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Sample records for hierarchical multi-label decision

  1. Predicting gene function using hierarchical multi-label decision tree ensembles

    Directory of Open Access Journals (Sweden)

    Kocev Dragi

    2010-01-01

    Full Text Available Abstract Background S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability. Results We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO. We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use. Conclusions Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.

  2. Decision rule classifiers for multi-label decision tables

    KAUST Repository

    Alsolami, Fawaz

    2014-01-01

    Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International Publishing.

  3. Decision rule classifiers for multi-label decision tables

    KAUST Repository

    Alsolami, Fawaz; Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail

    2014-01-01

    for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International

  4. Minimization of decision tree depth for multi-label decision tables

    KAUST Repository

    Azad, Mohammad

    2014-10-01

    In this paper, we consider multi-label decision tables that have a set of decisions attached to each row. Our goal is to find one decision from the set of decisions for each row by using decision tree as our tool. Considering our target to minimize the depth of the decision tree, we devised various kinds of greedy algorithms as well as dynamic programming algorithm. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of depth of decision trees.

  5. Minimization of decision tree depth for multi-label decision tables

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2014-01-01

    In this paper, we consider multi-label decision tables that have a set of decisions attached to each row. Our goal is to find one decision from the set of decisions for each row by using decision tree as our tool. Considering our target to minimize the depth of the decision tree, we devised various kinds of greedy algorithms as well as dynamic programming algorithm. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of depth of decision trees.

  6. Minimizing size of decision trees for multi-label decision tables

    KAUST Repository

    Azad, Mohammad

    2014-09-29

    We used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).

  7. Minimizing size of decision trees for multi-label decision tables

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2014-01-01

    We used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).

  8. A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

    Directory of Open Access Journals (Sweden)

    Jie Hu

    2018-01-01

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

  9. Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification.

    Science.gov (United States)

    Zhang, Jingpu; Zhang, Zuping; Wang, Zixiang; Liu, Yuting; Deng, Lei

    2018-05-15

    Long non-coding RNAs (lncRNAs) are an enormous collection of functional non-coding RNAs. Over the past decades, a large number of novel lncRNA genes have been identified. However, most of the lncRNAs remain function uncharacterized at present. Computational approaches provide a new insight to understand the potential functional implications of lncRNAs. Considering that each lncRNA may have multiple functions and a function may be further specialized into sub-functions, here we describe NeuraNetL2GO, a computational ontological function prediction approach for lncRNAs using hierarchical multi-label classification strategy based on multiple neural networks. The neural networks are incrementally trained level by level, each performing the prediction of gene ontology (GO) terms belonging to a given level. In NeuraNetL2GO, we use topological features of the lncRNA similarity network as the input of the neural networks and employ the output results to annotate the lncRNAs. We show that NeuraNetL2GO achieves the best performance and the overall advantage in maximum F-measure and coverage on the manually annotated lncRNA2GO-55 dataset compared to other state-of-the-art methods. The source code and data are available at http://denglab.org/NeuraNetL2GO/. leideng@csu.edu.cn. Supplementary data are available at Bioinformatics online.

  10. mPLR-Loc: an adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction.

    Science.gov (United States)

    Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

    2015-03-15

    Proteins located in appropriate cellular compartments are of paramount importance to exert their biological functions. Prediction of protein subcellular localization by computational methods is required in the post-genomic era. Recent studies have been focusing on predicting not only single-location proteins but also multi-location proteins. However, most of the existing predictors are far from effective for tackling the challenges of multi-label proteins. This article proposes an efficient multi-label predictor, namely mPLR-Loc, based on penalized logistic regression and adaptive decisions for predicting both single- and multi-location proteins. Specifically, for each query protein, mPLR-Loc exploits the information from the Gene Ontology (GO) database by using its accession number (AC) or the ACs of its homologs obtained via BLAST. The frequencies of GO occurrences are used to construct feature vectors, which are then classified by an adaptive decision-based multi-label penalized logistic regression classifier. Experimental results based on two recent stringent benchmark datasets (virus and plant) show that mPLR-Loc remarkably outperforms existing state-of-the-art multi-label predictors. In addition to being able to rapidly and accurately predict subcellular localization of single- and multi-label proteins, mPLR-Loc can also provide probabilistic confidence scores for the prediction decisions. For readers' convenience, the mPLR-Loc server is available online (http://bioinfo.eie.polyu.edu.hk/mPLRLocServer). Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Constructing multi-labelled decision trees for junction design using the predicted probabilities

    NARCIS (Netherlands)

    Bezembinder, Erwin M.; Wismans, Luc J. J.; Van Berkum, Eric C.

    2017-01-01

    In this paper, we evaluate the use of traditional decision tree algorithms CRT, CHAID and QUEST to determine a decision tree which can be used to predict a set of (Pareto optimal) junction design alternatives (e.g. signal or roundabout) for a given traffic demand pattern and available space. This is

  12. 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...

  13. Multi-label literature classification based on the Gene Ontology graph

    Directory of Open Access Journals (Sweden)

    Lu Xinghua

    2008-12-01

    Full Text Available Abstract Background The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. Results In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Conclusion Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate

  14. Hierarchical decision modeling essays in honor of Dundar F. Kocaoglu

    CERN Document Server

    2016-01-01

    This volume, developed in honor of Dr. Dundar F. Kocaoglu, aims to demonstrate the applications of the Hierarchical Decision Model (HDM) in different sectors and its capacity in decision analysis. It is comprised of essays from noted scholars, academics and researchers of engineering and technology management around the world. This book is organized into four parts: Technology Assessment, Strategic Planning, National Technology Planning and Decision Making Tools. Dr. Dundar F. Kocaoglu is one of the pioneers of multiple decision models using hierarchies, and creator of the HDM in decision analysis. HDM is a mission-oriented method for evaluation and/or selection among alternatives. A wide range of alternatives can be considered, including but not limited to, different technologies, projects, markets, jobs, products, cities to live in, houses to buy, apartments to rent, and schools to attend. Dr. Kocaoglu’s approach has been adopted for decision problems in many industrial sectors, including electronics rese...

  15. A hierarchical instrumental decision theory of nicotine dependence.

    Science.gov (United States)

    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.

  16. A hierarchical Markov decision process modeling feeding and marketing decisions of growing pigs

    DEFF Research Database (Denmark)

    Pourmoayed, Reza; Nielsen, Lars Relund; Kristensen, Anders Ringgaard

    2016-01-01

    Feeding is the most important cost in the production of growing pigs and has a direct impact on the marketing decisions, growth and the final quality of the meat. In this paper, we address the sequential decision problem of when to change the feed-mix within a finisher pig pen and when to pick pigs...... for marketing. We formulate a hierarchical Markov decision process with three levels representing the decision process. The model considers decisions related to feeding and marketing and finds the optimal decision given the current state of the pen. The state of the system is based on information from on...

  17. Hierarchical Modelling of Flood Risk for Engineering Decision Analysis

    DEFF Research Database (Denmark)

    Custer, Rocco

    protection structures in the hierarchical flood protection system - is identified. To optimise the design of protection structures, fragility and vulnerability models must allow for consideration of decision alternatives. While such vulnerability models are available for large protection structures (e...... systems, as well as the implementation of the flood risk analysis methodology and the vulnerability modelling approach are illustrated with an example application. In summary, the present thesis provides a characterisation of hierarchical flood protection systems as well as several methodologies to model...... and robust. Traditional risk management solutions, e.g. dike construction, are not particularly flexible, as they are difficult to adapt to changing risk. Conversely, the recent concept of integrated flood risk management, entailing a combination of several structural and non-structural risk management...

  18. Regulator Loss Functions and Hierarchical Modeling for Safety Decision Making.

    Science.gov (United States)

    Hatfield, Laura A; Baugh, Christine M; Azzone, Vanessa; Normand, Sharon-Lise T

    2017-07-01

    Regulators must act to protect the public when evidence indicates safety problems with medical devices. This requires complex tradeoffs among risks and benefits, which conventional safety surveillance methods do not incorporate. To combine explicit regulator loss functions with statistical evidence on medical device safety signals to improve decision making. In the Hospital Cost and Utilization Project National Inpatient Sample, we select pediatric inpatient admissions and identify adverse medical device events (AMDEs). We fit hierarchical Bayesian models to the annual hospital-level AMDE rates, accounting for patient and hospital characteristics. These models produce expected AMDE rates (a safety target), against which we compare the observed rates in a test year to compute a safety signal. We specify a set of loss functions that quantify the costs and benefits of each action as a function of the safety signal. We integrate the loss functions over the posterior distribution of the safety signal to obtain the posterior (Bayes) risk; the preferred action has the smallest Bayes risk. Using simulation and an analysis of AMDE data, we compare our minimum-risk decisions to a conventional Z score approach for classifying safety signals. The 2 rules produced different actions for nearly half of hospitals (45%). In the simulation, decisions that minimize Bayes risk outperform Z score-based decisions, even when the loss functions or hierarchical models are misspecified. Our method is sensitive to the choice of loss functions; eliciting quantitative inputs to the loss functions from regulators is challenging. A decision-theoretic approach to acting on safety signals is potentially promising but requires careful specification of loss functions in consultation with subject matter experts.

  19. Compromise decision support problems for hierarchical design involving uncertainty

    Science.gov (United States)

    Vadde, S.; Allen, J. K.; Mistree, F.

    1994-08-01

    In this paper an extension to the traditional compromise Decision Support Problem (DSP) formulation is presented. Bayesian statistics is used in the formulation to model uncertainties associated with the information being used. In an earlier paper a compromise DSP that accounts for uncertainty using fuzzy set theory was introduced. The Bayesian Decision Support Problem is described in this paper. The method for hierarchical design is demonstrated by using this formulation to design a portal frame. The results are discussed and comparisons are made with those obtained using the fuzzy DSP. Finally, the efficacy of incorporating Bayesian statistics into the traditional compromise DSP formulation is discussed and some pending research issues are described. Our emphasis in this paper is on the method rather than the results per se.

  20. A Framework for a Decision Support System in a Hierarchical Extended Enterprise Decision Context

    Science.gov (United States)

    Boza, Andrés; Ortiz, Angel; Vicens, Eduardo; Poler, Raul

    Decision Support System (DSS) tools provide useful information to decision makers. In an Extended Enterprise, a new goal, changes in the current objectives or small changes in the extended enterprise configuration produce a necessary adjustment in its decision system. A DSS in this context must be flexible and agile to make suitable an easy and quickly adaptation to this new context. This paper proposes to extend the Hierarchical Production Planning (HPP) structure to an Extended Enterprise decision making context. In this way, a framework for DSS in Extended Enterprise context is defined using components of HPP. Interoperability details have been reviewed to identify the impact in this framework. The proposed framework allows overcoming some interoperability barriers, identifying and organizing components for a DSS in Extended Enterprise context, and working in the definition of an architecture to be used in the design process of a flexible DSS in Extended Enterprise context which can reuse components for futures Extended Enterprise configurations.

  1. Automatic single- and multi-label enzymatic function prediction by machine learning

    Directory of Open Access Journals (Sweden)

    Shervine Amidi

    2017-03-01

    Full Text Available The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing different reactions and can hence be directly associated with multiple enzymatic functions. In the present work, we propose a multi-label enzymatic function classification scheme that combines structural and amino acid sequence information. We investigate two fusion approaches (in the feature level and decision level and assess the methodology for general enzymatic function prediction indicated by the first digit of the enzyme commission (EC code (six main classes on 40,034 enzymes from the PDB database. The proposed single-label and multi-label models predict correctly the actual functional activities in 97.8% and 95.5% (based on Hamming-loss of the cases, respectively. Also the multi-label model predicts all possible enzymatic reactions in 85.4% of the multi-labeled enzymes when the number of reactions is unknown. Code and datasets are available at https://figshare.com/s/a63e0bafa9b71fc7cbd7.

  2. Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model.

    Science.gov (United States)

    Damij, Nadja; Boškoski, Pavle; Bohanec, Marko; Mileva Boshkoska, Biljana

    2016-01-01

    The omnipresent need for optimisation requires constant improvements of companies' business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and "what-if" scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results.

  3. A diagram retrieval method with multi-label learning

    Science.gov (United States)

    Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

    2015-01-01

    In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

  4. Multi-Label Object Categorization Using Histograms of Global Relations

    DEFF Research Database (Denmark)

    Mustafa, Wail; Xiong, Hanchen; Kraft, Dirk

    2015-01-01

    In this paper, we present an object categorization system capable of assigning multiple and related categories for novel objects using multi-label learning. In this system, objects are described using global geometric relations of 3D features. We propose using the Joint SVM method for learning......). The experiments are carried out on a dataset of 100 objects belonging to 13 visual and action-related categories. The results indicate that multi-label methods are able to identify the relation between the dependent categories and hence perform categorization accordingly. It is also found that extracting...

  5. Instance annotation for multi-instance multi-label learning

    Science.gov (United States)

    F. Briggs; X.Z. Fern; R. Raich; Q. Lou

    2013-01-01

    Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and associated with a list of objects it contains. Prior work on MIML has focused on predicting label sets for previously unseen...

  6. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

    Science.gov (United States)

    Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

    2018-05-01

    Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

  7. Coordinating Information and Decisions of Hierarchical Distributed Decision Units in Crises

    National Research Council Canada - National Science Library

    Rose, Gerald

    1997-01-01

    A program of research is described. The research addressed decision making by distributed decision makers using either consensus or leader structures and confronted by both routine tasks and different kinds of information system crisis...

  8. Interval prediction for graded multi-label classification

    CERN Document Server

    Lastra, Gerardo; Bahamonde, Antonio

    2014-01-01

    Multi-label was introduced as an extension of multi-class classification. The aim is to predict a set of classes (called labels in this context) instead of a single one, namely the set of relevant labels. If membership to the set of relevant labels is defined to a certain degree, the learning task is called graded multi-label classification. These learning tasks can be seen as a set of ordinal classifications. Hence, recommender systems can be considered as multi-label classification tasks. In this paper, we present a new type of nondeterministic learner that, for each instance, tries to predict at the same time the true grade for each label. When the classification is uncertain for a label, however, the hypotheses predict a set of consecutive grades, i.e., an interval. The goal is to keep the set of predicted grades as small as possible; while still containing the true grade. We shall see that these classifiers take advantage of the interrelations of labels. The result is that, with quite narrow intervals, i...

  9. Modeling activity recognition of multi resident using label combination of multi label classification in smart home

    Science.gov (United States)

    Mohamed, Raihani; Perumal, Thinagaran; Sulaiman, Md Nasir; Mustapha, Norwati; Zainudin, M. N. Shah

    2017-10-01

    Pertaining to the human centric concern and non-obtrusive way, the ambient sensor type technology has been selected, accepted and embedded in the environment in resilient style. Human activities, everyday are gradually becoming complex and thus complicate the inferences of activities when it involving the multi resident in the same smart environment. Current works solutions focus on separate model between the resident, activities and interactions. Some study use data association and extra auxiliary of graphical nodes to model human tracking information in an environment and some produce separate framework to incorporate the auxiliary for interaction feature model. Thus, recognizing the activities and which resident perform the activity at the same time in the smart home are vital for the smart home development and future applications. This paper will cater the above issue by considering the simplification and efficient method using the multi label classification framework. This effort eliminates time consuming and simplifies a lot of pre-processing tasks comparing with previous approach. Applications to the multi resident multi label learning in smart home problems shows the LC (Label Combination) using Decision Tree (DT) as base classifier can tackle the above problems.

  10. Decision support in hierarchical planning systems: The case of procurement planning in oil refining industries

    DEFF Research Database (Denmark)

    Kallestrup, Kasper Bislev; Lynge, Lasse Hadberg; Akkerman, Renzo

    2014-01-01

    In this paper, we discuss the development of decision support systems for hierarchically structured planning approaches, such as commercially available advanced planning systems. We develop a framework to show how such a decision support system can be designed with the existing organization in mind...... and from the perspective of the organizational aspects involved. To exemplify and develop our framework, we use a case study of crude oil procurement planning in the refining industry. The results of the case study indicate an improved organizational embedding of the DSS, leading to significant savings...... in terms of planning efforts and procurement costs. In general, our framework aims to support the continuous improvement of advanced planning systems, increasing planning quality in complex supply chain settings....

  11. Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis

    Directory of Open Access Journals (Sweden)

    Wenjun Bai

    2018-02-01

    Full Text Available Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training and between tasks knowledge transfer. The applications that rely heavily on continuous prediction on emotional valance, e.g., to monitor prisoners’ emotional stability in jail, can be directly benefited from our framework. Abstract: To lower the single-label dependency on affective facial analysis, it urges the fruition of multi-label affective learning. The impediment to practical implementation of existing multi-label algorithms pertains to scarcity of scalable multi-label training datasets. To resolve this, an inductive transfer learning based framework, i.e.,Uncertainty Flow, is put forward in this research to allow knowledge transfer from a single labelled emotion recognition task to a multi-label affective recognition task. I.e., the model uncertainty—which can be quantified in Uncertainty Flow—is distilled from a single-label learning task. The distilled model uncertainty ensures the later efficient zero-shot multi-label affective learning. On the theoretical perspective, within our proposed Uncertainty Flow framework, the feasibility of applying weakly informative priors, e.g., uniform and Cauchy prior, is fully explored in this research. More importantly, based on the derived weight uncertainty, three sets of prediction related uncertainty indexes, i.e., soft-max uncertainty, pure uncertainty and uncertainty plus are proposed to produce reliable and accurate multi-label predictions. Validated on our manual annotated evaluation dataset, i.e., the multi-label annotated FER2013, our proposed Uncertainty Flow in multi-label facial expression analysis exhibited superiority to conventional multi-label learning algorithms and multi-label compatible neural networks. The success of our

  12. Predicting the decision to pursue mediation in civil disputes: a hierarchical classes analysis.

    Science.gov (United States)

    Reich, Warren A; Kressel, Kenneth; Scanlon, Kathleen M; Weiner, Gary A

    2007-11-01

    Clients (N = 185) involved in civil court cases completed the CPR Institute's Mediation Screen, which is designed to assist in making a decision about pursuing mediation. The authors modeled data using hierarchical classes analysis (HICLAS), a clustering algorithm that places clients into 1 set of classes and CPRMS items into another set of classes. HICLAS then links the sets of classes so that any class of clients can be identified in terms of the classes of items they endorsed. HICLAS-derived item classes reflected 2 underlying themes: (a) suitability of the dispute for a problem-solving process and (b) potential benefits of mediation. All clients who perceived that mediation would be beneficial also believed that the context of their conflict was favorable to mediation; however, not all clients who saw a favorable context believed they would benefit from mediation. The majority of clients who agreed to pursue mediation endorsed items reflecting both contextual suitability and perceived benefits of mediation.

  13. Developing a Hierarchical Decision Model to Evaluate Nuclear Power Plant Alternative Siting Technologies

    Science.gov (United States)

    Lingga, Marwan Mossa

    A strong trend of returning to nuclear power is evident in different places in the world. Forty-five countries are planning to add nuclear power to their grids and more than 66 nuclear power plants are under construction. Nuclear power plants that generate electricity and steam need to improve safety to become more acceptable to governments and the public. One novel practical solution to increase nuclear power plants' safety factor is to build them away from urban areas, such as offshore or underground. To date, Land-Based siting is the dominant option for siting all commercial operational nuclear power plants. However, the literature reveals several options for building nuclear power plants in safer sitings than Land-Based sitings. The alternatives are several and each has advantages and disadvantages, and it is difficult to distinguish among them and choose the best for a specific project. In this research, we recall the old idea of using the alternatives of offshore and underground sitings for new nuclear power plants and propose a tool to help in choosing the best siting technology. This research involved the development of a decision model for evaluating several potential nuclear power plant siting technologies, both those that are currently available and future ones. The decision model was developed based on the Hierarchical Decision Modeling (HDM) methodology. The model considers five major dimensions, social, technical, economic, environmental, and political (STEEP), and their related criteria and sub-criteria. The model was designed and developed by the author, and its elements' validation and evaluation were done by a large number of experts in the field of nuclear energy. The decision model was applied in evaluating five potential siting technologies and ranked the Natural Island as the best in comparison to Land-Based, Floating Plant, Artificial Island, and Semi-Embedded plant.

  14. FAST LABEL: Easy and efficient solution of joint multi-label and estimation problems

    KAUST Repository

    Sundaramoorthi, Ganesh; Hong, Byungwoo

    2014-01-01

    that plague local solutions. Further, in comparison to global methods for the multi-label problem, the method is more efficient and it is easy for a non-specialist to implement. We give sample Matlab code for the multi-label Chan-Vese problem in this paper

  15. Multi-label learning with fuzzy hypergraph regularization for protein subcellular location prediction.

    Science.gov (United States)

    Chen, Jing; Tang, Yuan Yan; Chen, C L Philip; Fang, Bin; Lin, Yuewei; Shang, Zhaowei

    2014-12-01

    Protein subcellular location prediction aims to predict the location where a protein resides within a cell using computational methods. Considering the main limitations of the existing methods, we propose a hierarchical multi-label learning model FHML for both single-location proteins and multi-location proteins. The latent concepts are extracted through feature space decomposition and label space decomposition under the nonnegative data factorization framework. The extracted latent concepts are used as the codebook to indirectly connect the protein features to their annotations. We construct dual fuzzy hypergraphs to capture the intrinsic high-order relations embedded in not only feature space, but also label space. Finally, the subcellular location annotation information is propagated from the labeled proteins to the unlabeled proteins by performing dual fuzzy hypergraph Laplacian regularization. The experimental results on the six protein benchmark datasets demonstrate the superiority of our proposed method by comparing it with the state-of-the-art methods, and illustrate the benefit of exploiting both feature correlations and label correlations.

  16. Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

    Science.gov (United States)

    Colas, Jaron T

    2017-01-01

    In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.

  17. Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

    Directory of Open Access Journals (Sweden)

    Jaron T Colas

    Full Text Available In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.

  18. Value-based decision making via sequential sampling with hierarchical competition and attentional modulation

    Science.gov (United States)

    2017-01-01

    In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes “winner-take-all” processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans’ value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light. PMID:29077746

  19. Multi-Label Classification Based on Low Rank Representation for Image Annotation

    Directory of Open Access Journals (Sweden)

    Qiaoyu Tan

    2017-01-01

    Full Text Available Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels. To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR. MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover. We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.

  20. Detailed Sponge City Planning Based on Hierarchical Fuzzy Decision-Making: A Case Study on Yangchen Lake

    Directory of Open Access Journals (Sweden)

    Junyu Zhang

    2017-11-01

    Full Text Available We proposed a Hierarchical Fuzzy Inference System (HFIS framework to offer better decision supports with fewer user-defined data (uncertainty. The framework consists two parts: a fuzzified Geographic Information System (GIS and a HFIS system. The former provides comprehensive information on the criterion unit and the latter helps in making more robust decisions. The HFIS and the traditional Multi-Criteria Decision Making (MCDM method were applied to a case study and compared. The fuzzified GIS maps maintained a majority of the dominant characteristics of the criterion unit but also revealed some non-significant information according to the surrounding environment. The urban planning map generated by the two methods shares similar strategy choices (6% difference, while the spatial distribution of strategies shares 69.7% in common. The HFIS required fewer subjective decisions than the MCDM (34 user-defined decision rules vs. 141 manual evaluations.

  1. hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression.

    Science.gov (United States)

    Wang, Pu; Ge, Ruiquan; Xiao, Xuan; Zhou, Manli; Zhou, Fengfeng

    2017-01-01

    Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each sample with only one class label, and the multi-label classification problem remains to be a challenge for biomedical researchers. This study proposes a novel multi-label learning algorithm, hMuLab, by integrating both feature-based and neighbor-based similarity scores. The multiple linear regression modeling techniques make hMuLab capable of producing multiple label assignments for a query sample. The comparison results over six commonly-used multi-label performance measurements suggest that hMuLab performs accurately and stably for the biomedical datasets, and may serve as a complement to the existing literature.

  2. MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING

    Data.gov (United States)

    National Aeronautics and Space Administration — MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract....

  3. Soft computing based on hierarchical evaluation approach and criteria interdependencies for energy decision-making problems: A case study

    International Nuclear Information System (INIS)

    Gitinavard, Hossein; Mousavi, S. Meysam; Vahdani, Behnam

    2017-01-01

    In numerous real-world energy decision problems, decision makers often encounter complex environments, in which existent imprecise data and uncertain information lead us to make an appropriate decision. In this paper, a new soft computing group decision-making approach is introduced based on novel compromise ranking method and interval-valued hesitant fuzzy sets (IVHFSs) for energy decision-making problems under multiple criteria. In the proposed approach, the assessment information is provided by energy experts or decision makers based on interval-valued hesitant fuzzy elements under incomplete criteria weights. In this respect, a new ranking index is presented respecting to interval-valued hesitant fuzzy Hamming distance measure to prioritize energy candidates, and criteria weights are computed based on an extended maximizing deviation method by considering the preferences experts' judgments about the relative importance of each criterion. Also, a decision making trial and evaluation laboratory (DEMATEL) method is extended under an IVHF-environment to compute the interdependencies between and within the selected criteria in the hierarchical structure. Accordingly, to demonstrate the applicability of the presented approach a case study and a practical example are provided regarding to hierarchical structure and criteria interdependencies relations for renewable energy and energy policy selection problems. Hence, the obtained computational results are compared with a fuzzy decision-making method from the recent literature based on some comparison parameters to show the advantages and constraints of the proposed approach. Finally, a sensitivity analysis is prepared to indicate effects of different criteria weights on ranking results to present the robustness or sensitiveness of the proposed soft computing approach versus the relative importance of criteria. - Highlights: • Introducing a novel interval-valued hesitant fuzzy compromise ranking method. • Presenting

  4. Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

    Directory of Open Access Journals (Sweden)

    Jaesung Lee

    2016-11-01

    Full Text Available Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets.

  5. Using hierarchical Bayesian methods to examine the tools of decision-making

    OpenAIRE

    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 ...

  6. Distributed Smart Decision-Making for a Multimicrogrid System Based on a Hierarchical Interactive Architecture

    DEFF Research Database (Denmark)

    Marzband, Mousa; Parhizi, Narges; Savaghebi, Mehdi

    2016-01-01

    In this paper, a comprehensive real-time interactive EMS framework for the utility and multiple electrically-coupled MGs is proposed. A hierarchical bi-level control scheme-BLCS with primary and secondary level controllers is applied in this regard. The proposed hierarchical architecture consists...... are treated as uncertainties in the proposed structure. In order to handle the uncertainties, Taguchi0s orthogonal array testing-TOAT approach is utilized. Then, the shortage or surplus of the MGs power should be submitted to a central EMS-CEMS in the secondary-level. In order to validate the proposed control...

  7. FAST LABEL: Easy and efficient solution of joint multi-label and estimation problems

    KAUST Repository

    Sundaramoorthi, Ganesh

    2014-06-01

    We derive an easy-to-implement and efficient algorithm for solving multi-label image partitioning problems in the form of the problem addressed by Region Competition. These problems jointly determine a parameter for each of the regions in the partition. Given an estimate of the parameters, a fast approximate solution to the multi-label sub-problem is derived by a global update that uses smoothing and thresholding. The method is empirically validated to be robust to fine details of the image that plague local solutions. Further, in comparison to global methods for the multi-label problem, the method is more efficient and it is easy for a non-specialist to implement. We give sample Matlab code for the multi-label Chan-Vese problem in this paper! Experimental comparison to the state-of-the-art in multi-label solutions to Region Competition shows that our method achieves equal or better accuracy, with the main advantage being speed and ease of implementation.

  8. A new model and simple algorithms for multi-label mumford-shah problems

    KAUST Repository

    Hong, Byungwoo

    2013-06-01

    In this work, we address the multi-label Mumford-Shah problem, i.e., the problem of jointly estimating a partitioning of the domain of the image, and functions defined within regions of the partition. We create algorithms that are efficient, robust to undesirable local minima, and are easy-to-implement. Our algorithms are formulated by slightly modifying the underlying statistical model from which the multi-label Mumford-Shah functional is derived. The advantage of this statistical model is that the underlying variables: the labels and the functions are less coupled than in the original formulation, and the labels can be computed from the functions with more global updates. The resulting algorithms can be tuned to the desired level of locality of the solution: from fully global updates to more local updates. We demonstrate our algorithm on two applications: joint multi-label segmentation and denoising, and joint multi-label motion segmentation and flow estimation. We compare to the state-of-the-art in multi-label Mumford-Shah problems and show that we achieve more promising results. © 2013 IEEE.

  9. Rectified-Linear-Unit-Based Deep Learning for Biomedical Multi-label Data.

    Science.gov (United States)

    Wang, Pu; Ge, Ruiquan; Xiao, Xuan; Cai, Yunpeng; Wang, Guoqing; Zhou, Fengfeng

    2017-09-01

    Disease diagnosis is one of the major data mining questions by the clinicians. The current diagnosis models usually have a strong assumption that one patient has only one disease, i.e. a single-label data mining problem. But the patients, especially when at the late stages, may have more than one disease and require a multi-label diagnosis. The multi-label data mining is much more difficult than a single-label one, and very few algorithms have been developed for this situation. Deep learning is a data mining algorithm with highly dense inner structure and has achieved many successful applications in the other areas. We propose a hypothesis that rectified-linear-unit-based deep learning algorithm may also be good at the clinical questions, by revising the last layer as a multi-label output. The proof-of-concept experimental data support the hypothesis, and the community may be interested in trying more applications.

  10. A Coupled k-Nearest Neighbor Algorithm for Multi-Label Classification

    Science.gov (United States)

    2015-05-22

    classification, an image may contain several concepts simultaneously, such as beach, sunset and kangaroo . Such tasks are usually denoted as multi-label...informatics, a gene can belong to both metabolism and transcription classes; and in music categorization, a song may labeled as Mozart and sad. In the

  11. ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

    KAUST Repository

    Wu, Baoyuan; Lyu, Siwei; Ghanem, Bernard

    2015-01-01

    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some

  12. Multi-label Learning with Missing Labels Using Mixed Dependency Graphs

    KAUST Repository

    Wu, Baoyuan; Jia, Fan; Liu, Wei; Ghanem, Bernard; Lyu, Siwei

    2018-01-01

    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e., some

  13. HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source.

    Science.gov (United States)

    Wan, Shixiang; Duan, Yucong; Zou, Quan

    2017-09-01

    Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine learning approaches to predict the subcellular location of proteins. There are two main challenges among the state-of-the-art prediction methods. First, most of the existing techniques are designed to deal with multi-class rather than multi-label classification, which ignores connections between multiple labels. In reality, multiple locations of particular proteins imply that there are vital and unique biological significances that deserve special focus and cannot be ignored. Second, techniques for handling imbalanced data in multi-label classification problems are necessary, but never employed. For solving these two issues, we have developed an ensemble multi-label classifier called HPSLPred, which can be applied for multi-label classification with an imbalanced protein source. For convenience, a user-friendly webserver has been established at http://server.malab.cn/HPSLPred. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Manifold regularized matrix completion for multi-label learning with ADMM.

    Science.gov (United States)

    Liu, Bin; Li, Yingming; Xu, Zenglin

    2018-05-01

    Multi-label learning is a common machine learning problem arising from numerous real-world applications in diverse fields, e.g, natural language processing, bioinformatics, information retrieval and so on. Among various multi-label learning methods, the matrix completion approach has been regarded as a promising approach to transductive multi-label learning. By constructing a joint matrix comprising the feature matrix and the label matrix, the missing labels of test samples are regarded as missing values of the joint matrix. With the low-rank assumption of the constructed joint matrix, the missing labels can be recovered by minimizing its rank. Despite its success, most matrix completion based approaches ignore the smoothness assumption of unlabeled data, i.e., neighboring instances should also share a similar set of labels. Thus they may under exploit the intrinsic structures of data. In addition, the matrix completion problem can be less efficient. To this end, we propose to efficiently solve the multi-label learning problem as an enhanced matrix completion model with manifold regularization, where the graph Laplacian is used to ensure the label smoothness over it. To speed up the convergence of our model, we develop an efficient iterative algorithm, which solves the resulted nuclear norm minimization problem with the alternating direction method of multipliers (ADMM). Experiments on both synthetic and real-world data have shown the promising results of the proposed approach. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Deciziile amenajistice ca procese ierarhizate [Managerial decisions as hierarchical analytic processes

    Directory of Open Access Journals (Sweden)

    Drăgoi M

    2002-07-01

    Full Text Available The paper deals with a step-wise HAP, applied by a group of decision makers, wherein nobody has a dominant position and it is unlikely to come to terms with respect to either the weights of different objectives or expected utilities of different alternatives. One of HAP outcome, namely the consistency index, is computed for each decision maker, for all other decision makers but that one, and for the whole group. Doing so, the group is able to assess to which extent each decision maker alters the group consistency index and a better consistency index could be achieved if the assessment procedure is being resumed by the most influential decision maker in terms of consistency. A case study is used to demonstrate how the step-wise process succeeds in improving the group's consistency index and how the weights of criteria are being changed during the negotiation process. The main contribution of the new approach is the algorithm presented in the figure of the paper where the condition to stop the process might be either a threshold value for the consistency index, or a given number of iterations per group or per person.

  16. Construction and application of hierarchical decision tree for classification of ultrasonographic prostate images

    NARCIS (Netherlands)

    Giesen, R. J.; Huynen, A. L.; Aarnink, R. G.; de la Rosette, J. J.; Debruyne, F. M.; Wijkstra, H.

    1996-01-01

    A non-parametric algorithm is described for the construction of a binary decision tree classifier. This tree is used to correlate textural features, computed from ultrasonographic prostate images, with the histopathology of the imaged tissue. The algorithm consists of two parts; growing and pruning.

  17. A hierarchical decision making model for the prioritization of distributed generation technologies: A case study for Iran

    International Nuclear Information System (INIS)

    Zangeneh, Ali; Jadid, Shahram; Rahimi-Kian, Ashkan

    2009-01-01

    The purpose of this paper is to present an assessment and evaluation model for the prioritization of distributed generation (DG) technologies, both conventional and renewable, to meet the increasing load due to the growth rate in Iran, while considering the issue of sustainable development. The proposed hierarchical decision making strategy is presented from the viewpoint of either the distribution company (DisCo) or the independent power producer (IPP) as a private entity. Nowadays, DG is a broadly-used term that covers various technologies; however, it is difficult to find a unique DG technology that takes into account multiple considerations, such as economic, technical, and environmental attributes. For this purpose, a multi-attribute decision making (MADM) approach is used to assess the alternatives for DG technology with respect to their economic, technical and environmental attributes. In addition, a regional primary energy attribute is also included in the hierarchy to express the potential of various kinds of energy resources in the regions under study. The obtained priority of DG technologies help decision maker in each region how allocate their total investment budget to the various technologies. From the performed analysis, it is observed that gas turbines are almost the best technologies for investing in various regions of Iran. At the end of the decision making process, a sensitivity analysis is performed based on the state regulations to indicate how the variations of the attributes' weights influence the DG alternatives' priority. This proposed analytical framework is implemented in seven parts of Iran with different climatic conditions and energy resources.

  18. A hierarchical decision aid in a debate on national energy policy

    International Nuclear Information System (INIS)

    Haemaelaeinen, R.P.; Seppaelaeinen, T.; Oehladt, K.; Ruusunen, J.

    1985-12-01

    A wide public depate on the future energy policy of Finland has been going on for the past few years, and at the moment the discussion is centered around the question whether a new nuclear power plant should be built or not. To clarify the differences between anti-nuclear and pro-nuclear opinions, a decision analysis of the issue was conducted with a microcomputer-based decision aid which utilizes the Analytic Hierarchy Process. The participants representing the opposite opinions were the Minister of Finance and a chief industrial executive. This paper presents the preference profiles of the participants and sensitivity analyses of the results, and discusses the implications of the results for the depate. Essential sources of opinion differences are pointed out and a deeper understanding of the issue is gained. This enables focusing the depate on the critical questions and elimination of less important criteria, which otherwise might receive disproportonate attention

  19. Assessing urban logistics pooling sustainability via a hierarchic dashboard from a group decision perspective

    OpenAIRE

    Gonzalez-Feliu, Jesus; Morana, Joëlle

    2014-01-01

    This paper aims to propose, via an experimental collaborative decision support method, to define a grid of indicators and a reference situation database to measure the sustainable performance of urban logistics pooling systems. To do this, we start by defining the notion of sustainability in the 4As approach, after what we identify the main sustainability indicators from an overview of the literature, and class them into four categories (one for each A of the approach). Then, a group of 20 ex...

  20. Low-Complexity Hierarchical Mode Decision Algorithms Targeting VLSI Architecture Design for the H.264/AVC Video Encoder

    Directory of Open Access Journals (Sweden)

    Guilherme Corrêa

    2012-01-01

    Full Text Available In H.264/AVC, the encoding process can occur according to one of the 13 intraframe coding modes or according to one of the 8 available interframes block sizes, besides the SKIP mode. In the Joint Model reference software, the choice of the best mode is performed through exhaustive executions of the entire encoding process, which significantly increases the encoder's computational complexity and sometimes even forbids its use in real-time applications. Considering this context, this work proposes a set of heuristic algorithms targeting hardware architectures that lead to earlier selection of one encoding mode. The amount of repetitions of the encoding process is reduced by 47 times, at the cost of a relatively small cost in compression performance. When compared to other works, the fast hierarchical mode decision results are expressively more satisfactory in terms of computational complexity reduction, quality, and bit rate. The low-complexity mode decision architecture proposed is thus a very good option for real-time coding of high-resolution videos. The solution is especially interesting for embedded and mobile applications with support to multimedia systems, since it yields good compression rates and image quality with a very high reduction in the encoder complexity.

  1. Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

    Science.gov (United States)

    Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

    2015-08-01

    In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

  2. Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Ying Yin

    2016-05-01

    Full Text Available Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1 the user-specific parameter for the number of clusters may incur the effective problem; (2 SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML-extreme learning machine (ELM, to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.

  3. An optimal hierarchical decision model for a regional logistics network with environmental impact consideration.

    Science.gov (United States)

    Zhang, Dezhi; Li, Shuangyan; Qin, Jin

    2014-01-01

    This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

  4. An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    2014-01-01

    Full Text Available This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users’ demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators’ service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

  5. An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

    Science.gov (United States)

    Zhang, Dezhi; Li, Shuangyan

    2014-01-01

    This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level. PMID:24977209

  6. Deep learning architectures for multi-label classification of intelligent health risk prediction.

    Science.gov (United States)

    Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

    2017-12-28

    Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging

  7. A Hierarchical Reliability Control Method for a Space Manipulator Based on the Strategy of Autonomous Decision-Making

    Directory of Open Access Journals (Sweden)

    Xin Gao

    2016-01-01

    Full Text Available In order to maintain and enhance the operational reliability of a robotic manipulator deployed in space, an operational reliability system control method is presented in this paper. First, a method to divide factors affecting the operational reliability is proposed, which divides the operational reliability factors into task-related factors and cost-related factors. Then the models describing the relationships between the two kinds of factors and control variables are established. Based on this, a multivariable and multiconstraint optimization model is constructed. Second, a hierarchical system control model which incorporates the operational reliability factors is constructed. The control process of the space manipulator is divided into three layers: task planning, path planning, and motion control. Operational reliability related performance parameters are measured and used as the system’s feedback. Taking the factors affecting the operational reliability into consideration, the system can autonomously decide which control layer of the system should be optimized and how to optimize it using a control level adjustment decision module. The operational reliability factors affect these three control levels in the form of control variable constraints. Simulation results demonstrate that the proposed method can achieve a greater probability of meeting the task accuracy requirements, while extending the expected lifetime of the space manipulator.

  8. A multi-label learning based kernel automatic recommendation method for support vector machine.

    Science.gov (United States)

    Zhang, Xueying; Song, Qinbao

    2015-01-01

    Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

  9. Research of ddi based on multi-label conditional random field

    Directory of Open Access Journals (Sweden)

    Yu Yangzhi

    2017-01-01

    Full Text Available The detection of drug name and drug-drug interaction(DDI is considered as a sequence labeling task in this paper. We present the multi-label CRF method to complete it. Compared to the traditional method, our method can not only identify drug names, but also can identify drug-drug interaction. According to the characteristics of medical texts, this paper extracts the good features of the description of DDI. The proposed method has good performance in DDIExtraction 2013 evaluation corpus.

  10. iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins.

    Science.gov (United States)

    Lin, Wei-Zhong; Fang, Jian-An; Xiao, Xuan; Chou, Kuo-Chen

    2013-04-05

    Predicting protein subcellular localization is a challenging problem, particularly when query proteins have multi-label features meaning that they may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing methods can only be used to deal with the single-label proteins. Actually, multi-label proteins should not be ignored because they usually bear some special function worthy of in-depth studies. By introducing the "multi-label learning" approach, a new predictor, called iLoc-Animal, has been developed that can be used to deal with the systems containing both single- and multi-label animal (metazoan except human) proteins. Meanwhile, to measure the prediction quality of a multi-label system in a rigorous way, five indices were introduced; they are "Absolute-True", "Absolute-False" (or Hamming-Loss"), "Accuracy", "Precision", and "Recall". As a demonstration, the jackknife cross-validation was performed with iLoc-Animal on a benchmark dataset of animal proteins classified into the following 20 location sites: (1) acrosome, (2) cell membrane, (3) centriole, (4) centrosome, (5) cell cortex, (6) cytoplasm, (7) cytoskeleton, (8) endoplasmic reticulum, (9) endosome, (10) extracellular, (11) Golgi apparatus, (12) lysosome, (13) mitochondrion, (14) melanosome, (15) microsome, (16) nucleus, (17) peroxisome, (18) plasma membrane, (19) spindle, and (20) synapse, where many proteins belong to two or more locations. For such a complicated system, the outcomes achieved by iLoc-Animal for all the aforementioned five indices were quite encouraging, indicating that the predictor may become a useful tool in this area. It has not escaped our notice that the multi-label approach and the rigorous measurement metrics can also be used to investigate many other multi-label problems in molecular biology. As a user-friendly web-server, iLoc-Animal is freely accessible to the public at the web-site .

  11. Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning.

    Science.gov (United States)

    Huang, Chang-Qin; Yang, Shang-Ming; Pan, Yan; Lai, Han-Jiang

    2018-09-01

    Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.

  12. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

    Science.gov (United States)

    Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

    2017-12-01

    Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

  13. EnzML: multi-label prediction of enzyme classes using InterPro signatures

    Directory of Open Access Journals (Sweden)

    De Ferrari Luna

    2012-04-01

    Full Text Available Abstract Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN.

  14. Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans

    Science.gov (United States)

    Efrain Humpire-Mamani, Gabriel; Arindra Adiyoso Setio, Arnaud; van Ginneken, Bram; Jacobs, Colin

    2018-04-01

    Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient method for simultaneous localization of multiple structures in 3D thorax-abdomen CT scans. Our approach predicts the location of multiple structures using a single multi-label convolutional neural network for each orthogonal view. Each network takes extra slices around the current slice as input to provide extra context. A sigmoid layer is used to perform multi-label classification. The output of the three networks is subsequently combined to compute a 3D bounding box for each structure. We used our approach to locate 11 structures of interest. The neural network was trained and evaluated on a large set of 1884 thorax-abdomen CT scans from patients undergoing oncological workup. Reference bounding boxes were annotated by human observers. The performance of our method was evaluated by computing the wall distance to the reference bounding boxes. The bounding boxes annotated by the first human observer were used as the reference standard for the test set. Using the best configuration, we obtained an average wall distance of 3.20~+/-~7.33 mm in the test set. The second human observer achieved 1.23~+/-~3.39 mm. For all structures, the results were better than those reported in previously published studies. In conclusion, we proposed an efficient method for the accurate localization of multiple organs. Our method uses multiple slices as input to provide more context around the slice under analysis, and we have shown that this improves performance. This method can easily be adapted to handle more organs.

  15. In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data

    KAUST Repository

    Raies, Arwa B.

    2017-12-05

    One goal of toxicity testing, among others, is identifying harmful effects of chemicals. Given the high demand for toxicity tests, it is necessary to conduct these tests for multiple toxicity endpoints for the same compound. Current computational toxicology methods aim at developing models mainly to predict a single toxicity endpoint. When chemicals cause several toxicity effects, one model is generated to predict toxicity for each endpoint, which can be labor and computationally intensive when the number of toxicity endpoints is large. Additionally, this approach does not take into consideration possible correlation between the endpoints. Therefore, there has been a recent shift in computational toxicity studies toward generating predictive models able to predict several toxicity endpoints by utilizing correlations between these endpoints. Applying such correlations jointly with compounds\\' features may improve model\\'s performance and reduce the number of required models. This can be achieved through multi-label classification methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology. Therefore, we performed extensive benchmarking and analysis of over 19,000 multi-label classification models generated using combinations of the state-of-the-art methods. The methods have been evaluated from different perspectives using various metrics to assess their effectiveness. We were able to illustrate variability in the performance of the methods under several conditions. This review will help researchers to select the most suitable method for the problem at hand and provide a baseline for evaluating new approaches. Based on this analysis, we provided recommendations for potential future directions in this area.

  16. In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data

    KAUST Repository

    Raies, Arwa B.; Bajic, Vladimir B.

    2017-01-01

    One goal of toxicity testing, among others, is identifying harmful effects of chemicals. Given the high demand for toxicity tests, it is necessary to conduct these tests for multiple toxicity endpoints for the same compound. Current computational toxicology methods aim at developing models mainly to predict a single toxicity endpoint. When chemicals cause several toxicity effects, one model is generated to predict toxicity for each endpoint, which can be labor and computationally intensive when the number of toxicity endpoints is large. Additionally, this approach does not take into consideration possible correlation between the endpoints. Therefore, there has been a recent shift in computational toxicity studies toward generating predictive models able to predict several toxicity endpoints by utilizing correlations between these endpoints. Applying such correlations jointly with compounds' features may improve model's performance and reduce the number of required models. This can be achieved through multi-label classification methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology. Therefore, we performed extensive benchmarking and analysis of over 19,000 multi-label classification models generated using combinations of the state-of-the-art methods. The methods have been evaluated from different perspectives using various metrics to assess their effectiveness. We were able to illustrate variability in the performance of the methods under several conditions. This review will help researchers to select the most suitable method for the problem at hand and provide a baseline for evaluating new approaches. Based on this analysis, we provided recommendations for potential future directions in this area.

  17. Page Layout Analysis of the Document Image Based on the Region Classification in a Decision Hierarchical Structure

    Directory of Open Access Journals (Sweden)

    Hossein Pourghassem

    2010-10-01

    Full Text Available The conversion of document image to its electronic version is a very important problem in the saving, searching and retrieval application in the official automation system. For this purpose, analysis of the document image is necessary. In this paper, a hierarchical classification structure based on a two-stage segmentation algorithm is proposed. In this structure, image is segmented using the proposed two-stage segmentation algorithm. Then, the type of the image regions such as document and non-document image is determined using multiple classifiers in the hierarchical classification structure. The proposed segmentation algorithm uses two algorithms based on wavelet transform and thresholding. Texture features such as correlation, homogeneity and entropy that extracted from co-occurrenc matrix and also two new features based on wavelet transform are used to classifiy and lable the regions of the image. The hierarchical classifier is consisted of two Multilayer Perceptron (MLP classifiers and a Support Vector Machine (SVM classifier. The proposed algorithm is evaluated on a database consisting of document and non-document images that provides from Internet. The experimental results show the efficiency of the proposed approach in the region segmentation and classification. The proposed algorithm provides accuracy rate of 97.5% on classification of the regions.

  18. "Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping out and Hierarchical Cluster Analysis"

    Directory of Open Access Journals (Sweden)

    Alex J. Bowers

    2010-05-01

    Full Text Available School personnel currently lack an effective method to pattern and visually interpret disaggregated achievement data collected on students as a means to help inform decision making. This study, through the examination of longitudinal K-12 teacher assigned grading histories for entire cohorts of students from a school district (n=188, demonstrates a novel application of hierarchical cluster analysis and pattern visualization in which all data points collected on every student in a cohort can be patterned, visualized and interpreted to aid in data driven decision making by teachers and administrators. Additionally, as a proof-of-concept study, overall schooling outcomes, such as student dropout or taking a college entrance exam, are identified from the data patterns and compared to past methods of dropout identification as one example of the usefulness of the method. Hierarchical cluster analysis correctly identified over 80% of the students who dropped out using the entire student grade history patterns from either K-12 or K-8.

  19. A Hierarchical Analysis of Bridge Decision Makers ... The Role of New Technology Adoption in the Timber Bridge Market: Special Project

    Science.gov (United States)

    Robert L. Smith; Robert J. Bush; Daniel L. Schmoldt

    1995-01-01

    Bridge design engineers and local highway officials make bridge replacement decisions across the United States. The Analytical Hierarchy Process was used to characterize the bridge material selection decision of these individuals. State Department of Transportation engineers, private consulting engineers, and local highway officials were personally interviewed in...

  20. Multi-Label Learning via Random Label Selection for Protein Subcellular Multi-Locations Prediction.

    Science.gov (United States)

    Wang, Xiao; Li, Guo-Zheng

    2013-03-12

    Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multi-location proteins to multiple proteins with single location, which doesn't take correlations among different subcellular locations into account. In this paper, a novel method named RALS (multi-label learning via RAndom Label Selection), is proposed to learn from multi-location proteins in an effective and efficient way. Through five-fold cross validation test on a benchmark dataset, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark datasets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multi-locations of proteins. The prediction web server is available at http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

  1. ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

    KAUST Repository

    Wu, Baoyuan

    2015-12-07

    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some of their labels are missing). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges. Unlike most MLML methods, We formulate this learning problem transductively as a convex quadratic matrix optimization problem that encourages training label consistency and encodes both types of label dependencies (i.e. undirected and directed edges) using quadratic terms and hard linear constraints. The alternating direction method of multipliers (ADMM) can be used to exactly and efficiently solve this problem. To evaluate our proposed method, we consider two popular applications (image and video annotation), where the label hierarchy can be derived from Wordnet. Experimental results show that our method achieves a significant improvement over state-of-the-art methods in performance and robustness to missing labels.

  2. Multi-Label Classification by Semi-Supervised Singular Value Decomposition.

    Science.gov (United States)

    Jing, Liping; Shen, Chenyang; Yang, Liu; Yu, Jian; Ng, Michael K

    2017-10-01

    Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods.

  3. A Comprehensive Decision-Making Approach Based on Hierarchical Attribute Model for Information Fusion Algorithms’ Performance Evaluation

    Directory of Open Access Journals (Sweden)

    Lianhui Li

    2014-01-01

    Full Text Available Aiming at the problem of fusion algorithm performance evaluation in multiradar information fusion system, firstly the hierarchical attribute model of track relevance performance evaluation model is established based on the structural model and functional model and quantization methods of evaluation indicators are given; secondly a combination weighting method is proposed to determine the weights of evaluation indicators, in which the objective and subjective weights are separately determined by criteria importance through intercriteria correlation (CRITIC and trapezoidal fuzzy scale analytic hierarchy process (AHP, and then experience factor is introduced to obtain the combination weight; at last the improved technique for order preference by similarity to ideal solution (TOPSIS replacing Euclidean distance with Kullback-Leibler divergence (KLD is used to sort the weighted indicator value of the evaluation object. An example is given to illustrate the correctness and feasibility of the proposed method.

  4. A novel immunohistochemical sequential multi-labelling and erasing technique enables epitope characterization of bone marrow pericytes in primary myelofibrosis

    DEFF Research Database (Denmark)

    Madelung, Ann; Bzorek, Michael; Bondo, Henrik

    2012-01-01

    : In Philadelphia (Ph)-negative chronic myeloproliferative neoplasms, increased microvascular density, bizarre vessel architecture and increased number of pericytes are among the distinct histopathological features. The aim of this study was to characterize bone marrow pericytes in primary myelofibrosis (PMF) using...... a novel multi-labelling immunohistochemical technique. Methods and results: Bone marrow biopsies from a normal donor (n = 1) and patients with PMF (n = 3) were subjected to an immunohistochemical sequential multi-labelling and erasing technique (SE-technique). Antigens of interest in the first and....../or second layer were detected with an immunoperoxidase system and visualized with aminoethylcarbazole. After imaging, erasing and blocking of immunoreagents, the slides were stained with a traditional double immunolabelling procedure. In addition, we applied a Photoshop(®) colour palette, creating a single...

  5. Facial Action Unit Recognition under Incomplete Data Based on Multi-label Learning with Missing Labels

    KAUST Repository

    Li, Yongqiang

    2016-07-07

    Facial action unit (AU) recognition has been applied in a wild range of fields, and has attracted great attention in the past two decades. Most existing works on AU recognition assumed that the complete label assignment for each training image is available, which is often not the case in practice. Labeling AU is expensive and time consuming process. Moreover, due to the AU ambiguity and subjective difference, some AUs are difficult to label reliably and confidently. Many AU recognition works try to train the classifier for each AU independently, which is of high computation cost and ignores the dependency among different AUs. In this work, we formulate AU recognition under incomplete data as a multi-label learning with missing labels (MLML) problem. Most existing MLML methods usually employ the same features for all classes. However, we find this setting is unreasonable in AU recognition, as the occurrence of different AUs produce changes of skin surface displacement or face appearance in different face regions. If using the shared features for all AUs, much noise will be involved due to the occurrence of other AUs. Consequently, the changes of the specific AUs cannot be clearly highlighted, leading to the performance degradation. Instead, we propose to extract the most discriminative features for each AU individually, which are learned by the supervised learning method. The learned features are further embedded into the instance-level label smoothness term of our model, which also includes the label consistency and the class-level label smoothness. Both a global solution using st-cut and an approximated solution using conjugate gradient (CG) descent are provided. Experiments on both posed and spontaneous facial expression databases demonstrate the superiority of the proposed method in comparison with several state-of-the-art works.

  6. Facial Action Unit Recognition under Incomplete Data Based on Multi-label Learning with Missing Labels

    KAUST Repository

    Li, Yongqiang; Wu, Baoyuan; Ghanem, Bernard; Zhao, Yongping; Yao, Hongxun; Ji, Qiang

    2016-01-01

    Facial action unit (AU) recognition has been applied in a wild range of fields, and has attracted great attention in the past two decades. Most existing works on AU recognition assumed that the complete label assignment for each training image is available, which is often not the case in practice. Labeling AU is expensive and time consuming process. Moreover, due to the AU ambiguity and subjective difference, some AUs are difficult to label reliably and confidently. Many AU recognition works try to train the classifier for each AU independently, which is of high computation cost and ignores the dependency among different AUs. In this work, we formulate AU recognition under incomplete data as a multi-label learning with missing labels (MLML) problem. Most existing MLML methods usually employ the same features for all classes. However, we find this setting is unreasonable in AU recognition, as the occurrence of different AUs produce changes of skin surface displacement or face appearance in different face regions. If using the shared features for all AUs, much noise will be involved due to the occurrence of other AUs. Consequently, the changes of the specific AUs cannot be clearly highlighted, leading to the performance degradation. Instead, we propose to extract the most discriminative features for each AU individually, which are learned by the supervised learning method. The learned features are further embedded into the instance-level label smoothness term of our model, which also includes the label consistency and the class-level label smoothness. Both a global solution using st-cut and an approximated solution using conjugate gradient (CG) descent are provided. Experiments on both posed and spontaneous facial expression databases demonstrate the superiority of the proposed method in comparison with several state-of-the-art works.

  7. Multi-label Learning with Missing Labels Using Mixed Dependency Graphs

    KAUST Repository

    Wu, Baoyuan

    2018-04-06

    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e., some of their labels are missing). The key point to handle missing labels is propagating the label information from the provided labels to missing labels, through a dependency graph that each label of each instance is treated as a node. We build this graph by utilizing different types of label dependencies. Specifically, the instance-level similarity is served as undirected edges to connect the label nodes across different instances and the semantic label hierarchy is used as directed edges to connect different classes. This base graph is referred to as the mixed dependency graph, as it includes both undirected and directed edges. Furthermore, we present another two types of label dependencies to connect the label nodes across different classes. One is the class co-occurrence, which is also encoded as undirected edges. Combining with the above base graph, we obtain a new mixed graph, called mixed graph with co-occurrence (MG-CO). The other is the sparse and low rank decomposition of the whole label matrix, to embed high-order dependencies over all labels. Combining with the base graph, the new mixed graph is called as MG-SL (mixed graph with sparse and low rank decomposition). Based on MG-CO and MG-SL, we further propose two convex transductive formulations of the MLML problem, denoted as MLMG-CO and MLMG-SL respectively. In both formulations, the instance-level similarity is embedded through a quadratic smoothness term, while the semantic label hierarchy is used as a linear constraint. In MLMG-CO, the class co-occurrence is also formulated as a quadratic smoothness term, while the sparse and low rank decomposition is incorporated into MLMG-SL, through two additional matrices (one is assumed as sparse, and the other is assumed as low

  8. Suitability Evaluation of Specific Shallow Geothermal Technologies Using a GIS-Based Multi Criteria Decision Analysis Implementing the Analytic Hierarchic Process

    Directory of Open Access Journals (Sweden)

    Francesco Tinti

    2018-02-01

    Full Text Available The exploitation potential of shallow geothermal energy is usually defined in terms of site-specific ground thermal characteristics. While true, this assumption limits the complexity of the analysis, since feasibility studies involve many other components that must be taken into account when calculating the effective market viability of a geothermal technology or the economic value of a shallow geothermal project. In addition, the results of a feasibility study are not simply the sum of the various factors since some components may be conflicting while others will be of a qualitative nature only. Different approaches are therefore needed to evaluate the suitability of an area for shallow geothermal installation. This paper introduces a new GIS platform-based multicriteria decision analysis method aimed at comparing as many different shallow geothermal relevant factors as possible. Using the Analytic Hierarchic Process Tool, a geolocalized Suitability Index was obtained for a specific technological case: the integrated technologies developed within the GEOTeCH Project. A suitability map for the technologies in question was drawn up for Europe.

  9. Hierarchical energy and frequency security pricing in a smart microgrid: An equilibrium-inspired epsilon constraint based multi-objective decision making approach

    International Nuclear Information System (INIS)

    Rezaei, Navid; Kalantar, Mohsen

    2015-01-01

    Highlights: • Proposing a multi-objective security pricing mechanism for islanded microgrids. • Generating Pareto points using epsilon constraint methodology. • Best compromise solution using a novel decision making approach. • An equilibrium-inspired technique is used as an efficient decision making method. • Stochastic management of hierarchical reserves in a droop controlled microgrid. - Abstract: The present paper formulates a frequency security constrained energy management system for an islanded microgrid. Static and dynamic securities of the microgrids have been modeled in depth based on droop control paradigm. The derived frequency dependent modeling is incorporated into a multi-objective energy management system. Microgrid central controller is in charge to determine optimal prices of energy and frequency security such that technical, economic and environmental targets are satisfied simultaneously. The associated prices are extracted based on calculating related Lagrange multipliers corresponding to providing the microgrid hourly energy and reserve requirements. Besides, to generate optimal Pareto solutions of the proposed multi-objective framework augmented epsilon constraint method is applied. Moreover, a novel methodology on the basis of Nash equilibrium strategy is devised and employed to select the best compromise solution from the generated Pareto front. Comprehensive analysis tool is implemented in a typical test microgrid and executed over a 24 h scheduling time horizon. The energy, primary and secondary frequency control reserves have been scheduled appropriately in three different case-studies which are defined based on the microgrid various operational policies. The optimization results verify that the operational policies adopted by means of the microgrid central controller have direct impacts on determined energy and security prices. The illustrative implementations can give the microgrid central controller an insight view to provide

  10. Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions

    Science.gov (United States)

    Li, Ji; Ren, Fuji

    Weblogs have greatly changed the communication ways of mankind. Affective analysis of blog posts is found valuable for many applications such as text-to-speech synthesis or computer-assisted recommendation. Traditional emotion recognition in text based on single-label classification can not satisfy higher requirements of affective computing. In this paper, the automatic identification of sentence emotion in weblogs is modeled as a multi-label text categorization task. Experiments are carried out on 12273 blog sentences from the Chinese emotion corpus Ren_CECps with 8-dimension emotion annotation. An ensemble algorithm RAKEL is used to recognize dominant emotions from the writer's perspective. Our emotion feature using detailed intensity representation for word emotions outperforms the other main features such as the word frequency feature and the traditional lexicon-based feature. In order to deal with relatively complex sentences, we integrate grammatical characteristics of punctuations, disjunctive connectives, modification relations and negation into features. It achieves 13.51% and 12.49% increases for Micro-averaged F1 and Macro-averaged F1 respectively compared to the traditional lexicon-based feature. Result shows that multiple-dimension emotion representation with grammatical features can efficiently classify sentence emotion in a multi-label problem.

  11. Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.

    Science.gov (United States)

    Wang, Xiao; Zhang, Jun; Li, Guo-Zheng

    2015-01-01

    It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins. In this article, two efficient multi-label predictors, Gpos-ECC-mPLoc and Gneg-ECC-mPLoc, are developed to predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. The two multi-label predictors construct the GO vectors by using the GO terms of homologous proteins of query proteins and then adopt a powerful multi-label ensemble classifier to make the final multi-label prediction. The two multi-label predictors have the following advantages: (1) they improve the prediction performance of multi-label proteins by taking the correlations among different labels into account; (2) they ensemble multiple CC classifiers and further generate better prediction results by ensemble learning; and (3) they construct the GO vectors by using the frequency of occurrences of GO terms in the typical homologous set instead of using 0/1 values. Experimental results show that Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently improve prediction accuracy of subcellular localization of multi-location gram-positive and gram-negative bacterial proteins respectively. The online web servers for Gpos-ECC-mPLoc and Gneg-ECC-mPLoc predictors are freely accessible

  12. Hierarchical species distribution models

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.

    2016-01-01

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

  13. Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification

    Science.gov (United States)

    Li, Shuanghong; Cao, Hongliang; Yang, Yupu

    2018-02-01

    Fault diagnosis is a key process for the reliability and safety of solid oxide fuel cell (SOFC) systems. However, it is difficult to rapidly and accurately identify faults for complicated SOFC systems, especially when simultaneous faults appear. In this research, a data-driven Multi-Label (ML) pattern identification approach is proposed to address the simultaneous fault diagnosis of SOFC systems. The framework of the simultaneous-fault diagnosis primarily includes two components: feature extraction and ML-SVM classifier. The simultaneous-fault diagnosis approach can be trained to diagnose simultaneous SOFC faults, such as fuel leakage, air leakage in different positions in the SOFC system, by just using simple training data sets consisting only single fault and not demanding simultaneous faults data. The experimental result shows the proposed framework can diagnose the simultaneous SOFC system faults with high accuracy requiring small number training data and low computational burden. In addition, Fault Inference Tree Analysis (FITA) is employed to identify the correlations among possible faults and their corresponding symptoms at the system component level.

  14. An integrated multi-label classifier with chemical-chemical interactions for prediction of chemical toxicity effects.

    Science.gov (United States)

    Liu, Tao; Chen, Lei; Pan, Xiaoyong

    2018-05-31

    Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. Predicting Multiple Functions of Sustainable Flood Retention Basins under Uncertainty via Multi-Instance Multi-Label Learning

    Directory of Open Access Journals (Sweden)

    Qinli Yang

    2015-03-01

    Full Text Available The ambiguity of diverse functions of sustainable flood retention basins (SFRBs may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input uncertainty, and the MIML-support vector machine (SVM algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty. Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%. The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management.

  16. Hierarchical analysis of bridge decision makers : the role of new technology adoption in the timber bridge market : special project fiscal year 1992

    Science.gov (United States)

    1995-08-01

    Bridge design engineers and local highway officials make bridge replacement decisions across the : United States. The Analytical Hierarchy Process was used to characterize the bridge material selection : decision of these individuals. State Departmen...

  17. Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

    KAUST Repository

    Azad, Mohammad

    2017-06-16

    We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.

  18. Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2017-01-01

    We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.

  19. Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory.

    Science.gov (United States)

    Qiu, Wang-Ren; Zheng, Quan-Shu; Sun, Bi-Qian; Xiao, Xuan

    2017-03-01

    Predicting phosphorylation protein is a challenging problem, particularly when query proteins have multi-label features meaning that they may be phosphorylated at two or more different type amino acids. In fact, human protein usually be phosphorylated at serine, threonine and tyrosine. By introducing the "multi-label learning" approach, a novel predictor has been developed that can be used to deal with the systems containing both single- and multi-label phosphorylation protein. Here we proposed a predictor called Multi-iPPseEvo by (1) incorporating the protein sequence evolutionary information into the general pseudo amino acid composition (PseAAC) via the grey system theory, (2) balancing out the skewed training datasets by the asymmetric bootstrap approach, and (3) constructing an ensemble predictor by fusing an array of individual random forest classifiers thru a voting system. Rigorous cross-validations via a set of multi-label metrics indicate that the multi-label phosphorylation predictor is very promising and encouraging. The current approach represents a new strategy to deal with the multi-label biological problems, and the software is freely available for academic use at http://www.jci-bioinfo.cn/Multi-iPPseEvo. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound.

    Science.gov (United States)

    Nanni, Loris; Brahnam, Sheryl

    2017-09-15

    Given an unknown compound, is it possible to predict its Anatomical Therapeutic Chemical class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological and chemical properties, thereby substantially expediting the pace of drug development. The problem is challenging because some drugs and compounds belong to two or more ATC classes, making machine learning extremely difficult. In this article a multi-label classifier system is proposed that incorporates information about a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes. The proposed system reshapes a 1D feature vector to obtain a 2D matrix representation of the compound. This matrix is then described by a histogram of gradients that is fed into a Multi-Label Learning with Label-Specific Features classifier. Rigorous cross-validations demonstrate the superior prediction quality of this method compared with other state-of-the-art approaches developed for this problem, a superiority that is reflected particularly in the absolute true rate, the most important and harshest metric for assessing multi-label systems. The MATLAB code for replicating the experiments presented in this article is available at https://www.dropbox.com/s/7v1mey48tl9bfgz/ToolPaperATC.rar?dl=0 . loris.nanni@unipd.it. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  1. Hierarchical screening for multiple mental disorders.

    Science.gov (United States)

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

    2013-10-01

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

  2. 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...

  3. Hierarchical Network Design

    DEFF Research Database (Denmark)

    Thomadsen, Tommy

    2005-01-01

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

  4. 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...

  5. 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....

  6. Introduction into Hierarchical Matrices

    KAUST Repository

    Litvinenko, Alexander

    2013-12-05

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

  7. Introduction into Hierarchical Matrices

    KAUST Repository

    Litvinenko, Alexander

    2013-01-01

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

  8. 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.

  9. A hierarchical anatomical classification schema for prediction of phenotypic side effects.

    Science.gov (United States)

    Wadhwa, Somin; Gupta, Aishwarya; Dokania, Shubham; Kanji, Rakesh; Bagler, Ganesh

    2018-01-01

    Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a 'hierarchical anatomical classification schema' which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

  10. 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.

  11. 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)

  12. 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.

  13. The Hierarchical Perspective

    Directory of Open Access Journals (Sweden)

    Daniel Sofron

    2015-05-01

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

  14. Bayesian nonparametric hierarchical modeling.

    Science.gov (United States)

    Dunson, David B

    2009-04-01

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

  15. Reliable multi-label learning via conformal predictor and random forest for syndrome differentiation of chronic fatigue in traditional Chinese medicine.

    Directory of Open Access Journals (Sweden)

    Huazhen Wang

    Full Text Available OBJECTIVE: Chronic Fatigue (CF still remains unclear about its etiology, pathophysiology, nomenclature and diagnostic criteria in the medical community. Traditional Chinese medicine (TCM adopts a unique diagnostic method, namely 'bian zheng lun zhi' or syndrome differentiation, to diagnose the CF with a set of syndrome factors, which can be regarded as the Multi-Label Learning (MLL problem in the machine learning literature. To obtain an effective and reliable diagnostic tool, we use Conformal Predictor (CP, Random Forest (RF and Problem Transformation method (PT for the syndrome differentiation of CF. METHODS AND MATERIALS: In this work, using PT method, CP-RF is extended to handle MLL problem. CP-RF applies RF to measure the confidence level (p-value of each label being the true label, and then selects multiple labels whose p-values are larger than the pre-defined significance level as the region prediction. In this paper, we compare the proposed CP-RF with typical CP-NBC(Naïve Bayes Classifier, CP-KNN(K-Nearest Neighbors and ML-KNN on CF dataset, which consists of 736 cases. Specifically, 95 symptoms are used to identify CF, and four syndrome factors are employed in the syndrome differentiation, including 'spleen deficiency', 'heart deficiency', 'liver stagnation' and 'qi deficiency'. THE RESULTS: CP-RF demonstrates an outstanding performance beyond CP-NBC, CP-KNN and ML-KNN under the general metrics of subset accuracy, hamming loss, one-error, coverage, ranking loss and average precision. Furthermore, the performance of CP-RF remains steady at the large scale of confidence levels from 80% to 100%, which indicates its robustness to the threshold determination. In addition, the confidence evaluation provided by CP is valid and well-calibrated. CONCLUSION: CP-RF not only offers outstanding performance but also provides valid confidence evaluation for the CF syndrome differentiation. It would be well applicable to TCM practitioners and

  16. Hierarchically Structured Electrospun Fibers

    Science.gov (United States)

    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

  17. Hierarchical video summarization

    Science.gov (United States)

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

    1998-12-01

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

  18. 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.

  19. A Hierarchical multi-input and output Bi-GRU Model for Sentiment Analysis on Customer Reviews

    Science.gov (United States)

    Zhang, Liujie; Zhou, Yanquan; Duan, Xiuyu; Chen, Ruiqi

    2018-03-01

    Multi-label sentiment classification on customer reviews is a practical challenging task in Natural Language Processing. In this paper, we propose a hierarchical multi-input and output model based bi-directional recurrent neural network, which both considers the semantic and lexical information of emotional expression. Our model applies two independent Bi-GRU layer to generate part of speech and sentence representation. Then the lexical information is considered via attention over output of softmax activation on part of speech representation. In addition, we combine probability of auxiliary labels as feature with hidden layer to capturing crucial correlation between output labels. The experimental result shows that our model is computationally efficient and achieves breakthrough improvements on customer reviews dataset.

  20. 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.

  1. 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....

  2. 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)

  3. 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

  4. Nested and Hierarchical Archimax copulas

    KAUST Repository

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

    2017-01-01

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

  5. Nested and Hierarchical Archimax copulas

    KAUST Repository

    Hofert, Marius

    2017-07-03

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

  6. Hierarchical Planning Methodology for a Supply Chain Management

    Directory of Open Access Journals (Sweden)

    Virna ORTIZ-ARAYA

    2012-01-01

    Full Text Available Hierarchical production planning is a widely utilized methodology for real world capacitated production planning systems with the aim of establishing different decision–making levels of the planning issues on the time horizon considered. This paper presents a hierarchical approach proposed to a company that produces reusable shopping bags in Chile and Perú, to determine the optimal allocation of resources at the tactical level as well as over the most immediate planning horizon to meet customer demands for the next weeks. Starting from an aggregated production planning model, the aggregated decisions are disaggregated into refined decisions in two levels, using a couple of optimization models that impose appropriate constraints to keep coherence of the plan on the production system. The main features of the hierarchical solution approach are presented.

  7. Expert hierarchical selection of oil and gas distribution systems

    International Nuclear Information System (INIS)

    Frankel, E.G.

    1991-01-01

    Selection and design of oil and gas distribution systems involves a large number of decision makers and interest groups, as well as many alternative technical, financial, network, operating, management and regulatory options. Their objectives and measures of performance are different. Decision models can be effectively represented by hierarchical structures. A simple deterministic analytic hierarchy process is presented with application to oil and gas distribution systems

  8. 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

  9. Trees and Hierarchical Structures

    CERN Document Server

    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.

  10. Optimisation by hierarchical search

    Science.gov (United States)

    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.

  11. How hierarchical is language use?

    Science.gov (United States)

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

    2012-01-01

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

  12. Do Group Decision Rules Affect Trust? A Laboratory Experiment on Group Decision Rules and Trust

    DEFF Research Database (Denmark)

    Nielsen, Julie Hassing

    2016-01-01

    Enhanced participation has been prescribed as the way forward for improving democratic decision making while generating positive attributes like trust. Yet we do not know the extent to which rules affect the outcome of decision making. This article investigates how different group decision rules......-hierarchical decision-making procedures enhance trust vis-à-vis other more hierarchical decision-making procedures....... affect group trust by testing three ideal types of decision rules (i.e., a Unilateral rule, a Representative rule and a 'Non-rule') in a laboratory experiment. The article shows significant differences between the three decision rules on trust after deliberation. Interestingly, however, it finds...

  13. 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.

  14. Hierarchical Linked Views

    Energy Technology Data Exchange (ETDEWEB)

    Erbacher, Robert; Frincke, Deb

    2007-07-02

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

  15. Hierarchical Discrete Event Supervisory Control of Aircraft Propulsion Systems

    Science.gov (United States)

    Yasar, Murat; Tolani, Devendra; Ray, Asok; Shah, Neerav; Litt, Jonathan S.

    2004-01-01

    This paper presents a hierarchical application of Discrete Event Supervisory (DES) control theory for intelligent decision and control of a twin-engine aircraft propulsion system. A dual layer hierarchical DES controller is designed to supervise and coordinate the operation of two engines of the propulsion system. The two engines are individually controlled to achieve enhanced performance and reliability, necessary for fulfilling the mission objectives. Each engine is operated under a continuously varying control system that maintains the specified performance and a local discrete-event supervisor for condition monitoring and life extending control. A global upper level DES controller is designed for load balancing and overall health management of the propulsion system.

  16. A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs.

    Science.gov (United States)

    Forkan, Abdur Rahim Mohammad; Khalil, Ibrahim

    2017-02-01

    In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs. In this paper, multi-label classification algorithms are applied in classifier design to forecast these values and related abnormalities. We proposed a completely new approach of patient-specific vital sign prediction system using their correlations. The developed technique can guide healthcare professionals to make accurate clinical decisions. Moreover, our model can support many patients with various clinical conditions concurrently by utilizing the power of cloud computing technology. The developed method also reduces the rate of false predictions in remote monitoring centres. In the experimental settings, the statistical features and correlations of six vital signs are formulated as multi-label classification problem. Eight multi-label classification algorithms along with three fundamental machine learning algorithms are used and tested on a public dataset of 85 patients. Different multi-label classification evaluation measures such as Hamming score, F1-micro average, and accuracy are used for interpreting the prediction performance of patient-specific situation classifications. We achieved 90-95% Hamming score values across 24 classifier combinations for 85 different patients used in our experiment. The results are compared with single-label classifiers and without considering the correlations among the vitals. The comparisons show that multi-label method is the best technique for this problem

  17. Direct hierarchical assembly of nanoparticles

    Science.gov (United States)

    Xu, Ting; Zhao, Yue; Thorkelsson, Kari

    2014-07-22

    The present invention provides hierarchical assemblies of a block copolymer, a bifunctional linking compound and a nanoparticle. The block copolymers form one micro-domain and the nanoparticles another micro-domain.

  18. 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...

  19. 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

  20. 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)

  1. Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models

    Science.gov (United States)

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

    2017-12-01

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

  2. Deferential vulnerability and patient decision-making

    African Journals Online (AJOL)

    2017-12-01

    Dec 1, 2017 ... cultures where certain hierarchical systems exist within the family or community. ... as a community, as opposed to a Western individualistic decision ... according to some ethicists, it is considered autonomous behaviour.

  3. Mitigating Herding in Hierarchical Crowdsourcing Networks.

    Science.gov (United States)

    Yu, Han; Miao, Chunyan; Leung, Cyril; Chen, Yiqiang; Fauvel, Simon; Lesser, Victor R; Yang, Qiang

    2016-12-05

    Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other workers idle. Existing herding control mechanisms designed for typical crowdsourcing systems are not effective in HCNs. In order to bridge this gap, we investigate the herding dynamics in HCNs and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Sub-delegation approach with dynamic worker effort Pricing (RTS-P) - with objective functions aiming to achieve superlinear time-averaged collective productivity in an HCN. By considering the workers' current reputation, workload, eagerness to work, and trust relationships, RTS-P provides a systematic approach to mitigate herding by helping workers make joint decisions on task sub-delegation, task acceptance, and effort pricing in a distributed manner. It is an individual-level decision support approach which results in the emergence of productive and robust collective patterns in HCNs. High resolution simulations demonstrate that RTS-P mitigates herding more effectively than state-of-the-art approaches.

  4. Inferring hierarchical clustering structures by deterministic annealing

    International Nuclear Information System (INIS)

    Hofmann, T.; Buhmann, J.M.

    1996-01-01

    The unsupervised detection of hierarchical structures is a major topic in unsupervised learning and one of the key questions in data analysis and representation. We propose a novel algorithm for the problem of learning decision trees for data clustering and related problems. In contrast to many other methods based on successive tree growing and pruning, we propose an objective function for tree evaluation and we derive a non-greedy technique for tree growing. Applying the principles of maximum entropy and minimum cross entropy, a deterministic annealing algorithm is derived in a meanfield approximation. This technique allows us to canonically superimpose tree structures and to fit parameters to averaged or open-quote fuzzified close-quote trees

  5. 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...

  6. Fuzzy compromise: An effective way to solve hierarchical design problems

    Science.gov (United States)

    Allen, J. K.; Krishnamachari, R. S.; Masetta, J.; Pearce, D.; Rigby, D.; Mistree, F.

    1990-01-01

    In this paper, we present a method for modeling design problems using a compromise decision support problem (DSP) incorporating the principles embodied in fuzzy set theory. Specifically, the fuzzy compromise decision support problem is used to study hierarchical design problems. This approach has the advantage that although the system modeled has an element of uncertainty associated with it, the solution obtained is crisp and precise. The efficacy of incorporating fuzzy sets into the solution process is discussed in the context of results obtained for a portal frame.

  7. 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 ...

  8. Hierarchical Microaggressions in Higher Education

    Science.gov (United States)

    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…

  9. Hierarchically structured, nitrogen-doped carbon membranes

    KAUST Repository

    Wang, Hong; Wu, Tao

    2017-01-01

    The present invention is a structure, method of making and method of use for a novel macroscopic hierarchically structured, nitrogen-doped, nano-porous carbon membrane (HNDCMs) with asymmetric and hierarchical pore architecture that can be produced

  10. Hierarchical effects on target detection and conflict monitoring

    Science.gov (United States)

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

    2016-01-01

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

  11. 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.

  12. Hierarchical matrices algorithms and analysis

    CERN Document Server

    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 ...

  13. 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.

  14. Hybrid and hierarchical composite materials

    CERN Document Server

    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.

  15. Hierarchical analysis of urban space

    OpenAIRE

    Kataeva, Y.

    2014-01-01

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

  16. Statistical Significance for Hierarchical Clustering

    Science.gov (United States)

    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

  17. Agent-based distributed hierarchical control of dc microgrid systems

    DEFF Research Database (Denmark)

    Meng, Lexuan; Vasquez, Juan Carlos; Guerrero, Josep M.

    2014-01-01

    In order to enable distributed control and management for microgrids, this paper explores the application of information consensus and local decisionmaking methods formulating an agent based distributed hierarchical control system. A droop controlled paralleled DC/DC converter system is taken as ....... Standard genetic algorithm is applied in each local control system in order to search for a global optimum. Hardware-in-Loop simulation results are shown to demonstrate the effectiveness of the method.......In order to enable distributed control and management for microgrids, this paper explores the application of information consensus and local decisionmaking methods formulating an agent based distributed hierarchical control system. A droop controlled paralleled DC/DC converter system is taken...... as a case study. The objective is to enhance the system efficiency by finding the optimal sharing ratio of load current. Virtual resistances in local control systems are taken as decision variables. Consensus algorithms are applied for global information discovery and local control systems coordination...

  18. How Firms Substitute for Authority in Strategic Decision-Making

    DEFF Research Database (Denmark)

    Dobrajska, Magdalena; Billinger, Stephan; Becker, Markus C.

    Strategic decisions are often made by multiple organizational members who form decision-making structures specialized for a given strategic decision. We study a series of strategic decisions in a business unit of a global Fortune 500 firm, identifying for each decision the hierarchical...... takes place in response to changes in decision characteristics, including decision complexity, decision importance, CEO proximity, and the degree to which a decision is routine. We show various manifestations of the substitution mechanism and discuss implications for strategic decision-making....... and departmental positions of all participating organizational members. We find that firms substitute between different structural components in decision-making structures to combine hierarchical authority with cross-departmental coordination and redundant knowledge. This substitution between structural components...

  19. 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

  20. Models for Rational Decision Making. Analysis of Literature and Selected Bibliography. Analysis and Bibliography Series, No. 6.

    Science.gov (United States)

    Hall, John S.

    This review analyzes the trend in educational decision making to replace hierarchical authority structures with more rational models for decision making drawn from management science. Emphasis is also placed on alternatives to a hierarchical decision-making model, including governing models, union models, and influence models. A 54-item…

  1. Hierarchical nonlinear dynamics of human attention.

    Science.gov (United States)

    Rabinovich, Mikhail I; Tristan, Irma; Varona, Pablo

    2015-08-01

    Attention is the process of focusing mental resources on a specific cognitive/behavioral task. Such brain dynamics involves different partially overlapping brain functional networks whose interconnections change in time according to the performance stage, and can be stimulus-driven or induced by an intrinsically generated goal. The corresponding activity can be described by different families of spatiotemporal discrete patterns or sequential dynamic modes. Since mental resources are finite, attention modalities compete with each other at all levels of the hierarchy, from perception to decision making and behavior. Cognitive activity is a dynamical process and attention possesses some universal dynamical characteristics. Thus, it is time to apply nonlinear dynamical theory for the description and prediction of hierarchical attentional tasks. Such theory has to include the analyses of attentional control stability, the time cost of attention switching, the finite capacity of informational resources in the brain, and the normal and pathological bifurcations of attention sequential dynamics. In this paper we have integrated today's knowledge, models and results in these directions. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Hierarchical trigger of the ALICE calorimeters

    CERN Document Server

    Muller, Hans; Novitzky, Norbert; Kral, Jiri; Rak, Jan; Schambach, Joachim; Wang, Ya-Ping; Wang, Dong; Zhou, Daicui

    2010-01-01

    The trigger of the ALICE electromagnetic calorimeters is implemented in 2 hierarchically connected layers of electronics. In the lower layer, level-0 algorithms search shower energy above threshold in locally confined Trigger Region Units (TRU). The top layer is implemented as a single, global trigger unit that receives the trigger data from all TRUs as input to the level-1 algorithm. This architecture was first developed for the PHOS high pT photon trigger before it was adopted by EMCal also for the jet trigger. TRU units digitize up to 112 analogue input signals from the Front End Electronics (FEE) and concentrate their digital stream in a single FPGA. A charge and time summing algorithm is combined with a peakfinder that suppresses spurious noise and is precise to single LHC bunches. With a peak-to-peak noise level of 150 MeV the linear dynamic range above threshold spans from MIP energies at 215 up to 50 GeV. Local level-0 decisions take less than 600 ns after LHC collisions, upon which all TRUs transfer ...

  3. Loops in hierarchical channel networks

    Science.gov (United States)

    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.

  4. Hierarchically nested river landform sequences

    Science.gov (United States)

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

    2017-12-01

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

  5. Stability of glassy hierarchical networks

    Science.gov (United States)

    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.

  6. 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)

  7. 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

  8. A hierarchical classification method for finger knuckle print recognition

    Science.gov (United States)

    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.

  9. Applying of forecasting at decision making in power systems

    International Nuclear Information System (INIS)

    Sapundjiev, G.

    2007-01-01

    The problems concerning forecast and decision making are analyzed. The typical tasks arising in the forecasting process of the power systems with hierarchical structure formulated and brought to formal description

  10. Hierarchically structured identification and classification method for vibrational monitoring of reactor components

    International Nuclear Information System (INIS)

    Saedtler, E.

    1981-01-01

    The dissertation discusses: 1. Approximative filter algorithms for identification of systems and hierarchical structures. 2. Adaptive statistical pattern recognition and classification. 3. Parameter selection, extraction, and modelling for an automatic control system. 4. Design of a decision tree and an adaptive diagnostic system. (orig./RW) [de

  11. A Hierarchical Algorithm for Integrated Scheduling and Control With Applications to Power Systems

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Dinesen, Peter Juhler; Jørgensen, John Bagterp

    2016-01-01

    The contribution of this paper is a hierarchical algorithm for integrated scheduling and control via model predictive control of hybrid systems. The controlled system is a linear system composed of continuous control, state, and output variables. Binary variables occur as scheduling decisions in ...

  12. Multicollinearity in hierarchical linear models.

    Science.gov (United States)

    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.

  13. 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)

  14. 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...

  15. Silver Films with Hierarchical Chirality.

    Science.gov (United States)

    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.

  16. Hierarchical coarse-graining transform.

    Science.gov (United States)

    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.

  17. Adaptive hierarchical multi-agent organizations

    NARCIS (Netherlands)

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

    2010-01-01

    In this chapter, we discuss the design of adaptive hierarchical organizations for multi-agent systems (MAS). Hierarchical organizations have a number of advantages such as their ability to handle complex problems and their scalability to large organizations. By introducing adaptivity in the

  18. The Case for a Hierarchical Cosmology

    Science.gov (United States)

    Vaucouleurs, G. de

    1970-01-01

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

  19. 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...

  20. 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 ...

  1. Clinical time series prediction: Toward a hierarchical dynamical system framework.

    Science.gov (United States)

    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.

  2. Clinical time series prediction: towards a hierarchical dynamical system framework

    Science.gov (United States)

    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

  3. 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.

  4. Hierarchical Rhetorical Sentence Categorization for Scientific Papers

    Science.gov (United States)

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

    2018-03-01

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

  5. Processing of hierarchical syntactic structure in music.

    Science.gov (United States)

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

    2013-09-17

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

  6. Hierarchical Control with Virtual Resistance Optimization for Efficiency Enhancement and State-of-Charge Balancing in DC Microgrids

    DEFF Research Database (Denmark)

    Meng, Lexuan; Dragicevic, Tomislav; Quintero, Juan Carlos Vasquez

    2015-01-01

    This paper proposes a hierarchical control scheme which applies optimization method into DC microgrids in order to improve the system overall efficiency while considering the State-of-Charge (SoC) balancing at the same time. Primary droop controller, secondary voltage restoration controller...... and tertiary optimization tool formulate the complete hierarchical control system. Virtual resistances are taken as the decision variables for achieving the objective. simulation results are presented to verify the proposed approach....

  7. UNCERTAINTY HANDLING IN DISASTER MANAGEMENT USING HIERARCHICAL ROUGH SET GRANULATION

    Directory of Open Access Journals (Sweden)

    H. Sheikhian

    2015-08-01

    Full Text Available Uncertainty is one of the main concerns in geospatial data analysis. It affects different parts of decision making based on such data. In this paper, a new methodology to handle uncertainty for multi-criteria decision making problems is proposed. It integrates hierarchical rough granulation and rule extraction to build an accurate classifier. Rough granulation provides information granules with a detailed quality assessment. The granules are the basis for the rule extraction in granular computing, which applies quality measures on the rules to obtain the best set of classification rules. The proposed methodology is applied to assess seismic physical vulnerability in Tehran. Six effective criteria reflecting building age, height and material, topographic slope and earthquake intensity of the North Tehran fault have been tested. The criteria were discretized and the data set was granulated using a hierarchical rough method, where the best describing granules are determined according to the quality measures. The granules are fed into the granular computing algorithm resulting in classification rules that provide the highest prediction quality. This detailed uncertainty management resulted in 84% accuracy in prediction in a training data set. It was applied next to the whole study area to obtain the seismic vulnerability map of Tehran. A sensitivity analysis proved that earthquake intensity is the most effective criterion in the seismic vulnerability assessment of Tehran.

  8. Rough multiple objective decision making

    CERN Document Server

    Xu, Jiuping

    2011-01-01

    Rough Set TheoryBasic concepts and properties of rough sets Rough Membership Rough Intervals Rough FunctionApplications of Rough SetsMultiple Objective Rough Decision Making Reverse Logistics Problem with Rough Interval Parameters MODM based Rough Approximation for Feasible RegionEVRMCCRMDCRM Reverse Logistics Network Design Problem of Suji Renewable Resource MarketBilevel Multiple Objective Rough Decision Making Hierarchical Supply Chain Planning Problem with Rough Interval Parameters Bilevel Decision Making ModelBL-EVRM BL-CCRMBL-DCRMApplication to Supply Chain Planning of Mianyang Co., LtdStochastic Multiple Objective Rough Decision Multi-Objective Resource-Constrained Project Scheduling UnderRough Random EnvironmentRandom Variable Stochastic EVRM Stochastic CCRM Stochastic DCRM Multi-Objective rc-PSP/mM/Ro-Ra for Longtan Hydropower StationFuzzy Multiple Objective Rough Decision Making Allocation Problem under Fuzzy Environment Fuzzy Variable Fu-EVRM Fu-CCRM Fu-DCRM Earth-Rock Work Allocation Problem.

  9. Hierarchical ordering with partial pairwise hierarchical relationships on the macaque brain data sets.

    Directory of Open Access Journals (Sweden)

    Woosang Lim

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

  10. 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...

  11. 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.

  12. Hierarchical modeling and analysis for spatial data

    CERN Document Server

    Banerjee, Sudipto; Gelfand, Alan E

    2003-01-01

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

  13. Hierarchically structured, nitrogen-doped carbon membranes

    KAUST Repository

    Wang, Hong

    2017-08-03

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

  14. Zeolitic materials with hierarchical porous structures.

    Science.gov (United States)

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

    2011-06-17

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

  15. 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.

  16. 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...

  17. Packaging glass with hierarchically nanostructured surface

    KAUST Repository

    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.

  18. Packaging glass with hierarchically nanostructured surface

    KAUST Repository

    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

  19. Hierarchical organization versus self-organization

    OpenAIRE

    Busseniers, Evo

    2014-01-01

    In this paper we try to define the difference between hierarchical organization and self-organization. Organization is defined as a structure with a function. So we can define the difference between hierarchical organization and self-organization both on the structure as on the function. In the next two chapters these two definitions are given. For the structure we will use some existing definitions in graph theory, for the function we will use existing theory on (self-)organization. In the t...

  20. 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.

  1. Evaluating Hierarchical Structure in Music Annotations.

    Science.gov (United States)

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

    2017-01-01

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

  2. 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.

  3. Decision Making

    Directory of Open Access Journals (Sweden)

    Pier Luigi Baldi

    2006-06-01

    Full Text Available This article points out some conditions which significantly exert an influence upon decision and compares decision making and problem solving as interconnected processes. Some strategies of decision making are also examined.

  4. Fuzzy hierarchical model for risk assessment principles, concepts, and practical applications

    CERN Document Server

    Chan, Hing Kai

    2013-01-01

    Risk management is often complicated by situational uncertainties and the subjective preferences of decision makers. Fuzzy Hierarchical Model for Risk Assessment introduces a fuzzy-based hierarchical approach to solve risk management problems considering both qualitative and quantitative criteria to tackle imprecise information.   This approach is illustrated through number of case studies using examples from the food, fashion and electronics sectors to cover a range of applications including supply chain management, green product design and green initiatives. These practical examples explore how this method can be adapted and fine tuned to fit other industries as well.   Supported by an extensive literature review, Fuzzy Hierarchical Model for Risk Assessment  comprehensively introduces a new method for project managers across all industries as well as researchers in risk management.

  5. Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

    Directory of Open Access Journals (Sweden)

    Itoh Hideaki

    2015-09-01

    Full Text Available The theory of partially observable Markov decision processes (POMDPs is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden Markov models and neural networks. However, little attention has been paid to bottom-up approaches for learning POMDP models. In this paper, we present a novel bottom-up learning algorithm for hierarchical POMDP models and prove that, by using this algorithm, a perfect model (i.e., a model that can perfectly predict future observations can be learned at least in a class of deterministic POMDP environments

  6. Hierarchical structure for audio-video based semantic classification of sports video sequences

    Science.gov (United States)

    Kolekar, M. H.; Sengupta, S.

    2005-07-01

    A hierarchical structure for sports event classification based on audio and video content analysis is proposed in this paper. Compared to the event classifications in other games, those of cricket are very challenging and yet unexplored. We have successfully solved cricket video classification problem using a six level hierarchical structure. The first level performs event detection based on audio energy and Zero Crossing Rate (ZCR) of short-time audio signal. In the subsequent levels, we classify the events based on video features using a Hidden Markov Model implemented through Dynamic Programming (HMM-DP) using color or motion as a likelihood function. For some of the game-specific decisions, a rule-based classification is also performed. Our proposed hierarchical structure can easily be applied to any other sports. Our results are very promising and we have moved a step forward towards addressing semantic classification problems in general.

  7. Habits as action sequences: hierarchical action control and changes in outcome value.

    Science.gov (United States)

    Dezfouli, Amir; Lingawi, Nura W; Balleine, Bernard W

    2014-11-05

    Goal-directed action involves making high-level choices that are implemented using previously acquired action sequences to attain desired goals. Such a hierarchical schema is necessary for goal-directed actions to be scalable to real-life situations, but results in decision-making that is less flexible than when action sequences are unfolded and the decision-maker deliberates step-by-step over the outcome of each individual action. In particular, from this perspective, the offline revaluation of any outcomes that fall within action sequence boundaries will be invisible to the high-level planner resulting in decisions that are insensitive to such changes. Here, within the context of a two-stage decision-making task, we demonstrate that this property can explain the emergence of habits. Next, we show how this hierarchical account explains the insensitivity of over-trained actions to changes in outcome value. Finally, we provide new data that show that, under extended extinction conditions, habitual behaviour can revert to goal-directed control, presumably as a consequence of decomposing action sequences into single actions. This hierarchical view suggests that the development of action sequences and the insensitivity of actions to changes in outcome value are essentially two sides of the same coin, explaining why these two aspects of automatic behaviour involve a shared neural structure. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  8. Hierarchical self-organization of non-cooperating individuals.

    Directory of Open Access Journals (Sweden)

    Tamás Nepusz

    Full Text Available Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.

  9. Decision making.

    Science.gov (United States)

    Chambers, David W

    2011-01-01

    A decision is a commitment of resources under conditions of risk in expectation of the best future outcome. The smart decision is always the strategy with the best overall expected value-the best combination of facts and values. Some of the special circumstances involved in decision making are discussed, including decisions where there are multiple goals, those where more than one person is involved in making the decision, using trigger points, framing decisions correctly, commitments to lost causes, and expert decision makers. A complex example of deciding about removal of asymptomatic third molars, with and without an EBD search, is discussed.

  10. Analysis hierarchical model for discrete event systems

    Science.gov (United States)

    Ciortea, E. M.

    2015-11-01

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

  11. Self-assembled biomimetic superhydrophobic hierarchical arrays.

    Science.gov (United States)

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

    2013-09-01

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

  12. Preference for oddity: uniqueness heuristic or hierarchical choice process?

    Science.gov (United States)

    Waite, Thomas A

    2008-10-01

    Traditional economic theories assume decision makers in multialternative choice tasks "assign" a value to each option and then express rational preferences. Here, I report an apparent violation of such rationality in gray jays (Perisoreus canadensis). I tested the jays' preference in a quaternary choice task where three options were the same color and the fourth option was a different color. All options offered an identical food reward and so the strictly rational expectation was that subjects would choose the odd-colored option in 25% of choices. In clear disagreement, every subject chose the odd option more frequently than expected. I speculate as to how this surprising preference for oddity might have been ecologically rational: by using a unique-choice heuristic, the jays might have been able to bypass a deliberative phase of the decision process and devote more attention to scanning for predators. Alternatively, it is conceivable that the jays did not prefer oddity per se. Instead, they might have used a hierarchical process, assigning options to color categories and then choosing between categories. If so, their behavior matches expectation after all (on average, subjects chose the odd option 50% of the time). It should be straightforward to test these competing hypotheses. The current results can be viewed as a new example of how simple mechanisms sometimes produce economically puzzling yet ecologically rational decision making.

  13. 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.

  14. Market Competitiveness Evaluation of Mechanical Equipment with a Pairwise Comparisons Hierarchical Model.

    Science.gov (United States)

    Hou, Fujun

    2016-01-01

    This paper provides a description of how market competitiveness evaluations concerning mechanical equipment can be made in the context of multi-criteria decision environments. It is assumed that, when we are evaluating the market competitiveness, there are limited number of candidates with some required qualifications, and the alternatives will be pairwise compared on a ratio scale. The qualifications are depicted as criteria in hierarchical structure. A hierarchical decision model called PCbHDM was used in this study based on an analysis of its desirable traits. Illustration and comparison shows that the PCbHDM provides a convenient and effective tool for evaluating the market competitiveness of mechanical equipment. The researchers and practitioners might use findings of this paper in application of PCbHDM.

  15. Static and dynamic friction of hierarchical surfaces.

    Science.gov (United States)

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

    2016-12-01

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

  16. Learning with hierarchical-deep models.

    Science.gov (United States)

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

    2013-08-01

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

  17. 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...

  18. Deep hierarchical attention network for video description

    Science.gov (United States)

    Li, Shuohao; Tang, Min; Zhang, Jun

    2018-03-01

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

  19. 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.

  20. Hierarchical Micro-Nano Coatings by Painting

    Science.gov (United States)

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

    2016-03-01

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

  1. 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

  2. Hierarchical Traces for Reduced NSM Memory Requirements

    Science.gov (United States)

    Dahl, Torbjørn S.

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

  3. Inferring on the Intentions of Others by Hierarchical Bayesian Learning

    Science.gov (United States)

    Diaconescu, Andreea O.; Mathys, Christoph; Weber, Lilian A. E.; Daunizeau, Jean; Kasper, Lars; Lomakina, Ekaterina I.; Fehr, Ernst; Stephan, Klaas E.

    2014-01-01

    Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition. PMID:25187943

  4. Uncertainty in perception and the Hierarchical Gaussian Filter

    Directory of Open Access Journals (Sweden)

    Christoph Daniel Mathys

    2014-11-01

    Full Text Available In its full sense, perception rests on an agent’s model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the hierarchical Gaussian filter (HGF offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (instability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF’s hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder-Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient - but at the same time intuitive - framework for the resolution of perceptual uncertainty in behaving agents.

  5. 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...

  6. 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...

  7. 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)

  8. 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.

  9. 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.

  10. Human capital, social capital and scientific research in Europe: an application of linear hierarchical models

    OpenAIRE

    Mathieu Goudard; Michel Lubrano

    2011-01-01

    The theory of human capital is one way to explain individual decisions to produce scientific research. However, this theory, even if it reckons the importance of time in science, is too short for explaining the existing diversity of scientific output. The present paper introduces the social capital of Bourdieu (1980), Coleman (1988) and Putnam (1995) as a necessary complement to explain the creation of scientific human capital. This paper connects these two concepts by means of a hierarchical...

  11. Explicit and Latent Authority in Hierarchical Organizations

    NARCIS (Netherlands)

    van den Brink, J.R.; Gilles, R.P.

    2005-01-01

    We consider the problem of control of access to a firm's productive asset, embedding the decision makers into a structure of formal authority relationships.Within such a structure, decision makers act as principal to some decision makers, while they act as agent to other decision makers.We study

  12. Explicit and Latent Authority in Hierarchical Organizations

    OpenAIRE

    van den Brink, J.R.; Gilles, R.P.

    2005-01-01

    In this paper we consider the problem of the control of access to a firm's productive asset, embedding the relevant decision makers into a general structure of formal authority relations. Within such an authority structure, each decision maker acts as a principal to some decision makers, while she acts as an agent in relation to certain other decision makers. We study under which conditions decision makers decide to exercise their own authority and to accept their superiors' authority. We dis...

  13. 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

  14. Hierarchical Broadcasting in the Future Mobile Internet

    NARCIS (Netherlands)

    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

  15. Hierarchical regression analysis in structural Equation Modeling

    NARCIS (Netherlands)

    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

  16. 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 ...

  17. 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...

  18. 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...

  19. 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.

  20. 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...

  1. 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...

  2. Hierarchical pre-segmentation without prior knowledge

    NARCIS (Netherlands)

    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 –

  3. 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

  4. Hierarchical Context Modeling for Video Event Recognition.

    Science.gov (United States)

    Wang, Xiaoyang; Ji, Qiang

    2016-10-11

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

  5. Hierarchical production planning for consumer goods

    NARCIS (Netherlands)

    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

  6. Hierarchical Bayesian Models of Subtask Learning

    Science.gov (United States)

    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…

  7. 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.

  8. The decision-making process between rationality and emotions

    OpenAIRE

    Alvino, Letizia; Franco, Massimo

    2017-01-01

    The decision-making process has been analyzed in several disciplines (economics, social sciences, humanities, etc.) with the aim of creating models to help decision-makers in strategy formulation. The Organizational theory takes into account both the decision-making process of individuals and groups of a company. Numerous models have been built, which include a wide range of psychological, environmental, hierarchical factors, all of which only account the notion of rationality. In time, such ...

  9. 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...

  10. 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...

  11. Virtual timers in hierarchical real-time systems

    NARCIS (Netherlands)

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

    2009-01-01

    Hierarchical scheduling frameworks (HSFs) provide means for composing complex real-time systems from welldefined subsystems. This paper describes an approach to provide hierarchically scheduled real-time applications with virtual event timers, motivated by the need for integrating priority

  12. Rational decisions

    CERN Document Server

    Binmore, Ken

    2008-01-01

    It is widely held that Bayesian decision theory is the final word on how a rational person should make decisions. However, Leonard Savage--the inventor of Bayesian decision theory--argued that it would be ridiculous to use his theory outside the kind of small world in which it is always possible to ""look before you leap."" If taken seriously, this view makes Bayesian decision theory inappropriate for the large worlds of scientific discovery and macroeconomic enterprise. When is it correct to use Bayesian decision theory--and when does it need to be modified? Using a minimum of mathematics,

  13. Individual v. community-level measures of women's decision-making ...

    African Journals Online (AJOL)

    These questions captured information on women's participation in household decision-making, gender-related hurdles in accessing healthcare and women's .... estimated to account for the hierarchical structure of the data. In order to ...

  14. A hierarchical framework for air traffic control

    Science.gov (United States)

    Roy, Kaushik

    Air travel in recent years has been plagued by record delays, with over $8 billion in direct operating costs being attributed to 100 million flight delay minutes in 2007. Major contributing factors to delay include weather, congestion, and aging infrastructure; the Next Generation Air Transportation System (NextGen) aims to alleviate these delays through an upgrade of the air traffic control system. Changes to large-scale networked systems such as air traffic control are complicated by the need for coordinated solutions over disparate temporal and spatial scales. Individual air traffic controllers must ensure aircraft maintain safe separation locally with a time horizon of seconds to minutes, whereas regional plans are formulated to efficiently route flows of aircraft around weather and congestion on the order of every hour. More efficient control algorithms that provide a coordinated solution are required to safely handle a larger number of aircraft in a fixed amount of airspace. Improved estimation algorithms are also needed to provide accurate aircraft state information and situational awareness for human controllers. A hierarchical framework is developed to simultaneously solve the sometimes conflicting goals of regional efficiency and local safety. Careful attention is given in defining the interactions between the layers of this hierarchy. In this way, solutions to individual air traffic problems can be targeted and implemented as needed. First, the regional traffic flow management problem is posed as an optimization problem and shown to be NP-Hard. Approximation methods based on aggregate flow models are developed to enable real-time implementation of algorithms that reduce the impact of congestion and adverse weather. Second, the local trajectory design problem is solved using a novel slot-based sector model. This model is used to analyze sector capacity under varying traffic patterns, providing a more comprehensive understanding of how increased automation

  15. 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...

  16. Application of hierarchical matrices for partial inverse

    KAUST Repository

    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.

  17. 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...

  18. 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

  19. Biominerals- hierarchical nanocomposites: the example of bone

    Science.gov (United States)

    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

  20. Noise enhances information transfer in hierarchical networks.

    Science.gov (United States)

    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.

  1. 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

  2. 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.

  3. 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)

  4. 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

  5. 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

  6. Hierarchical Fiber Structures Made by Electrospinning Polymers

    Science.gov (United States)

    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.

  7. Hierarchical video summarization based on context clustering

    Science.gov (United States)

    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.

  8. Internet advertising effectiveness by using hierarchical model

    OpenAIRE

    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...

  9. A Hierarchical Agency Model of Deposit Insurance

    OpenAIRE

    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...

  10. 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

  11. A decision model for energy resource selection in China

    International Nuclear Information System (INIS)

    Wang Bing; Kocaoglu, Dundar F.; Daim, Tugrul U.; Yang Jiting

    2010-01-01

    This paper evaluates coal, petroleum, natural gas, nuclear energy and renewable energy resources as energy alternatives for China through use of a hierarchical decision model. The results indicate that although coal is still the major preferred energy alternative, it is followed closely by renewable energy. The sensitivity analysis indicates that the most critical criterion for energy selection is the current energy infrastructure. A hierarchical decision model is used, and expert judgments are quantified, to evaluate the alternatives. Criteria used for the evaluations are availability, current energy infrastructure, price, safety, environmental impacts and social impacts.

  12. On hierarchical solutions to the BBGKY hierarchy

    Science.gov (United States)

    Hamilton, A. J. S.

    1988-01-01

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

  13. 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.

  14. 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)

  15. Anisotropic and Hierarchical Porosity in Multifunctional Ceramics

    Science.gov (United States)

    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.

  16. 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

  17. Local Approximation and Hierarchical Methods for Stochastic Optimization

    Science.gov (United States)

    Cheng, Bolong

    In this thesis, we present local and hierarchical approximation methods for two classes of stochastic optimization problems: optimal learning and Markov decision processes. For the optimal learning problem class, we introduce a locally linear model with radial basis function for estimating the posterior mean of the unknown objective function. The method uses a compact representation of the function which avoids storing the entire history, as is typically required by nonparametric methods. We derive a knowledge gradient policy with the locally parametric model, which maximizes the expected value of information. We show the policy is asymptotically optimal in theory, and experimental works suggests that the method can reliably find the optimal solution on a range of test functions. For the Markov decision processes problem class, we are motivated by an application where we want to co-optimize a battery for multiple revenue, in particular energy arbitrage and frequency regulation. The nature of this problem requires the battery to make charging and discharging decisions at different time scales while accounting for the stochastic information such as load demand, electricity prices, and regulation signals. Computing the exact optimal policy becomes intractable due to the large state space and the number of time steps. We propose two methods to circumvent the computation bottleneck. First, we propose a nested MDP model that structure the co-optimization problem into smaller sub-problems with reduced state space. This new model allows us to understand how the battery behaves down to the two-second dynamics (that of the frequency regulation market). Second, we introduce a low-rank value function approximation for backward dynamic programming. This new method only requires computing the exact value function for a small subset of the state space and approximate the entire value function via low-rank matrix completion. We test these methods on historical price data from the

  18. Decision Accuracy in Computer-Mediated versus Face-to-Face Decision-Making Teams.

    Science.gov (United States)

    Hedlund; Ilgen; Hollenbeck

    1998-10-01

    Changes in the way organizations are structured and advances in communication technologies are two factors that have altered the conditions under which group decisions are made. Decisions are increasingly made by teams that have a hierarchical structure and whose members have different areas of expertise. In addition, many decisions are no longer made via strictly face-to-face interaction. The present study examines the effects of two modes of communication (face-to-face or computer-mediated) on the accuracy of teams' decisions. The teams are characterized by a hierarchical structure and their members differ in expertise consistent with the framework outlined in the Multilevel Theory of team decision making presented by Hollenbeck, Ilgen, Sego, Hedlund, Major, and Phillips (1995). Sixty-four four-person teams worked for 3 h on a computer simulation interacting either face-to-face (FtF) or over a computer network. The communication mode had mixed effects on team processes in that members of FtF teams were better informed and made recommendations that were more predictive of the correct team decision, but leaders of CM teams were better able to differentiate staff members on the quality of their decisions. Controlling for the negative impact of FtF communication on staff member differentiation increased the beneficial effect of the FtF mode on overall decision making accuracy. Copyright 1998 Academic Press.

  19. Principal-subordinate hierarchical multi-objective programming model of initial water rights allocation

    Directory of Open Access Journals (Sweden)

    Dan Wu

    2009-06-01

    Full Text Available The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.

  20. TWO-LEVEL HIERARCHICAL COORDINATION QUEUING METHOD FOR TELECOMMUNICATION NETWORK NODES

    Directory of Open Access Journals (Sweden)

    M. V. Semenyaka

    2014-07-01

    Full Text Available The paper presents hierarchical coordination queuing method. Within the proposed method a queuing problem has been reduced to optimization problem solving that was presented as two-level hierarchical structure. The required distribution of flows and bandwidth allocation was calculated at the first level independently for each macro-queue; at the second level solutions obtained on lower level for each queue were coordinated in order to prevent probable network link overload. The method of goal coordination has been determined for multilevel structure managing, which makes it possible to define the order for consideration of queue cooperation restrictions and calculation tasks distribution between levels of hierarchy. Decisions coordination was performed by the method of Lagrange multipliers. The study of method convergence has been carried out by analytical modeling.

  1. Simplifying decision making: a practical framework

    OpenAIRE

    Chaudhry, Muhammad Shirjeel Riaz; Sidek, Mohmad Safhree

    2014-01-01

    Approved for public release; distribution is unlimited This thesis proposes a decision-making model based on PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) analysis, AHP (Analytical Hierarchical Process), and game theory. The case study used to demonstrate the concept is a 2013 Malaysian crisis wherein foreign intruders occupied a village in Sabah state. The Malaysian government, ultimately, launched a military operation to clear the area. The focus of our st...

  2. DECISION-COMPONENTS OF NICE'S TECHNOLOGY APPRAISALS ASSESSMENT FRAMEWORK.

    Science.gov (United States)

    de Folter, Joost; Trusheim, Mark; Jonsson, Pall; Garner, Sarah

    2018-01-01

    Value assessment frameworks have gained prominence recently in the context of U.S. healthcare. Such frameworks set out a series of factors that are considered in funding decisions. The UK's National Institute of Health and Care Excellence (NICE) is an established health technology assessment (HTA) agency. We present a novel application of text analysis that characterizes NICE's Technology Appraisals in the context of the newer assessment frameworks and present the results in a visual way. A total of 243 documents of NICE's medicines guidance from 2007 to 2016 were analyzed. Text analysis was used to identify a hierarchical set of decision factors considered in the assessments. The frequency of decision factors stated in the documents was determined and their association with terms related to uncertainty. The results were incorporated into visual representations of hierarchical factors. We identified 125 decision factors, and hierarchically grouped these into eight domains: Clinical Effectiveness, Cost Effectiveness, Condition, Current Practice, Clinical Need, New Treatment, Studies, and Other Factors. Textual analysis showed all domains appeared consistently in the guidance documents. Many factors were commonly associated with terms relating to uncertainty. A series of visual representations was created. This study reveals the complexity and consistency of NICE's decision-making processes and demonstrates that cost effectiveness is not the only decision-criteria. The study highlights the importance of processes and methodology that can take both quantitative and qualitative information into account. Visualizations can help effectively communicate this complex information during the decision-making process and subsequently to stakeholders.

  3. 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....

  4. Constructing storyboards based on hierarchical clustering analysis

    Science.gov (United States)

    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.

  5. 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....

  6. 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...

  7. 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....

  8. Broca's area: a supramodal hierarchical processor?

    Science.gov (United States)

    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.

  9. 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...

  10. 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.

  11. 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

  12. 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.

  13. Technique for fast and efficient hierarchical clustering

    Science.gov (United States)

    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.

  14. The Hierarchical Clustering of Tax Burden in the EU27

    Directory of Open Access Journals (Sweden)

    Simkova Nikola

    2015-09-01

    Full Text Available The issue of taxation has become more important due to a significant share of the government revenue. There are several ways of expressing the tax burden of countries. This paper describes the traditional approach as a share of tax revenue to GDP which is applied to the total taxation and the capital taxation as a part of tax systems affecting investment decisions. The implicit tax rate on capital created by Eurostat also offers a possible explanation of the tax burden on capital, so its components are analysed in detail. This study uses one of the econometric methods called the hierarchical clustering. The data on which the clustering is based comprises countries in the EU27 for the period of 1995 – 2012. The aim of this paper is to reveal clusters of countries in the EU27 with similar tax burden or tax changes. The findings suggest that mainly newly acceding countries (2004 and 2007 are in a group of countries with a low tax burden which tried to encourage investors by favourable tax rates. On the other hand, there are mostly countries from the original EU15. Some clusters may be explained by similar historical development, geographic and demographic characteristics.

  15. Applying Hierarchical Task Analysis Method to Discovery Layer Evaluation

    Directory of Open Access Journals (Sweden)

    Marlen Promann

    2015-03-01

    Full Text Available Libraries are implementing discovery layers to offer better user experiences. While usability tests have been helpful in evaluating the success or failure of implementing discovery layers in the library context, the focus has remained on its relative interface benefits over the traditional federated search. The informal site- and context specific usability tests have offered little to test the rigor of the discovery layers against the user goals, motivations and workflow they have been designed to support. This study proposes hierarchical task analysis (HTA as an important complementary evaluation method to usability testing of discovery layers. Relevant literature is reviewed for the discovery layers and the HTA method. As no previous application of HTA to the evaluation of discovery layers was found, this paper presents the application of HTA as an expert based and workflow centered (e.g. retrieving a relevant book or a journal article method to evaluating discovery layers. Purdue University’s Primo by Ex Libris was used to map eleven use cases as HTA charts. Nielsen’s Goal Composition theory was used as an analytical framework to evaluate the goal carts from two perspectives: a users’ physical interactions (i.e. clicks, and b user’s cognitive steps (i.e. decision points for what to do next. A brief comparison of HTA and usability test findings is offered as a way of conclusion.

  16. Testing adaptive toolbox models: a Bayesian hierarchical approach.

    Science.gov (United States)

    Scheibehenne, Benjamin; Rieskamp, Jörg; Wagenmakers, Eric-Jan

    2013-01-01

    Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

  17. 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)

  18. Hierarchical feature selection for erythema severity estimation

    Science.gov (United States)

    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.

  19. Hierarchical Diagnosis of Vocal Fold Disorders

    Science.gov (United States)

    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%.

  20. Discrete hierarchical organization of social group sizes.

    Science.gov (United States)

    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.

  1. The concept of a hierarchical cosmos

    Science.gov (United States)

    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.

  2. A self-defining hierarchical data system

    Science.gov (United States)

    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.

  3. Hierarchical prisoner’s dilemma in hierarchical game for resource competition

    Science.gov (United States)

    Fujimoto, Yuma; Sagawa, Takahiro; Kaneko, Kunihiko

    2017-07-01

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

  4. Hierarchically organized layout for visualization of biochemical pathways.

    Science.gov (United States)

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

    2010-01-01

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

  5. Statistical framework for decision making in mine action

    DEFF Research Database (Denmark)

    2007-01-01

    The lecture discusses the basics of statistical decision making in connection with humanitarian mine action. There is special focus on: 1) requirements for mine detection; 2) design and evaluation and confidence of mine equipment; 3) efficient mine action by hierarchical approaches; 4) performance...... improvement by statistical learning and information fusion; 5) the advantage of using combined methods....

  6. Statistical framework for decision making in mine action

    DEFF Research Database (Denmark)

    Larsen, Jan

    The lecture discusses the basics of statistical decision making in connection with humanitarian mine action. There is special focus on: 1) requirements for mine detection; 2) design and evaluation and confidence of mine equipment; 3) efficient mine action by hierarchical approaches; 4) performance...

  7. Decision making in prioritization of required operational capabilities

    Science.gov (United States)

    Andreeva, P.; Karev, M.; Kovacheva, Ts.

    2015-10-01

    The paper describes an expert heuristic approach to prioritization of required operational capabilities in the field of defense. Based on expert assessment and by application of the method of Analytical Hierarchical Process, a methodology for their prioritization has been developed. It has been applied to practical simulation decision making games.

  8. Real-Time Pricing-Based Scheduling Strategy in Smart Grids: A Hierarchical Game Approach

    Directory of Open Access Journals (Sweden)

    Jie Yang

    2014-01-01

    Full Text Available This paper proposes a scheduling strategy based on real-time pricing in smart grids. A hierarchical game is employed to analyze the decision-making process of generators and consumers. We prove the existence and uniqueness of Nash equilibrium and utilize a backward induction method to obtain the generation and consumption strategies. Then, we propose two dynamic algorithms for the generators and consumers to search for the equilibrium in a distributed fashion. Simulation results demonstrate that the proposed scheduling strategy can match supply with demand and shift load away from peak time.

  9. Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

    Science.gov (United States)

    Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

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

  10. Dissociable mechanisms of speed-accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift diffusion model

    Directory of Open Access Journals (Sweden)

    Jiaxiang eZhang

    2014-04-01

    Full Text Available Two phenomena are commonly observed in decision-making. First, there is a speed-accuracy tradeoff such that decisions are slower and more accurate when instructions emphasize accuracy over speed, and vice versa. Second, decision performance improves with practice, as a task is learnt. The speed-accuracy tradeoff and learning effects have been explained under a well-established evidence-accumulation framework for decision-making, which suggests that evidence supporting each choice is accumulated over time, and a decision is committed to when the accumulated evidence reaches a decision boundary. This framework suggests that changing the decision boundary creates the tradeoff between decision speed and accuracy, while increasing the rate of accumulation leads to more accurate and faster decisions after learning. However, recent studies challenged the view that speed-accuracy tradeoff and learning are associated with changes in distinct, single decision parameters. Further, the influence of speed-accuracy instructions over the course of learning remains largely unknown. Here, we used a hierarchical drift-diffusion model to examine the speed-accuracy tradeoff during learning of a coherent motion discrimination task across multiple training sessions, and a transfer test session. The influence of speed-accuracy instructions was robust over training and generalized across untrained stimulus features. Emphasizing decision accuracy rather than speed was associated with increased boundary separation, drift rate and non-decision time at the beginning of training. However, after training, an emphasis on decision accuracy was only associated with increased boundary separation. In addition, faster and more accurate decisions after learning were due to a gradual decrease in boundary separation and an increase in drift rate. The results suggest that speed-accuracy instructions and learning differentially shape decision-making processes at different time scales.

  11. Epidemic spreading in a hierarchical social network.

    Science.gov (United States)

    Grabowski, A; Kosiński, R A

    2004-09-01

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

  12. Epidemics and dimensionality in hierarchical networks

    Science.gov (United States)

    Zheng, Da-Fang; Hui, P. M.; Trimper, Steffen; Zheng, Bo

    2005-07-01

    Epidemiological processes are studied within a recently proposed hierarchical network model using the susceptible-infected-refractory dynamics of an epidemic. Within the network model, a population may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveal that for H>1, global spreading results regardless of the degree of homophily of the individuals forming a social circle. For H=1, a transition from global to local spread occurs as the population becomes decomposed into increasingly homophilous groups. Multiple dimensions in classifying individuals (nodes) thus make a society (computer network) highly susceptible to large-scale outbreaks of infectious diseases (viruses).

  13. Hierarchical image segmentation for learning object priors

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.; Li, Nan [TEMPLE UNIV.

    2010-11-10

    The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

  14. Optimization of Hierarchical System for Data Acquisition

    Directory of Open Access Journals (Sweden)

    V. Novotny

    2011-04-01

    Full Text Available Television broadcasting over IP networks (IPTV is one of a number of network applications that are except of media distribution also interested in data acquisition from group of information resources of variable size. IP-TV uses Real-time Transport Protocol (RTP protocol for media streaming and RTP Control Protocol (RTCP protocol for session quality feedback. Other applications, for example sensor networks, have data acquisition as the main task. Current solutions have mostly problem with scalability - how to collect and process information from large amount of end nodes quickly and effectively? The article deals with optimization of hierarchical system of data acquisition. Problem is mathematically described, delay minima are searched and results are proved by simulations.

  15. Optimization of Hierarchically Scheduled Heterogeneous Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Traian; Pop, Paul; Eles, Petru

    2005-01-01

    We present an approach to the analysis and optimization of heterogeneous distributed embedded systems. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling policies. When several scheduling policies share a resource......, they are organized in a hierarchy. In this paper, we address design problems that are characteristic to such hierarchically scheduled systems: assignment of scheduling policies to tasks, mapping of tasks to hardware components, and the scheduling of the activities. We present algorithms for solving these problems....... Our heuristics are able to find schedulable implementations under limited resources, achieving an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

  16. Growing hierarchical probabilistic self-organizing graphs.

    Science.gov (United States)

    López-Rubio, Ezequiel; Palomo, Esteban José

    2011-07-01

    Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.

  17. Supervisory, hierarchical control for a multimodular ALMR

    International Nuclear Information System (INIS)

    Otaduy, P.J.; Brittain, C.R.; Rovere, L.A.

    1989-01-01

    This paper describes the directions and present status of research in supervisory control for multimodular nuclear plants at ORNL as part of DOE's advanced controls program ACTO. The hierarchical supervisory structure envisioned for a PRISM-like supervisor closest to the process actuators and how it has actually been implemented for demonstration in a network of CPU's is presented next. Two demonstrations of supervisory control with an expert system are also described, one for control of a plant with a single reactor and turbine, the other for control of a plant with three reactors and one turbine. An appendix contains the mathematical basis for the novel approach to large scale system decomposition we have used in the demonstrations of supervisory distributed control of the single reactor plant. 6 refs., 5 figs

  18. Entrepreneurial intention modeling using hierarchical multiple regression

    Directory of Open Access Journals (Sweden)

    Marina Jeger

    2014-12-01

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

  19. 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.

  20. Hierarchical reorganization of dimensions in OLAP visualizations.

    Science.gov (United States)

    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.

  1. Coulomb blockade in hierarchical quantum Hall droplets

    International Nuclear Information System (INIS)

    Cappelli, Andrea; Georgiev, Lachezar S; Zemba, Guillermo R

    2009-01-01

    The degeneracy of energy levels in a quantum dot of Hall fluid, leading to conductance peaks, can be readily derived from the partition functions of conformal field theory. Their complete expressions can be found for Hall states with both Abelian and non-Abelian statistics, upon adapting known results for the annulus geometry. We analyze the Abelian states with hierarchical filling fractions, ν = m/(mp ± 1), and find a non-trivial pattern of conductance peaks. In particular, each one of them occurs with a characteristic multiplicity, which is due to the extended symmetry of the m-folded edge. Experimental tests of the multiplicity can shed more light on the dynamics of this composite edge. (fast track communication)

  2. Galactic chemical evolution in hierarchical formation models

    Science.gov (United States)

    Arrigoni, Matias

    2010-10-01

    The chemical properties and abundance ratios of galaxies provide important information about their formation histories. Galactic chemical evolution has been modelled in detail within the monolithic collapse scenario. These models have successfully described the abundance distributions in our Galaxy and other spiral discs, as well as the trends of metallicity and abundance ratios observed in early-type galaxies. In the last three decades, however, the paradigm of hierarchical assembly in a Cold Dark Matter (CDM) cosmology has revised the picture of how structure in the Universe forms and evolves. In this scenario, galaxies form when gas radiatively cools and condenses inside dark matter haloes, which themselves follow dissipationless gravitational collapse. The CDM picture has been successful at predicting many observed properties of galaxies (for example, the luminosity and stellar mass function of galaxies, color-magnitude or star formation rate vs. stellar mass distributions, relative numbers of early and late-type galaxies, gas fractions and size distributions of spiral galaxies, and the global star formation history), though many potential problems and open questions remain. It is therefore interesting to see whether chemical evolution models, when implemented within this modern cosmological context, are able to correctly predict the observed chemical properties of galaxies. With the advent of more powerfull telescopes and detectors, precise observations of chemical abundances and abundance ratios in various phases (stellar, ISM, ICM) offer the opportunity to obtain strong constraints on galaxy formation histories and the physics that shapes them. However, in order to take advantage of these observations, it is necessary to implement detailed modeling of chemical evolution into a modern cosmological model of hierarchical assembly.

  3. Hierarchical imaging of the human knee

    Science.gov (United States)

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

    2016-10-01

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

  4. HIERARCHICAL FRAGMENTATION OF THE ORION MOLECULAR FILAMENTS

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  5. Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

    Science.gov (United States)

    Klauer, Karl Christoph

    2010-01-01

    Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into…

  6. Discursive Hierarchical Patterning in Law and Management Cases

    Science.gov (United States)

    Lung, Jane

    2008-01-01

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

  7. Hierarchical Factoring Based On Image Analysis And Orthoblique Rotations.

    Science.gov (United States)

    Stankov, L

    1979-07-01

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

  8. Hierarchical Sets: Analyzing Pangenome Structure through Scalable Set Visualizations

    DEFF Research Database (Denmark)

    Pedersen, Thomas Lin

    2017-01-01

    of hierarchical sets by applying it to a pangenome based on 113 Escherichia and Shigella genomes and find it provides a powerful addition to pangenome analysis. The described clustering algorithm and visualizations are implemented in the hierarchicalSets R package available from CRAN (https...

  9. Hierarchical structure of moral stages assessed by a sorting task

    NARCIS (Netherlands)

    Boom, J.; Brugman, D.; Van der Heijden, P.G.M.

    2001-01-01

    Following criticism of Kohlberg’s theory of moral judgment, an empirical re-examination of hierarchical stage structure was desirable. Utilizing Piaget’s concept of reflective abstraction as a basis, the hierarchical stage structure was investigated using a new method. Study participants (553 Dutch

  10. Hierarchical clustering using correlation metric and spatial continuity constraint

    Science.gov (United States)

    Stork, Christopher L.; Brewer, Luke N.

    2012-10-02

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

  11. Decision rules for decision tables with many-valued decisions

    KAUST Repository

    Chikalov, Igor; Zielosko, Beata

    2011-01-01

    In the paper, authors presents a greedy algorithm for construction of exact and partial decision rules for decision tables with many-valued decisions. Exact decision rules can be 'over-fitted', so instead of exact decision rules with many attributes

  12. 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.

  13. 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.

  14. Improved Adhesion and Compliancy of Hierarchical Fibrillar Adhesives.

    Science.gov (United States)

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

    2015-08-05

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

  15. HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

    Science.gov (United States)

    Wiecki, Thomas V; Sofer, Imri; Frank, Michael J

    2013-01-01

    The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

  16. A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Feng Lu

    2016-10-01

    Full Text Available Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster–Shaffer evidence theory (WDS is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach.

  17. HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python

    Directory of Open Access Journals (Sweden)

    Thomas V Wiecki

    2013-08-01

    Full Text Available The diffusion model is a commonly used tool to infer latent psychological processes underlying decision making, and to link them to neural mechanisms based on reaction times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of reaction time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model, which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject / condition than non-hierarchical method, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g. fMRI influence decision making parameters. This paper will first describe the theoretical background of drift-diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the chi-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs

  18. Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

    Science.gov (United States)

    Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

    2012-01-01

    In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

  19. Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ning Yu

    2011-12-01

    Full Text Available In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

  20. Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

    Science.gov (United States)

    Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

    2012-01-01

    In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

  1. Nuclear decisions

    International Nuclear Information System (INIS)

    Dunster, H.J.; Farmer, F.R.; Gaines, M.J.

    1979-01-01

    In the Correspondence section of this issue of Nature three letters are published each of which has a bearing on the article by P. Taylor in the issue of 22 February. In that article Taylor calls for changes in the way decisions are taken on nuclear energy matters. The three articles are by H.J. Dunster of the UKAEA Health and Safety Executive, F.R. Farmer, Safety Adviser to the UKAEA, and M.J. Gaines, National Radiological Protection Board. (UK)

  2. Failing Decision

    DEFF Research Database (Denmark)

    Knudsen, Morten

    2014-01-01

    Recently the Danish subway trains have begun to announce “on time” when they arrive at a station on time. This action reflects a worrying acceptance of the normality of failure. If trains were generally expected to be on time, there would be no reason to – triumphantly – announce it. This chapter...... by an interest in failure as one way of improving understanding of present-day decision making in organizations.......Recently the Danish subway trains have begun to announce “on time” when they arrive at a station on time. This action reflects a worrying acceptance of the normality of failure. If trains were generally expected to be on time, there would be no reason to – triumphantly – announce it. This chapter...... deals not with traffic delays, but with failing decisions in organizations. The assumption of this chapter is that failing decisions today are as normal as delayed trains. Instead of being the exception, failure is part of the everyday reproduction of organizations – as an uncontrolled effect but also...

  3. Nuclear decisions

    International Nuclear Information System (INIS)

    Fischhoff, B.

    1991-01-01

    This attempts to identify the role of judgmental processes in various nuclear arms/war decisions. That task analysis is done with some confidence. The next two steps, diagnosing potential weaknesses in those judgments and suggesting ameliorative procedures, are done with increasing timidity. The empirical research base is not (ever) as large as one would like. It is particularly weak with regard to studies of experts forced to go beyond hard data and rely on intuitive judgments within their field of expertise. further evidence is needed to substantiate the claim that experts think like everyone else unless they have had the opportunity to master a particular kind of judgment as a learned skill. Although there is both theoretical and empirical reason to believe that various forms of decision aiding are possible, the high stakes involved mandate caution before proposing any intervention. Ineffective steps may make matters worse if they raise confidence without improving performance, or if they disrupt the cognitive ecology within which decision makers are accustomed to functioning, so that they lose touch with their own imperfect intuitions without acquiring viable alternatives

  4. Decision rules for decision tables with many-valued decisions

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    In the paper, authors presents a greedy algorithm for construction of exact and partial decision rules for decision tables with many-valued decisions. Exact decision rules can be \\'over-fitted\\', so instead of exact decision rules with many attributes, it is more appropriate to work with partial decision rules with smaller number of attributes. Based on results for set cover problem authors study bounds on accuracy of greedy algorithm for exact and partial decision rule construction, and complexity of the problem of minimization of decision rule length. © 2011 Springer-Verlag.

  5. Action recognition using mined hierarchical compound features.

    Science.gov (United States)

    Gilbert, Andrew; Illingworth, John; Bowden, Richard

    2011-05-01

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

  6. Star Cluster Structure from Hierarchical Star Formation

    Science.gov (United States)

    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.

  7. HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR

    International Nuclear Information System (INIS)

    Schneider, Michael D.; Dawson, William A.; Hogg, David W.; Marshall, Philip J.; Bard, Deborah J.; Meyers, Joshua; Lang, Dustin

    2015-01-01

    Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics

  8. 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.

  9. Hierarchical regular small-world networks

    International Nuclear Information System (INIS)

    Boettcher, Stefan; Goncalves, Bruno; Guclu, Hasan

    2008-01-01

    Two new networks are introduced that resemble small-world properties. These networks are recursively constructed but retain a fixed, regular degree. They possess a unique one-dimensional lattice backbone overlaid by a hierarchical sequence of long-distance links, mixing real-space and small-world features. Both networks, one 3-regular and the other 4-regular, lead to distinct behaviors, as revealed by renormalization group studies. The 3-regular network is planar, has a diameter growing as √N with system size N, and leads to super-diffusion with an exact, anomalous exponent d w = 1.306..., but possesses only a trivial fixed point T c = 0 for the Ising ferromagnet. In turn, the 4-regular network is non-planar, has a diameter growing as ∼2 √(log 2 N 2 ) , exhibits 'ballistic' diffusion (d w = 1), and a non-trivial ferromagnetic transition, T c > 0. It suggests that the 3-regular network is still quite 'geometric', while the 4-regular network qualifies as a true small world with mean-field properties. As an engineering application we discuss synchronization of processors on these networks. (fast track communication)

  10. Hierarchical theory of quantum adiabatic evolution

    International Nuclear Information System (INIS)

    Zhang, Qi; Wu, Biao; Gong, Jiangbin

    2014-01-01

    Quantum adiabatic evolution is a dynamical evolution of a quantum system under slow external driving. According to the quantum adiabatic theorem, no transitions occur between nondegenerate instantaneous energy eigenstates in such a dynamical evolution. However, this is true only when the driving rate is infinitesimally small. For a small nonzero driving rate, there are generally small transition probabilities between the energy eigenstates. We develop a classical mechanics framework to address the small deviations from the quantum adiabatic theorem order by order. A hierarchy of Hamiltonians is constructed iteratively with the zeroth-order Hamiltonian being determined by the original system Hamiltonian. The kth-order deviations are governed by a kth-order Hamiltonian, which depends on the time derivatives of the adiabatic parameters up to the kth-order. Two simple examples, the Landau–Zener model and a spin-1/2 particle in a rotating magnetic field, are used to illustrate our hierarchical theory. Our analysis also exposes a deep, previously unknown connection between classical adiabatic theory and quantum adiabatic theory. (paper)

  11. 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.

  12. Hierarchical analysis of dependency in metabolic networks.

    Science.gov (United States)

    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

  13. Hierarchical Design of Tissue Regenerative Constructs.

    Science.gov (United States)

    Rose, Jonas C; De Laporte, Laura

    2018-03-01

    The worldwide shortage of organs fosters significant advancements in regenerative therapies. Tissue engineering and regeneration aim to supply or repair organs or tissues by combining material scaffolds, biochemical signals, and cells. The greatest challenge entails the creation of a suitable implantable or injectable 3D macroenvironment and microenvironment to allow for ex vivo or in vivo cell-induced tissue formation. This review gives an overview of the essential components of tissue regenerating scaffolds, ranging from the molecular to the macroscopic scale in a hierarchical manner. Further, this review elaborates about recent pivotal technologies, such as photopatterning, electrospinning, 3D bioprinting, or the assembly of micrometer-scale building blocks, which enable the incorporation of local heterogeneities, similar to most native extracellular matrices. These methods are applied to mimic a vast number of different tissues, including cartilage, bone, nerves, muscle, heart, and blood vessels. Despite the tremendous progress that has been made in the last decade, it remains a hurdle to build biomaterial constructs in vitro or in vivo with a native-like structure and architecture, including spatiotemporal control of biofunctional domains and mechanical properties. New chemistries and assembly methods in water will be crucial to develop therapies that are clinically translatable and can evolve into organized and functional tissues. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. The hierarchical brain network for face recognition.

    Science.gov (United States)

    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.

  15. Hierarchical control of a nuclear reactor using uncertain dynamics techniques

    International Nuclear Information System (INIS)

    Rovere, L.A.; Otaduy, P.J.; Brittain, C.R.; Perez, R.B.

    1988-01-01

    Recent advances in the nonlinear optimal control area are opening new possibilities towards its implementation in process control. Algorithms for multivariate control, hierarchical decomposition, parameter tracking, model uncertainties actuator saturation effects and physical limits to state variables can be implemented on the basis of a consistent mathematical formulation. In this paper, good agreement is shown between a centralized and a hierarchical implementation of a controller for a hypothetical nuclear power plant subject to multiple demands. The performance of the hierarchical distributed system in the presence of localized subsystem failures is analyzed. 4 refs., 13 figs

  16. Analytical and numerical studies of creation probabilities of hierarchical trees

    Directory of Open Access Journals (Sweden)

    S.S. Borysov

    2011-03-01

    Full Text Available We consider the creation conditions of diverse hierarchical trees both analytically and numerically. A connection between the probabilities to create hierarchical levels and the probability to associate these levels into a united structure is studied. We argue that a consistent probabilistic picture requires the use of deformed algebra. Our consideration is based on the study of the main types of hierarchical trees, among which both regular and degenerate ones are studied analytically, while the creation probabilities of Fibonacci, scale-free and arbitrary trees are determined numerically.

  17. MULTIPLE CRITERIA DECISION MAKING APPROACH FOR INDUSTRIAL ENGINEER SELECTION USING FUZZY AHP-FUZZY TOPSIS

    OpenAIRE

    Deliktaş, Derya; ÜSTÜN, Özden

    2018-01-01

    In this study, a fuzzy multiple criteria decision-making approach is proposed to select an industrial engineer among ten candidates in a manufacturing environment. The industrial engineer selection problem is a special case of the personal selection problem. This problem, which has hierarchical structure of criteria and many decision makers, contains many criteria. The evaluation process of decision makers also includes ambiguous parameters. The fuzzy AHP is used to determin...

  18. Comparisons of Flow Patterns over a Hierarchical and a Non-hierarchical Surface in Relation to Biofouling Control

    Directory of Open Access Journals (Sweden)

    Bin Ahmad Fawzan Mohammed Ridha

    2018-01-01

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

  19. Metastable states in the hierarchical Dyson model drive parallel processing in the hierarchical Hopfield network

    International Nuclear Information System (INIS)

    Agliari, Elena; Barra, Adriano; Guerra, Francesco; Galluzzi, Andrea; Tantari, Daniele; Tavani, Flavia

    2015-01-01

    In this paper, we introduce and investigate the statistical mechanics of hierarchical neural networks. First, we approach these systems à la Mattis, by thinking of the Dyson model as a single-pattern hierarchical neural network. We also discuss the stability of different retrievable states as predicted by the related self-consistencies obtained both from a mean-field bound and from a bound that bypasses the mean-field limitation. The latter is worked out by properly reabsorbing the magnetization fluctuations related to higher levels of the hierarchy into effective fields for the lower levels. Remarkably, mixing Amit's ansatz technique for selecting candidate-retrievable states with the interpolation procedure for solving for the free energy of these states, we prove that, due to gauge symmetry, the Dyson model accomplishes both serial and parallel processing. We extend this scenario to multiple stored patterns by implementing the Hebb prescription for learning within the couplings. This results in Hopfield-like networks constrained on a hierarchical topology, for which, by restricting to the low-storage regime where the number of patterns grows at its most logarithmical with the amount of neurons, we prove the existence of the thermodynamic limit for the free energy, and we give an explicit expression of its mean-field bound and of its related improved bound. We studied the resulting self-consistencies for the Mattis magnetizations, which act as order parameters, are studied and the stability of solutions is analyzed to get a picture of the overall retrieval capabilities of the system according to both mean-field and non-mean-field scenarios. Our main finding is that embedding the Hebbian rule on a hierarchical topology allows the network to accomplish both serial and parallel processing. By tuning the level of fast noise affecting it or triggering the decay of the interactions with the distance among neurons, the system may switch from sequential retrieval to

  20. Reprocessing decision

    International Nuclear Information System (INIS)

    Heising, C.D.

    1978-01-01

    The United States must decide whether to permit, delay, or prohibit the reprocessing and recycling of nuclear spent fuel. To permit reprocessing would allow recycle as early as 1985; to delay the decision for a later administration to deal with means spent fuel would mount up at nuclear reactor sites; to prohibit would eliminate recycling and mandate permanent storage. Bayesian decision analysis was used to examine reprocessing costs associated with risks and economic benefits. Three distinct categories of risk that are important in the nuclear fuel cycle are discussed. These are: health, environment, and safety risks; nuclear theft and sabotage; and nuclear weapons proliferation risks. Results are discussed from comparing nine routes to weapons-usuable mterial available to nonweapons states that desire a nuclear capability. These are: production reactor and military reporcessor; research reacotr and military reprocessor; power plant plus military reprocessor or commercial reprocessor; enrichment (centrifuge, gaseous diffusion, electromagnetic separation, or aerodynamic jet cascade); and accelerator. It was found that the commercial power reactor-commercial reprocessor route is comparatively unattractive to a nonweapons state. In summary, allowing nuclear fuel reprocessing to go forward in the United States can be expected to increase the costs to society by a maximum $360 million a year. This is approximately one-seventh of the expected benefit (reduced electricity bills) to be dderived by society from closing the fuel cycle. It appears that the permitting reprocessing now is logically preferable to delaying or prohibiting the technology, the author concludes

  1. Hierarchical matrix techniques for the solution of elliptic equations

    KAUST Repository

    Chá vez, Gustavo; Turkiyyah, George; Yokota, Rio; Keyes, David E.

    2014-01-01

    Hierarchical matrix approximations are a promising tool for approximating low-rank matrices given the compactness of their representation and the economy of the operations between them. Integral and differential operators have been the major

  2. Scalable Hierarchical Algorithms for stochastic PDEs and UQ

    KAUST Repository

    Litvinenko, Alexander; Chá vez, Gustavo; Keyes,David; Ltaief, Hatem; Yokota, Rio

    2015-01-01

    number of degrees of freedom in the discretization. The storage is reduced to the log-linear as well. This hierarchical structure is a good starting point for parallel algorithms. Parallelization on shared and distributed memory systems was pioneered

  3. A Hierarchical Clustering Methodology for the Estimation of Toxicity

    Science.gov (United States)

    A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

  4. Carbon fiber reinforced hierarchical orthogrid stiffened cylinder: Fabrication and testing

    Science.gov (United States)

    Wu, Hao; Lai, Changlian; Sun, Fangfang; Li, Ming; Ji, Bin; Wei, Weiyi; Liu, Debo; Zhang, Xi; Fan, Hualin

    2018-04-01

    To get strong, stiff and light cylindrical shell, carbon fiber reinforced hierarchical orthogrid stiffened cylinders are designed and fabricated. The cylinder is stiffened by two-scale orthogrid. The primary orthogrid has thick and high ribs and contains several sub-orthogrid cells whose rib is much thinner and lower. The primary orthogrid stiffens the bending rigidity of the cylinder to resist the global instability while the sub-orthogrid stiffens the bending rigidity of the skin enclosed by the primary orthogrid to resist local buckling. The cylinder is fabricated by filament winding method based on a silicone rubber mandrel with hierarchical grooves. Axial compression tests are performed to reveal the failure modes. With hierarchical stiffeners, the cylinder fails at skin fracture and has high specific strength. The cylinder will fail at end crushing if the end of the cylinder is not thickened. Global instability and local buckling are well restricted by the hierarchical stiffeners.

  5. What are hierarchical models and how do we analyze them?

    Science.gov (United States)

    Royle, Andy

    2016-01-01

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

  6. Slow logarithmic relaxation in models with hierarchically constrained dynamics

    OpenAIRE

    Brey, J. J.; Prados, A.

    2000-01-01

    A general kind of models with hierarchically constrained dynamics is shown to exhibit logarithmic anomalous relaxation, similarly to a variety of complex strongly interacting materials. The logarithmic behavior describes most of the decay of the response function.

  7. Bayesian disease mapping: hierarchical modeling in spatial epidemiology

    National Research Council Canada - National Science Library

    Lawson, Andrew

    2013-01-01

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

  8. 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 ...

  9. Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

    Science.gov (United States)

    Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

  10. Facile synthesis and photocatalytic activity of zinc oxide hierarchical microcrystals

    KAUST Repository

    Xu, Xinjiang

    2013-04-04

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

  11. Bayesian hierarchical modelling of North Atlantic windiness

    Science.gov (United States)

    Vanem, E.; Breivik, O. N.

    2013-03-01

    Extreme weather conditions represent serious natural hazards to ship operations and may be the direct cause or contributing factor to maritime accidents. Such severe environmental conditions can be taken into account in ship design and operational windows can be defined that limits hazardous operations to less extreme conditions. Nevertheless, possible changes in the statistics of extreme weather conditions, possibly due to anthropogenic climate change, represent an additional hazard to ship operations that is less straightforward to account for in a consistent way. Obviously, there are large uncertainties as to how future climate change will affect the extreme weather conditions at sea and there is a need for stochastic models that can describe the variability in both space and time at various scales of the environmental conditions. Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular, pertaining to long-term trends in the wave climate. In this paper, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over an area in the North Atlantic ocean is investigated. When the results from the model for North Atlantic windiness is compared to the results for significant wave height over the same area, it is interesting to observe that whereas an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

  12. Bayesian hierarchical modelling of North Atlantic windiness

    Directory of Open Access Journals (Sweden)

    E. Vanem

    2013-03-01

    Full Text Available Extreme weather conditions represent serious natural hazards to ship operations and may be the direct cause or contributing factor to maritime accidents. Such severe environmental conditions can be taken into account in ship design and operational windows can be defined that limits hazardous operations to less extreme conditions. Nevertheless, possible changes in the statistics of extreme weather conditions, possibly due to anthropogenic climate change, represent an additional hazard to ship operations that is less straightforward to account for in a consistent way. Obviously, there are large uncertainties as to how future climate change will affect the extreme weather conditions at sea and there is a need for stochastic models that can describe the variability in both space and time at various scales of the environmental conditions. Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular, pertaining to long-term trends in the wave climate. In this paper, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over an area in the North Atlantic ocean is investigated. When the results from the model for North Atlantic windiness is compared to the results for significant wave height over the same area, it is interesting to observe that whereas an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

  13. Hierarchical Representation Learning for Kinship Verification.

    Science.gov (United States)

    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.

  14. Object recognition with hierarchical discriminant saliency networks

    Directory of Open Access Journals (Sweden)

    Sunhyoung eHan

    2014-09-01

    Full Text Available The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognitionmodel, the hierarchical discriminant saliency network (HDSN, whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. The HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a neuralnetwork implementation, all layers are convolutional and implement acombination of filtering, rectification, and pooling. The rectificationis performed with a parametric extension of the now popular rectified linearunits (ReLUs, whose parameters can be tuned for the detection of targetobject classes. This enables a number of functional enhancementsover neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation ofsaliency responses by the discriminant power of the underlying features,and the ability to detect both feature presence and absence.In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity totarget object classes and invariance. The resulting performance demonstrates benefits for all the functional enhancements of the HDSN.

  15. 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

  16. A hierarchical stochastic model for bistable perception.

    Science.gov (United States)

    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

  17. Hierarchical Trust Management of COI in Heterogeneous Mobile Networks

    Science.gov (United States)

    2017-08-01

    Report: Hierarchical Trust Management of COI in Heterogeneous Mobile Networks The views, opinions and/or findings contained in this report are those of...Institute & State University Title: Hierarchical Trust Management of COI in Heterogeneous Mobile Networks Report Term: 0-Other Email: irchen@vt.edu...Reconfigurability, Survivability and Intrusion Tolerance for Community of Interest (COI) Applications – Our proposed COI trust management protocol will

  18. Hierarchical control of vehicular fuel cell / battery hybrid powertrain

    OpenAIRE

    Xu, Liangfei; Ouyang, Minggao; Li, Jianqiu; Hua, Jianfeng

    2010-01-01

    In a proton exchange membrane (PEM) fuel cell/battery hybrid vehicle, a fuel cell system fulfills the stationary power demand, and a traction battery provides the accelerating power and recycles braking energy. The entire system is coordinated by a distributed control system, incorporating three key strategies: 1) vehicle control, 2) fuel cell control and 3) battery management. They make up a hierarchical control system. This paper introduces a hierarchical control strategy for a fuel cell / ...

  19. A Hierarchical Dispatch Structure for Distribution Network Pricing

    OpenAIRE

    Yuan, Zhao; Hesamzadeh, Mohammad Reza

    2015-01-01

    This paper presents a hierarchical dispatch structure for efficient distribution network pricing. The dispatch coordination problem in the context of hierarchical network operators are addressed. We formulate decentralized generation dispatch into a bilevel optimization problem in which main network operator and the connected distribution network operator optimize their costs in two levels. By using Karush-Kuhn-Tucker conditions and Fortuny-Amat McCarl linearization, the bilevel optimization ...

  20. The Revised Hierarchical Model: A critical review and assessment

    OpenAIRE

    Kroll, Judith F.; van Hell, Janet G.; Tokowicz, Natasha; Green, David W.

    2010-01-01

    Brysbaert and Duyck (2009) suggest that it is time to abandon the Revised Hierarchical Model (Kroll and Stewart, 1994) in favor of connectionist models such as BIA+ (Dijkstra and Van Heuven, 2002) that more accurately account for the recent evidence on nonselective access in bilingual word recognition. In this brief response, we first review the history of the Revised Hierarchical Model (RHM), consider the set of issues that it was proposed to address, and then evaluate the evidence that supp...

  1. Proposing a Hierarchical Utility Package with Reference to Mobile Advertising

    OpenAIRE

    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 consumers’ psyche. This study proposes a hierarchical utility package (in the consumers’ 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...

  2. Cluster Based Hierarchical Routing Protocol for Wireless Sensor Network

    OpenAIRE

    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...

  3. Facile fabrication of superhydrophobic surfaces with hierarchical structures.

    Science.gov (United States)

    Lee, Eunyoung; Lee, Kun-Hong

    2018-03-06

    Hierarchical structures were fabricated on the surfaces of SUS304 plates using a one-step process of direct microwave irradiation under a carbon dioxide atmosphere. The surface nanostructures were composed of chrome-doped hematite single crystals. Superhydrophobic surfaces with a water contact angle up to 169° were obtained by chemical modification of the hierarchical structures. The samples maintained superhydrophobicity under NaCl solution up to 2 weeks.

  4. Decision making in matters of pollution in France

    International Nuclear Information System (INIS)

    Galle, Marion

    1989-01-01

    Decision making in matters of pollution, although taking place in a hierarchically determined institutional setting, can be interpreted as the result of a conflictual process between the different groups involved. 1. The statutory setting: coherence of laws and adaptability of norms; a hierarchical, pyramidal, formal structure; relative autonomy and flexibility of local decision making: possibility of developing local organizations for the control of pollution. 2. The conflictual process: Within the institutional setting, decision making is the result of interaction between: the state and the authorities, who make the rules and possess the formal power; industry, which has the economic power and is the arbiter of technical feasibility; the public, who are guardians of social values and judges of the acceptability of final decisions; the experts, who possess the scientific knowledge on which decisions are based. The relative weights of these different interests in decision making are linked with the existence of today's society of communication which increases the importance of communicators and especially the media. Conclusion: decision making in questions of environmental risk and pollution necessarily consists in taking into account all the interests involved in order to arrive at a solution acceptable to the public. The interests of society are not necessarily compatible with technical or scientific logic. (author)

  5. Leadership styles across hierarchical levels in nursing departments.

    Science.gov (United States)

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

    2000-01-01

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

  6. Hierarchical Neural Regression Models for Customer Churn Prediction

    Directory of Open Access Journals (Sweden)

    Golshan Mohammadi

    2013-01-01

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

  7. 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.

  8. 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.

  9. A sow replacement model using Bayesian updating in a three-level hierarchic Markov process. I. Biological model

    DEFF Research Database (Denmark)

    Kristensen, Anders Ringgaard; Søllested, Thomas Algot

    2004-01-01

    that really uses all these methodological improvements. In this paper, the biological model describing the performance and feed intake of sows is presented. In particular, estimation of herd specific parameters is emphasized. The optimization model is described in a subsequent paper......Several replacement models have been presented in literature. In other applicational areas like dairy cow replacement, various methodological improvements like hierarchical Markov processes and Bayesian updating have been implemented, but not in sow models. Furthermore, there are methodological...... improvements like multi-level hierarchical Markov processes with decisions on multiple time scales, efficient methods for parameter estimations at herd level and standard software that has been hardly implemented at all in any replacement model. The aim of this study is to present a sow replacement model...

  10. A sow replacement model using Bayesian updating in a three-level hierarchic Markov process. II. Optimization model

    DEFF Research Database (Denmark)

    Kristensen, Anders Ringgaard; Søllested, Thomas Algot

    2004-01-01

    improvements. The biological model of the replacement model is described in a previous paper and in this paper the optimization model is described. The model is developed as a prototype for use under practical conditions. The application of the model is demonstrated using data from two commercial Danish sow......Recent methodological improvements in replacement models comprising multi-level hierarchical Markov processes and Bayesian updating have hardly been implemented in any replacement model and the aim of this study is to present a sow replacement model that really uses these methodological...... herds. It is concluded that the Bayesian updating technique and the hierarchical structure decrease the size of the state space dramatically. Since parameter estimates vary considerably among herds it is concluded that decision support concerning sow replacement only makes sense with parameters...

  11. ESTABLISHING A PRIORITY HIERARCHICAL FOR REGIONAL AIRPORT INFRASTRUCTURE INVESTMENTS ACCORDING TO TOURISM DEVELOPMENT CRITERIA: A BRAZILIAN CASE STUDY

    Directory of Open Access Journals (Sweden)

    Elisangela A. M. Silva

    2017-12-01

    Full Text Available The Brazilian government stepped up investments in works related to improving access to cities, when it was announced as the venue for the 2014 Football World Cup. The difficulties observed to guide these investments were to select regional airports of greater relevance for the tourism sector. Given the unavailability of data that could serve this purpose, the present study sought to define, from secondary and qualitative information extracted from government documents, five indicators to allow a priority hierarchical. The methodology applied the hierarchical analysis method (AHP and standardization procedures, resulting in the selection of five airports in the North region, four airports in the Southeast, South and Northeast regions, and one in the Center-West region of Brazil. The main advantage of the methodology was the significant reduction of cost and time in the decision-making process. An important conclusion was the understanding of tourism as an option for regional economic diversification.

  12. Group Decision Process Support

    DEFF Research Database (Denmark)

    Gøtze, John; Hijikata, Masao

    1997-01-01

    Introducing the notion of Group Decision Process Support Systems (GDPSS) to traditional decision-support theorists.......Introducing the notion of Group Decision Process Support Systems (GDPSS) to traditional decision-support theorists....

  13. The rational choice model in family decision making at the end of life.

    Science.gov (United States)

    Karasz, Alison; Sacajiu, Galit; Kogan, Misha; Watkins, Liza

    2010-01-01

    Most end-of-life decisions are made by family members. Current ethical guidelines for family decision making are based on a hierarchical model that emphasizes the patient's wishes over his or her best interests. Evidence suggests that the model poorly reflects the strategies and priorities of many families. Researchers observed and recorded 26 decision-making meetings between hospital staff and family members. Semi-structured follow-up interviews were conducted. Transcriptions were analyzed using qualitative techniques. For both staff and families, consideration of a patient's best interests generally took priority over the patient's wishes. Staff generally introduced discussion of the patient's wishes for rhetorical purposes, such as persuasion. Competing moral frameworks, which de-emphasized the salience of patients' autonomy and "right to choose," played a role in family decision making. The priority given to the patients' wishes in the hierarchical model does not reflect the priorities of staff and families in making decisions about end-of-life care.

  14. Object recognition with hierarchical discriminant saliency networks.

    Science.gov (United States)

    Han, Sunhyoung; Vasconcelos, Nuno

    2014-01-01

    The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

  15. COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance.

    Science.gov (United States)

    Cui, Licong

    Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on the knowledge provided within the targeted ontology. This paper proposes a novel cross-ontology analysis method, Cross-Ontology Hierarchical Relation Examination (COHeRE), to detect inconsistencies and possible errors in hierarchical relations across multiple ontologies. COHeRE leverages the Unified Medical Language System (UMLS) knowledge source and the MapReduce cloud computing technique for systematic, large-scale ontology quality assurance work. COHeRE consists of three main steps with the UMLS concepts and relations as the input. First, the relations claimed in source vocabularies are filtered and aggregated for each pair of concepts. Second, inconsistent relations are detected if a concept pair is related by different types of relations in different source vocabularies. Finally, the uncovered inconsistent relations are voted according to their number of occurrences across different source vocabularies. The voting result together with the inconsistent relations serve as the output of COHeRE for possible ontological change. The highest votes provide initial suggestion on how such inconsistencies might be fixed. In UMLS, 138,987 concept pairs were found to have inconsistent relationships across multiple source vocabularies. 40 inconsistent concept pairs involving hierarchical relationships were randomly selected and manually reviewed by a human expert. 95.8% of the inconsistent relations involved in these concept pairs indeed exist in their source vocabularies rather than being introduced by mistake in the UMLS integration process. 73.7% of the concept pairs with suggested relationship were agreed by the human expert. The effectiveness of COHeRE indicates that UMLS provides a promising environment to enhance

  16. Hierarchical Thinking: A Cognitive Tool for Guiding Coherent Decision Making in Design Problem Solving

    Science.gov (United States)

    Haupt, Grietjie

    2018-01-01

    This paper builds on two concepts, the first of which is the extended information processing model of expert design cognition. This proposes twelve internal psychological characteristics interacting with the external world of expert designers during the early phases of the design process. Here, I explore one of the characteristics, hierarchical…

  17. Modular Assembly of Hierarchically Structured Polymers

    Science.gov (United States)

    Leophairatana, Porakrit

    The synthesis of macromolecules with complex yet highly controlled molecular architectures has attracted significant attention in the past few decades due to the growing demand for specialty polymers that possess novel properties. Despite recent efforts, current synthetic routes lack the ability to control several important architectural variables while maintaining low polydispersity index. This dissertation explores a new synthetic scheme for the modular assembly of hierarchically structured polymers (MAHP) that allows virtually any complex polymer to be assembled from a few basic molecular building blocks using a single common coupling chemistry. Complex polymer structures can be assembled from a molecular toolkit consisting of (1) copper-catalyzed azide-alkyne cycloaddition (CuAAC), (2) linear heterobifunctional macromonomers, (3) a branching heterotrifunctional molecule, (4) a protection/deprotection strategy, (5) "click" functional solid substrates, and (6) functional and responsive polymers. This work addresses the different challenges that emerged during the development of this synthetic scheme, and presents strategies to overcome those challenges. Chapter 3 investigates the alkyne-alkyne (i.e. Glaser) coupling side reactions associated with the atom transfer radical polymerization (ATRP) synthesis of alkyne-functional macromonomers, as well as with the CuAAC reaction of alkyne functional building blocks. In typical ATRP synthesis of unprotected alkyne functional polymers, Glaser coupling reactions can significantly compromise the polymer functionality and undermine the success of subsequent click reactions in which the polymers are used. Two strategies are reported that effectively eliminate these coupling reactions: (1) maintaining low temperature post-ATRP upon exposure to air, followed by immediate removal of copper catalyst; and (2) adding excess reducing agents post-ATRP, which prevents the oxidation of Cu(I) catalyst required by the Glaser coupling

  18. Hierarchical vs non-hierarchical audio indexation and classification for video genres

    Science.gov (United States)

    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%.

  19. Information source exploitation/exploration and NPD decision-making

    DEFF Research Database (Denmark)

    Jespersen, Kristina Risom

    different Scandinavian companies. Data was analyzed using hierarchical regression models across decision criteria dimensions and NPD stages as well as analyzing the combination of selected information sources. Rather than forwarding one optimal search behavior for the entire NPD process, we find optimal...... information search behavior at either end of the exploitation/exploration continuum. Additionally, we find that overexploitation and overexploration is caused by managerial bias. This creates managerial misbehavior at gate decision-points of the NPD process.......The purpose of this study is to examine how the exploration/exploitation continuum is applied by decision-makers in new product gate decision-making. Specifically, we analyze at gate decision-points how the evaluation of a new product project is affected by the information source exploitation...

  20. Decision-making in schizophrenia: A predictive-coding perspective.

    Science.gov (United States)

    Sterzer, Philipp; Voss, Martin; Schlagenhauf, Florian; Heinz, Andreas

    2018-05-31

    Dysfunctional decision-making has been implicated in the positive and negative symptoms of schizophrenia. Decision-making can be conceptualized within the framework of hierarchical predictive coding as the result of a Bayesian inference process that uses prior beliefs to infer states of the world. According to this idea, prior beliefs encoded at higher levels in the brain are fed back as predictive signals to lower levels. Whenever these predictions are violated by the incoming sensory data, a prediction error is generated and fed forward to update beliefs encoded at higher levels. Well-documented impairments in cognitive decision-making support the view that these neural inference mechanisms are altered in schizophrenia. There is also extensive evidence relating the symptoms of schizophrenia to aberrant signaling of prediction errors, especially in the domain of reward and value-based decision-making. Moreover, the idea of altered predictive coding is supported by evidence for impaired low-level sensory mechanisms and motor processes. We review behavioral and neural findings from these research areas and provide an integrated view suggesting that schizophrenia may be related to a pervasive alteration in predictive coding at multiple hierarchical levels, including cognitive and value-based decision-making processes as well as sensory and motor systems. We relate these findings to decision-making processes and propose that varying degrees of impairment in the implicated brain areas contribute to the variety of psychotic experiences. Copyright © 2018 Elsevier Inc. All rights reserved.

  1. Hierarchical Ag mesostructures for single particle SERS substrate

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-01-30

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

  2. Hierarchical virtual screening approaches in small molecule drug discovery.

    Science.gov (United States)

    Kumar, Ashutosh; Zhang, Kam Y J

    2015-01-01

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

  3. Road network safety evaluation using Bayesian hierarchical joint model.

    Science.gov (United States)

    Wang, Jie; Huang, Helai

    2016-05-01

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

  4. Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran.

    Science.gov (United States)

    Malekmohammadi, Bahram; Tayebzadeh Moghadam, Negar

    2018-04-13

    Environmental risk assessment (ERA) is a commonly used, effective tool applied to reduce adverse effects of environmental risk factors. In this study, ERA was investigated using the Bayesian network (BN) model based on a hierarchical structure of variables in an influence diagram (ID). ID facilitated ranking of the different alternatives under uncertainty that were then used to evaluate comparisons of the different risk factors. BN was used to present a new model for ERA applicable to complicated development projects such as dam construction. The methodology was applied to the Gabric Dam, in southern Iran. The main environmental risk factors in the region, presented by the Gabric Dam, were identified based on the Delphi technique and specific features of the study area. These included the following: flood, water pollution, earthquake, changes in land use, erosion and sedimentation, effects on the population, and ecosensitivity. These risk factors were then categorized based on results from the output decision node of the BN, including expected utility values for risk factors in the decision node. ERA was performed for the Gabric Dam using the analytical hierarchy process (AHP) method to compare results of BN modeling with those of conventional methods. Results determined that a BN-based hierarchical structure to ERA present acceptable and reasonable risk assessment prioritization in proposing suitable solutions to reduce environmental risks and can be used as a powerful decision support system for evaluating environmental risks.

  5. Decision Making with Imperfect Decision Makers

    CERN Document Server

    Guy, Tatiana Valentine; Wolpert, David H

    2012-01-01

    Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies. To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, lit

  6. Scenarios for the hierarchical evaluation of the global sustainability of electric generator plants

    International Nuclear Information System (INIS)

    Roldan A, M.C.; Martinez F, M.

    2007-01-01

    The AHP multi criteria method was applied (Analytic Hierarchy Process-Analytic process of Hierarchization) to evaluate the sustainability in the whole life cycle of the electricity generation technologies (hydroelectric, carboelectric, thermoelectric natural fuel oil, natural gas thermoelectric, geothermal, nucleo electric, wind electric, photo thermic and photovoltaic) with the purpose of offering an useful method in the taking of decisions to impel the sustainable development. Eight scenarios are analyzed. The results in most of the scenarios reflect the benefit of the renewable energy: the hydroelectric energy, photo thermic and wind driven its are those more sustainable. To reach the sustainable development in Mexico, the energy politicians should be more near to the use of the renewable energy. (Author)

  7. A hierarchical approach to ecological assessment of contaminated soils at Aberdeen Proving Ground, USA

    Energy Technology Data Exchange (ETDEWEB)

    Kuperman, R.G.

    1995-12-31

    Despite the expansion of environmental toxicology studies over the past decade, soil ecosystems have largely been ignored in ecotoxicological studies in the United States. The objective of this project was to develop and test the efficacy of a comprehensive methodology for assessing ecological impacts of soil contamination. A hierarchical approach that integrates biotic parameters and ecosystem processes was used to give insight into the mechanisms that lead to alterations in the structure and function of soil ecosystems in contaminated areas. This approach involved (1) a thorough survey of the soil biota to determine community structure, (2) laboratory and field tests on critical ecosystem processes, (3) toxicity trials, and (4) the use of spatial analyses to provide input to the decision-making, process. This methodology appears to, offer an efficient and potentially cost-saving tool for remedial investigations of contaminated sites.

  8. Resilient control of cyber-physical systems against intelligent attacker: a hierarchal stackelberg game approach

    Science.gov (United States)

    Yuan, Yuan; Sun, Fuchun; Liu, Huaping

    2016-07-01

    This paper is concerned with the resilient control under denial-of-service attack launched by the intelligent attacker. The resilient control system is modelled as a multi-stage hierarchical game with a corresponding hierarchy of decisions made at cyber and physical layer, respectively. Specifically, the interaction in the cyber layer between different security agents is modelled as a static infinite Stackelberg game, while in the underlying physical layer the full-information H∞ minimax control with package drops is modelled as a different Stackelberg game. Both games are solved sequentially, which is consistent with the actual situations. Finally, the proposed method is applied to the load frequency control of the power system, which demonstrates its effectiveness.

  9. Enhanced fuzzy-connective-based hierarchical aggregation network using particle swarm optimization

    Science.gov (United States)

    Wang, Fang-Fang; Su, Chao-Ton

    2014-11-01

    The fuzzy-connective-based aggregation network is similar to the human decision-making process. It is capable of aggregating and propagating degrees of satisfaction of a set of criteria in a hierarchical manner. Its interpreting ability and transparency make it especially desirable. To enhance its effectiveness and further applicability, a learning approach is successfully developed based on particle swarm optimization to determine the weights and parameters of the connectives in the network. By experimenting on eight datasets with different characteristics and conducting further statistical tests, it has been found to outperform the gradient- and genetic algorithm-based learning approaches proposed in the literature; furthermore, it is capable of generating more accurate estimates. The present approach retains the original benefits of fuzzy-connective-based aggregation networks and is widely applicable. The characteristics of the learning approaches are also discussed and summarized, providing better understanding of the similarities and differences among these three approaches.

  10. A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony

    Directory of Open Access Journals (Sweden)

    Farshad Faezy Razi

    2014-06-01

    Full Text Available Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers.

  11. Hierarchical structure of stock price fluctuations in financial markets

    International Nuclear Information System (INIS)

    Gao, Ya-Chun; Cai, Shi-Min; Wang, Bing-Hong

    2012-01-01

    The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they share. In this paper, the She–Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviating from Kolmogorov monofractal scaling of the velocity fluctuation in fluid turbulence, is applied to study and quantify the hierarchical structure of stock price fluctuations in financial markets. We therefore observed certain interesting results: (i) the hierarchical structure related to multifractal scaling generally presents in all the stock price fluctuations we investigated. (ii) The quantitatively statistical parameters that describe SL hierarchy are different between developed financial markets and emerging ones, distinctively. (iii) For the high-frequency stock price fluctuation, the hierarchical structure varies with different time periods. All these results provide a novel analogy in turbulence and financial market dynamics and an insight to deeply understand multifractality in financial markets. (paper)

  12. Hierarchical classification with a competitive evolutionary neural tree.

    Science.gov (United States)

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

    1999-04-01

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

  13. Hierarchical modeling of molecular energies using a deep neural network

    Science.gov (United States)

    Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

    2018-06-01

    We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

  14. 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.

  15. 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.

  16. A Hierarchal Risk Assessment Model Using the Evidential Reasoning Rule

    Directory of Open Access Journals (Sweden)

    Xiaoxiao Ji

    2017-02-01

    Full Text Available This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1 formulation of risk assessment hierarchy; (2 representation of both qualitative and quantitative information; (3 elicitation of attribute weights and information reliabilities; (4 aggregation of assessment information using the ER rule and (5 quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

  17. Ways of looking ahead: hierarchical planning in language production.

    Science.gov (United States)

    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.

  18. Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

    Science.gov (United States)

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

    2017-11-01

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

  19. Hierarchical surfaces for enhanced self-cleaning applications

    Science.gov (United States)

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

    2017-04-01

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

  20. New insight in magnetic saturation behavior of nickel hierarchical structures

    Science.gov (United States)

    Ma, Ji; Zhang, Jianxing; Liu, Chunting; Chen, Kezheng

    2017-09-01

    It is unanimously accepted that non-ferromagnetic inclusions in a ferromagnetic system will lower down total saturation magnetization in unit of emu/g. In this study, ;lattice strain; was found to be another key factor to have critical impact on magnetic saturation behavior of the system. The lattice strain determined assembling patterns of primary nanoparticles in hierarchical structures and was intimately related with the formation process of these architectures. Therefore, flower-necklace-like and cauliflower-like nickel hierarchical structures were used as prototype systems to evidence the relationship between assembling patterns of primary nanoparticles and magnetic saturation behaviors of these architectures. It was found that the influence of lattice strain on saturation magnetization outperformed that of non-ferromagnetic inclusions in these hierarchical structures. This will enable new insights into fundamental understanding of related magnetic effects.

  1. Hierarchical surfaces for enhanced self-cleaning applications

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  2. Interneuronal Mechanism for Tinbergen’s Hierarchical Model of Behavioral Choice

    Science.gov (United States)

    Pirger, Zsolt; Crossley, Michael; László, Zita; Naskar, Souvik; Kemenes, György; O’Shea, Michael; Benjamin, Paul R.; Kemenes, Ildikó

    2014-01-01

    Summary Recent studies of behavioral choice support the notion that the decision to carry out one behavior rather than another depends on the reconfiguration of shared interneuronal networks [1]. We investigated another decision-making strategy, derived from the classical ethological literature [2, 3], which proposes that behavioral choice depends on competition between autonomous networks. According to this model, behavioral choice depends on inhibitory interactions between incompatible hierarchically organized behaviors. We provide evidence for this by investigating the interneuronal mechanisms mediating behavioral choice between two autonomous circuits that underlie whole-body withdrawal [4, 5] and feeding [6] in the pond snail Lymnaea. Whole-body withdrawal is a defensive reflex that is initiated by tactile contact with predators. As predicted by the hierarchical model, tactile stimuli that evoke whole-body withdrawal responses also inhibit ongoing feeding in the presence of feeding stimuli. By recording neurons from the feeding and withdrawal networks, we found no direct synaptic connections between the interneuronal and motoneuronal elements that generate the two behaviors. Instead, we discovered that behavioral choice depends on the interaction between two unique types of interneurons with asymmetrical synaptic connectivity that allows withdrawal to override feeding. One type of interneuron, the Pleuro-Buccal (PlB), is an extrinsic modulatory neuron of the feeding network that completely inhibits feeding when excited by touch-induced monosynaptic input from the second type of interneuron, Pedal-Dorsal12 (PeD12). PeD12 plays a critical role in behavioral choice by providing a synaptic pathway joining the two behavioral networks that underlies the competitive dominance of whole-body withdrawal over feeding. PMID:25155505

  3. Surface Water Quality Assessment and Prioritize the Factors Pollute This Water Using Topsis Fuzzy Hierarchical Analysis

    Directory of Open Access Journals (Sweden)

    Mehdi Komasi

    2017-03-01

    Full Text Available Background & Objective: Nowadays, according to growth of industry and increasing population, water resources are seriousely shortened. This lack of water resources will require special management to be considered in industry and agriculture. Among the various sources of water, surface waters are more susceptible to infection. The most important of these sources of pollution are industrial pollution, detergent, pesticides, radioactive materials, heat and salt concentration.  Materials & methods: In this article, at first the importance of each pollutant will be evaluated base on the effects and its results and then quality evaluation of surface water will be studied. In order to assess the relative importance of these pollutants primarily using TOPSIS software, prioritize these factors as one of the hierarchical analysis and then is modeled with decision tree method using Weka software, the importance of each factor is evaluated and if it does not meet the minimal importance of the decision tree will be removed. Results: The results obtained from the Topsis fuzzy analysis indicate that surface water and groundwater are exposed to pollution about 74% and 26% respectively among the six pollutants examined in this study. In addition, results obtaned from the hierarchical tree in software Weka has shown that the heat factor, soluble salts and industrial pollutants give impac factor or purity about 0.1338, 0.0523 and 1.2694 respectively. Conclusion: Surface water is at greater risk of being polluted compared with groundwater. The heat factor and low concentration of dissolved salts have the low impact and industrial pollutants are considered as the most influential factors in surface water pollution.

  4. [Decision making in cariology

    NARCIS (Netherlands)

    Verdonschot, E.H.A.M.; Liem, S.L.; Palenstein Helderman, W.H. van

    2003-01-01

    By conducting an oral examination, during radiographic examination and in treatment planning procedures dentists make numerous decisions. A dentist will be required to make his decisions explicit. Decision trees and decision analyses may play an important role. In a decision analysis, the

  5. Molecular simulation of adsorption and transport in hierarchical porous materials.

    Science.gov (United States)

    Coasne, Benoit; Galarneau, Anne; Gerardin, Corine; Fajula, François; Villemot, François

    2013-06-25

    Adsorption and transport in hierarchical porous solids with micro- (~1 nm) and mesoporosities (>2 nm) are investigated by molecular simulation. Two models of hierarchical solids are considered: microporous materials in which mesopores are carved out (model A) and mesoporous materials in which microporous nanoparticles are inserted (model B). Adsorption isotherms for model A can be described as a linear combination of the adsorption isotherms for pure mesoporous and microporous solids. In contrast, adsorption in model B departs from adsorption in pure microporous and mesoporous solids; the inserted microporous particles act as defects, which help nucleate the liquid phase within the mesopore and shift capillary condensation toward lower pressures. As far as transport under a pressure gradient is concerned, the flux in hierarchical materials consisting of microporous solids in which mesopores are carved out obeys the Navier-Stokes equation so that Darcy's law is verified within the mesopore. Moreover, the flow in such materials is larger than in a single mesopore, due to the transfer between micropores and mesopores. This nonzero velocity at the mesopore surface implies that transport in such hierarchical materials involves slippage at the mesopore surface, although the adsorbate has a strong affinity for the surface. In contrast to model A, flux in model B is smaller than in a single mesopore, as the nanoparticles act as constrictions that hinder transport. By a subtle effect arising from fast transport in the mesopores, the presence of mesopores increases the number of molecules in the microporosity in hierarchical materials and, hence, decreases the flow in the micropores (due to mass conservation). As a result, we do not observe faster diffusion in the micropores of hierarchical materials upon flow but slower diffusion, which increases the contact time between the adsorbate and the surface of the microporosity.

  6. 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

  7. Urban pattern: Layout design by hierarchical domain splitting

    KAUST Repository

    Yang, Yongliang; Wang, Jun; Vouga, Etienne; Wonka, Peter

    2013-01-01

    We present a framework for generating street networks and parcel layouts. Our goal is the generation of high-quality layouts that can be used for urban planning and virtual environments. We propose a solution based on hierarchical domain splitting using two splitting types: streamline-based splitting, which splits a region along one or multiple streamlines of a cross field, and template-based splitting, which warps pre-designed templates to a region and uses the interior geometry of the template as the splitting lines. We combine these two splitting approaches into a hierarchical framework, providing automatic and interactive tools to explore the design space.

  8. Hierarchical structures of ZnO spherical particles synthesized solvothermally

    Science.gov (United States)

    Saito, Noriko; Haneda, Hajime

    2011-12-01

    We review the solvothermal synthesis, using a mixture of ethylene glycol (EG) and water as the solvent, of zinc oxide (ZnO) particles having spherical and flower-like shapes and hierarchical nanostructures. The preparation conditions of the ZnO particles and the microscopic characterization of the morphology are summarized. We found the following three effects of the ratio of EG to water on the formation of hierarchical structures: (i) EG restricts the growth of ZnO microcrystals, (ii) EG promotes the self-assembly of small crystallites into spheroidal particles and (iii) the high water content of EG results in hollow spheres.

  9. Urban pattern: Layout design by hierarchical domain splitting

    KAUST Repository

    Yang, Yongliang

    2013-11-06

    We present a framework for generating street networks and parcel layouts. Our goal is the generation of high-quality layouts that can be used for urban planning and virtual environments. We propose a solution based on hierarchical domain splitting using two splitting types: streamline-based splitting, which splits a region along one or multiple streamlines of a cross field, and template-based splitting, which warps pre-designed templates to a region and uses the interior geometry of the template as the splitting lines. We combine these two splitting approaches into a hierarchical framework, providing automatic and interactive tools to explore the design space.

  10. Topology of foreign exchange markets using hierarchical structure methods

    Science.gov (United States)

    Naylor, Michael J.; Rose, Lawrence C.; Moyle, Brendan J.

    2007-08-01

    This paper uses two physics derived hierarchical techniques, a minimal spanning tree and an ultrametric hierarchical tree, to extract a topological influence map for major currencies from the ultrametric distance matrix for 1995-2001. We find that these two techniques generate a defined and robust scale free network with meaningful taxonomy. The topology is shown to be robust with respect to method, to time horizon and is stable during market crises. This topology, appropriately used, gives a useful guide to determining the underlying economic or regional causal relationships for individual currencies and to understanding the dynamics of exchange rate price determination as part of a complex network.

  11. Topology-based hierarchical scheduling using deficit round robin

    DEFF Research Database (Denmark)

    Yu, Hao; Yan, Ying; Berger, Michael Stubert

    2009-01-01

    according to the topology. The mapping process could be completed through the network management plane or by manual configuration. Based on the knowledge of the network, the scheduler can manage the traffic on behalf of other less advanced nodes, avoid potential traffic congestion, and provide flow...... protection and isolation. Comparisons between hierarchical scheduling, flow-based scheduling, and class-based scheduling schemes have been carried out under a symmetric tree topology. Results have shown that the hierarchical scheduling scheme provides better flow protection and isolation from attack...

  12. Hierarchical Control for Multiple DC-Microgrids Clusters

    DEFF Research Database (Denmark)

    Shafiee, Qobad; Dragicevic, Tomislav; Vasquez, Juan Carlos

    2014-01-01

    DC microgrids (MGs) have gained research interest during the recent years because of many potential advantages as compared to the ac system. To ensure reliable operation of a low-voltage dc MG as well as its intelligent operation with the other DC MGs, a hierarchical control is proposed in this p......DC microgrids (MGs) have gained research interest during the recent years because of many potential advantages as compared to the ac system. To ensure reliable operation of a low-voltage dc MG as well as its intelligent operation with the other DC MGs, a hierarchical control is proposed...

  13. Chimera states in networks of logistic maps with hierarchical connectivities

    Science.gov (United States)

    zur Bonsen, Alexander; Omelchenko, Iryna; Zakharova, Anna; Schöll, Eckehard

    2018-04-01

    Chimera states are complex spatiotemporal patterns consisting of coexisting domains of coherence and incoherence. We study networks of nonlocally coupled logistic maps and analyze systematically how the dilution of the network links influences the appearance of chimera patterns. The network connectivities are constructed using an iterative Cantor algorithm to generate fractal (hierarchical) connectivities. Increasing the hierarchical level of iteration, we compare the resulting spatiotemporal patterns. We demonstrate that a high clustering coefficient and symmetry of the base pattern promotes chimera states, and asymmetric connectivities result in complex nested chimera patterns.

  14. Control of discrete event systems modeled as hierarchical state machines

    Science.gov (United States)

    Brave, Y.; Heymann, M.

    1991-01-01

    The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.

  15. Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

    Czech Academy of Sciences Publication Activity Database

    Scarpa, G.; Gaetano, R.; Haindl, Michal; Zerubia, J.

    2009-01-01

    Roč. 18, č. 8 (2009), s. 1830-1843 ISSN 1057-7149 R&D Projects: GA ČR GA102/08/0593 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : Classification * texture analysis * segmentation * hierarchical image models * Markov process Subject RIV: BD - Theory of Information Impact factor: 2.848, year: 2009 http://library.utia.cas.cz/separaty/2009/RO/haindl-hierarchical multiple markov chain model for unsupervised texture segmentation.pdf

  16. HiPS - Hierarchical Progressive Survey Version 1.0

    Science.gov (United States)

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

    2017-05-01

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

  17. MR-AFS: a global hierarchical file-system

    International Nuclear Information System (INIS)

    Reuter, H.

    2000-01-01

    The next generation of fusion experiments will use object-oriented technology creating the need for world wide sharing of an underlying hierarchical file-system. The Andrew file system (AFS) is a well known and widely spread global distributed file-system. Multiple-resident-AFS (MR-AFS) combines the features of AFS with hierarchical storage management systems. Files in MR-AFS therefore may be migrated on secondary storage, such as roboted tape libraries. MR-AFS is in use at IPP for the current experiments and data originating from super-computer applications. Experiences and scalability issues are discussed

  18. Drifting through Basic Subprocesses of Reading: A Hierarchical Diffusion Model Analysis of Age Effects on Visual Word Recognition.

    Science.gov (United States)

    Froehlich, Eva; Liebig, Johanna; Ziegler, Johannes C; Braun, Mario; Lindenberger, Ulman; Heekeren, Hauke R; Jacobs, Arthur M

    2016-01-01

    Reading is one of the most popular leisure activities and it is routinely performed by most individuals even in old age. Successful reading enables older people to master and actively participate in everyday life and maintain functional independence. Yet, reading comprises a multitude of subprocesses and it is undoubtedly one of the most complex accomplishments of the human brain. Not surprisingly, findings of age-related effects on word recognition and reading have been partly contradictory and are often confined to only one of four central reading subprocesses, i.e., sublexical, orthographic, phonological and lexico-semantic processing. The aim of the present study was therefore to systematically investigate the impact of age on each of these subprocesses. A total of 1,807 participants (young, N = 384; old, N = 1,423) performed four decision tasks specifically designed to tap one of the subprocesses. To account for the behavioral heterogeneity in older adults, this subsample was split into high and low performing readers. Data were analyzed using a hierarchical diffusion modeling approach, which provides more information than standard response time/accuracy analyses. Taking into account incorrect and correct response times, their distributions and accuracy data, hierarchical diffusion modeling allowed us to differentiate between age-related changes in decision threshold, non-decision time and the speed of information uptake. We observed longer non-decision times for older adults and a more conservative decision threshold. More importantly, high-performing older readers outperformed younger adults at the speed of information uptake in orthographic and lexico-semantic processing, whereas a general age-disadvantage was observed at the sublexical and phonological levels. Low-performing older readers were slowest in information uptake in all four subprocesses. Discussing these results in terms of computational models of word recognition, we propose age

  19. Drifting through basic subprocesses of reading: A hierarchical diffusion model analysis of age effects on visual word recognition

    Directory of Open Access Journals (Sweden)

    Eva Froehlich

    2016-11-01

    Full Text Available Reading is one of the most popular leisure activities and it is routinely performed by most individuals even in old age. Successful reading enables older people to master and actively participate in everyday life and maintain functional independence. Yet, reading comprises a multitude of subprocesses and it is undoubtedly one of the most complex accomplishments of the human brain. Not surprisingly, findings of age-related effects on word recognition and reading have been partly contradictory and are often confined to only one of four central reading subprocesses, i.e., sublexical, orthographic, phonological and lexico-semantic processing. The aim of the present study was therefore to systematically investigate the impact of age on each of these subprocesses. A total of 1,807 participants (young, N = 384; old, N = 1,423 performed four decision tasks specifically designed to tap one of the subprocesses. To account for the behavioral heterogeneity in older adults, this subsample was split into high and low performing readers. Data were analyzed using a hierarchical diffusion modelling approach which provides more information than standard response times/accuracy analyses. Taking into account incorrect and correct response times, their distributions and accuracy data, hierarchical diffusion modelling allowed us to differentiate between age-related changes in decision threshold, non-decision time and the speed of information uptake. We observed longer non-decision times for older adults and a more conservative decision threshold. More importantly, high-performing older readers outperformed younger adults at the speed of information uptake in orthographic and lexico-semantic processing whereas a general age-disadvantage was observed at the sublexical and phonological levels. Low-performing older readers were slowest in information uptake in all four subprocesses. Discussing these results in terms of computational models of word recognition, we propose

  20. Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach

    Directory of Open Access Journals (Sweden)

    Kai Ma

    2017-05-01

    Full Text Available This paper studies an energy trading and pricing problem for microgrids with uncertain energy supply. The energy provider with the renewable energy (RE generation (wind power determines the energy purchase from the electricity markets and the pricing strategy for consumers to maximize its profit, and then the consumers determine their energy demands to maximize their payoffs. The hierarchical game is established between the energy provider and the consumers. The energy provider is the leader and the consumers are the followers in the hierarchical game. We consider two types of consumers according to their response to the price, i.e., the price-taking consumers and the price-anticipating consumers. We derive the equilibrium point of the hierarchical game through the backward induction method. Comparing the two types of consumers, we study the influence of the types of consumers on the equilibrium point. In particular, the uncertainty of the energy supply from the energy provider is considered. Simulation results show that the energy provider can obtain more profit using the proposed decision-making scheme.

  1. The fluency of social hierarchy: the ease with which hierarchical relationships are seen, remembered, learned, and liked.

    Science.gov (United States)

    Zitek, Emily M; Tiedens, Larissa Z

    2012-01-01

    We tested the hypothesis that social hierarchies are fluent social stimuli; that is, they are processed more easily and therefore liked better than less hierarchical stimuli. In Study 1, pairs of people in a hierarchy based on facial dominance were identified faster than pairs of people equal in their facial dominance. In Study 2, a diagram representing hierarchy was memorized more quickly than a diagram representing equality or a comparison diagram. This faster processing led the hierarchy diagram to be liked more than the equality diagram. In Study 3, participants were best able to learn a set of relationships that represented hierarchy (asymmetry of power)--compared to relationships in which there was asymmetry of friendliness, or compared to relationships in which there was symmetry--and this processing ease led them to like the hierarchy the most. In Study 4, participants found it easier to make decisions about a company that was more hierarchical and thus thought the hierarchical organization had more positive qualities. In Study 5, familiarity as a basis for the fluency of hierarchy was demonstrated by showing greater fluency for male than female hierarchies. This study also showed that when social relationships are difficult to learn, people's preference for hierarchy increases. Taken together, these results suggest one reason people might like hierarchies--hierarchies are easy to process. This fluency for social hierarchies might contribute to the construction and maintenance of hierarchies.

  2. Decision Support Methods for Supply Processes in the Floral Industry

    Directory of Open Access Journals (Sweden)

    Kutyba Agata

    2017-12-01

    Full Text Available The aim of this paper was to show the application of the ABC and AHP (multi-criteria method for hierarchical analysis of decision processes as an important part of decision making in supply processes which are realized in the floral industry. The ABC analysis was performed in order to classify the product mix from the perspective of the demand values. This in consequence enabled us to identify the most important products which were then used as a variant in the AHP method.

  3. Shared decision making

    Science.gov (United States)

    ... page: //medlineplus.gov/ency/patientinstructions/000877.htm Shared decision making To use the sharing features on this page, ... treatment you both support. When to use Shared Decision Making Shared decision making is often used when you ...

  4. Supporting Communication and Decision Making in Finnish Intensive Care with Language Technology

    Directory of Open Access Journals (Sweden)

    Hanna J. Suominen

    2010-01-01

    Full Text Available A fluent flow of health information is critical for health communication and decision making. However, the flow is fragmented by the large amount of textual records and their specific jargon. This creates risks for both patient safety and cost-effective health services. Language technology for the automated processing of textual health records is emerging. In this paper, we describe method development for building topical overviews in Finnish intensive care. Our topical search methods are based on supervised multi-label classification and regression, as well as supervised and unsupervised multi-class classification. Our linguistic analysis methods are based on rule-based and statistical parsing, as well as tailoring of a commercial morphological analyser. According to our experimental results, the supervised methods generalise for multiple topics and human annotators, and the unsupervised method enables an ad hoc information search. Tailored linguistic analysis improves performance in the experiments and, in addition, improves text comprehensibility for health professionals and laypeople. In conclusion, the performance of our methods is promising for real-life applications.

  5. Decision Making and Cancer

    OpenAIRE

    Reyna, Valerie F.; Nelson, Wendy L.; Han, Paul K.; Pignone, Michael P.

    2015-01-01

    We review decision-making along the cancer continuum in the contemporary context of informed and shared decision making, in which patients are encouraged to take a more active role in their health care. We discuss challenges to achieving informed and shared decision making, including cognitive limitations and emotional factors, but argue that understanding the mechanisms of decision making offers hope for improving decision support. Theoretical approaches to decision making that explain cogni...

  6. A hierarchical approach for simulating northern forest dynamics

    Science.gov (United States)

    Don C. Bragg; David W. Roberts; Thomas R. Crow

    2004-01-01

    Complexity in ecological systems has challenged forest simulation modelers for years, resulting in a number of approaches with varying degrees of success. Arguments in favor of hierarchical modeling are made, especially for considering a complex environmental issue like widespread eastern hemlock regeneration failure. We present the philosophy and basic framework for...

  7. Tailoring the porosity of hierarchical zeolites by carbon-templating

    DEFF Research Database (Denmark)

    Zhu, Kake; Egeblad, Kresten; Christensen, Claus H.

    2008-01-01

    We report the synthesis and characterization of a series of hierarchical porous zeolite single crystal materials with a range of porosities made available by carbon-templating using differently-sized carbon particles as templates for the additional non-micropore porosity. The materials were...

  8. Applying Hierarchical Model Calibration to Automatically Generated Items.

    Science.gov (United States)

    Williamson, David M.; Johnson, Matthew S.; Sinharay, Sandip; Bejar, Isaac I.

    This study explored the application of hierarchical model calibration as a means of reducing, if not eliminating, the need for pretesting of automatically generated items from a common item model prior to operational use. Ultimately the successful development of automatic item generation (AIG) systems capable of producing items with highly similar…

  9. Hierarchical Scheduling Framework Based on Compositional Analysis Using Uppaal

    DEFF Research Database (Denmark)

    Boudjadar, Jalil; David, Alexandre; Kim, Jin Hyun

    2014-01-01

    This paper introduces a reconfigurable compositional scheduling framework, in which the hierarchical structure, the scheduling policies, the concrete task behavior and the shared resources can all be reconfigured. The behavior of each periodic preemptive task is given as a list of timed actions, ...

  10. Scalable Hierarchical Algorithms for stochastic PDEs and Uncertainty Quantification

    KAUST Repository

    Litvinenko, Alexander; Chavez, Gustavo; Keyes, David E.; Ltaief, Hatem; Yokota, Rio

    2015-01-01

    number of degrees of freedom in the discretization. The storage is reduced to the log-linear as well. This hierarchical structure is a good starting point for parallel algorithms. Parallelization on shared and distributed memory systems was pioneered by R

  11. A hierarchical spatial framework for forest landscape planning.

    Science.gov (United States)

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

    2005-01-01

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

  12. A HIERARCHICAL SET OF MODELS FOR SPECIES RESPONSE ANALYSIS

    NARCIS (Netherlands)

    HUISMAN, J; OLFF, H; FRESCO, LFM

    Variation in the abundance of species in space and/or time can be caused by a wide range of underlying processes. Before such causes can be analysed we need simple mathematical models which can describe the observed response patterns. For this purpose a hierarchical set of models is presented. These

  13. A hierarchical set of models for species response analysis

    NARCIS (Netherlands)

    Huisman, J.; Olff, H.; Fresco, L.F.M.

    1993-01-01

    Variation in the abundance of species in space and/or time can be caused by a wide range of underlying processes. Before such causes can be analysed we need simple mathematical models which can describe the observed response patterns. For this purpose a hierarchical set of models is presented. These

  14. Clinical fracture risk evaluated by hierarchical agglomerative clustering

    DEFF Research Database (Denmark)

    Kruse, C; Eiken, P; Vestergaard, P

    2017-01-01

    reimbursement, primary healthcare sector use and comorbidity of female subjects were combined. Standardized variable means, Euclidean distances and Ward's D2 method of hierarchical agglomerative clustering (HAC), were used to form the clustering object. K number of clusters was selected with the lowest cluster...

  15. Hierarchical structure of correlation functions for single jets

    International Nuclear Information System (INIS)

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

    1993-01-01

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

  16. Hierarchical structure of correlation functions for single jets

    Energy Technology Data Exchange (ETDEWEB)

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

    1993-08-01

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

  17. Accelerated Hierarchical Collision Detection for Simulation using CUDA

    DEFF Research Database (Denmark)

    Jørgensen, Jimmy Alison; Fugl, Andreas Rune; Petersen, Henrik Gordon

    2011-01-01

    . The hierarchical nature of the bounding volume structure complicates an efficient implementation on massively parallel architectures such as modern graphics cards and we therefore propose a hybrid method where only box and triangle overlap tests and transformations are offloaded to the graphics card. When...

  18. On Structure, Family and Parameter Estimation of Hierarchical Archimedean Copulas

    Czech Academy of Sciences Publication Activity Database

    Górecki, J.; Hofert, M.; Holeňa, Martin

    2017-01-01

    Roč. 87, č. 17 (2017), s. 3261-3324 ISSN 0094-9655 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : copula estimation * goodness-of-fit * Hierarchical Archimedean copula * structure determination Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability Impact factor: 0.757, year: 2016

  19. Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

    Science.gov (United States)

    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…

  20. High-speed hierarchical pipeline for radiological projection data acquisition

    International Nuclear Information System (INIS)

    Kang Kejun; Wang Jingjin; Xu Qichun; Gao Wenhuan; Fu Changqing

    1993-01-01

    Modern radiological imaging system needs fast data acquisition technology of high density projection acquiring. For this reason, a new type of pipeline, which has multi-stage and hierarchical structure, was developed. By this pipeline, the dead time of the data acquisition system was greatly reduced, and then the throughput of the whole system was increased

  1. Content Consumption and Hierarchical Structures of Web-Supported Courses

    Science.gov (United States)

    Hershkovitz, Arnon; Hardof-Jaffe, Sharon; Nachmias, Rafi

    2014-01-01

    This study presents an empirical investigation of the relationship between the hierarchical structure of content delivered to students within a Learning Management System (LMS) and its actual consumption. To this end, campus-wide data relating to 1,203 courses were collected from the LMS' servers and were subsequently analyzed using data mining…

  2. The Revised Hierarchical Model: A critical review and assessment

    NARCIS (Netherlands)

    Kroll, J.F.; Hell, J.G. van; Tokowicz, N.; Green, D.W.

    2010-01-01

    Brysbaert and Duyck (this issue) suggest that it is time to abandon the Revised Hierarchical Model (Kroll and Stewart, 1994) in favor of connectionist models such as BIA+ (Dijkstra and Van Heuven, 2002) that more accurately account for the recent evidence on non-selective access in bilingual word

  3. Micro-nanofibers with hierarchical structure by bubbfil-spinning

    Directory of Open Access Journals (Sweden)

    Liu Peng

    2015-01-01

    Full Text Available Bubbfil spinning is used to fabricate micro/nanofibers with hierarchical structure. The wall of a polymer film is attenuated unevenly by a blowing air. The burst of the bubble results in film fragments with different thickness, as a result, different sizes of fibers are obtained.

  4. Hierarchical emc analysis approach for power electronics applications

    NARCIS (Netherlands)

    Zhao, D.; Ferreira, B.; Roc'h, A.; Leferink, Frank Bernardus Johannes

    2008-01-01

    In this paper, a novel method for EMI (Electromagnetic Interference) level prediction is proposed. The method is based on the hierarchical structure of the generation of EMI. That is, the determination of EMI level can be divided into three levels, namely the functional level, the transient level

  5. Selective hierarchical patterning of silicon nanostructures via soft nanostencil lithography.

    Science.gov (United States)

    Du, Ke; Ding, Junjun; Wathuthanthri, Ishan; Choi, Chang-Hwan

    2017-11-17

    It is challenging to hierarchically pattern high-aspect-ratio nanostructures on microstructures using conventional lithographic techniques, where photoresist (PR) film is not able to uniformly cover on the microstructures as the aspect ratio increases. Such non-uniformity causes poor definition of nanopatterns over the microstructures. Nanostencil lithography can provide an alternative means to hierarchically construct nanostructures on microstructures via direct deposition or plasma etching through a free-standing nanoporous membrane. In this work, we demonstrate the multiscale hierarchical fabrication of high-aspect-ratio nanostructures on microstructures of silicon using a free-standing nanostencil, which is a nanoporous membrane consisting of metal (Cr), PR, and anti-reflective coating. The nanostencil membrane is used as a deposition mask to define Cr nanodot patterns on the predefined silicon microstructures. Then, deep reactive ion etching is used to hierarchically create nanostructures on the microstructures using the Cr nanodots as an etch mask. With simple modification of the main fabrication processes, high-aspect-ratio nanopillars are selectively defined only on top of the microstructures, on bottom, or on both top and bottom.

  6. Enhanced water desalination performance through hierarchically-structured ceramic membranes

    NARCIS (Netherlands)

    Liu, Tong; Lei, Libin; Gu, Jianqiang; Wang, Yao; Winnubst, Louis; Chen, Chusheng; Ye, Chunsong; Chen, Fanglin

    2017-01-01

    Developments of membrane water desalination are impeded by low water vapor flux across the membrane. We present an innovative membrane design to significantly enhance the water vapor flux. A bilayer zirconia-based membrane with a thick hierarchically-structured support and a thin functional layer is

  7. A Hierarchical Biology Concept Framework: A Tool for Course Design

    OpenAIRE

    Khodor, Julia; Halme, Dina Gould; Walker, Graham C.

    2004-01-01

    A typical undergraduate biology curriculum covers a very large number of concepts and details. We describe the development of a Biology Concept Framework (BCF) as a possible way to organize this material to enhance teaching and learning. Our BCF is hierarchical, places details in context, nests related concepts, and articulates concepts that are inherently obvious to experts but often difficult ...

  8. Hierarchical Bayesian sparse image reconstruction with application to MRFM.

    Science.gov (United States)

    Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

    2009-09-01

    This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

  9. A hierarchical model exhibiting the Kosterlitz-Thouless fixed point

    International Nuclear Information System (INIS)

    Marchetti, D.H.U.; Perez, J.F.

    1985-01-01

    A hierarchical model for 2-d Coulomb gases displaying a line stable of fixed points describing the Kosterlitz-Thouless phase transition is constructed. For Coulomb gases corresponding to Z sub(N)- models these fixed points are stable for an intermediate temperature interval. (Author) [pt

  10. The evolutionary strata of DARPP-32 tail implicates hierarchical ...

    Indian Academy of Sciences (India)

    The evolutionary strata of DARPP-32 tail implicates hierarchical functional expansion in higher vertebrates. CHOONG YONG UNG and TEOW CHONG TEOH. Supplementary data 1. Protein sequences of PPP1R1 family from 39 vertebrate species in FASTA format. Supplementary data 2. Multiple sequence alignment results ...

  11. Online credit card fraud prediction based on hierarchical temporal ...

    African Journals Online (AJOL)

    This understanding gave birth to the Hierarchical Temporal Memory (HTM) which holds a lot of promises in the area of time-series prediction and anomaly detection problems. This paper demonstrates the behaviour of an HTM model with respect to its learning and prediction of online credit card fraud. The model was ...

  12. Comparing hierarchical models via the marginalized deviance information criterion.

    Science.gov (United States)

    Quintero, Adrian; Lesaffre, Emmanuel

    2018-07-20

    Hierarchical models are extensively used in pharmacokinetics and longitudinal studies. When the estimation is performed from a Bayesian approach, model comparison is often based on the deviance information criterion (DIC). In hierarchical models with latent variables, there are several versions of this statistic: the conditional DIC (cDIC) that incorporates the latent variables in the focus of the analysis and the marginalized DIC (mDIC) that integrates them out. Regardless of the asymptotic and coherency difficulties of cDIC, this alternative is usually used in Markov chain Monte Carlo (MCMC) methods for hierarchical models because of practical convenience. The mDIC criterion is more appropriate in most cases but requires integration of the likelihood, which is computationally demanding and not implemented in Bayesian software. Therefore, we consider a method to compute mDIC by generating replicate samples of the latent variables that need to be integrated out. This alternative can be easily conducted from the MCMC output of Bayesian packages and is widely applicable to hierarchical models in general. Additionally, we propose some approximations in order to reduce the computational complexity for large-sample situations. The method is illustrated with simulated data sets and 2 medical studies, evidencing that cDIC may be misleading whilst mDIC appears pertinent. Copyright © 2018 John Wiley & Sons, Ltd.

  13. Zinc oxide's hierarchical nanostructure and its photocatalytic properties

    DEFF Research Database (Denmark)

    Kanjwal, Muzafar Ahmed; Sheikh, Faheem A.; Barakat, Nasser A. M.

    2012-01-01

    In this study, a new hierarchical nanostructure that consists of zinc oxide (ZnO) was produced by the electrospinning process followed by a hydrothermal technique. First, electrospinning of a colloidal solution that consisted of zinc nanoparticles, zinc acetate dihydrate and poly(vinyl alcohol...

  14. Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets

    KAUST Repository

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

    2017-01-01

    algebra, we approximate the discretized covariance function in the hierarchical (H-) matrix format. The H-matrix format has a log-linear computational cost and storage O(kn log n), where the rank k is a small integer and n is the number of locations. The H

  15. Human Activity Recognition Using Hierarchically-Mined Feature Constellations

    NARCIS (Netherlands)

    Oikonomopoulos, A.; Pantic, Maja

    In this paper we address the problem of human activity modelling and recognition by means of a hierarchical representation of mined dense spatiotemporal features. At each level of the hierarchy, the proposed method selects feature constellations that are increasingly discriminative and

  16. Hierarchical modelling for the environmental sciences statistical methods and applications

    CERN Document Server

    Clark, James S

    2006-01-01

    New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide a consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complicated, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences.

  17. Conceptual hierarchical modeling to describe wetland plant community organization

    Science.gov (United States)

    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.

  18. Complementary Person-Culture Values Fit and Hierarchical Career Status

    Science.gov (United States)

    Holtschlag, Claudia; Morales, Carlos E.; Masuda, Aline D.; Maydeu-Olivares, Alberto

    2013-01-01

    Although career success is an issue of global concern, few studies have examined the antecedents of career success across cultures. In this study we test whether the relationship between individuals' self-enhancement values (achievement and power) and hierarchical status differs across 29 countries and whether this variation depends on countries'…

  19. Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure

    NARCIS (Netherlands)

    Veldhoen, S.; Hupkes, D.; Zuidema, W.

    2016-01-01

    We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that

  20. Facile synthesis and photocatalytic activity of zinc oxide hierarchical microcrystals

    KAUST Repository

    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

  1. Generic Formal Framework for Compositional Analysis of Hierarchical Scheduling Systems

    DEFF Research Database (Denmark)

    Boudjadar, Jalil; Hyun Kim, Jin; Thi Xuan Phan, Linh

    We present a compositional framework for the specification and analysis of hierarchical scheduling systems (HSS). Firstly we provide a generic formal model, which can be used to describe any type of scheduling system. The concept of Job automata is introduced in order to model job instantiation...

  2. Ferritic Alloys with Extreme Creep Resistance via Coherent Hierarchical Precipitates.

    Science.gov (United States)

    Song, Gian; Sun, Zhiqian; Li, Lin; Xu, Xiandong; Rawlings, Michael; Liebscher, Christian H; Clausen, Bjørn; Poplawsky, Jonathan; Leonard, Donovan N; Huang, Shenyan; Teng, Zhenke; Liu, Chain T; Asta, Mark D; Gao, Yanfei; Dunand, David C; Ghosh, Gautam; Chen, Mingwei; Fine, Morris E; Liaw, Peter K

    2015-11-09

    There have been numerous efforts to develop creep-resistant materials strengthened by incoherent particles at high temperatures and stresses in response to future energy needs for steam turbines in thermal-power plants. However, the microstructural instability of the incoherent-particle-strengthened ferritic steels limits their application to temperatures below 900 K. Here, we report a novel ferritic alloy with the excellent creep resistance enhanced by coherent hierarchical precipitates, using the integrated experimental (transmission-electron microscopy/scanning-transmission-electron microscopy, in-situ neutron diffraction, and atom-probe tomography) and theoretical (crystal-plasticity finite-element modeling) approaches. This alloy is strengthened by nano-scaled L21-Ni2TiAl (Heusler phase)-based precipitates, which themselves contain coherent nano-scaled B2 zones. These coherent hierarchical precipitates are uniformly distributed within the Fe matrix. Our hierarchical structure material exhibits the superior creep resistance at 973 K in terms of the minimal creep rate, which is four orders of magnitude lower than that of conventional ferritic steels. These results provide a new alloy-design strategy using the novel concept of hierarchical precipitates and the fundamental science for developing creep-resistant ferritic alloys. The present research will broaden the applications of ferritic alloys to higher temperatures.

  3. Higher-Order Hierarchical Legendre Basis Functions in Applications

    DEFF Research Database (Denmark)

    Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter

    2007-01-01

    The higher-order hierarchical Legendre basis functions have been developed for effective solution of integral equations with the method of moments. They are derived from orthogonal Legendre polynomials modified to enforce normal continuity between neighboring mesh elements, while preserving a high...

  4. Parental Influences, Career Decision-Making Attributions, and Self-Efficacy: Differences for Men and Women?

    Science.gov (United States)

    Lease, Suzanne H.; Dahlbeck, David T.

    2009-01-01

    This study investigated the relations of maternal and paternal attachment, parenting styles, and career locus of control to college students' career decision self-efficacy and explored whether these relations differed by student gender. Data analysis using hierarchical multiple regression revealed that attachment was relevant for females' career…

  5. Developing an electronic health record (EHR) for methadone treatment recording and decision support.

    LENUS (Irish Health Repository)

    Xiao, Liang

    2011-02-01

    In this paper, we give an overview of methadone treatment in Ireland and outline the rationale for designing an electronic health record (EHR) with extensibility, interoperability and decision support functionality. Incorporating several international standards, a conceptual model applying a problem orientated approach in a hierarchical structure has been proposed for building the EHR.

  6. A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

    Science.gov (United States)

    Rey S. Ofren; Edward Harvey

    2000-01-01

    A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

  7. Application of Expectation Maximization Method for Purchase Decision-Making Support in Welding Branch

    Directory of Open Access Journals (Sweden)

    Kujawińska Agnieszka

    2016-06-01

    Full Text Available The article presents a study of applying the proposed method of cluster analysis to support purchasing decisions in the welding industry. The authors analyze the usefulness of the non-hierarchical method, Expectation Maximization (EM, in the selection of material (212 combinations of flux and wire melt for the SAW (Submerged Arc Welding method process. The proposed approach to cluster analysis is proved as useful in supporting purchase decisions.

  8. Intensity-based hierarchical elastic registration using approximating splines.

    Science.gov (United States)

    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

  9. Hierarchical sets: analyzing pangenome structure through scalable set visualizations

    Science.gov (United States)

    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

  10. The path to glory is paved with hierarchy: When hierarchical differentiation increases group effectiveness.

    NARCIS (Netherlands)

    Ronay, R.D.; Greenaway, K; Anicich, E.M; Galinsky, A.D.

    2012-01-01

    Two experiments examined the psychological and biological antecedents of hierarchical differentiation and the resulting consequences for productivity and conflict within small groups. In Experiment 1, which used a priming manipulation, hierarchically differentiated groups (i.e., groups comprising 1

  11. Medical decision making

    NARCIS (Netherlands)

    Stiggelbout, A.M.; Vries, M. de; Scherer, L.; Keren, G.; Wu, G.

    2016-01-01

    This chapter presents an overview of the field of medical decision making. It distinguishes the levels of decision making seen in health-care practice and shows how research in judgment and decision making support or improve decision making. Most of the research has been done at the micro level,

  12. Project Decision Chain

    DEFF Research Database (Denmark)

    Rolstadås, Asbjørn; Pinto, Jeffrey K.; Falster, Peter

    2015-01-01

    To add value to project performance and help obtain project success, a new framework for decision making in projects is defined. It introduces the project decision chain inspired by the supply chain thinking in the manufacturing sector and uses three types of decisions: authorization, selection......, and plan decision. A primitive decision element is defined where all the three decision types can be accommodated. Each task in the primitive element can in itself contain subtasks that in turn will comprise new primitive elements. The primitive elements are nested together in a project decision chain....

  13. DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING

    Directory of Open Access Journals (Sweden)

    M. Safish Mary

    2012-04-01

    Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.

  14. Decision Making Under Uncertainty

    Science.gov (United States)

    2010-11-01

    A sound approach to rational decision making requires a decision maker to establish decision objectives, identify alternatives, and evaluate those...often violate the axioms of rationality when making decisions under uncertainty. The systematic description of such observations may lead to the...which leads to “anchoring” on the initial value. The fact that individuals have been shown to deviate from rationality when making decisions

  15. Hierarchical composites: Analysis of damage evolution based on fiber bundle model

    DEFF Research Database (Denmark)

    Mishnaevsky, Leon

    2011-01-01

    A computational model of multiscale composites is developed on the basis of the fiber bundle model with the hierarchical load sharing rule, and employed to study the effect of the microstructures of hierarchical composites on their damage resistance. Two types of hierarchical materials were consi...

  16. Facile preparation of hierarchically porous polymer microspheres for superhydrophobic coating

    Science.gov (United States)

    Gao, Jiefeng; Wong, Julia Shuk-Ping; Hu, Mingjun; Li, Wan; Li, Robert. K. Y.

    2013-12-01

    A facile method, i.e., nonsolvent assisted electrospraying, is proposed to fabricate hierarchically porous microspheres. The pore size on the microsphere surface ranges from a few tens to several hundred nanometers. Thermally and nonsolvent induced phase separation as well as breath figure is responsible for the formation of the hierarchical structures with different nano-sized pores. The nonsolvent could not only induce phase separation, but also stabilize the interface between the droplet and air, which can prevent the droplet from strong deformation, and is therefore beneficial to the formation of regular and uniform microspheres. On the other hand, solvent evaporation, polymer diffusion and Coulomb fission during electrospraying influence the morphology of finally obtained products. In this paper, the influence of polymer concentration, the weight ratio between nonsolvent and polymer and the flowing rate on the morphology of the porous microsphere is carefully studied. The hierarchically porous microsphere significantly increases the surface roughness and thus the hydrophobicity, and the contact angle can reach as high as 152.2 +/- 1.2°. This nonsolvent assisted electrospraying opens a new way to fabricate superhydrophobic coating materials.A facile method, i.e., nonsolvent assisted electrospraying, is proposed to fabricate hierarchically porous microspheres. The pore size on the microsphere surface ranges from a few tens to several hundred nanometers. Thermally and nonsolvent induced phase separation as well as breath figure is responsible for the formation of the hierarchical structures with different nano-sized pores. The nonsolvent could not only induce phase separation, but also stabilize the interface between the droplet and air, which can prevent the droplet from strong deformation, and is therefore beneficial to the formation of regular and uniform microspheres. On the other hand, solvent evaporation, polymer diffusion and Coulomb fission during

  17. Probabilistic daily ILI syndromic surveillance with a spatio-temporal Bayesian hierarchical model.

    Directory of Open Access Journals (Sweden)

    Ta-Chien Chan

    Full Text Available BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs.

  18. A hierarchical probabilistic model for rapid object categorization in natural scenes.

    Directory of Open Access Journals (Sweden)

    Xiaofu He

    Full Text Available Humans can categorize objects in complex natural scenes within 100-150 ms. This amazing ability of rapid categorization has motivated many computational models. Most of these models require extensive training to obtain a decision boundary in a very high dimensional (e.g., ∼6,000 in a leading model feature space and often categorize objects in natural scenes by categorizing the context that co-occurs with objects when objects do not occupy large portions of the scenes. It is thus unclear how humans achieve rapid scene categorization.To address this issue, we developed a hierarchical probabilistic model for rapid object categorization in natural scenes. In this model, a natural object category is represented by a coarse hierarchical probability distribution (PD, which includes PDs of object geometry and spatial configuration of object parts. Object parts are encoded by PDs of a set of natural object structures, each of which is a concatenation of local object features. Rapid categorization is performed as statistical inference. Since the model uses a very small number (∼100 of structures for even complex object categories such as animals and cars, it requires little training and is robust in the presence of large variations within object categories and in their occurrences in natural scenes. Remarkably, we found that the model categorized animals in natural scenes and cars in street scenes with a near human-level performance. We also found that the model located animals and cars in natural scenes, thus overcoming a flaw in many other models which is to categorize objects in natural context by categorizing contextual features. These results suggest that coarse PDs of object categories based on natural object structures and statistical operations on these PDs may underlie the human ability to rapidly categorize scenes.

  19. How does aging affect recognition-based inference? A hierarchical Bayesian modeling approach.

    Science.gov (United States)

    Horn, Sebastian S; Pachur, Thorsten; Mata, Rui

    2015-01-01

    The recognition heuristic (RH) is a simple strategy for probabilistic inference according to which recognized objects are judged to score higher on a criterion than unrecognized objects. In this article, a hierarchical Bayesian extension of the multinomial r-model is applied to measure use of the RH on the individual participant level and to re-evaluate differences between younger and older adults' strategy reliance across environments. Further, it is explored how individual r-model parameters relate to alternative measures of the use of recognition and other knowledge, such as adherence rates and indices from signal-detection theory (SDT). Both younger and older adults used the RH substantially more often in an environment with high than low recognition validity, reflecting adaptivity in strategy use across environments. In extension of previous analyses (based on adherence rates), hierarchical modeling revealed that in an environment with low recognition validity, (a) older adults had a stronger tendency than younger adults to rely on the RH and (b) variability in RH use between individuals was larger than in an environment with high recognition validity; variability did not differ between age groups. Further, the r-model parameters correlated moderately with an SDT measure expressing how well people can discriminate cases where the RH leads to a correct vs. incorrect inference; this suggests that the r-model and the SDT measures may offer complementary insights into the use of recognition in decision making. In conclusion, younger and older adults are largely adaptive in their application of the RH, but cognitive aging may be associated with an increased tendency to rely on this strategy. Copyright © 2014 Elsevier B.V. All rights reserved.

  20. Inertia and Decision Making.

    Science.gov (United States)

    Alós-Ferrer, Carlos; Hügelschäfer, Sabine; Li, Jiahui

    2016-01-01

    Decision inertia is the tendency to repeat previous choices independently of the outcome, which can give rise to perseveration in suboptimal choices. We investigate this tendency in probability-updating tasks. Study 1 shows that, whenever decision inertia conflicts with normatively optimal behavior (Bayesian updating), error rates are larger and decisions are slower. This is consistent with a dual-process view of decision inertia as an automatic process conflicting with a more rational, controlled one. We find evidence of decision inertia in both required and autonomous decisions, but the effect of inertia is more clear in the latter. Study 2 considers more complex decision situations where further conflict arises due to reinforcement processes. We find the same effects of decision inertia when reinforcement is aligned with Bayesian updating, but if the two latter processes conflict, the effects are limited to autonomous choices. Additionally, both studies show that the tendency to rely on decision inertia is positively associated with preference for consistency.