pangenome based on 113 Escherichia and Shigella genomes and find it provides a powerful addition to pangenome analysis. Availability and Implementation: The described clustering algorithm and visualizations are implemented in the hierarchicalSets R package available from CRAN (https://cran.r-project.org/web/packages/hierarchicalSets) Contact: thomasp85@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28130242
DEFF Research Database (Denmark)
Sandvik, Kjetil; Laursen, Ditte
2014-01-01
User interaction with radio and television programmes is not a new thing. However, with new cross-media production concepts such as X Factor and Voice, this is changing dramatically. The second-screen logic of these productions encourages viewers, along with TV’s traditional one-way communication...... mode, to communicate on interactive (dialogue-enabling) devices such as laptops, smartphones and tablets. Using the TV show Voice as our example, this article shows how the technological and situational set-up of the production invites viewers to engage in new ways of interaction and communication...
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.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
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......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...... 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....
Moore, Mitzi Ruth
1992-01-01
Proposes having students perform skits in which they play the roles of the science concepts they are trying to understand. Provides the dialog for a skit in which hot and cold gas molecules are interviewed on a talk show to study how these properties affect wind, rain, and other weather phenomena. (MDH)
Corbee, R J
2014-12-01
Obesity is an important disease with a high prevalence in cats. Because obesity is related to several other diseases, it is important to identify the population at risk. Several risk factors for obesity have been described in the literature. A higher incidence of obesity in certain cat breeds has been suggested. The aim of this study was to determine whether obesity occurs more often in certain breeds. The second aim was to relate the increased prevalence of obesity in certain breeds to the official standards of that breed. To this end, 268 cats of 22 different breeds investigated by determining their body condition score (BCS) on a nine-point scale by inspection and palpation, at two different cat shows. Overall, 45.5% of the show cats had a BCS > 5, and 4.5% of the show cats had a BCS > 7. There were significant differences between breeds, which could be related to the breed standards. Most overweight and obese cats were in the neutered group. It warrants firm discussions with breeders and cat show judges to come to different interpretations of the standards in order to prevent overweight conditions in certain breeds from being the standard of beauty. Neutering predisposes for obesity and requires early nutritional intervention to prevent obese conditions. Journal of Animal Physiology and Animal Nutrition © 2014 Blackwell Verlag GmbH.
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)
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
Hierarchical Bayesian modelling of mobility metrics for hazard model input calibration
Calder, Eliza; Ogburn, Sarah; Spiller, Elaine; Rutarindwa, Regis; Berger, Jim
2015-04-01
In this work we present a method to constrain flow mobility input parameters for pyroclastic flow models using hierarchical Bayes modeling of standard mobility metrics such as H/L and flow volume etc. The advantage of hierarchical modeling is that it can leverage the information in global dataset for a particular mobility metric in order to reduce the uncertainty in modeling of an individual volcano, especially important where individual volcanoes have only sparse datasets. We use compiled pyroclastic flow runout data from Colima, Merapi, Soufriere Hills, Unzen and Semeru volcanoes, presented in an open-source database FlowDat (https://vhub.org/groups/massflowdatabase). While the exact relationship between flow volume and friction varies somewhat between volcanoes, dome collapse flows originating from the same volcano exhibit similar mobility relationships. Instead of fitting separate regression models for each volcano dataset, we use a variation of the hierarchical linear model (Kass and Steffey, 1989). The model presents a hierarchical structure with two levels; all dome collapse flows and dome collapse flows at specific volcanoes. The hierarchical model allows us to assume that the flows at specific volcanoes share a common distribution of regression slopes, then solves for that distribution. We present comparisons of the 95% confidence intervals on the individual regression lines for the data set from each volcano as well as those obtained from the hierarchical model. The results clearly demonstrate the advantage of considering global datasets using this technique. The technique developed is demonstrated here for mobility metrics, but can be applied to many other global datasets of volcanic parameters. In particular, such methods can provide a means to better contain parameters for volcanoes for which we only have sparse data, a ubiquitous problem in volcanology.
International Nuclear Information System (INIS)
1988-01-01
The Energy Show is a new look at the problems of world energy, where our supplies come from, now and in the future. The programme looks at how we need energy to maintain our standards of living. Energy supply is shown as the complicated set of problems it is - that Fossil Fuels are both raw materials and energy sources, that some 'alternatives' so readily suggested as practical options are in reality a long way from being effective. (author)
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
Intensity-based hierarchical elastic registration using approximating splines.
Serifovic-Trbalic, Amira; Demirovic, Damir; Cattin, Philippe C
2014-01-01
We introduce a new hierarchical approach for elastic medical image registration using approximating splines. In order to obtain the dense deformation field, we employ Gaussian elastic body splines (GEBS) that incorporate anisotropic landmark errors and rotation information. Since the GEBS approach is based on a physical model in form of analytical solutions of the Navier equation, it can very well cope with the local as well as global deformations present in the images by varying the standard deviation of the Gaussian forces. The proposed GEBS approximating model is integrated into the elastic hierarchical image registration framework, which decomposes a nonrigid registration problem into numerous local rigid transformations. The approximating GEBS registration scheme incorporates anisotropic landmark errors as well as rotation information. The anisotropic landmark localization uncertainties can be estimated directly from the image data, and in this case, they represent the minimal stochastic localization error, i.e., the Cramér-Rao bound. The rotation information of each landmark obtained from the hierarchical procedure is transposed in an additional angular landmark, doubling the number of landmarks in the GEBS model. The modified hierarchical registration using the approximating GEBS model is applied to register 161 image pairs from a digital mammogram database. The obtained results are very encouraging, and the proposed approach significantly improved all registrations comparing the mean-square error in relation to approximating TPS with the rotation information. On artificially deformed breast images, the newly proposed method performed better than the state-of-the-art registration algorithm introduced by Rueckert et al. (IEEE Trans Med Imaging 18:712-721, 1999). The average error per breast tissue pixel was less than 2.23 pixels compared to 2.46 pixels for Rueckert's method. The proposed hierarchical elastic image registration approach incorporates the GEBS
Panel Smooth Transition Regression Models
DEFF Research Database (Denmark)
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
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
Testing discontinuities in nonparametric regression
Dai, Wenlin; Zhou, Yuejin; Tong, Tiejun
2017-01-01
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…
Classification using Hierarchical Naive Bayes models
DEFF Research Database (Denmark)
Langseth, Helge; Dyhre Nielsen, Thomas
2006-01-01
Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe......, termed Hierarchical Naïve Bayes models. Hierarchical Naïve Bayes models extend the modeling flexibility of Naïve Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naïve Bayes models...
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
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.
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 (c) 2016 APA, all rights reserved).
International Nuclear Information System (INIS)
Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei
2007-01-01
Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age
Biomedical application of hierarchically built structures based on metal oxides
Korovin, M. S.; Fomenko, A. N.
2017-12-01
Nowadays, the use of hierarchically built structures in biology and medicine arouses much interest. The aim of this work is to review and summarize the available literature data about hierarchically organized structures in biomedical application. Nanoparticles can serve as an example of such structures. Medicine holds a special place among various application methods of similar systems. Special attention is paid to inorganic nanoparticles based on different metal oxides and hydroxides, such as iron, zinc, copper, and aluminum. Our investigations show that low-dimensional nanostructures based on aluminum oxides and hydroxides have an inhibitory effect on tumor cells and possess an antimicrobial activity. At the same time, it is obvious that the large-scale use of nanoparticles by humans needs to thoroughly study their properties. Special attention should be paid to the study of nanoparticle interaction with living biological objects. The numerous data show that there is no clear understanding of interaction mechanisms between nanoparticles and various cell types.
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
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...
Regularized Label Relaxation Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung; Fang, Bingwu
2018-04-01
Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on -norm and -norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.
A mechanical model of biomimetic adhesive pads with tilted and hierarchical structures.
Schargott, M
2009-06-01
A 3D model for hierarchical biomimetic adhesive pads is constructed. It is based on the main principles of the adhesive pads of the Tokay gecko and consists of hierarchical layers of vertical or tilted beams, where each layer is constructed in such a way that no cohesion between adjacent beams can occur. The elastic and adhesive properties are calculated analytically and numerically. For the adhesive contact on stochastically rough surfaces, the maximum adhesion force increases with increasing number of hierarchical layers. Additional calculations show that the adhesion force also depends on the height spectrum of the rough surface.
A mechanical model of biomimetic adhesive pads with tilted and hierarchical structures
Energy Technology Data Exchange (ETDEWEB)
Schargott, M [Institute of Mechanics, Technische Universitaet Berlin, Strd 17 Juni 135, 10623 Berlin (Germany)], E-mail: martin.schargott@tu-berlin.de
2009-06-01
A 3D model for hierarchical biomimetic adhesive pads is constructed. It is based on the main principles of the adhesive pads of the Tokay gecko and consists of hierarchical layers of vertical or tilted beams, where each layer is constructed in such a way that no cohesion between adjacent beams can occur. The elastic and adhesive properties are calculated analytically and numerically. For the adhesive contact on stochastically rough surfaces, the maximum adhesion force increases with increasing number of hierarchical layers. Additional calculations show that the adhesion force also depends on the height spectrum of the rough surface.
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
A mechanical model of biomimetic adhesive pads with tilted and hierarchical structures
International Nuclear Information System (INIS)
Schargott, M
2009-01-01
A 3D model for hierarchical biomimetic adhesive pads is constructed. It is based on the main principles of the adhesive pads of the Tokay gecko and consists of hierarchical layers of vertical or tilted beams, where each layer is constructed in such a way that no cohesion between adjacent beams can occur. The elastic and adhesive properties are calculated analytically and numerically. For the adhesive contact on stochastically rough surfaces, the maximum adhesion force increases with increasing number of hierarchical layers. Additional calculations show that the adhesion force also depends on the height spectrum of the rough surface
Algorithm of parallel: hierarchical transformation and its implementation on FPGA
Timchenko, Leonid I.; Petrovskiy, Mykola S.; Kokryatskay, Natalia I.; Barylo, Alexander S.; Dembitska, Sofia V.; Stepanikuk, Dmytro S.; Suleimenov, Batyrbek; Zyska, Tomasz; Uvaysova, Svetlana; Shedreyeva, Indira
2017-08-01
In this paper considers the algorithm of laser beam spots image classification in atmospheric-optical transmission systems. It discusses the need for images filtering using adaptive methods, using, for example, parallel-hierarchical networks. The article also highlights the need to create high-speed memory devices for such networks. Implementation and simulation results of the developed method based on the PLD are demonstrated, which shows that the presented method gives 15-20% better prediction results than similar methods.
Advanced statistics: linear regression, part II: multiple linear regression.
Marill, Keith A
2004-01-01
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Logic regression and its extensions.
Schwender, Holger; Ruczinski, Ingo
2010-01-01
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.
Fluorocarbon adsorption in hierarchical porous frameworks
Energy Technology Data Exchange (ETDEWEB)
Motkuri, RK; Annapureddy, HVR; Vijaykumar, M; Schaef, HT; Martin, PF; McGrail, BP; Dang, LX; Krishna, R; Thallapally, PK
2014-07-09
Metal-organic frameworks comprise an important class of solid-state materials and have potential for many emerging applications such as energy storage, separation, catalysis and bio-medical. Here we report the adsorption behaviour of a series of fluorocarbon derivatives on a set of microporous and hierarchical mesoporous frameworks. The microporous frameworks show a saturation uptake capacity for dichlorodifluoromethane of >4 mmol g(-1) at a very low relative saturation pressure (P/P-o) of 0.02. In contrast, the mesoporous framework shows an exceptionally high uptake capacity reaching >14 mmol g(-1) at P/P-o of 0.4. Adsorption affinity in terms of mass loading and isosteric heats of adsorption is found to generally correlate with the polarizability and boiling point of the refrigerant, with dichlorodifluoromethane >chlorodifluoromethane >chlorotrifluoromethane >tetrafluoromethane >methane. These results suggest the possibility of exploiting these sorbents for separation of azeotropic mixtures of fluorocarbons and use in eco-friendly fluorocarbon-based adsorption cooling.
National Research Council Canada - National Science Library
Aberg, P
2001-01-01
... before and after application of chemicals on volar forearms of volunteers, Tegobetaine and sodium lauryl sulphate were used to induce the irritations, The spectra were filtered using orthogonal signal correction (OSC...
Zeolitic materials with hierarchical porous structures.
Lopez-Orozco, Sofia; Inayat, Amer; Schwab, Andreas; Selvam, Thangaraj; Schwieger, Wilhelm
2011-06-17
During the past several years, different kinds of hierarchical structured zeolitic materials have been synthesized due to their highly attractive properties, such as superior mass/heat transfer characteristics, lower restriction of the diffusion of reactants in the mesopores, and low pressure drop. Our contribution provides general information regarding types and preparation methods of hierarchical zeolitic materials and their relative advantages and disadvantages. Thereafter, recent advances in the preparation and characterization of hierarchical zeolitic structures within the crystallites by post-synthetic treatment methods, such as dealumination or desilication; and structured devices by in situ and ex situ zeolite coatings on open-cellular ceramic foams as (non-reactive as well as reactive) supports are highlighted. Specific advantages of using hierarchical zeolitic catalysts/structures in selected catalytic reactions, such as benzene to phenol (BTOP) and methanol to olefins (MTO) are presented. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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 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...
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.
Packaging glass with hierarchically nanostructured surface
He, Jr-Hau; Fu, Hui-Chun
2017-01-01
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
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.
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 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.
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 screening for multiple mental disorders.
Batterham, Philip J; Calear, Alison L; Sunderland, Matthew; Carragher, Natacha; Christensen, Helen; Mackinnon, Andrew J
2013-10-01
There is a need for brief, accurate screening when assessing multiple mental disorders. Two-stage hierarchical screening, consisting of brief pre-screening followed by a battery of disorder-specific scales for those who meet diagnostic criteria, may increase the efficiency of screening without sacrificing precision. This study tested whether more efficient screening could be gained using two-stage hierarchical screening than by administering multiple separate tests. Two Australian adult samples (N=1990) with high rates of psychopathology were recruited using Facebook advertising to examine four methods of hierarchical screening for four mental disorders: major depressive disorder, generalised anxiety disorder, panic disorder and social phobia. Using K6 scores to determine whether full screening was required did not increase screening efficiency. However, pre-screening based on two decision tree approaches or item gating led to considerable reductions in the mean number of items presented per disorder screened, with estimated item reductions of up to 54%. The sensitivity of these hierarchical methods approached 100% relative to the full screening battery. Further testing of the hierarchical screening approach based on clinical criteria and in other samples is warranted. The results demonstrate that a two-phase hierarchical approach to screening multiple mental disorders leads to considerable increases efficiency gains without reducing accuracy. Screening programs should take advantage of prescreeners based on gating items or decision trees to reduce the burden on respondents. © 2013 Elsevier B.V. All rights reserved.
Fabrication of Superhydrophobic Surface with Controlled Wetting Property by Hierarchical Particles.
Xu, Jianxiong; Liu, Weiwei; Du, Jingjing; Tang, Zengmin; Xu, Lijian; Li, Na
2015-04-01
Hierarchical particles were prepared by synthetically joining appropriately functionalized polystyrene spheres of poly[styrene-co-(3-(4-vinylphenyl)pentane-2,4-dione)] (PS-co-PVPD) nanoparticles and poly(styrene-co-chloromethylstyrene) (PS-co-PCMS) microparticles. The coupling reaction of nucleophilic substitution of pendent β-diketone groups with benzyl chloride was used to form the hierarchical particles. Since the polymeric nanoparticles and microparticles were synthesized by dispersion polymerization and emulsion polymerization, respectively, both the core microparticles and the surface nanoparticles can be different size and chemical composition. By means of changing the size of the PS-co-PVPD surface nanoparticles, a series of hierarchical particles with different scale ratio of the micro/nano surface structure were successfully prepared. Moreover, by employing the PS-co-PVPD microparticles and PS-co-PCMS nanoparticles as building blocks, hierarchical particles with surface nanoaprticles of different composition were made. These as-prepared hierarchical particles were subsequently assembled on glass substrates to form particulate films. Contact angle measurement shows that superhydrophobic surfaces can be obtained and the contact angle of water on the hierarchically structured surface can be adjusted by the scale ratio of the micro/nano surface structure and surface chemical component of hierarchical particles.
Abstract Expression Grammar Symbolic Regression
Korns, Michael F.
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2009-01-01
. 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
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
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....
Testing Heteroscedasticity in Robust Regression
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2011-01-01
Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
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.
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.
Band structures of two dimensional solid/air hierarchical phononic crystals
Energy Technology Data Exchange (ETDEWEB)
Xu, Y.L.; Tian, X.G. [State Key Laboratory for Mechanical Structure Strength and Vibration, Xi' an Jiaotong University, Xi' an 710049 (China); Chen, C.Q., E-mail: chencq@tsinghua.edu.cn [Department of Engineering Mechanics, AML and CNMM, Tsinghua University, Beijing 100084 (China)
2012-06-15
The hierarchical phononic crystals to be considered show a two-order 'hierarchical' feature, which consists of square array arranged macroscopic periodic unit cells with each unit cell itself including four sub-units. Propagation of acoustic wave in such two dimensional solid/air phononic crystals is investigated by the finite element method (FEM) with the Bloch theory. Their band structure, wave filtering property, and the physical mechanism responsible for the broadened band gap are explored. The corresponding ordinary phononic crystal without hierarchical feature is used for comparison. Obtained results show that the solid/air hierarchical phononic crystals possess tunable outstanding band gap features, which are favorable for applications such as sound insulation and vibration attenuation.
Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun
2014-12-01
Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.
Multiple Linear Regression: A Realistic Reflector.
Nutt, A. T.; Batsell, R. R.
Examples of the use of Multiple Linear Regression (MLR) techniques are presented. This is done to show how MLR aids data processing and decision-making by providing the decision-maker with freedom in phrasing questions and by accurately reflecting the data on hand. A brief overview of the rationale underlying MLR is given, some basic definitions…
Hierarchical vs non-hierarchical audio indexation and classification for video genres
Dammak, Nouha; BenAyed, Yassine
2018-04-01
In this paper, Support Vector Machines (SVMs) are used for segmenting and indexing video genres based on only audio features extracted at block level, which has a prominent asset by capturing local temporal information. The main contribution of our study is to show the wide effect on the classification accuracies while using an hierarchical categorization structure based on Mel Frequency Cepstral Coefficients (MFCC) audio descriptor. In fact, the classification consists in three common video genres: sports videos, music clips and news scenes. The sub-classification may divide each genre into several multi-speaker and multi-dialect sub-genres. The validation of this approach was carried out on over 360 minutes of video span yielding a classification accuracy of over 99%.
Hierarchical optimal control of large-scale nonlinear chemical processes.
Ramezani, Mohammad Hossein; Sadati, Nasser
2009-01-01
In this paper, a new approach is presented for optimal control of large-scale chemical processes. In this approach, the chemical process is decomposed into smaller sub-systems at the first level, and a coordinator at the second level, for which a two-level hierarchical control strategy is designed. For this purpose, each sub-system in the first level can be solved separately, by using any conventional optimization algorithm. In the second level, the solutions obtained from the first level are coordinated using a new gradient-type strategy, which is updated by the error of the coordination vector. The proposed algorithm is used to solve the optimal control problem of a complex nonlinear chemical stirred tank reactor (CSTR), where its solution is also compared with the ones obtained using the centralized approach. The simulation results show the efficiency and the capability of the proposed hierarchical approach, in finding the optimal solution, over the centralized method.
Poincaré Embeddings for Learning Hierarchical Representations
CERN. Geneva
2018-01-01
Abstracts: Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically do not account for this property. In this talk, I will discuss a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincaré ball. Due to the underlying hyperbolic geometry, this allows us to learn parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. We introduce an efficient algorithm to learn the embeddings based on Riemannian optimization and show experimentally that Poincaré embeddings outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability. &...
Hierarchical tone mapping for high dynamic range image visualization
Qiu, Guoping; Duan, Jiang
2005-07-01
In this paper, we present a computationally efficient, practically easy to use tone mapping techniques for the visualization of high dynamic range (HDR) images in low dynamic range (LDR) reproduction devices. The new method, termed hierarchical nonlinear linear (HNL) tone-mapping operator maps the pixels in two hierarchical steps. The first step allocates appropriate numbers of LDR display levels to different HDR intensity intervals according to the pixel densities of the intervals. The second step linearly maps the HDR intensity intervals to theirs allocated LDR display levels. In the developed HNL scheme, the assignment of LDR display levels to HDR intensity intervals is controlled by a very simple and flexible formula with a single adjustable parameter. We also show that our new operators can be used for the effective enhancement of ordinary images.
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.
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Using hierarchical Bayesian methods to examine the tools of decision-making
Michael D. Lee; Benjamin R. Newell
2011-01-01
Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in a simulated binary comparison judgment task. The simulation involves 20 subjects making 100 forced choice comparisons about the relative magnitudes of two objects (which of two German cities has more inhabitants). Two worked-examples show how hierarchical models can be developed to account for and ...
da Silva, Natal Santos; Undurraga, Eduardo A; da Silva Ferreira, Elis Regina; Estofolete, Cássia Fernanda; Nogueira, Maurício Lacerda
2018-01-01
In Brazil, the incidence of hospitalization due to dengue, as an indicator of severity, has drastically increased since 1998. The objective of our study was to identify risk factors associated with subsequent hospitalization related to dengue. We analyzed 7613 dengue confirmed via serology (ELISA), non-structural protein 1, or polymerase chain reaction amplification. We used a hierarchical framework to generate a multivariate logistic regression based on a variety of risk variables. This was followed by multiple statistical analyses to assess hierarchical model accuracy, variance, goodness of fit, and whether or not this model reliably represented the population. The final model, which included age, sex, ethnicity, previous dengue infection, hemorrhagic manifestations, plasma leakage, and organ failure, showed that all measured parameters, with the exception of previous dengue, were statistically significant. The presence of organ failure was associated with the highest risk of subsequent dengue hospitalization (OR=5·75; CI=3·53-9·37). Therefore, plasma leakage and organ failure were the main indicators of hospitalization due to dengue, although other variables of minor importance should also be considered to refer dengue patients to hospital treatment, which may lead to a reduction in avoidable deaths as well as costs related to dengue. Copyright © 2017 Elsevier B.V. All rights reserved.
Star Cluster Structure from Hierarchical Star Formation
Grudic, Michael; Hopkins, Philip; Murray, Norman; Lamberts, Astrid; Guszejnov, David; Schmitz, Denise; Boylan-Kolchin, Michael
2018-01-01
Young massive star clusters (YMCs) spanning 104-108 M⊙ in mass generally have similar radial surface density profiles, with an outer power-law index typically between -2 and -3. This similarity suggests that they are shaped by scale-free physics at formation. Recent multi-physics MHD simulations of YMC formation have also produced populations of YMCs with this type of surface density profile, allowing us to narrow down the physics necessary to form a YMC with properties as observed. We show that the shallow density profiles of YMCs are a natural result of phase-space mixing that occurs as they assemble from the clumpy, hierarchically-clustered configuration imprinted by the star formation process. We develop physical intuition for this process via analytic arguments and collisionless N-body experiments, elucidating the connection between star formation physics and star cluster structure. This has implications for the early-time structure and evolution of proto-globular clusters, and prospects for simulating their formation in the FIRE cosmological zoom-in simulations.
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.
Hierarchical analysis of dependency in metabolic networks.
Gagneur, Julien; Jackson, David B; Casari, Georg
2003-05-22
Elucidation of metabolic networks for an increasing number of organisms reveals that even small networks can contain thousands of reactions and chemical species. The intimate connectivity between components complicates their decomposition into biologically meaningful sub-networks. Moreover, traditional higher-order representations of metabolic networks as metabolic pathways, suffers from the lack of rigorous definition, yielding pathways of disparate content and size. We introduce a hierarchical representation that emphasizes the gross organization of metabolic networks in largely independent pathways and sub-systems at several levels of independence. The approach highlights the coupling of different pathways and the shared compounds responsible for those couplings. By assessing our results on Escherichia coli (E.coli metabolic reactions, Genetic Circuits Research Group, University of California, San Diego, http://gcrg.ucsd.edu/organisms/ecoli.html, 'model v 1.01. reactions') against accepted biochemical annotations, we provide the first systematic synopsis of an organism's metabolism. Comparison with operons of E.coli shows that low-level clusters are reflected in genome organization and gene regulation. Source code, data sets and supplementary information are available at http://www.mas.ecp.fr/labo/equipe/gagneur/hierarchy/hierarchy.html
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.
On Solving Lq-Penalized Regressions
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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.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, Veerle
2012-01-01
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 logisti...
Logistic regression for dichotomized counts.
Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W
2016-12-01
Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.
Determinants of falls in community-dwelling elderly: hierarchical analysis.
Brito, Thais Alves; Coqueiro, Raildo da Silva; Fernandes, Marcos Henrique; de Jesus, Cleber Souza
2014-01-01
To analyze the fall-related factors in community-dwelling elderly. Epidemiologic cross-sectional population-based household study with hierarchical interrelationships among the potential risk factors. The sample was made up of noninstitutionalized individuals over age 60, who were resident of a city in Brazil's Northeast Region. The dependent variable was fall occurrence in the last 12 months; independent variables were sociodemographic, behavioral, health, and functional status factors. Multivariate hierarchical Poisson regression analysis was used based on a proposed theoretic model. Three hundred and sixteen (89.0%) elderly participated of the survey, average age 74.2 years; the majority was female, with limited literacy and had low-medium family income. The fall prevalence was of 25.8%; occurrence was related to depression symptoms (PR = 1.55) and balance limitation (PR = 1.56). The high fall prevalence among elderly necessitates the identification of fall-related factors for action planning prevention programs with this group. © 2014 Wiley Periodicals, Inc.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... 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...
Poisson Mixture Regression Models for Heart Disease Prediction.
Mufudza, Chipo; Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
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.
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.
Correlation and simple linear regression.
Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G
2003-06-01
In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.
Regression filter for signal resolution
International Nuclear Information System (INIS)
Matthes, W.
1975-01-01
The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)
Prediction of road accidents: A Bayesian hierarchical approach
DEFF Research Database (Denmark)
Deublein, Markus; Schubert, Matthias; Adey, Bryan T.
2013-01-01
the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link......In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson......-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks...
COMPOSITE METHOD OF RELIABILITY RESEARCH FOR HIERARCHICAL MULTILAYER ROUTING SYSTEMS
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R. B. Tregubov
2016-09-01
Full Text Available The paper deals with the idea of a research method for hierarchical multilayer routing systems. The method represents a composition of methods of graph theories, reliability, probabilities, etc. These methods are applied to the solution of different private analysis and optimization tasks and are systemically connected and coordinated with each other through uniform set-theoretic representation of the object of research. The hierarchical multilayer routing systems are considered as infrastructure facilities (gas and oil pipelines, automobile and railway networks, systems of power supply and communication with distribution of material resources, energy or information with the use of hierarchically nested functions of routing. For descriptive reasons theoretical constructions are considered on the example of task solution of probability determination for up state of specific infocommunication system. The author showed the possibility of constructive combination of graph representation of structure of the object of research and a logic probable analysis method of its reliability indices through uniform set-theoretic representation of its elements and processes proceeding in them.
Hierarchical capitalism in Latin America: Comparative analysis with other economies
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Edgar J. Saucedo A.
2015-12-01
Full Text Available Purpose – The purpose of this paper is to compare the three largest economies in Latin America (Brazil, Mexico and Argentina with other economies that have another type of capitalism, in that way we can extract some effects of the hierarchical capitalism in Latin America Design/methodology/approach – The data were taken from World Economic Outlook (IMF, The Global Innovation Index (INSEADand the Democracy Index (The Economist. The selected countries are: Argentina, Brazil, Mexico, South Korea, Spain and Croatia. We establish a comparison among countries in the following dimensions: economic growth, innovation and democracy. Findings – The comparison shows that Argentina, Brazil and Mexico have lower level of economic growth, innovation performance and democracy level than South Korea, Spain and Croatia. The variety of capitalism in Latin America (hierarchical has lower performance than others kinds of capitalism in other regions of the world. Research limitations/implications – We have compared Latin American countries with countries from other regions of the world. However, a comparison may include more countries and results could vary. Originality/value – The results tend to support the idea that hierarchical capitalism has poor results in comparison with other varieties of capitalism.
Hierarchical adaptive experimental design for Gaussian process emulators
International Nuclear Information System (INIS)
Busby, Daniel
2009-01-01
Large computer simulators have usually complex and nonlinear input output functions. This complicated input output relation can be analyzed by global sensitivity analysis; however, this usually requires massive Monte Carlo simulations. To effectively reduce the number of simulations, statistical techniques such as Gaussian process emulators can be adopted. The accuracy and reliability of these emulators strongly depend on the experimental design where suitable evaluation points are selected. In this paper a new sequential design strategy called hierarchical adaptive design is proposed to obtain an accurate emulator using the least possible number of simulations. The hierarchical design proposed in this paper is tested on various standard analytic functions and on a challenging reservoir forecasting application. Comparisons with standard one-stage designs such as maximin latin hypercube designs show that the hierarchical adaptive design produces a more accurate emulator with the same number of computer experiments. Moreover a stopping criterion is proposed that enables to perform the number of simulations necessary to obtain required approximation accuracy.
Hierarchical organization of brain functional networks during visual tasks.
Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie
2011-09-01
The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.
Hierarchical and coupling model of factors influencing vessel traffic flow.
Directory of Open Access Journals (Sweden)
Zhao Liu
Full Text Available Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.
Hierarchical and coupling model of factors influencing vessel traffic flow.
Liu, Zhao; Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi
2017-01-01
Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.
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.
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
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.
A nontransferring dry adhesive with hierarchical polymer nanohairs.
Jeong, Hoon Eui; Lee, Jin-Kwan; Kim, Hong Nam; Moon, Sang Heup; Suh, Kahp Y
2009-04-07
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.
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…
Regression Models for Repairable Systems
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
Survival analysis II: Cox regression
Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.
2011-01-01
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the
Kernel regression with functional response
Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe
2011-01-01
We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.
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.
Hierarchical silica particles by dynamic multicomponent assembly
DEFF Research Database (Denmark)
Wu, Z. W.; Hu, Q. Y.; Pang, J. B.
2005-01-01
Abstract: Aerosol-assisted assembly of mesoporous silica particles with hierarchically controllable pore structure has been prepared using cetyltrimethylammonium bromide (CTAB) and poly(propylene oxide) (PPO, H[OCH(CH3)CH2],OH) as co-templates. Addition of the hydrophobic PPO significantly...... influences the delicate hydrophilic-hydrophobic balance in the well-studied CTAB-silicate co-assembling system, resulting in various mesostructures (such as hexagonal, lamellar, and hierarchical structure). The co-assembly of CTAB, silicate clusters, and a low-molecular-weight PPO (average M-n 425) results...... in a uniform lamellar structure, while the use of a high-molecular-weight PPO (average M-n 2000), which is more hydrophobic, leads to the formation of hierarchical pore structure that contains meso-meso or meso-macro pore structure. The role of PPO additives on the mesostructure evolution in the CTAB...
On Utmost Multiplicity of Hierarchical Stellar Systems
Directory of Open Access Journals (Sweden)
Gebrehiwot Y. M.
2016-12-01
Full Text Available According to theoretical considerations, multiplicity of hierarchical stellar systems can reach, depending on masses and orbital parameters, several hundred, while observational data confirm the existence of at most septuple (seven-component systems. In this study, we cross-match the stellar systems of very high multiplicity (six and more components in modern catalogues of visual double and multiple stars to find among them the candidates to hierarchical systems. After cross-matching the catalogues of closer binaries (eclipsing, spectroscopic, etc., some of their components were found to be binary/multiple themselves, what increases the system's degree of multiplicity. Optical pairs, known from literature or filtered by the authors, were flagged and excluded from the statistics. We compiled a list of hierarchical systems with potentially very high multiplicity that contains ten objects. Their multiplicity does not exceed 12, and we discuss a number of ways to explain the lack of extremely high multiplicity systems.
Hierarchical Micro-Nano Coatings by Painting
Kirveslahti, Anna; Korhonen, Tuulia; Suvanto, Mika; Pakkanen, Tapani A.
2016-03-01
In this paper, the wettability properties of coatings with hierarchical surface structures and low surface energy were studied. Hierarchically structured coatings were produced by using hydrophobic fumed silica nanoparticles and polytetrafluoroethylene (PTFE) microparticles as additives in polyester (PES) and polyvinyldifluoride (PVDF). These particles created hierarchical micro-nano structures on the paint surfaces and lowered or supported the already low surface energy of the paint. Two standard application techniques for paint application were employed and the presented coatings are suitable for mass production and use in large surface areas. By regulating the particle concentrations, it was possible to modify wettability properties gradually. Highly hydrophobic surfaces were achieved with the highest contact angle of 165∘. Dynamic contact angle measurements were carried out for a set of selected samples and low hysteresis was obtained. Produced coatings possessed long lasting durability in the air and in underwater conditions.
Hierarchical capillary adhesion of microcantilevers or hairs
International Nuclear Information System (INIS)
Liu Jianlin; Feng Xiqiao; Xia Re; Zhao Hongping
2007-01-01
As a result of capillary forces, animal hairs, carbon nanotubes or nanowires of a periodically or randomly distributed array often assemble into hierarchical structures. In this paper, the energy method is adopted to analyse the capillary adhesion of microsized hairs, which are modelled as clamped microcantilevers wetted by liquids. The critical conditions for capillary adhesion of two hairs, three hairs or two bundles of hairs are derived in terms of Young's contact angle, elastic modulus and geometric sizes of the beams. Then, the hierarchical capillary adhesion of hairs is addressed. It is found that for multiple hairs or microcantilevers, the system tends to take a hierarchical structure as a result of the minimization of the total potential energy of the system. The level number of structural hierarchy increases with the increase in the number of hairs if they are sufficiently long. Additionally, we performed experiments to verify our theoretical solutions for the adhesion of microbeams
Yi Huang; Francesca Dominici; Michelle Bell
2004-01-01
In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994. At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP ...
Zhaoxuan Li; SM Mahbobur Rahman; Rolando Vega; Bing Dong
2016-01-01
We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statisti...
Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M
2007-09-01
Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.
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...
Analyzing security protocols in hierarchical networks
DEFF Research Database (Denmark)
Zhang, Ye; Nielson, Hanne Riis
2006-01-01
Validating security protocols is a well-known hard problem even in a simple setting of a single global network. But a real network often consists of, besides the public-accessed part, several sub-networks and thereby forms a hierarchical structure. In this paper we first present a process calculus...... capturing the characteristics of hierarchical networks and describe the behavior of protocols on such networks. We then develop a static analysis to automate the validation. Finally we demonstrate how the technique can benefit the protocol development and the design of network systems by presenting a series...
Hierarchical MAS based control strategy for microgrid
Energy Technology Data Exchange (ETDEWEB)
Xiao, Z.; Li, T.; Huang, M.; Shi, J.; Yang, J.; Yu, J. [School of Information Science and Engineering, Yunnan University, Kunming 650091 (China); Xiao, Z. [School of Electrical and Electronic Engineering, Nanyang Technological University, Western Catchment Area, 639798 (Singapore); Wu, W. [Communication Branch of Yunnan Power Grid Corporation, Kunming, Yunnan 650217 (China)
2010-09-15
Microgrids have become a hot topic driven by the dual pressures of environmental protection concerns and the energy crisis. In this paper, a challenge for the distributed control of a modern electric grid incorporating clusters of residential microgrids is elaborated and a hierarchical multi-agent system (MAS) is proposed as a solution. The issues of how to realize the hierarchical MAS and how to improve coordination and control strategies are discussed. Based on MATLAB and ZEUS platforms, bilateral switching between grid-connected mode and island mode is performed under control of the proposed MAS to enhance and support its effectiveness. (authors)
Multiparty hierarchical quantum-information splitting
International Nuclear Information System (INIS)
Wang Xinwen; Zhang Dengyu; Tang Shiqing; Xie Lijun
2011-01-01
We propose a scheme for multiparty hierarchical quantum-information splitting (QIS) with a multipartite entangled state, where a boss distributes a secret quantum state to two grades of agents asymmetrically. The agents who belong to different grades have different authorities for recovering the boss's secret. Except for the boss's Bell-state measurement, no nonlocal operation is involved. The presented scheme is also shown to be secure against eavesdropping. Such a hierarchical QIS is expected to find useful applications in the field of modern multipartite quantum cryptography.
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.
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…
Controlling attribute effect in linear regression
Calders, Toon; Karim, Asim A.; Kamiran, Faisal; Ali, Wasif Mohammad; Zhang, Xiangliang
2013-01-01
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.
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.
Intermediate and advanced topics in multilevel logistic regression analysis.
Austin, Peter C; Merlo, Juan
2017-09-10
Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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.
A hierarchical stochastic model for bistable perception.
Directory of Open Access Journals (Sweden)
Stefan Albert
2017-11-01
Full Text Available Viewing of ambiguous stimuli can lead to bistable perception alternating between the possible percepts. During continuous presentation of ambiguous stimuli, percept changes occur as single events, whereas during intermittent presentation of ambiguous stimuli, percept changes occur at more or less regular intervals either as single events or bursts. Response patterns can be highly variable and have been reported to show systematic differences between patients with schizophrenia and healthy controls. Existing models of bistable perception often use detailed assumptions and large parameter sets which make parameter estimation challenging. Here we propose a parsimonious stochastic model that provides a link between empirical data analysis of the observed response patterns and detailed models of underlying neuronal processes. Firstly, we use a Hidden Markov Model (HMM for the times between percept changes, which assumes one single state in continuous presentation and a stable and an unstable state in intermittent presentation. The HMM captures the observed differences between patients with schizophrenia and healthy controls, but remains descriptive. Therefore, we secondly propose a hierarchical Brownian model (HBM, which produces similar response patterns but also provides a relation to potential underlying mechanisms. The main idea is that neuronal activity is described as an activity difference between two competing neuronal populations reflected in Brownian motions with drift. This differential activity generates switching between the two conflicting percepts and between stable and unstable states with similar mechanisms on different neuronal levels. With only a small number of parameters, the HBM can be fitted closely to a high variety of response patterns and captures group differences between healthy controls and patients with schizophrenia. At the same time, it provides a link to mechanistic models of bistable perception, linking the group
A hierarchical stochastic model for bistable perception.
Albert, Stefan; Schmack, Katharina; Sterzer, Philipp; Schneider, Gaby
2017-11-01
Viewing of ambiguous stimuli can lead to bistable perception alternating between the possible percepts. During continuous presentation of ambiguous stimuli, percept changes occur as single events, whereas during intermittent presentation of ambiguous stimuli, percept changes occur at more or less regular intervals either as single events or bursts. Response patterns can be highly variable and have been reported to show systematic differences between patients with schizophrenia and healthy controls. Existing models of bistable perception often use detailed assumptions and large parameter sets which make parameter estimation challenging. Here we propose a parsimonious stochastic model that provides a link between empirical data analysis of the observed response patterns and detailed models of underlying neuronal processes. Firstly, we use a Hidden Markov Model (HMM) for the times between percept changes, which assumes one single state in continuous presentation and a stable and an unstable state in intermittent presentation. The HMM captures the observed differences between patients with schizophrenia and healthy controls, but remains descriptive. Therefore, we secondly propose a hierarchical Brownian model (HBM), which produces similar response patterns but also provides a relation to potential underlying mechanisms. The main idea is that neuronal activity is described as an activity difference between two competing neuronal populations reflected in Brownian motions with drift. This differential activity generates switching between the two conflicting percepts and between stable and unstable states with similar mechanisms on different neuronal levels. With only a small number of parameters, the HBM can be fitted closely to a high variety of response patterns and captures group differences between healthy controls and patients with schizophrenia. At the same time, it provides a link to mechanistic models of bistable perception, linking the group differences to
Parameter-Invariant Hierarchical Exclusive Alphabet Design for 2-WRC with HDF Strategy
Directory of Open Access Journals (Sweden)
T. Uřičář
2010-01-01
Full Text Available Hierarchical eXclusive Code (HXC for the Hierarchical Decode and Forward (HDF strategy in the Wireless 2-Way Relay Channel (2-WRC has the achievable rate region extended beyond the classical MAC region. Although direct HXC design is in general highly complex, a layered approach to HXC design is a feasible solution. While the outer layer code of the layered HXC can be any state-of-the-art capacity approaching code, the inner layer must be designed in such a way that the exclusive property of hierarchical symbols (received at the relay will be provided. The simplest case of the inner HXC layer is a simple signal space channel symbol memoryless mapper called Hierarchical eXclusive Alphabet (HXA. The proper design of HXA is important, especially in the case of parametric channels, where channel parametrization (e.g. phase rotation can violate the exclusive property of hierarchical symbols (as seen by the relay, resulting in significant capacity degradation. In this paper we introduce an example of a geometrical approach to Parameter-Invariant HXA design, and we show that the corresponding hierarchical MAC capacity region extends beyond the classical MAC region, irrespective of the channel pametrization.
Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.
Nitta, Tohru
2017-10-01
We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).
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.
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Regression algorithm for emotion detection
Berthelon , Franck; Sander , Peter
2013-01-01
International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...
Directional quantile regression in R
Czech Academy of Sciences Publication Activity Database
Boček, Pavel; Šiman, Miroslav
2017-01-01
Roč. 53, č. 3 (2017), s. 480-492 ISSN 0023-5954 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * regression quantile * halfspace depth * depth contour Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0476587.pdf
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
Hierarchical drivers of reef-fish metacommunity structure.
MacNeil, M Aaron; Graham, Nicholas A J; Polunin, Nicholas V C; Kulbicki, Michel; Galzin, René; Harmelin-Vivien, Mireille; Rushton, Steven P
2009-01-01
Coral reefs are highly complex ecological systems, where multiple processes interact across scales in space and time to create assemblages of exceptionally high biodiversity. Despite the increasing frequency of hierarchically structured sampling programs used in coral-reef science, little progress has been made in quantifying the relative importance of processes operating across multiple scales. The vast majority of reef studies are conducted, or at least analyzed, at a single spatial scale, ignoring the implicitly hierarchical structure of the overall system in favor of small-scale experiments or large-scale observations. Here we demonstrate how alpha (mean local number of species), beta diversity (degree of species dissimilarity among local sites), and gamma diversity (overall species richness) vary with spatial scale, and using a hierarchical, information-theoretic approach, we evaluate the relative importance of site-, reef-, and atoll-level processes driving the fish metacommunity structure among 10 atolls in French Polynesia. Process-based models, representing well-established hypotheses about drivers of reef-fish community structure, were assembled into a candidate set of 12 hierarchical linear models. Variation in fish abundance, biomass, and species richness were unevenly distributed among transect, reef, and atoll levels, establishing the relative contribution of variation at these spatial scales to the structure of the metacommunity. Reef-fish biomass, species richness, and the abundance of most functional-groups corresponded primarily with transect-level habitat diversity and atoll-lagoon size, whereas detritivore and grazer abundances were largely correlated with potential covariates of larval dispersal. Our findings show that (1) within-transect and among-atoll factors primarily drive the relationship between alpha and gamma diversity in this reef-fish metacommunity; (2) habitat is the primary correlate with reef-fish metacommunity structure at
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Runtime Concepts of Hierarchical Software Components
Czech Academy of Sciences Publication Activity Database
Bureš, Tomáš; Hnětynka, P.; Plášil, František
2007-01-01
Roč. 8, special (2007), s. 454-463 ISSN 1525-9293 R&D Projects: GA AV ČR 1ET400300504 Institutional research plan: CEZ:AV0Z10300504 Keywords : component-based development * hierarchical components * connectors * controlers * runtime environment Subject RIV: JC - Computer Hardware ; Software
Hierarchical Broadcasting in the Future Mobile Internet
Hesselman, C.E.W.; Eertink, E.H.; Fernandez, Milagros; Crnkovic, Ivica; Fohler, Gerhard; Griwodz, Carsten; Plagemann, Thomas; Gruenbacher, Paul
2002-01-01
We describe an architecture for the hierarchical distribution of multimedia broadcasts in the future mobile Internet. The architecture supports network as well as application-layer mobility solutions, and uses stream control functions that are influenced by available network resources, user-defined
Modular networks with hierarchical organization: The dynamical ...
Indian Academy of Sciences (India)
Most of the complex systems seen in real life also have associated dynamics [10], and the ... another example, this time a hierarchical structure, viz., the Cayley tree with b ..... natural constraints operating on networks in real life, such as the ...
A hierarchical model for ordinal matrix factorization
DEFF Research Database (Denmark)
Paquet, Ulrich; Thomson, Blaise; Winther, Ole
2012-01-01
This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based...
Hierarchical Control for Multiple DC Microgrids Clusters
DEFF Research Database (Denmark)
Shafiee, Qobad; Dragicevic, Tomislav; Vasquez, Juan Carlos
2014-01-01
This paper presents a distributed hierarchical control framework to ensure reliable operation of dc Microgrid (MG) clusters. In this hierarchy, primary control is used to regulate the common bus voltage inside each MG locally. An adaptive droop method is proposed for this level which determines...
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.
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...
A hierarchical classification scheme of psoriasis images
DEFF Research Database (Denmark)
Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær
2003-01-01
A two-stage hierarchical classification scheme of psoriasis lesion images is proposed. These images are basically composed of three classes: normal skin, lesion and background. The scheme combines conventional tools to separate the skin from the background in the first stage, and the lesion from...
Hierarchical pre-segmentation without prior knowledge
Kuijper, A.; Florack, L.M.J.
2001-01-01
A new method to pre-segment images by means of a hierarchical description is proposed. This description is obtained from an investigation of the deep structure of a scale space image – the input image and the Gaussian filtered ones simultaneously. We concentrate on scale space critical points –
Hierarchical spatial organization of geographical networks
International Nuclear Information System (INIS)
Travencolo, Bruno A N; Costa, Luciano da F
2008-01-01
In this work, we propose a hierarchical extension of the polygonality index as the means to characterize geographical planar networks. By considering successive neighborhoods around each node, it is possible to obtain more complete information about the spatial order of the network at progressive spatial scales. The potential of the methodology is illustrated with respect to synthetic and real geographical networks
Hierarchical production planning for consumer goods
Kok, de A.G.
1990-01-01
Abstract In this paper the mathematical logic behind a hierarchical planning procedure is discussed. The planning procedure is used to derive production volumes of consumer products. The essence of the planning procedure is that first a commitment is made concerning the production volume for a
Hierarchical Bayesian Models of Subtask Learning
Anglim, Jeromy; Wynton, Sarah K. A.
2015-01-01
The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…
Hierarchical subtask discovery with non-negative matrix factorization
CSIR Research Space (South Africa)
Earle, AC
2018-04-01
Full Text Available Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We...
Hierarchical subtask discovery with non-negative matrix factorization
CSIR Research Space (South Africa)
Earle, AC
2017-08-01
Full Text Available Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We...
Virtual timers in hierarchical real-time systems
Heuvel, van den M.M.H.P.; Holenderski, M.J.; Cools, W.A.; Bril, R.J.; Lukkien, J.J.; Zhu, D.
2009-01-01
Hierarchical scheduling frameworks (HSFs) provide means for composing complex real-time systems from welldefined subsystems. This paper describes an approach to provide hierarchically scheduled real-time applications with virtual event timers, motivated by the need for integrating priority
A hierarchical classification method for finger knuckle print recognition
Kong, Tao; Yang, Gongping; Yang, Lu
2014-12-01
Finger knuckle print has recently been seen as an effective biometric technique. In this paper, we propose a hierarchical classification method for finger knuckle print recognition, which is rooted in traditional score-level fusion methods. In the proposed method, we firstly take Gabor feature as the basic feature for finger knuckle print recognition and then a new decision rule is defined based on the predefined threshold. Finally, the minor feature speeded-up robust feature is conducted for these users, who cannot be recognized by the basic feature. Extensive experiments are performed to evaluate the proposed method, and experimental results show that it can achieve a promising performance.
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.
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.
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 the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest....
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 the fin...... the finite-sample distribution of estimators of the mean and variance in a simple location-scale model under the null that no impulses matter. A Monte Carlo simulation confirms the null distribution, and shows power against an alternative of interest...
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.
Confidence bands for inverse regression models
International Nuclear Information System (INIS)
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
Hierarchically Nanoporous Bioactive Glasses for High Efficiency Immobilization of Enzymes
DEFF Research Database (Denmark)
He, W.; Min, D.D.; Zhang, X.D.
2014-01-01
Bioactive glasses with hierarchical nanoporosity and structures have been heavily involved in immobilization of enzymes. Because of meticulous design and ingenious hierarchical nanostructuration of porosities from yeast cell biotemplates, hierarchically nanostructured porous bioactive glasses can...... and products of catalytic reactions can freely diffuse through open mesopores (2–40 nm). The formation mechanism of hierarchically structured porous bioactive glasses, the immobilization mechanism of enzyme and the catalysis mechanism of immobilized enzyme are then discussed. The novel nanostructure...
Parental Vaccine Acceptance: A Logistic Regression Model Using Previsit Decisions.
Lee, Sara; Riley-Behringer, Maureen; Rose, Jeanmarie C; Meropol, Sharon B; Lazebnik, Rina
2017-07-01
This study explores how parents' intentions regarding vaccination prior to their children's visit were associated with actual vaccine acceptance. A convenience sample of parents accompanying 6-week-old to 17-year-old children completed a written survey at 2 pediatric practices. Using hierarchical logistic regression, for hospital-based participants (n = 216), vaccine refusal history ( P < .01) and vaccine decision made before the visit ( P < .05) explained 87% of vaccine refusals. In community-based participants (n = 100), vaccine refusal history ( P < .01) explained 81% of refusals. Over 1 in 5 parents changed their minds about vaccination during the visit. Thirty parents who were previous vaccine refusers accepted current vaccines, and 37 who had intended not to vaccinate choose vaccination. Twenty-nine parents without a refusal history declined vaccines, and 32 who did not intend to refuse before the visit declined vaccination. Future research should identify key factors to nudge parent decision making in favor of vaccination.
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.
Superhydrophobic SERS substrates based on silicon hierarchical nanostructures
Chen, Xuexian; Wen, Jinxiu; Zhou, Jianhua; Zheng, Zebo; An, Di; Wang, Hao; Xie, Weiguang; Zhan, Runze; Xu, Ningsheng; Chen, Jun; She, Juncong; Chen, Huanjun; Deng, Shaozhi
2018-02-01
Silicon nanostructures have been cultivated as promising surface enhanced Raman scattering (SERS) substrates in terms of their low-loss optical resonance modes, facile functionalization, and compatibility with today’s state-of-the-art CMOS techniques. However, unlike their plasmonic counterparts, the electromagnetic field enhancements induced by silicon nanostructures are relatively small, which restrict their SERS sensing limit to around 10-7 M. To tackle this problem, we propose here a strategy for improving the SERS performance of silicon nanostructures by constructing silicon hierarchical nanostructures with a superhydrophobic surface. The hierarchical nanostructures are binary structures consisted of silicon nanowires (NWs) grown on micropyramids (MPs). After being modified with perfluorooctyltriethoxysilane (PFOT), the nanostructure surface shows a stable superhydrophobicity with a high contact angle of ˜160°. The substrate can allow for concentrating diluted analyte solutions into a specific area during the evaporation of the liquid droplet, whereby the analytes are aggregated into a small volume and can be easily detected by the silicon nanostructure SERS substrate. The analyte molecules (methylene blue: MB) enriched from an aqueous solution lower than 10-8 M can be readily detected. Such a detection limit is ˜100-fold lower than the conventional SERS substrates made of silicon nanostructures. Additionally, the detection limit can be further improved by functionalizing gold nanoparticles onto silicon hierarchical nanostructures, whereby the superhydrophobic characteristics and plasmonic field enhancements can be combined synergistically to give a detection limit down to ˜10-11 M. A gold nanoparticle-functionalized superhydrophobic substrate was employed to detect the spiked melamine in liquid milk. The results showed that the detection limit can be as low as 10-5 M, highlighting the potential of the proposed superhydrophobic SERS substrate in
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
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...
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.
Logistic regression applied to natural hazards: rare event logistic regression with replications
Guns, M.; Vanacker, V.
2012-06-01
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.
Hierarchical models in the brain.
Directory of Open Access Journals (Sweden)
Karl Friston
2008-11-01
Full Text Available This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain.
Shan, Mingqiu; Li, Sam Fong Yau; Yu, Sheng; Qian, Yan; Guo, Shuchen; Zhang, Li; Ding, Anwei
2018-01-01
Platycladi cacumen (dried twigs and leaves of Platycladus orientalis (L.) Franco) is a frequently utilized Chinese medicinal herb. To evaluate the quality of the phytomedcine, an ultra-performance liquid chromatographic method with diode array detection was established for chemical fingerprinting and quantitative analysis. In this study, 27 batches of P. cacumen from different regions were collected for analysis. A chemical fingerprint with 20 common peaks was obtained using Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2004A). Among these 20 components, seven flavonoids (myricitrin, isoquercitrin, quercitrin, afzelin, cupressuflavone, amentoflavone and hinokiflavone) were identified and determined simultaneously. In the method validation, the seven analytes showed good regressions (R ≥ 0.9995) within linear ranges and good recoveries from 96.4% to 103.3%. Furthermore, with the contents of these seven flavonoids, hierarchical clustering analysis was applied to distinguish the 27 batches into five groups. The chemometric results showed that these groups were almost consistent with geographical positions and climatic conditions of the production regions. Integrating fingerprint analysis, simultaneous determination and hierarchical clustering analysis, the established method is rapid, sensitive, accurate and readily applicable, and also provides a significant foundation for quality control of P. cacumen efficiently. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Modeling oil production based on symbolic regression
International Nuclear Information System (INIS)
Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju
2015-01-01
Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak
On logistic regression analysis of dichotomized responses.
Lu, Kaifeng
2017-01-01
We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
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.
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...... 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...... seen as necessary in order to identify aggregate level effects of policy measures, but are questioned by many advocates of critical realist ontology. Using research into the relationship between urban structure and travel as an example, the paper discusses relevant research methods and the kinds...
Application of hierarchical matrices for partial inverse
Litvinenko, Alexander
2013-11-26
In this work we combine hierarchical matrix techniques (Hackbusch, 1999) and domain decomposition methods to obtain fast and efficient algorithms for the solution of multiscale problems. This combination results in the hierarchical domain decomposition (HDD) method, which can be applied for solution multi-scale problems. Multiscale problems are problems that require the use of different length scales. Using only the finest scale is very expensive, if not impossible, in computational time and memory. Domain decomposition methods decompose the complete problem into smaller systems of equations corresponding to boundary value problems in subdomains. Then fast solvers can be applied to each subdomain. Subproblems in subdomains are independent, much smaller and require less computational resources as the initial problem.
Translating Management Practices in Hierarchical Organizations
DEFF Research Database (Denmark)
Wæraas, Arild; Nielsen, Jeppe Agger
structures affect translators’ approaches taken towards management ideas. This paper reports the findings from a longitudinal case study of the translation of Leadership Pipeline in a Danish fire department and how the translators’ approach changed over time from a modifying to a reproducing mode. The study......This study examines how translators in a hierarchical context approach the translation of management practices. Although current translation theory and research emphasize the importance of contextual factors in translation processes, little research has investigated how strongly hierarchical...... finds that translation does not necessarily imply transformation of the management idea, pointing instead to aspects of exact imitation and copying of an ”original” idea. It also highlights how translation is likely to involve multiple and successive translation modes and, furthermore, that strongly...
Hierarchical structure in the distribution of galaxies
International Nuclear Information System (INIS)
Schulman, L.S.; Seiden, P.E.; Technion - Israel Institute of Technology, Haifa; IBM Thomas J. Watson Research Center, Yorktown Heights, NY)
1986-01-01
The distribution of galaxies has a hierarchical structure with power-law correlations. This is usually thought to arise from gravity alone acting on an originally uniform distributioon. If, however, the original process of galaxy formation occurs through the stimulated birth of one galaxy due to a nearby recently formed galaxy, and if this process occurs near its percolation threshold, then a hierarchical structure with power-law correlations arises at the time of galaxy formation. If subsequent gravitational evolution within an expanding cosmology is such as to retain power-law correlations, the initial r exp -1 dropoff can steepen to the observed r exp -1.8. The distribution of galaxies obtained by this process produces clustering and voids, as observed. 23 references
Biominerals- hierarchical nanocomposites: the example of bone
Beniash, Elia
2010-01-01
Many organisms incorporate inorganic solids in their tissues to enhance their functional, primarily mechanical, properties. These mineralized tissues, also called biominerals, are unique organo-mineral nanocomposites, organized at several hierarchical levels, from nano- to macroscale. Unlike man made composite materials, which often are simple physical blends of their components, the organic and inorganic phases in biominerals interface at the molecular level. Although these tissues are made of relatively weak components at ambient conditions, their hierarchical structural organization and intimate interactions between different elements lead to superior mechanical properties. Understanding basic principles of formation, structure and functional properties of these tissues might lead to novel bioinspired strategies for material design and better treatments for diseases of the mineralized tissues. This review focuses on general principles of structural organization, formation and functional properties of biominerals on the example the bone tissues. PMID:20827739
Noise enhances information transfer in hierarchical networks.
Czaplicka, Agnieszka; Holyst, Janusz A; Sloot, Peter M A
2013-01-01
We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.
Quantum Ising model on hierarchical structures
International Nuclear Information System (INIS)
Lin Zhifang; Tao Ruibao.
1989-11-01
A quantum Ising chain with both the exchange couplings and the transverse fields arranged in a hierarchical way is considered. Exact analytical results for the critical line and energy gap are obtained. It is shown that when R 1 not= R 2 , where R 1 and R 2 are the hierarchical parameters for the exchange couplings and the transverse fields, respectively, the system undergoes a phase transition in a different universality class from the pure quantum Ising chain with R 1 =R 2 =1. On the other hand, when R 1 =R 2 =R, there exists a critical value R c dependent on the furcating number of the hierarchy. In case of R > R c , the system is shown to exhibit as Ising-like critical point with the critical behaviour the same as in the pure case, while for R c the system belongs to another universality class. (author). 19 refs, 2 figs
Hierarchical State Machines as Modular Horn Clauses
Directory of Open Access Journals (Sweden)
Pierre-Loïc Garoche
2016-07-01
Full Text Available In model based development, embedded systems are modeled using a mix of dataflow formalism, that capture the flow of computation, and hierarchical state machines, that capture the modal behavior of the system. For safety analysis, existing approaches rely on a compilation scheme that transform the original model (dataflow and state machines into a pure dataflow formalism. Such compilation often result in loss of important structural information that capture the modal behaviour of the system. In previous work we have developed a compilation technique from a dataflow formalism into modular Horn clauses. In this paper, we present a novel technique that faithfully compile hierarchical state machines into modular Horn clauses. Our compilation technique preserves the structural and modal behavior of the system, making the safety analysis of such models more tractable.
Hierarchical control system of advanced robot manipulator
International Nuclear Information System (INIS)
Oomichi, Takeo; Okino, Akihisa; Nishihara, Masatoshi; Sakamoto, Taizou; Matsuda, Koichi; Ohnishi, Ken
1990-01-01
We introduce a double arm with 4-finger's manipulator system which process the large volume of information at high speed. This is under research/development many type of works in the harsh condition. Namely, hierarchization of instruction unit in which motion control system as real time processing unit, and task planning unit as non-real time processing unit, interface with operation through the task planning unit has been made. Also, high speed processing of large volume information has been realized by decentralizing the motion control unit by function, hierarchizing the high speed processing unit, and developing high speed transmission, IC which does not depend on computer OS to avoid the delay in transmission. (author)
Effect of the sheet thickness of hierarchical SnO_2 on the gas sensing performance
International Nuclear Information System (INIS)
Zhang, Wenlong; Zeng, Wen; BinMiao; Wang, Zhongchang
2015-01-01
Graphical abstract: - Highlights: • A unique flower-like SnO_2 hierarchical architecture assembled with nanosheets were successfully synthesized. • The thickness of the unique hierarchical nanoflowers was precisely controlled. • The nanoflowers composed of thinner nanosheets show a significantly enhanced gas sensing properties. • A possible growth mechanism for the unique hierarchical SnO_2 nanoflower assembled with nanosheets of different thickness is proposed. - Abstract: A unique hierarchical SnO_2 nanoflower was successfully synthesized via a facile one-step hydrothermal method. The nanoflower was analyzed in detail using X ray diffraction, field-emission electron microscope and transmission electron microscope. It was found that the nanoflowers are all assembled from nanosheets. The nanosheet thickness could be precisely controlled by tuning the dosage of NaOH. Gas sensing tests demonstrated that the thickness of the sheet significantly affects the gas sensing performance. The improved gas sensing properties are attributed to the thinned petals as well as their pores and defects. These results show that the thickness and morphology of hierarchical nanostructures affect the functionality of gas sensors.
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…
Statistical analysis of sediment toxicity by additive monotone regression splines
Boer, de W.J.; Besten, den P.J.; Braak, ter C.J.F.
2002-01-01
Modeling nonlinearity and thresholds in dose-effect relations is a major challenge, particularly in noisy data sets. Here we show the utility of nonlinear regression with additive monotone regression splines. These splines lead almost automatically to the estimation of thresholds. We applied this
A Bayesian hierarchical model for demand curve analysis.
Ho, Yen-Yi; Nhu Vo, Tien; Chu, Haitao; Luo, Xianghua; Le, Chap T
2018-07-01
Drug self-administration experiments are a frequently used approach to assessing the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration's policy on tobacco regulation, because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.
Molenaar, Dylan; Tuerlinckx, Francis; van der Maas, Han L J
2015-05-01
We show how the hierarchical model for responses and response times as developed by van der Linden (2007), Fox, Klein Entink, and van der Linden (2007), Klein Entink, Fox, and van der Linden (2009), and Glas and van der Linden (2010) can be simplified to a generalized linear factor model with only the mild restriction that there is no hierarchical model at the item side. This result is valuable as it enables all well-developed modelling tools and extensions that come with these methods. We show that the restriction we impose on the hierarchical model does not influence parameter recovery under realistic circumstances. In addition, we present two illustrative real data analyses to demonstrate the practical benefits of our approach. © 2014 The British Psychological Society.
Band structures of two dimensional solid/air hierarchical phononic crystals
International Nuclear Information System (INIS)
Xu, Y.L.; Tian, X.G.; Chen, C.Q.
2012-01-01
The hierarchical phononic crystals to be considered show a two-order “hierarchical” feature, which consists of square array arranged macroscopic periodic unit cells with each unit cell itself including four sub-units. Propagation of acoustic wave in such two dimensional solid/air phononic crystals is investigated by the finite element method (FEM) with the Bloch theory. Their band structure, wave filtering property, and the physical mechanism responsible for the broadened band gap are explored. The corresponding ordinary phononic crystal without hierarchical feature is used for comparison. Obtained results show that the solid/air hierarchical phononic crystals possess tunable outstanding band gap features, which are favorable for applications such as sound insulation and vibration attenuation.
Hierarchically structured distributed microprocessor network for control
International Nuclear Information System (INIS)
Greenwood, J.R.; Holloway, F.W.; Rupert, P.R.; Ozarski, R.G.; Suski, G.J.
1979-01-01
To satisfy a broad range of control-analysis and data-acquisition requirements for Shiva, a hierarchical, computer-based, modular-distributed control system was designed. This system handles the more than 3000 control elements and 1000 data acquisition units in a severe high-voltage, high-current environment. The control system design gives one a flexible and reliable configuration to meet the development milestones for Shiva within critical time limits
Preliminary results from the hierarchical glitch pipeline
International Nuclear Information System (INIS)
Mukherjee, Soma
2007-01-01
This paper reports on the preliminary results obtained from the hierarchical glitch classification pipeline on LIGO data. The pipeline that has been under construction for the past year is now complete and end-to-end tested. It is ready to generate analysis results on a daily basis. The details of the pipeline, the classification algorithms employed and the results obtained with one days analysis on the gravitational wave and several auxiliary and environmental channels from all three LIGO detectors are discussed
Hierarchical Fiber Structures Made by Electrospinning Polymers
Reneker, Darrell H.
2009-03-01
A filter for water purification that is very thin, with small interstices and high surface area per unit mass, can be made with nanofibers. The mechanical strength of a very thin sheet of nanofibers is not great enough to withstand the pressure drop of the fluid flowing through. If the sheet of nanofibers is made thicker, the strength will increase, but the flow will be reduced to an impractical level. An optimized filter can be made with nanometer scale structures supported on micron scale structures, which are in turn supported on millimeter scale structures. This leads to a durable hierarchical structure to optimize the filtration efficiency with a minimum amount of material. Buckling coils,ootnotetextTao Han, Darrell H Reneker, Alexander L. Yarin, Polymer, Volume 48, issue 20 (September 21, 2007), p. 6064-6076. electrical bending coilsootnotetextDarrell H. Reneker and Alexander L. Yarin, Polymer, Volume 49, Issue 10 (2008) Pages 2387-2425, DOI:10.1016/j.polymer.2008.02.002. Feature Article. and pendulum coilsootnotetextT. Han, D.H. Reneker, A.L. Yarin, Polymer, Volume 49, (2008) Pages 2160-2169, doi:10.1016/jpolymer.2008.01.0487878. spanning dimensions from a few microns to a few centimeters can be collected from a single jet by controlling the position and motion of a collector. Attractive routes to the design and construction of hierarchical structures for filtration are based on nanofibers supported on small coils that are in turn supported on larger coils, which are supported on even larger overlapping coils. ``Such top-down'' hierarchical structures are easy to make by electrospinning. In one example, a thin hierarchical structure was made, with a high surface area and small interstices, having an open area of over 50%, with the thinnest fibers supported at least every 15 microns.
Hierarchical video summarization based on context clustering
Tseng, Belle L.; Smith, John R.
2003-11-01
A personalized video summary is dynamically generated in our video personalization and summarization system based on user preference and usage environment. The three-tier personalization system adopts the server-middleware-client architecture in order to maintain, select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. In this paper, the metadata includes visual semantic annotations and automatic speech transcriptions. Our personalization and summarization engine in the middleware selects the optimal set of desired video segments by matching shot annotations and sentence transcripts with user preferences. Besides finding the desired contents, the objective is to present a coherent summary. There are diverse methods for creating summaries, and we focus on the challenges of generating a hierarchical video summary based on context information. In our summarization algorithm, three inputs are used to generate the hierarchical video summary output. These inputs are (1) MPEG-7 metadata descriptions of the contents in the server, (2) user preference and usage environment declarations from the user client, and (3) context information including MPEG-7 controlled term list and classification scheme. In a video sequence, descriptions and relevance scores are assigned to each shot. Based on these shot descriptions, context clustering is performed to collect consecutively similar shots to correspond to hierarchical scene representations. The context clustering is based on the available context information, and may be derived from domain knowledge or rules engines. Finally, the selection of structured video segments to generate the hierarchical summary efficiently balances between scene representation and shot selection.
Internet advertising effectiveness by using hierarchical model
RAHMANI, Samaneh
2015-01-01
Abstract. Present paper has been developed with the title of internet advertising effectiveness by using hierarchical model. Presenting the question: Today Internet is an important channel in marketing and advertising. The reason for this could be the ability of the Internet to reduce costs and people’s access to online services[1]. Also advertisers can easily access a multitude of users and communicate with them at low cost [9]. On the other hand, compared to traditional advertising, interne...
A Hierarchical Agency Model of Deposit Insurance
Jonathan Carroll; Shino Takayama
2010-01-01
This paper develops a hierarchical agency model of deposit insurance. The main purpose is to undertake a game theoretic analysis of the consequences of deposit insurance schemes and their effects on monitoring incentives for banks. Using this simple framework, we analyze both risk- independent and risk-dependent premium schemes along with reserve requirement constraints. The results provide policymakers with not only a better understanding of the effects of deposit insurance on welfare and th...
Hierarchical antifouling brushes for biosensing applications
Czech Academy of Sciences Publication Activity Database
de los Santos Pereira, Andres; Riedel, Tomáš; Brynda, Eduard; Rodriguez-Emmenegger, Cesar
2014-01-01
Roč. 202, 31 October (2014), s. 1313-1321 ISSN 0925-4005 R&D Projects: GA ČR GAP205/12/1702; GA MŠk(CZ) EE2.3.30.0029; GA MŠk(CZ) ED1.1.00/02.0109 Institutional support: RVO:61389013 Keywords : hierarchically structured brushes * affinity biosensors * fouling Subject RIV: CE - Biochemistry Impact factor: 4.097, year: 2014
A hierarchical bayesian model to quantify uncertainty of stream water temperature forecasts.
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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.
Category theoretic analysis of hierarchical protein materials and social networks.
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David I Spivak
Full Text Available Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a "concept web" or "semantic network" except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine.
On hierarchical solutions to the BBGKY hierarchy
Hamilton, A. J. S.
1988-01-01
It is thought that the gravitational clustering of galaxies in the universe may approach a scale-invariant, hierarchical form in the small separation, large-clustering regime. Past attempts to solve the Born-Bogoliubov-Green-Kirkwood-Yvon (BBGKY) hierarchy in this regime have assumed a certain separable hierarchical form for the higher order correlation functions of galaxies in phase space. It is shown here that such separable solutions to the BBGKY equations must satisfy the condition that the clustered component of the solution has cluster-cluster correlations equal to galaxy-galaxy correlations to all orders. The solutions also admit the presence of an arbitrary unclustered component, which plays no dyamical role in the large-clustering regime. These results are a particular property of the specific separable model assumed for the correlation functions in phase space, not an intrinsic property of spatially hierarchical solutions to the BBGKY hierarchy. The observed distribution of galaxies does not satisfy the required conditions. The disagreement between theory and observation may be traced, at least in part, to initial conditions which, if Gaussian, already have cluster correlations greater than galaxy correlations.
Hierarchically structured materials for lithium batteries
International Nuclear Information System (INIS)
Xiao, Jie; Zheng, Jianming; Li, Xiaolin; Shao, Yuyan; Zhang, Ji-Guang
2013-01-01
The lithium-ion battery (LIB) is one of the most promising power sources to be deployed in electric vehicles, including solely battery powered vehicles, plug-in hybrid electric vehicles, and hybrid electric vehicles. With the increasing demand for devices of high-energy densities (>500 Wh kg −1 ), new energy storage systems, such as lithium–oxygen (Li–O 2 ) batteries and other emerging systems beyond the conventional LIB, have attracted worldwide interest for both transportation and grid energy storage applications in recent years. It is well known that the electrochemical performance of these energy storage systems depends not only on the composition of the materials, but also on the structure of the electrode materials used in the batteries. Although the desired performance characteristics of batteries often have conflicting requirements with the micro/nano-structure of electrodes, hierarchically designed electrodes can be tailored to satisfy these conflicting requirements. This work will review hierarchically structured materials that have been successfully used in LIB and Li–O 2 batteries. Our goal is to elucidate (1) how to realize the full potential of energy materials through the manipulation of morphologies, and (2) how the hierarchical structure benefits the charge transport, promotes the interfacial properties and prolongs the electrode stability and battery lifetime. (paper)
Anisotropic and Hierarchical Porosity in Multifunctional Ceramics
Lichtner, Aaron Zev
The performance of multifunctional porous ceramics is often hindered by the seemingly contradictory effects of porosity on both mechanical and non-structural properties and yet a sufficient body of knowledge linking microstructure to these properties does not exist. Using a combination of tailored anisotropic and hierarchical materials, these disparate effects may be reconciled. In this project, a systematic investigation of the processing, characterization and properties of anisotropic and isotropic hierarchically porous ceramics was conducted. The system chosen was a composite ceramic intended as the cathode for a solid oxide fuel cell (SOFC). Comprehensive processing investigations led to the development of approaches to make hierarchical, anisotropic porous microstructures using directional freeze-casting of well dispersed slurries. The effect of all the important processing parameters was investigated. This resulted in an ability to tailor and control the important microstructural features including the scale of the microstructure, the macropore size and total porosity. Comparable isotropic porous ceramics were also processed using fugitive pore formers. A suite of characterization techniques including x-ray tomography and 3-D sectional scanning electron micrographs (FIB-SEM) was used to characterize and quantify the green and partially sintered microstructures. The effect of sintering temperature on the microstructure was quantified and discrete element simulations (DEM) were used to explain the experimental observations. Finally, the comprehensive mechanical properties, at room temperature, were investigated, experimentally and using DEM, for the different microstructures.
Statistical dynamics of ultradiffusion in hierarchical systems
International Nuclear Information System (INIS)
Gardner, S.
1987-01-01
In many types of disordered systems which exhibit frustration and competition, an ultrametric topology is found to exist in the space of allowable states. This ultrametric topology of states is associated with a hierarchical relaxation process called ultradiffusion. Ultradiffusion occurs in hierarchical non-linear (HNL) dynamical systems when constraints cause large scale, slow modes of motion to be subordinated to small scale, fast modes. Examples of ultradiffusion are found throughout condensed matter physics and critical phenomena (e.g. the states of spin glasses), in biophysics (e.g. the states of Hopfield networks) and in many other fields including layered computing based upon nonlinear dynamics. The statistical dynamics of ultradiffusion can be treated as a random walk on an ultrametric space. For reversible bifurcating ultrametric spaces the evolution equation governing the probability of a particle being found at site i at time t has a highly degenerate transition matrix. This transition matrix has a fractal geometry similar to the replica form proposed for spin glasses. The authors invert this fractal matrix using a recursive quad-tree (QT) method. Possible applications of hierarchical systems to communications and symbolic computing are discussed briefly
Efficiently dense hierarchical graphene based aerogel electrode for supercapacitors
Wang, Xin; Lu, Chengxing; Peng, Huifen; Zhang, Xin; Wang, Zhenkun; Wang, Gongkai
2016-08-01
Boosting gravimetric and volumetric capacitances simultaneously at a high rate is still a discrepancy in development of graphene based supercapacitors. We report the preparation of dense hierarchical graphene/activated carbon composite aerogels via a reduction induced self-assembly process coupled with a drying post treatment. The compact and porous structures of composite aerogels could be maintained. The drying post treatment has significant effects on increasing the packing density of aerogels. The introduced activated carbons play the key roles of spacers and bridges, mitigating the restacking of adjacent graphene nanosheets and connecting lateral and vertical graphene nanosheets, respectively. The optimized aerogel with a packing density of 0.67 g cm-3 could deliver maximum gravimetric and volumetric capacitances of 128.2 F g-1 and 85.9 F cm-3, respectively, at a current density of 1 A g-1 in aqueous electrolyte, showing no apparent degradation to the specific capacitance at a current density of 10 A g-1 after 20000 cycles. The corresponding gravimetric and volumetric capacitances of 116.6 F g-1 and 78.1 cm-3 with an acceptable cyclic stability are also achieved in ionic liquid electrolyte. The results show a feasible strategy of designing dense hierarchical graphene based aerogels for supercapacitors.
Colloidal quantum dot solar cells exploiting hierarchical structuring
Labelle, André J.
2015-02-11
Extremely thin-absorber solar cells offer low materials utilization and simplified manufacture but require improved means to enhance photon absorption in the active layer. Here, we report enhanced-absorption colloidal quantum dot (CQD) solar cells that feature transfer-stamped solution-processed pyramid-shaped electrodes employed in a hierarchically structured device. The pyramids increase, by up to a factor of 2, the external quantum efficiency of the device at absorption-limited wavelengths near the absorber band edge. We show that absorption enhancement can be optimized with increased pyramid angle with an appreciable net improvement in power conversion efficiency, that is, with the gain in current associated with improved absorption and extraction overcoming the smaller fractional decrease in open-circuit voltage associated with increased junction area. We show that the hierarchical combination of micron-scale structured electrodes with nanoscale films provides for an optimized enhancement at absorption-limited wavelengths. We fabricate 54.7° pyramid-patterned electrodes, conformally apply the quantum dot films, and report pyramid CQD solar cells that exhibit a 24% improvement in overall short-circuit current density with champion devices providing a power conversion efficiency of 9.2%.
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. PMID:26089806
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.
Hierarchical CuO hollow microspheres: Controlled synthesis for enhanced lithium storage performance
International Nuclear Information System (INIS)
Guan Xiangfeng; Li Liping; Li Guangshe; Fu Zhengwei; Zheng Jing; Yan Tingjiang
2011-01-01
Graphical abstract: Hierarchical CuO microspheres with hollow interiors were formed through self-wrapping of a single layer of radically oriented CuO nanorods, and these microspheres showed excellent cycle performance and enhanced lithium storage capacity. Display Omitted Research highlights: → Hierarchical CuO hollow microspheres were prepared by a hydrothermal method. → The CuO hollow microspheres were assembled from radically oriented nanorods. → The growth mechanism was proposed to proceed via self-assembly and Ostwald's ripening. → The microspheres showed good cycle performance and enhanced lithium storage capacity. → Hierarchical microstructures with hollow interiors promote electrochemical property. - Abstract: In this work, hierarchical CuO hollow microspheres were hydrothermally prepared without use of any surfactants or templates. By controlling the formation reaction conditions and monitoring the relevant reaction processes using time-dependent experiments, it is demonstrated that hierarchical CuO microspheres with hollow interiors were formed through self-wrapping of a single layer of radically oriented CuO nanorods, and that hierarchical spheres could be tuned to show different morphologies and microstructures. As a consequence, the formation mechanism was proposed to proceed via a combined process of self-assembly and Ostwald's ripening. Further, these hollow microspheres were initiated as the anode material in lithium ion batteries, which showed excellent cycle performance and enhanced lithium storage capacity, most likely because of the synergetic effect of small diffusion lengths in building blocks of nanorods and proper void space that buffers the volume expansion. The strategy reported in this work is reproducible, which may help to significantly improve the electrochemical performance of transition metal oxide-based anode materials via designing the hollow structures necessary for developing lithium ion batteries and the relevant
Conjoined legs: Sirenomelia or caudal regression syndrome?
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Sakti Prasad Das
2013-01-01
Full Text Available Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
Conjoined legs: Sirenomelia or caudal regression syndrome?
Das, Sakti Prasad; Ojha, Niranjan; Ganesh, G Shankar; Mohanty, Ram Narayan
2013-07-01
Presence of single umbilical persistent vitelline artery distinguishes sirenomelia from caudal regression syndrome. We report a case of a12-year-old boy who had bilateral umbilical arteries presented with fusion of both legs in the lower one third of leg. Both feet were rudimentary. The right foot had a valgus rocker-bottom deformity. All toes were present but rudimentary. The left foot showed absence of all toes. Physical examination showed left tibia vara. The chest evaluation in sitting revealed pigeon chest and elevated right shoulder. Posterior examination of the trunk showed thoracic scoliosis with convexity to right. The patient was operated and at 1 year followup the boy had two separate legs with a good aesthetic and functional results.
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....
Regression away from the mean: Theory and examples.
Schwarz, Wolf; Reike, Dennis
2018-02-01
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.
International Nuclear Information System (INIS)
Canha-Gouveia, Analuce; Rita Costa-Pinto, Ana; Martins, Albino M; Sousa, Rui A; Reis, Rui L; Neves, Nuno M; Silva, Nuno A; Salgado, António J; Sousa, Nuno; Faria, Susana
2015-01-01
Hierarchical structures, constituted by polymeric nano and microfibers, have been considered promising scaffolds for tissue engineering strategies, mainly because they mimic, in some way, the complexity and nanoscale detail observed in real organs. The chondrogenic potential of these scaffolds has been previously demonstrated, but their osteogenic potential is not yet corroborated. In order to assess if a hierarchical structure, with nanoscale details incorporated, is an improved scaffold for bone tissue regeneration, we evaluate cell adhesion, proliferation, and osteogenic differentiation of human Wharton’s jelly derived stem cells (hWJSCs), seeded into hierarchical fibrous scaffolds. Biological data corroborates that hierarchical fibrous scaffolds show an enhanced cell entrapment when compared to rapid prototyped scaffolds without nanofibers. Furthermore, upregulation of bone specific genes and calcium phosphate deposition confirms the successful osteogenic differentiation of hWJSCs on these scaffolds. These results support our hypothesis that a scaffold with hierarchical structure, in conjugation with hWJSCs, represents a possible feasible strategy for bone tissue engineering applications. (paper)
Fabrication and properties of dual-level hierarchical structures mimicking gecko foot hairs.
Zhang, Peng; Liu, Shiyuan; Lv, Hao
2013-02-01
In nature, geckos have extraordinary adhesive capabilities. The multi-scale hierarchical structure of the gecko foot hairs, especially the high-aspect-ratio structure of its micro-scale seta and nano-scale spatulae is the critical factor of the gecko's ability to adopt and stick to any different surface with powerful adhesion force. In this paper, we present a simple and effective approach to fabricate dual-level hierarchical structures mimicking gecko foot hairs. Polydimethyl-siloxane (PDMS) hierarchical arrays were fabricated by demolding from a double stack mold that was composed of an SU-8 mold by thick film photolithography and a silicon mold by inductively coupled plasma (ICP) etching. Top pillars of the fabricated structures have 3 micom diameter and 18 microm in height, while base pillars have 25 microm diameter and 40 microm in height. The water droplet contact angle tests indicate that the hierarchical structures increase the hydrophobic property significantly compared with the single-level arrays and the unstructured polymers, exhibiting superhydrophobicity (154.2 degrees) like the Tokay gecko's (160.9 degrees). The shear force tests show that the top pillars make attachment through side contact with a value of about 0.25 N/cm2, and moreover, the hierarchical structures are demonstrated to be more suitable for contacting with rough surfaces.
Topology of the correlation networks among major currencies using hierarchical structure methods
Keskin, Mustafa; Deviren, Bayram; Kocakaplan, Yusuf
2011-02-01
We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 period. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.
Huang, Ying; Zha, Guangyu; Luo, Qiaojie; Zhang, Jianxiang; Zhang, Feng; Li, Xiaohui; Zhao, Shifang; Zhu, Weipu; Li, Xiaodong
2014-01-01
The deficient osseointegration and implant-associated infections are pivotal issues for the long-term clinical success of endosteal Ti implants, while development of functional surfaces that can simultaneously overcome these problems remains highly challenging. This study aimed to fabricate sophisticated Ti implant surface with both osteogenic inducing activity and inherent antibacterial ability simply via tailoring surface topographical features. Micro/submciro/nano-scale structure was constructed on Ti by three cumulative subtractive methods, including sequentially conducted sandblasting as well as primary and secondary acid etching treatment. Topographical features of this hierarchical structure can be well tuned by the time of the secondary acid treatment. Ti substrate with mere micro/submicro-scale structure (MS0-Ti) served as a control to examine the influence of hierarchical structures on surface properties and biological activities. Surface analysis indicated that all hierarchically structured surfaces possessed exactly the same surface chemistry as that of MS0-Ti, and all of them showed super-amphiphilicity, high surface free energy, and high protein adsorption capability. Biological evaluations revealed surprisingly antibacterial ability and excellent osteogenic activity for samples with optimized hierarchical structure (MS30-Ti) when compared with MS0-Ti. Consequently, for the first time, a hierarchically structured Ti surface with topography-induced inherent antibacterial capability and excellent osteogenic activity was constructed. PMID:25146099
A Hierarchical Bayesian Model to Predict Self-Thinning Line for Chinese Fir in Southern China.
Directory of Open Access Journals (Sweden)
Xiongqing Zhang
Full Text Available Self-thinning is a dynamic equilibrium between forest growth and mortality at full site occupancy. Parameters of the self-thinning lines are often confounded by differences across various stand and site conditions. For overcoming the problem of hierarchical and repeated measures, we used hierarchical Bayesian method to estimate the self-thinning line. The results showed that the self-thinning line for Chinese fir (Cunninghamia lanceolata (Lamb.Hook. plantations was not sensitive to the initial planting density. The uncertainty of model predictions was mostly due to within-subject variability. The simulation precision of hierarchical Bayesian method was better than that of stochastic frontier function (SFF. Hierarchical Bayesian method provided a reasonable explanation of the impact of other variables (site quality, soil type, aspect, etc. on self-thinning line, which gave us the posterior distribution of parameters of self-thinning line. The research of self-thinning relationship could be benefit from the use of hierarchical Bayesian method.
A Hierarchically Micro-Meso-Macroporous Zeolite CaA for Methanol Conversion to Dimethyl Ether
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Yan Wang
2016-11-01
Full Text Available A hierarchical zeolite CaA with microporous, mesoporous and macroporous structure was hydrothermally synthesized by a ”Bond-Blocking” method using organo-functionalized mesoporous silica (MS as a silica source. The characterization by XRD, SEM/TEM and N2 adsorption/desorption techniques showed that the prepared material had well-crystalline zeolite Linde Type A (LTA topological structure, microspherical particle morphologies, and hierarchically intracrystalline micro-meso-macropores structure. With the Bond-Blocking principle, the external surface area and macro-mesoporosity of the hierarchical zeolite CaA can be adjusted by varying the organo-functionalized degree of the mesoporous silica surface. Similarly, the distribution of the micro-meso-macroporous structure in the zeolite CaA can be controlled purposely. Compared with the conventional microporous zeolite CaA, the hierarchical zeolite CaA as a catalyst in the conversion of methanol to dimethyl ether (DME, exhibited complete DME selectivity and stable catalytic activity with high methanol conversion. The catalytic performances of the hierarchical zeolite CaA results clearly from the micro-meso-macroporous structure, improving diffusion properties, favoring the access to the active surface and avoiding secondary reactions (no hydrocarbon products were detected after 3 h of reaction.
Similarity maps and hierarchical clustering for annotating FT-IR spectral images.
Zhong, Qiaoyong; Yang, Chen; Großerüschkamp, Frederik; Kallenbach-Thieltges, Angela; Serocka, Peter; Gerwert, Klaus; Mosig, Axel
2013-11-20
Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization. We introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy. We demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward's clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.
Multiperiod Hierarchical Location Problem of Transit Hub in Urban Agglomeration Area
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Ting-ting Li
2017-01-01
Full Text Available With the rapid urbanization in developing countries, urban agglomeration area (UAA forms. Also, transportation demand in UAA grows rapidly and presents hierarchical feature. Therefore, it is imperative to develop models for transit hubs to guide the development of UAA and better meet the time-varying and hierarchical transportation demand. In this paper, the multiperiod hierarchical location problem of transit hub in urban agglomeration area (THUAA is studied. A hierarchical service network of THUAA with a multiflow, nested, and noncoherent structure is described. Then a multiperiod hierarchical mathematical programming model is proposed, aiming at minimizing the total demand weighted travel time. Moreover, an improved adaptive clonal selection algorithm is presented to solve the model. Both the model and algorithm are verified by the application to a real-life problem of Beijing-Tianjin-Hebei Region in China. The results of different scenarios in the case show that urban population migration has a great impact on the THUAA location scheme. Sustained and appropriate urban population migration helps to reduce travel time for urban residents.
Kaewkamnerdpong, Issarapong; Krisdapong, Sudaduang
2018-06-01
To assess the hierarchical associations between children's school performance and condition-specific (CS) oral health-related quality of life (OHRQoL), school absence, oral status, sociodemographic and economic status (SDES) and social capital; and to investigate the associations between CS OHRQoL and related oral status, adjusting for SDES and social capital. Data on 925 sixth grade children in Sakaeo province, Thailand, were collected through oral examinations for dental caries and oral hygiene, social capital questionnaires, OHRQoL interviews using the Child-Oral Impacts on Daily Performances index, parental self-administered questionnaires and school documents. A hierarchical conceptual framework was developed, and independent variables were hierarchically entered into multiple logistic models for CS OHRQoL and linear regression models for school performance. After adjusting for SDES and social capital, children with high DMFT or DT scores were significantly threefold more likely to have CS impacts attributed to dental caries. However, poor oral hygiene was not significantly associated with CS impacts attributed to gingival disease. High DMFT scores were significantly associated with lower school performance, whereas high Simplified Oral Hygiene Index scores were not. The final model showed that CS impacts attributed to dental caries and school absence accounted for the association between DMFT score and school performance. Dental caries was associated with CS impacts on OHRQoL, and exerted its effect on school performance through the CS impacts and school absence. There was no association between oral hygiene and CS impacts on OHRQoL or school performance. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Hierarchical traits distances explain grassland Fabaceae species' ecological niches distances
Fort, Florian; Jouany, Claire; Cruz, Pablo
2015-01-01
Fabaceae species play a key role in ecosystem functioning through their capacity to fix atmospheric nitrogen via their symbiosis with Rhizobium bacteria. To increase benefits of using Fabaceae in agricultural systems, it is necessary to find ways to evaluate species or genotypes having potential adaptations to sub-optimal growth conditions. We evaluated the relevance of phylogenetic distance, absolute trait distance and hierarchical trait distance for comparing the adaptation of 13 grassland Fabaceae species to different habitats, i.e., ecological niches. We measured a wide range of functional traits (root traits, leaf traits, and whole plant traits) in these species. Species phylogenetic and ecological distances were assessed from a species-level phylogenetic tree and species' ecological indicator values, respectively. We demonstrated that differences in ecological niches between grassland Fabaceae species were related more to their hierarchical trait distances than to their phylogenetic distances. We showed that grassland Fabaceae functional traits tend to converge among species with the same ecological requirements. Species with acquisitive root strategies (thin roots, shallow root systems) are competitive species adapted to non-stressful meadows, while conservative ones (coarse roots, deep root systems) are able to tolerate stressful continental climates. In contrast, acquisitive species appeared to be able to tolerate low soil-P availability, while conservative ones need high P availability. Finally we highlight that traits converge along the ecological gradient, providing the assumption that species with similar root-trait values are better able to coexist, regardless of their phylogenetic distance. PMID:25741353
Linguistic steganography on Twitter: hierarchical language modeling with manual interaction
Wilson, Alex; Blunsom, Phil; Ker, Andrew D.
2014-02-01
This work proposes a natural language stegosystem for Twitter, modifying tweets as they are written to hide 4 bits of payload per tweet, which is a greater payload than previous systems have achieved. The system, CoverTweet, includes novel components, as well as some already developed in the literature. We believe that the task of transforming covers during embedding is equivalent to unilingual machine translation (paraphrasing), and we use this equivalence to de ne a distortion measure based on statistical machine translation methods. The system incorporates this measure of distortion to rank possible tweet paraphrases, using a hierarchical language model; we use human interaction as a second distortion measure to pick the best. The hierarchical language model is designed to model the speci c language of the covers, which in this setting is the language of the Twitter user who is embedding. This is a change from previous work, where general-purpose language models have been used. We evaluate our system by testing the output against human judges, and show that humans are unable to distinguish stego tweets from cover tweets any better than random guessing.
Visual question answering using hierarchical dynamic memory networks
Shang, Jiayu; Li, Shiren; Duan, Zhikui; Huang, Junwei
2018-04-01
Visual Question Answering (VQA) is one of the most popular research fields in machine learning which aims to let the computer learn to answer natural language questions with images. In this paper, we propose a new method called hierarchical dynamic memory networks (HDMN), which takes both question attention and visual attention into consideration impressed by Co-Attention method, which is the best (or among the best) algorithm for now. Additionally, we use bi-directional LSTMs, which have a better capability to remain more information from the question and image, to replace the old unit so that we can capture information from both past and future sentences to be used. Then we rebuild the hierarchical architecture for not only question attention but also visual attention. What's more, we accelerate the algorithm via a new technic called Batch Normalization which helps the network converge more quickly than other algorithms. The experimental result shows that our model improves the state of the art on the large COCO-QA dataset, compared with other methods.
Likelihood Approximation With Parallel Hierarchical Matrices For Large Spatial Datasets
Litvinenko, Alexander
2017-11-01
The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Matérn covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\\\H$-) matrix format with computational cost $\\\\mathcal{O}(k^2n \\\\log^2 n/p)$ and storage $\\\\mathcal{O}(kn \\\\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.
Hierarchical structure of the otolith of adult wild carp
Energy Technology Data Exchange (ETDEWEB)
Li Zhuo; Gao Yonghua [State key laboratory of new ceramics and fine processing, Department of Materials Science and Engineering, Tsinghua University, Beijing 100084 (China); Feng Qingling, E-mail: biomater@mail.tsinghua.edu.cn [State key laboratory of new ceramics and fine processing, Department of Materials Science and Engineering, Tsinghua University, Beijing 100084 (China)
2009-04-30
The otolith of adult wild carp contains a pair of asterisci, a pair of lappilli and a pair of sagittae. Current research works are mainly restricted to the field of the daily ring structure. The purpose of this work is to explore the structural characteristics of carp's otolith in terms of hierarchy from nanometer to millimeter scale by transmission election microscope (TEM) and scanning electron microscope (SEM). Based on the observation, carp's lapillus is composed of ordered aragonite crystals. Seven hierarchical levels of the microstructure were proposed and described with the scheme representing a complete organization in detail. SEM studies show not only the clear daily growth increment, but also the morphology within the single daily increment. The domain structure of crystal orientation in otolith was observed for the first time. Furthermore, TEM investigation displays that the lapillus is composed of aragonite crystals with nanometer scale. Four hierarchical levels of the microstructure of the sagitta are also proposed. The asteriscus which is composed of nanometer scale vaterite crystals is considered to have a uniform structure.
Hierarchical structure of the otolith of adult wild carp
International Nuclear Information System (INIS)
Li Zhuo; Gao Yonghua; Feng Qingling
2009-01-01
The otolith of adult wild carp contains a pair of asterisci, a pair of lappilli and a pair of sagittae. Current research works are mainly restricted to the field of the daily ring structure. The purpose of this work is to explore the structural characteristics of carp's otolith in terms of hierarchy from nanometer to millimeter scale by transmission election microscope (TEM) and scanning electron microscope (SEM). Based on the observation, carp's lapillus is composed of ordered aragonite crystals. Seven hierarchical levels of the microstructure were proposed and described with the scheme representing a complete organization in detail. SEM studies show not only the clear daily growth increment, but also the morphology within the single daily increment. The domain structure of crystal orientation in otolith was observed for the first time. Furthermore, TEM investigation displays that the lapillus is composed of aragonite crystals with nanometer scale. Four hierarchical levels of the microstructure of the sagitta are also proposed. The asteriscus which is composed of nanometer scale vaterite crystals is considered to have a uniform structure.
Likelihood Approximation With Parallel Hierarchical Matrices For Large Spatial Datasets
Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.
2017-01-01
The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Matérn covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\H$-) matrix format with computational cost $\\mathcal{O}(k^2n \\log^2 n/p)$ and storage $\\mathcal{O}(kn \\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.
A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis
Directory of Open Access Journals (Sweden)
Shaoning Li
2017-01-01
Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.
LSTM-Based Hierarchical Denoising Network for Android Malware Detection
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Jinpei Yan
2018-01-01
Full Text Available Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN, a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.
Hollow Carbon Nanopolyhedra for Enhanced Electrocatalysis via Confined Hierarchical Porosity.
Song, Xiaokai; Guo, Linli; Liao, Xuemei; Liu, Jian; Sun, Jianhua; Li, Xiaopeng
2017-06-01
A novel strategy for the fabrication of hollow Co and N-codoped carbon nanopolyhedra (H-CoNC) from metal-organic framework (MOF) using in situ evaporation of ZnO nanosphere templates is proposed. The excess Zn supply during the pyrolysis process is found beneficial in terms of high nitrogen (≈9.75 at%), relatively homogenous CoN bonding, and the electrochemically accessible hierarchical porous system. Compared with other reported "solid" CoNC of identical surface areas, the newly developed H-CoNC shows enhanced kinetic current in 0.1 m KOH electrolyte and elevated oxygen reduction reaction (ORR) performance in 6 m KOH. The latter exceeds results obtained with the benchmark 20 wt% Pt/C, which is related to the strong confinement of O 2 molecules in the H-CoNC hierarchical porous system. Furthermore, the H-CoNC displays great tolerance toward the methanol crossover and KSCN poisoning. Finally, the assembled Zn-air batteries with H-CoNC yield a record open circuit potential (1.59 V vs Zn, stabilized at 1.52 V), high power density (331.0 mW cm -2 ), and promising rate performance. This work provides a new guideline for the design of MOF-derived carbon materials, as well as novel insights into spatial confinement effect toward the ORR activity. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Anticollusion Attack Noninteractive Security Hierarchical Key Agreement Scheme in WHMS
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Kefei Mao
2016-01-01
Full Text Available Wireless Health Monitoring Systems (WHMS have potential to change the way of health care and bring numbers of benefits to patients, physicians, hospitals, and society. However, there are crucial barriers not only to transmit the biometric information but also to protect the privacy and security of the patients’ information. The key agreement between two entities is an essential cryptography operation to clear the barriers. In particular, the noninteractive hierarchical key agreement scheme becomes an attractive direction in WHMS because each sensor node or gateway has limited resources and power. Recently, a noninteractive hierarchical key agreement scheme has been proposed by Kim for WHMS. However, we show that Kim’s cryptographic scheme is vulnerable to the collusion attack if the physicians can be corrupted. Obviously, it is a more practical security condition. Therefore, we proposed an improved key agreement scheme against the attack. Security proof, security analysis, and experimental results demonstrate that our proposed scheme gains enhanced security and more efficiency than Kim’s previous scheme while inheriting its qualities of one-round communication and security properties.
A hierarchical SVG image abstraction layer for medical imaging
Kim, Edward; Huang, Xiaolei; Tan, Gang; Long, L. Rodney; Antani, Sameer
2010-03-01
As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the "semantic gap", a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.
The Realized Hierarchical Archimedean Copula in Risk Modelling
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Ostap Okhrin
2017-06-01
Full Text Available This paper introduces the concept of the realized hierarchical Archimedean copula (rHAC. The proposed approach inherits the ability of the copula to capture the dependencies among financial time series, and combines it with additional information contained in high-frequency data. The considered model does not suffer from the curse of dimensionality, and is able to accurately predict high-dimensional distributions. This flexibility is obtained by using a hierarchical structure in the copula. The time variability of the model is provided by daily forecasts of the realized correlation matrix, which is used to estimate the structure and the parameters of the rHAC. Extensive simulation studies show the validity of the estimator based on this realized correlation matrix, and its performance, in comparison to the benchmark models. The application of the estimator to one-day-ahead Value at Risk (VaR prediction using high-frequency data exhibits good forecasting properties for a multivariate portfolio.
A hierarchical approach to reducing communication in parallel graph algorithms
Harshvardhan,
2015-01-01
Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. This is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. The hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5 × - 8× improvement for a variety of graph algorithms at 12, 000+ cores.
Hierarchical traits distances explain grassland Fabaceae species’ ecological niches distances
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Florian eFort
2015-02-01
Full Text Available Fabaceae species play a key role in ecosystem functioning through their capacity to fix atmospheric nitrogen via their symbiosis with Rhizobium bacteria. To increase benefits of using Fabaceae in agricultural systems, it is necessary to find ways to evaluate species or genotypes having potential adaptations to sub-optimal growth conditions. We evaluated the relevance of phylogenetic distance, absolute trait distance and hierarchical trait distance for comparing the adaptation of 13 grassland Fabaceae species to different habitats, i.e. ecological niches. We measured a wide range of functional traits (root traits, leaf traits and whole plant traits in these species. Species phylogenetic and ecological distances were assessed from a species-level phylogenetic tree and species’ ecological indicator values, respectively. We demonstrated that differences in ecological niches between grassland Fabaceae species were related more to their hierarchical trait distances than to their phylogenetic distances. We showed that grassland Fabaceae functional traits tend to converge among species with the same ecological requirements. Species with acquisitive root strategies (thin roots, shallow root systems are competitive species adapted to non-stressful meadows, while conservative ones (coarse roots, deep root systems are able to tolerate stressful continental climates. In contrast, acquisitive species appeared to be able to tolerate low soil-P availability, while conservative ones need high P availability. Finally we highlight that traits converge along the ecological gradient, providing the assumption that species with similar root-trait values are better able to coexist, regardless of their phylogenetic distance.
Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression
Halim Boukaram, Wajih
2017-09-14
We present high performance implementations of the QR and the singular value decomposition of a batch of small matrices hosted on the GPU with applications in the compression of hierarchical matrices. The one-sided Jacobi algorithm is used for its simplicity and inherent parallelism as a building block for the SVD of low rank blocks using randomized methods. We implement multiple kernels based on the level of the GPU memory hierarchy in which the matrices can reside and show substantial speedups against streamed cuSOLVER SVDs. The resulting batched routine is a key component of hierarchical matrix compression, opening up opportunities to perform H-matrix arithmetic efficiently on GPUs.
Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression
Halim Boukaram, Wajih; Turkiyyah, George; Ltaief, Hatem; Keyes, David E.
2017-01-01
We present high performance implementations of the QR and the singular value decomposition of a batch of small matrices hosted on the GPU with applications in the compression of hierarchical matrices. The one-sided Jacobi algorithm is used for its simplicity and inherent parallelism as a building block for the SVD of low rank blocks using randomized methods. We implement multiple kernels based on the level of the GPU memory hierarchy in which the matrices can reside and show substantial speedups against streamed cuSOLVER SVDs. The resulting batched routine is a key component of hierarchical matrix compression, opening up opportunities to perform H-matrix arithmetic efficiently on GPUs.
International Nuclear Information System (INIS)
Gupta, Sushant; Arjunan, Arul Chakkaravarthi; Deshpande, Sameer; Seal, Sudipta; Singh, Deepika; Singh, Rajiv K.
2009-01-01
Superhydrophobic polytetrafluoroethylene films with hierarchical surface roughness were deposited using pulse electron deposition technique. We were able to modulate roughness of the deposited films by controlling the beam energy and hence the electron penetration depth. The films deposited at higher beam energy showed contact angle as high as 166 o . The scanning electron and atomic force microscope studies revealed clustered growth and two level sub-micron asperities on films deposited at higher energies. Such dual-scale hierarchical roughness and heterogeneities at the water-surface interface was attributed to the observed contact angle and thus its superhydrophobic nature.
Energy Technology Data Exchange (ETDEWEB)
Gupta, Sushant, E-mail: sushant3@ufl.ed [Department of Materials Science and Engineering, University of Florida, Gainesville, FL 32611 (United States); Arjunan, Arul Chakkaravarthi [Sinmat Incorporated, 2153 SE Hawthorne Road, 129, Gainesville, Florida 32641 (United States); Deshpande, Sameer; Seal, Sudipta [Advanced Material Processing and Analysis Center, University of Central Florida, Orlando, Florida 32816 (United States); Singh, Deepika [Sinmat Incorporated, 2153 SE Hawthorne Road, 129, Gainesville, Florida 32641 (United States); Singh, Rajiv K. [Department of Materials Science and Engineering, University of Florida, Gainesville, FL 32611 (United States)
2009-06-30
Superhydrophobic polytetrafluoroethylene films with hierarchical surface roughness were deposited using pulse electron deposition technique. We were able to modulate roughness of the deposited films by controlling the beam energy and hence the electron penetration depth. The films deposited at higher beam energy showed contact angle as high as 166{sup o}. The scanning electron and atomic force microscope studies revealed clustered growth and two level sub-micron asperities on films deposited at higher energies. Such dual-scale hierarchical roughness and heterogeneities at the water-surface interface was attributed to the observed contact angle and thus its superhydrophobic nature.
A novel snowflake-like SnO2 hierarchical architecture with superior gas sensing properties
Li, Yanqiong
2018-02-01
Snowflake-like SnO2 hierarchical architecture has been synthesized via a facile hydrothermal method and followed by calcination. The SnO2 hierarchical structures are assembled with thin nanoflakes blocks, which look like snowflake shape. A possible mechanism for the formation of the SnO2 hierarchical structures is speculated. Moreover, gas sensing tests show that the sensor based on snowflake-like SnO2 architectures exhibited excellent gas sensing properties. The enhancement may be attributed to its unique structures, in which the porous feature on the snowflake surface could further increase the active surface area of the materials and provide facile pathways for the target gas.
A reward optimization method based on action subrewards in hierarchical reinforcement learning.
Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming
2014-01-01
Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.
Du, Shuting; Chen, Xiaoxin; Sun, Qiming; Wang, Ning; Jia, Mingjun; Valtchev, Valentin; Yu, Jihong
2016-02-28
Hierarchical TS-1 zeolites with secondary macropores have been successfully prepared by using two different fluoride-containing chemical etching post-treated routes. Hierarchical TS-1 zeolites exhibited a chemical composition similar to that of the parent material and showed remarkably enhanced catalytic activity in oxidative desulfurization reaction.
A flexible fuzzy regression algorithm for forecasting oil consumption estimation
International Nuclear Information System (INIS)
Azadeh, A.; Khakestani, M.; Saberi, M.
2009-01-01
Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.
International Nuclear Information System (INIS)
Yang, Zeheng; Wang, Kun; Shao, Zongming; Tian, Yuan; Chen, Gongde; Wang, Kai; Chen, Zhangxian; Dou, Yan; Zhang, Weixin
2017-01-01
Hierarchical array catalysts with micro/nano structures on substrates not only possess high reactivity from large surface area and suitable interface, but intensify mass transfer through shortening the diffusion paths of both reactants and products for high catalytic efficiency. Herein, we first demonstrate fabrication of Fe 2 O 3 hierarchical arrays grown on stainless-steel substrates via in-situ hydrothermal chemical oxidation followed by heat treatment in N 2 atmosphere. As a Fenton-like catalyst, Fe 2 O 3 hierarchical arrays exhibit excellent catalytic activity and life cycle performance for methylene blue (MB) dye degradation in aqueous solution in the presence of H 2 O 2 . The Fe 2 O 3 catalyst with unique hierarchical structures and efficient transport channels, effectively activates H 2 O 2 to generate large quantity of • OH radicals and highly promotes reaction kinetics between MB and • OH radicals. Immobilization of hierarchical array catalysts on stainless-steel can prevent particles agglomeration, facilitate the recovery and reuse of the catalysts, which is expected promising applications in wastewater remediation. - Graphical abstract: The in-situ synthesis of Fe 2 O 3 hierarchical arrays on stainless-steel substrates was reported for the first time, which exhibit excellent catalytic activity performance for methylene blue (MB) dye degradation in aqueous solution in the presence of H 2 O 2 . - Highlights: • Fe 2 O 3 hierarchical arrays was prepared by in-situ hydrothermal chemical oxidation. • F − ions play an important role in the formation of the Fe 2 O 3 hierarchical arrays. • Fe 2 O 3 hierarchical arrays show high catalytic activity to methylene blue degradation.
Bayesian semiparametric regression models to characterize molecular evolution
Directory of Open Access Journals (Sweden)
Datta Saheli
2012-10-01
Full Text Available Abstract Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. Results The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. Conclusions The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.
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.
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.
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.
Hierarchical Feature Extraction With Local Neural Response for Image Recognition.
Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P
2013-04-01
In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.
Pattern overlap implies runaway growth in hierarchical tile systems
Directory of Open Access Journals (Sweden)
David Doty
2015-11-01
Full Text Available We show that in the hierarchical tile assembly model, if there is a producible assembly that overlaps a nontrivial translation of itself consistently (i.e., the pattern of tile types in the overlap region is identical in both translations, then arbitrarily large assemblies are producible. The significance of this result is that tile systems intended to controllably produce finite structures must avoid pattern repetition in their producible assemblies that would lead to such overlap.This answers an open question of Chen and Doty (SODA 2012, who showed that so-called "partial-order" systems producing a unique finite assembly and avoiding such overlaps must require time linear in the assembly diameter. An application of our main result is that any system producing a unique finite assembly is automatically guaranteed to avoid such overlaps, simplifying the hypothesis of Chen and Doty's main theorem.
Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan
2011-11-01
To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.
DEFF Research Database (Denmark)
Yu, Cuiyan; Yu, Yanlong; Xu, Tao
2017-01-01
Hierarchical nanoflowers assembled with Au nanoparticles (NPs) decorated ZnO nanosheets (Au-ZnO nanosheet flowers, AZNSFs) were successful synthesized. The AZNSFs showed more efficient activity to photodegradation of RhB than that of pure ZnO nanosheet flowers and commercial ZnO nanopowders. The ...
Improved SIRAP analysis for synchronization in hierarchical scheduled real-time systems
Behnam, M.; Bril, R.J.; Nolte, T.
2009-01-01
We present our ongoing work on synchronization in hierarchical scheduled real-time systems, where tasks are scheduled using fixed-priority pre-emptive scheduling. In this paper, we show that the original local schedulability analysis of the synchronization protocol SIRAP [4] is very pessimistic when
Biomimetic "Cactus Spine" with Hierarchical Groove Structure for Efficient Fog Collection.
Bai, Fan; Wu, Juntao; Gong, Guangming; Guo, Lin
2015-07-01
A biomimetic "cactus spine" with hierarchical groove structure is designed and fabricated using simple electrospinning. This novel artificial cactus spine possesses excellent fog collection and water transportation ability. A model cactus equipped with artificial spines also shows a great water storage capacity. The results can be helpful in the development of water collectors and may make a contribution to the world water crisis.
Non-Hierarchical Clustering as a method to analyse an open-ended ...
African Journals Online (AJOL)
We show that the use of non-hierarchical analysis allows us to interpret the reasoning of students solving different mathematical problems using Algebra, and to separate them into different groups, that can be recognised and characterised by common traits in their answers, without any prior knowledge on the part of the ...
Schenning, A.P.H.J.; Bastiaansen, C.W.M.; Broer, D.J.; Debije, M.G.
2014-01-01
ABSTRACT: In this review, we show the important role of supramolecular chemistry in the fabrication of stimuli responsive and hierarchically structured liquid crystalline polymer networks. Supramolecular interactions can be used to create three dimensional order or as molecular triggers in materials
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...... the no-transfer rule (where no revenue bubbles up) and the full-transfer rule (where all the revenues bubble up to the top of the hierarchy). Intermediate rules within the family are reminiscent of popular incentive mechanisms for social mobilization or multi-level marketing....
Constructing storyboards based on hierarchical clustering analysis
Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu
2005-07-01
There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.
Hierarchical Network Design Using Simulated Annealing
DEFF Research Database (Denmark)
Thomadsen, Tommy; Clausen, Jens
2002-01-01
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....
Robust Pseudo-Hierarchical Support Vector Clustering
DEFF Research Database (Denmark)
Hansen, Michael Sass; Sjöstrand, Karl; Olafsdóttir, Hildur
2007-01-01
Support vector clustering (SVC) has proven an efficient algorithm for clustering of noisy and high-dimensional data sets, with applications within many fields of research. An inherent problem, however, has been setting the parameters of the SVC algorithm. Using the recent emergence of a method...... for calculating the entire regularization path of the support vector domain description, we propose a fast method for robust pseudo-hierarchical support vector clustering (HSVC). The method is demonstrated to work well on generated data, as well as for detecting ischemic segments from multidimensional myocardial...
Implementation of hierarchical control in DC microgrids
DEFF Research Database (Denmark)
Jin, Chi; Wang, Peng; Xiao, Jianfang
2014-01-01
of Technology, Singapore. The coordination control among multiple dc sources and energy storages is implemented using a novel hierarchical control technique. The bus voltage essentially acts as an indicator of supply-demand balance. A wireless control is implemented for the reliable operation of the grid....... A reasonable compromise between the maximum power harvest and effective battery management is further enhanced using the coordination control based on a central energy management system. The feasibility and effectiveness of the proposed control strategies have been tested by a dc microgrid in WERL....
Broca's area: a supramodal hierarchical processor?
Tettamanti, Marco; Weniger, Dorothea
2006-05-01
Despite the presence of shared characteristics across the different domains modulating Broca's area activity (e.g., structural analogies, as between language and music, or representational homologies, as between action execution and action observation), the question of what exactly the common denominator of such diverse brain functions is, with respect to the function of Broca's area, remains largely a debated issue. Here, we suggest that an important computational role of Broca's area may be to process hierarchical structures in a wide range of functional domains.
SORM applied to hierarchical parallel system
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager
2006-01-01
of a particular first order reliability method (FORM) was first described in a celebrated paper by Rackwitz and Fiessler more than a quarter of a century ago. The method has become known as the Rackwitz-Fiessler algorithm. The original RF-algorithm as applied to a hierarchical random variable model...... is recapitulated so that a simple but quite effective accuracy improving calculation can be explained. A limit state curvature correction factor on the probability approximation is obtained from the final stop results of the RF-algorithm. This correction factor is based on Breitung’s asymptotic formula for second...
Additive Manufacturing of Hierarchical Porous Structures
Energy Technology Data Exchange (ETDEWEB)
Grote, Christopher John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Materials Science and Technology Division. Polymers and Coatings
2016-08-30
Additive manufacturing has become a tool of choice for the development of customizable components. Developments in this technology have led to a powerful array of printers that t serve a variety of needs. However, resin development plays a crucial role in leading the technology forward. This paper addresses the development and application of printing hierarchical porous structures. Beginning with the development of a porous scaffold, which can be functionalized with a variety of materials, and concluding with customized resins for metal, ceramic, and carbon structures.
Flow and transport in hierarchically fractured systems
International Nuclear Information System (INIS)
Karasaki, K.
1993-01-01
Preliminary results indicate that flow in the saturated zone at Yucca Mountain is controlled by fractures. A current conceptual model assumes that the flow in the fracture system can be approximately by a three-dimensionally interconnected network of linear conduits. The overall flow system of rocks at Yucca Mountain is considered to consist of hierarchically structured heterogeneous fracture systems of multiple scales. A case study suggests that it is more appropriate to use the flow parameters of the large fracture system for predicting the first arrival time, rather than using the bulk average parameters of the total system
AN INTEGER PROGRAMMING MODEL FOR HIERARCHICAL WORKFORCE
Directory of Open Access Journals (Sweden)
BANU SUNGUR
2013-06-01
Full Text Available The model presented in this paper is based on the model developed by Billionnet for the hierarchical workforce problem. In Billionnet’s Model, while determining the workers’ weekly costs, weekly working hours of workers are not taken into consideration. In our model, the weekly costs per worker are reduced in proportion to the working hours per week. Our model is illustrated on the Billionnet’s Example. The models in question are compared and evaluated on the basis of the results obtained from the example problem. A reduction is achieved in the total cost by the proposed model.
Technique for fast and efficient hierarchical clustering
Stork, Christopher
2013-10-08
A fast and efficient technique for hierarchical clustering of samples in a dataset includes compressing the dataset to reduce a number of variables within each of the samples of the dataset. A nearest neighbor matrix is generated to identify nearest neighbor pairs between the samples based on differences between the variables of the samples. The samples are arranged into a hierarchy that groups the samples based on the nearest neighbor matrix. The hierarchy is rendered to a display to graphically illustrate similarities or differences between the samples.
Show me the money: incorporating financial motives into the gambling motives questionnaire.
Dechant, Kristianne
2014-12-01
Although research has only recently begun to measure what motivates all levels of gambling involvement, motives could offer a theoretically interesting and practical way to subtype gamblers in research and for responsible gambling initiatives. The Gambling Motives Questionnaire (GMQ) is one measure that weaves together much of the gambling motives literature, but it has been criticized for neglecting financial reasons for gambling. This study uses a series of factor analyses to explore the effect of adding nine financial motives to the GMQ in a heterogeneous sample of 1,014 adult past-year gamblers. After trimming trivial financial motives, the penultimate factor analysis of the 15 GMQ items and four financial motives led to a four-factor solution, with factors tapping enhancement, social, coping and financial motives, as predicted. A final factor analysis performed on a modified GMQ-F (i.e., 16 items, including a financial subscale) revealed the same four factors, and hierarchical regression showed that the financial motives improve the GMQ-F's prediction of gambling frequency. This study provides evidence that omitting financial motives is a clear gap in the GMQ, yet suggests that the GMQ is a promising tool that can be conceptually and empirically strengthened with the simple addition of financial items.
Prediction of road accidents: A Bayesian hierarchical approach.
Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H
2013-03-01
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 of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any
Hierarchical spatial structure of stream fish colonization and extinction
Hitt, N.P.; Roberts, J.H.
2012-01-01
Spatial variation in extinction and colonization is expected to influence community composition over time. In stream fish communities, local species richness (alpha diversity) and species turnover (beta diversity) are thought to be regulated by high extinction rates in headwater streams and high colonization rates in downstream areas. We evaluated the spatiotemporal structure of fish communities in streams originally surveyed by Burton and Odum 1945 (Ecology 26: 182-194) in Virginia, USA and explored the effects of species traits on extinction and colonization dynamics. We documented dramatic changes in fish community structure at both the site and stream scales. Of the 34 fish species observed, 20 (59%) were present in both time periods, but 11 (32%) colonized the study area and three (9%) were extirpated over time. Within streams, alpha diversity increased in two of three streams but beta diversity decreased dramatically in all streams due to fish community homogenization caused by colonization of common species and extirpation of rare species. Among streams, however, fish communities differentiated over time. Regression trees indicated that reproductive life-history traits such as spawning mound construction, associations with mound-building species, and high fecundity were important predictors of species persistence or colonization. Conversely, native fishes not associated with mound-building exhibited the highest rates of extirpation from streams. Our results demonstrate that stream fish colonization and extinction dynamics exhibit hierarchical spatial structure and suggest that mound-building fishes serve as keystone species for colonization of headwater streams.
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.
Regression calibration with more surrogates than mismeasured variables
Kipnis, Victor; Midthune, Douglas; Freedman, Laurence S.; Carroll, Raymond J.
2012-01-01
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.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan
2018-04-01
Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.
Directory of Open Access Journals (Sweden)
Dan Meng
2015-01-01
Full Text Available WO3 hierarchical porous structures were successfully synthesized via cetyltrimethylammonium bromide- (CTAB- assisted hydrothermal method. The structure and morphology were investigated using scanning electron microscope, X-ray diffractometer, transmission electron microscopy, X-ray photoelectron spectra, Brunauer-Emmett-Teller nitrogen adsorption-desorption, and thermogravimetry and differential thermal analysis. The result demonstrated that WO3 hierarchical porous structures with an orthorhombic structure were constructed by a number of nanoparticles about 50–100 nm in diameters. The H2 gas sensing measurements showed that well-defined WO3 hierarchical porous structures with a large specific surface area exhibited the higher sensitivity compared with products without CTAB at all operating temperatures. Moreover, the reversible and fast response to H2 gas and good selectivity were obtained. The results indicated that the WO3 hierarchical porous structures are promising materials for gas sensors.
Lin, Chin An
2015-12-01
Fused-silica packaging glass fabricated with a hierarchical structure by integrating small (ultrathin nanorods) and large (honeycomb nanowalls) structures was demonstrated with exceptional light-harvesting solar performance, which is attributed to the subwavelength feature of the nanorods and an efficient scattering ability of the honeycomb nanowalls. Si solar cells covered with the hierarchically structured packaging glass exhibit enhanced conversion efficiency by 5.2% at normal incidence, and the enhancement went up to 46% at the incident angle of 60°. The hierarchical structured packaging glass shows excellent self-cleaning characteristics: 98.8% of the efficiency is maintained after 6 weeks of outdoor exposure, indicating that the nanostructured surface effectively repels polluting dust/particles. The presented self-cleaning omnidirectional light-harvesting design using the hierarchical structured packaging glass is a potential universal scheme for practical solar applications.
Structural Break Tests Robust to Regression Misspecification
Directory of Open Access Journals (Sweden)
Alaa Abi Morshed
2018-05-01
Full Text Available Structural break tests 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 conditional mean dynamics are misspecified. We also show that the sup Wald test for breaks in the unconditional mean and variance does not have the same size distortions, yet benefits from similar power to its conditional counterpart in correctly specified models. Hence, we propose using it as an alternative and complementary test for breaks. We apply the unconditional and conditional mean and variance tests to three US series: unemployment, industrial production growth and interest rates. Both the unconditional and the conditional mean tests detect a break in the mean of interest rates. However, for the other two series, the unconditional mean test does not detect a break, while the conditional mean tests based on dynamic regression models occasionally detect a break, with the implied break-point estimator varying across different dynamic specifications. For all series, the unconditional variance does not detect a break while most tests for the conditional variance do detect a break which also varies across specifications.
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
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 and thoroughly…
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
A Seemingly Unrelated Poisson Regression Model
King, Gary
1989-01-01
This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.
Hierarchic modeling of heat exchanger thermal hydraulics
International Nuclear Information System (INIS)
Horvat, A.; Koncar, B.
2002-01-01
Volume Averaging Technique (VAT) is employed in order to model the heat exchanger cross-flow as a porous media flow. As the averaging of the transport equations lead to a closure problem, separate relations are introduced to model interphase momentum and heat transfer between fluid flow and the solid structure. The hierarchic modeling is used to calculate the local drag coefficient C d as a function of Reynolds number Re h . For that purpose a separate model of REV is built and DNS of flow through REV is performed. The local values of heat transfer coefficient h are obtained from available literature. The geometry of the simulation domain and boundary conditions follow the geometry of the experimental test section used at U.C.L.A. The calculated temperature fields reveal that the geometry with denser pin-fins arrangement (HX1) heats fluid flow faster. The temperature field in the HX2 exhibits the formation of thermal boundary layer between pin-fins, which has a significant role in overall thermal performance of the heat exchanger. Although presented discrepancies of the whole-section drag coefficient C d are large, we believe that hierarchic modeling is an appropriate strategy for calculation of complex transport phenomena in heat exchanger geometries.(author)
Hierarchical feature selection for erythema severity estimation
Wang, Li; Shi, Chenbo; Shu, Chang
2014-10-01
At present PASI system of scoring is used for evaluating erythema severity, which can help doctors to diagnose psoriasis [1-3]. The system relies on the subjective judge of doctors, where the accuracy and stability cannot be guaranteed [4]. This paper proposes a stable and precise algorithm for erythema severity estimation. Our contributions are twofold. On one hand, in order to extract the multi-scale redness of erythema, we design the hierarchical feature. Different from traditional methods, we not only utilize the color statistical features, but also divide the detect window into small window and extract hierarchical features. Further, a feature re-ranking step is introduced, which can guarantee that extracted features are irrelevant to each other. On the other hand, an adaptive boosting classifier is applied for further feature selection. During the step of training, the classifier will seek out the most valuable feature for evaluating erythema severity, due to its strong learning ability. Experimental results demonstrate the high precision and robustness of our algorithm. The accuracy is 80.1% on the dataset which comprise 116 patients' images with various kinds of erythema. Now our system has been applied for erythema medical efficacy evaluation in Union Hosp, China.
Hierarchical Diagnosis of Vocal Fold Disorders
Nikkhah-Bahrami, Mansour; Ahmadi-Noubari, Hossein; Seyed Aghazadeh, Babak; Khadivi Heris, Hossein
This paper explores the use of hierarchical structure for diagnosis of vocal fold disorders. The hierarchical structure is initially used to train different second-level classifiers. At the first level normal and pathological signals have been distinguished. Next, pathological signals have been classified into neurogenic and organic vocal fold disorders. At the final level, vocal fold nodules have been distinguished from polyps in organic disorders category. For feature selection at each level of hierarchy, the reconstructed signal at each wavelet packet decomposition sub-band in 5 levels of decomposition with mother wavelet of (db10) is used to extract the nonlinear features of self-similarity and approximate entropy. Also, wavelet packet coefficients are used to measure energy and Shannon entropy features at different spectral sub-bands. Davies-Bouldin criterion has been employed to find the most discriminant features. Finally, support vector machines have been adopted as classifiers at each level of hierarchy resulting in the diagnosis accuracy of 92%.
Discrete hierarchical organization of social group sizes.
Zhou, W-X; Sornette, D; Hill, R A; Dunbar, R I M
2005-02-22
The 'social brain hypothesis' for the evolution of large brains in primates has led to evidence for the coevolution of neocortical size and social group sizes, suggesting that there is a cognitive constraint on group size that depends, in some way, on the volume of neural material available for processing and synthesizing information on social relationships. More recently, work on both human and non-human primates has suggested that social groups are often hierarchically structured. We combine data on human grouping patterns in a comprehensive and systematic study. Using fractal analysis, we identify, with high statistical confidence, a discrete hierarchy of group sizes with a preferred scaling ratio close to three: rather than a single or a continuous spectrum of group sizes, humans spontaneously form groups of preferred sizes organized in a geometrical series approximating 3-5, 9-15, 30-45, etc. Such discrete scale invariance could be related to that identified in signatures of herding behaviour in financial markets and might reflect a hierarchical processing of social nearness by human brains.
The concept of a hierarchical cosmos
Grujić, P. V.
2003-10-01
The idea of a hierachically structured cosmos can be traced back to the Presocratic Hellada. In the fifth century BC Anaxagoras from Clazomenae developed an idea of a sort of fractal material world, by introducing the concept of seeds (spermata), or homoeomeries as Aristotle dubbed it later (Grujić 2001). Anaxagoras ideas have been grossly neglected during the Middle Ages, to be invoked by a number of post-Renaissance thinkers, like Leibniz, Kant, etc, though neither of them referred to their Greek predecessor. But the real resurrections of the hierarchical paradigm started at the beginning of the last century, with Fournier and Charlier (Grujić 2002). Second half of the 20th century witnessed an intensive development of the theoretical models based on the (multi)fractal paradigm, as well as a considerable body of the observational evidence in favour of the hierarchical cosmos (Saar 1988). We overview the state of the art of the cosmological fractal concept, both within the astrophysical (Sylos Labini et al 1998), methodological (Ribeiro 2001) and epistemological (Ribeiro and Videira 1998) context.
A self-defining hierarchical data system
Bailey, J.
1992-01-01
The Self-Defining Data System (SDS) is a system which allows the creation of self-defining hierarchical data structures in a form which allows the data to be moved between different machine architectures. Because the structures are self-defining they can be used for communication between independent modules in a distributed system. Unlike disk-based hierarchical data systems such as Starlink's HDS, SDS works entirely in memory and is very fast. Data structures are created and manipulated as internal dynamic structures in memory managed by SDS itself. A structure may then be exported into a caller supplied memory buffer in a defined external format. This structure can be written as a file or sent as a message to another machine. It remains static in structure until it is reimported into SDS. SDS is written in portable C and has been run on a number of different machine architectures. Structures are portable between machines with SDS looking after conversion of byte order, floating point format, and alignment. A Fortran callable version is also available for some machines.
Inheritance rules for Hierarchical Metadata Based on ISO 19115
Zabala, A.; Masó, J.; Pons, X.
2012-04-01
registry is complete for each metadata hierarchical level, but at the implementation level most of the metadata elements are not stored at both levels but only at more generic one. This communication defines a metadata system that covers 4 levels, describes which metadata has to support series-layer inheritance and in which way, and how hierarchical levels are defined and stored. Metadata elements are classified according to the type of inheritance between products, series, tiles and the datasets. It explains the metadata elements classification and exemplifies it using core metadata elements. The communication also presents a metadata viewer and edition tool that uses the described model to propagate metadata elements and to show to the user a complete set of metadata for each level in a transparent way. This tool is integrated in the MiraMon GIS software.
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.
Poisson Mixture Regression Models for Heart Disease Prediction
Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
Hierarchical prisoner’s dilemma in hierarchical game for resource competition
Fujimoto, Yuma; Sagawa, Takahiro; Kaneko, Kunihiko
2017-07-01
Dilemmas in cooperation are one of the major concerns in game theory. In a public goods game, each individual cooperates by paying a cost or defecting without paying it, and receives a reward from the group out of the collected cost. Thus, defecting is beneficial for each individual, while cooperation is beneficial for the group. Now, groups (say, countries) consisting of individuals also play games. To study such a multi-level game, we introduce a hierarchical game in which multiple groups compete for limited resources by utilizing the collected cost in each group, where the power to appropriate resources increases with the population of the group. Analyzing this hierarchical game, we found a hierarchical prisoner’s dilemma, in which groups choose the defecting policy (say, armament) as a Nash strategy to optimize each group’s benefit, while cooperation optimizes the total benefit. On the other hand, for each individual, refusing to pay the cost (say, tax) is a Nash strategy, which turns out to be a cooperation policy for the group, thus leading to a hierarchical dilemma. Here the group reward increases with the group size. However, we find that there exists an optimal group size that maximizes the individual payoff. Furthermore, when the population asymmetry between two groups is large, the smaller group will choose a cooperation policy (say, disarmament) to avoid excessive response from the larger group, and the prisoner’s dilemma between the groups is resolved. Accordingly, the relevance of this hierarchical game on policy selection in society and the optimal size of human or animal groups are discussed.
Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.
Notaro, Marco; Schubach, Max; Robinson, Peter N; Valentini, Giorgio
2017-10-12
The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.
BANK FAILURE PREDICTION WITH LOGISTIC REGRESSION
Directory of Open Access Journals (Sweden)
Taha Zaghdoudi
2013-04-01
Full Text Available In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.
Robust Mediation Analysis Based on Median Regression
Yuan, Ying; MacKinnon, David P.
2014-01-01
Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925
Learning Hierarchical User Interest Models from Web Pages
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
We propose an algorithm for learning hierarchical user interest models according to the Web pages users have browsed. In this algorithm, the interests of a user are represented into a tree which is called a user interest tree, the content and the structure of which can change simultaneously to adapt to the changes in a user's interests. This expression represents a user's specific and general interests as a continuum. In some sense, specific interests correspond to short-term interests, while general interests correspond to long-term interests. So this representation more really reflects the users' interests. The algorithm can automatically model a user's multiple interest domains, dynamically generate the interest models and prune a user interest tree when the number of the nodes in it exceeds given value. Finally, we show the experiment results in a Chinese Web Site.
Hierarchically porous carbon/polyaniline hybrid for use in supercapacitors.
Joo, Min Jae; Yun, Young Soo; Jin, Hyoung-Joon
2014-12-01
A hierarchically porous carbon (HPC)/polyaniline (PANI) hybrid electrode was prepared by the polymerization of PANI on the surface of the HPC via rapid-mixing polymerization. The surface morphologies and chemical composition of the HPC/PANI hybrid electrode were characterized using transmission electron microscopy and X-ray photoelectron spectroscopy (XPS), respectively. The surface morphologies and XPS results for the HPC, PANI and HPC/PANI hybrids indicate that PANI is coated on the surface of HPC in the HPC/PANI hybrids which have two different nitrogen groups as a benzenoid amine (-NH-) peak and positively charged nitrogen (N+) peak. The electrochemical performances of the HPC/PANI hybrids were analyzed by performing cyclic voltammetry and galvanostatic charge-discharge tests. The HPC/PANI hybrids showed a better specific capacitance (222 F/g) than HPC (111 F/g) because of effect of pseudocapacitor behavior. In addition, good cycle stabilities were maintained over 1000 cycles.
Hierarchic Analysis Method to Evaluate Rock Burst Risk
Directory of Open Access Journals (Sweden)
Ming Ji
2015-01-01
Full Text Available In order to reasonably evaluate the risk of rock bursts in mines, the factors impacting rock bursts and the existing grading criterion on the risk of rock bursts were studied. By building a model of hierarchic analysis method, the natural factors, technology factors, and management factors that influence rock bursts were analyzed and researched, which determined the degree of each factor’s influence (i.e., weight and comprehensive index. Then the grade of rock burst risk was assessed. The results showed that the assessment level generated by the model accurately reflected the actual risk degree of rock bursts in mines. The model improved the maneuverability and practicability of existing evaluation criteria and also enhanced the accuracy and science of rock burst risk assessment.
Hierarchical energy management mechanisms for an electricity market with microgrids
Directory of Open Access Journals (Sweden)
Hong-Tzer Yang
2014-08-01
Full Text Available This study addresses a micro-grid electricity market (MGEM with day-ahead (DA and real-time market mechanisms integrated. The bidding mechanisms for the market are described in this study, considering the generation cost of different distributed energy resources (DERs, like distributed generator, energy storage system and demand response. Including load and renewable generation forecasting systems and a fuzzy decision supporting system, a hierarchical micro-grid energy management system (MG-EMS is then proposed to ensure the benefits of involved micro-grid central controller, DER owners and customers. To verify the feasibility of the proposed system, the whole-year historical pricing and load data for New England independent system operator are employed. The numerical results show that the proposed MG-EMS is promising and effective for the operations of MGEM.
Complex networks as an emerging property of hierarchical preferential attachment
Hébert-Dufresne, Laurent; Laurence, Edward; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.
2015-12-01
Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.
A Heuristic Hierarchical Scheme for Academic Search and Retrieval
DEFF Research Database (Denmark)
Amolochitis, Emmanouil; Christou, Ioannis T.; Tan, Zheng-Hua
2013-01-01
and a graph-theoretic computed score that relates the paper’s index terms with each other. We designed and developed a meta-search engine that submits user queries to standard digital repositories of academic publications and re-ranks the repository results using the hierarchical heuristic scheme. We evaluate......, and by more than 907.5% in terms of LEX. We also re-rank the top-10 results of a subset of the original 58 user queries produced by Google Scholar, Microsoft Academic Search, and ArnetMiner; the results show that PubSearch compares very well against these search engines as well. The proposed scheme can...... be easily plugged in any existing search engine for retrieval of academic publications....
Hierarchical functional modularity in the resting-state human brain.
Ferrarini, Luca; Veer, Ilya M; Baerends, Evelinda; van Tol, Marie-José; Renken, Remco J; van der Wee, Nic J A; Veltman, Dirk J; Aleman, André; Zitman, Frans G; Penninx, Brenda W J H; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, Julien
2009-07-01
Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Copyright 2009 Wiley-Liss, Inc
Hierarchical structures in cold-drawn pearlitic steel wire
DEFF Research Database (Denmark)
Zhang, Xiaodan; Godfrey, Andrew; Hansen, Niels
2013-01-01
The microstructure and crystallography of drawn pearlitic steel wires have been quantified by a number of electron microscopy techniques including scanning electron microscopy, transmission electron microscopy, electron backscatter diffraction and nanobeam diffraction, with focus on the change...... in the structure and crystallography when a randomly oriented cementite structure in a patented wire during wire drawing is transformed into a lamellar structure parallel to the drawing axis. Changes in the interlamellar spacing and in the misorientation angle along and across the ferrite lamellae show significant...... through-diameter variations in wires drawn to large strains P 1.5. The structural evolution is hierarchical as the structural variations have their cause in a different macroscopic orientation of the cementite in the initial (patented) structure with respect to the wire axis. The through...
Hierarchical structures in cold-drawn pearlitic steel wire
DEFF Research Database (Denmark)
Zhang, Xiaodan; Godfrey, Andrew; Hansen, Niels
2013-01-01
The microstructure and crystallography of drawn pearlitic steel wires have been quantified by a number of electron microscopy techniques including scanning electron microscopy, transmission electron microscopy, electron backscatter diffraction and nanobeam diffraction, with focus on the change...... in the structure and crystallography when a randomly oriented cementite structure in a patented wire during wire drawing is transformed into a lamellar structure parallel to the drawing axis. Changes in the interlamellar spacing and in the misorientation angle along and across the ferrite lamellae show significant...... through-diameter variations in wires drawn to large strains ⩾ 1.5. The structural evolution is hierarchical as the structural variations have their cause in a different macroscopic orientation of the cementite in the initial (patented) structure with respect to the wire axis. The through...
Parallel iterative solvers and preconditioners using approximate hierarchical methods
Energy Technology Data Exchange (ETDEWEB)
Grama, A.; Kumar, V.; Sameh, A. [Univ. of Minnesota, Minneapolis, MN (United States)
1996-12-31
In this paper, we report results of the performance, convergence, and accuracy of a parallel GMRES solver for Boundary Element Methods. The solver uses a hierarchical approximate matrix-vector product based on a hybrid Barnes-Hut / Fast Multipole Method. We study the impact of various accuracy parameters on the convergence and show that with minimal loss in accuracy, our solver yields significant speedups. We demonstrate the excellent parallel efficiency and scalability of our solver. The combined speedups from approximation and parallelism represent an improvement of several orders in solution time. We also develop fast and paralellizable preconditioners for this problem. We report on the performance of an inner-outer scheme and a preconditioner based on truncated Green`s function. Experimental results on a 256 processor Cray T3D are presented.
DEFF Research Database (Denmark)
Andersen, Steffen; Harrison, Glenn W.; Lau, Morten I.
2008-01-01
We review the use of behavior from television game shows to infer risk attitudes. These shows provide evidence when contestants are making decisions over very large stakes, and in a replicated, structured way. Inferences are generally confounded by the subjective assessment of skill in some games......, and the dynamic nature of the task in most games. We consider the game shows Card Sharks, Jeopardy!, Lingo, and finally Deal Or No Deal. We provide a detailed case study of the analyses of Deal Or No Deal, since it is suitable for inference about risk attitudes and has attracted considerable attention....
Measuring performance at trade shows
DEFF Research Database (Denmark)
Hansen, Kåre
2004-01-01
Trade shows is an increasingly important marketing activity to many companies, but current measures of trade show performance do not adequately capture dimensions important to exhibitors. Based on the marketing literature's outcome and behavior-based control system taxonomy, a model is built...... that captures a outcome-based sales dimension and four behavior-based dimensions (i.e. information-gathering, relationship building, image building, and motivation activities). A 16-item instrument is developed for assessing exhibitors perceptions of their trade show performance. The paper presents evidence...
On a Robust MaxEnt Process Regression Model with Sample-Selection
Directory of Open Access Journals (Sweden)
Hea-Jung Kim
2018-04-01
Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.
The process and utility of classification and regression tree methodology in nursing research.
Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda
2014-06-01
This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Discussion paper. English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. © 2013 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.
HIERARCHICAL ADAPTIVE ROOD PATTERN SEARCH FOR MOTION ESTIMATION AT VIDEO SEQUENCE ANALYSIS
Directory of Open Access Journals (Sweden)
V. T. Nguyen
2016-05-01
Full Text Available Subject of Research.The paper deals with the motion estimation algorithms for the analysis of video sequences in compression standards MPEG-4 Visual and H.264. Anew algorithm has been offered based on the analysis of the advantages and disadvantages of existing algorithms. Method. Thealgorithm is called hierarchical adaptive rood pattern search (Hierarchical ARPS, HARPS. This new algorithm includes the classic adaptive rood pattern search ARPS and hierarchical search MP (Hierarchical search or Mean pyramid. All motion estimation algorithms have been implemented using MATLAB package and tested with several video sequences. Main Results. The criteria for evaluating the algorithms were: speed, peak signal to noise ratio, mean square error and mean absolute deviation. The proposed method showed a much better performance at a comparable error and deviation. The peak signal to noise ratio in different video sequences shows better and worse results than characteristics of known algorithms so it requires further investigation. Practical Relevance. Application of this algorithm in MPEG-4 and H.264 codecs instead of the standard can significantly reduce compression time. This feature enables to recommend it in telecommunication systems for multimedia data storing, transmission and processing.
Energy Technology Data Exchange (ETDEWEB)
Gao, Dahai; Jia, Mengqiu, E-mail: jiamq@mail.buct.edu.cn
2015-07-15
Graphical abstract: - Highlights: • The hierarchical particles were prepared by a simple, mild hydrothermal process. • The obtained “chestnut” ZnO particles show dual-scale morphology with high roughness. • FEVE derivative was creatively imported to graft onto hierarchical particles. • Superhydrophobic surfaces were obtained, on which the contact angles surpass 150°. • A special model was proposed to explain the wetting state in this work. - Abstract: Superhydrophobic surfaces on the basis of hierarchical ZnO particles grafted by fluoroethylene-vinylether (FEVE) polymer derivative were prepared using a facile, mild and low-cost method. X-ray diffraction (XRD) and scanning electron microscope (SEM) revealed that the resulting ZnO particles via hydrothermal process exhibit micro–nano dual-scale morphology with high purity under a suitable surfactant amount and alkali concentration. The grafting of FEVE derivative was confirmed by Fourier transform infrared spectroscopy (FTIR) and energy-dispersive X-ray spectrometer (EDS), suggesting that hierarchical surface of ZnO particles was an imported monomolecular layer of fluorocarbon polymer. The obtained surface fabricated by drop-casting shows considerably high contact angle and good resistance to water immersion. The wetting behavior in this work was furthermore analyzed by theoretical wetting model. This work demonstrates that the sufficient low-wettable surface and high roughness both take a vital role in the superhydrophobic behavior.
Liu, Yan
2014-03-01
Hierarchically porous SAPO-11 zeolite (H-SAPO-11) is rationally synthesized from a starting silicoaluminophosphate gel in the presence of polyhexamethylene biguanidine as a mesoscale template. The sample is well characterized by XRD, N2 sorption, SEM, TEM, NMR, XPS, NH3-TPD, and TG techniques. The results show that the sample obtained has good crystallinity, hierarchical porosity (mesopores at ca. 10nm and macropores at ca. 50-200nm), high BET surface area (226m2/g), large pore volume (0.25cm3/g), and abundant medium and strong acidic sites (0.36mmol/g). After loading Pt (0.5wt.%) on H-SAPO-11 by using wet impregnation method, catalytic hydroisomerization tests of n-dodecane show that the hierarchical Pt/SAPO-11 zeolite exhibits high conversion of n-dodecane and enhanced selectivity for branched products as well as reduced selectivity for cracking products, compared with conventional Pt/SAPO-11 zeolite. This phenomenon is reasonably attributed to the presence of hierarchical porosity, which is favorable for access of reactants on catalytically active sites. The improvement in catalytic performance in long-chain paraffin hydroisomerization over Pt/SAPO-11-based catalyst is of great importance for its industrial applications in the future. © 2013 Elsevier Inc.
Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.
Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong
Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep
DEFF Research Database (Denmark)
Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann
2012-01-01
Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... with sparse LAR, lasso and Elastic Net (EN) sparse regression methods. Due to the inconsistent measurement condition, Locally Weighted Scatter plot Smoothing (Loess) has been employed to alleviate the undesired variation in the estimated viscosity. The experimental results of applying different methods show...