WorldWideScience

Sample records for series clustering framework

  1. Modeling sports highlights using a time-series clustering framework and model interpretation

    Science.gov (United States)

    Radhakrishnan, Regunathan; Otsuka, Isao; Xiong, Ziyou; Divakaran, Ajay

    2005-01-01

    In our past work on sports highlights extraction, we have shown the utility of detecting audience reaction using an audio classification framework. The audio classes in the framework were chosen based on intuition. In this paper, we present a systematic way of identifying the key audio classes for sports highlights extraction using a time series clustering framework. We treat the low-level audio features as a time series and model the highlight segments as "unusual" events in a background of an "usual" process. The set of audio classes to characterize the sports domain is then identified by analyzing the consistent patterns in each of the clusters output from the time series clustering framework. The distribution of features from the training data so obtained for each of the key audio classes, is parameterized by a Minimum Description Length Gaussian Mixture Model (MDL-GMM). We also interpret the meaning of each of the mixture components of the MDL-GMM for the key audio class (the "highlight" class) that is correlated with highlight moments. Our results show that the "highlight" class is a mixture of audience cheering and commentator's excited speech. Furthermore, we show that the precision-recall performance for highlights extraction based on this "highlight" class is better than that of our previous approach which uses only audience cheering as the key highlight class.

  2. Clustering of financial time series

    Science.gov (United States)

    D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

    2013-05-01

    This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

  3. New Perspectives for Old Clusters: Anderson-Evans Anions as Building Blocks of Large Polyoxometalate Frameworks in a Series of Heterometallic 3 d-4 f Species.

    Science.gov (United States)

    Artetxe, Beñat; Reinoso, Santiago; San Felices, Leire; Lezama, Luis; Gutiérrez-Zorrilla, Juan M; Vicent, Cristian; Haso, Fadi; Liu, Tianbo

    2016-03-18

    A series of nine [Sb7W36O133Ln3M2(OAc)(H2O)8](17-) heterometallic anions (Ln3M2; Ln=La-Gd, M=Co; Ln=Ce, M=Ni and Zn) have been obtained by reacting 3 d metal disubstituted Krebs-type tungstoantimonates(III) with early lanthanides. Their unique tetrameric structure contains a novel {MW9O33} capping unit formed by a planar {MW6O24} fragment to which three {WO2} groups are condensed to form a tungstate skeleton identical to that of a hypothetical trilacunary derivative of the ɛ-Keggin cluster. It is shown, for the first time, that classical Anderson-Evans {MW6O24} anions can act as building blocks to construct purely inorganic large frameworks. Unprecedented reactivity in the outer ring of these disk-shaped species is also revealed. The Ln3M2 anions possess chirality owing to a {Sb4O4} cluster being encapsulated in left- or right-handed orientations. Their ability to self-associate in blackberry-type vesicles in solution has been assessed for the Ce3Co2 derivative. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. A Review of Subsequence Time Series Clustering

    Directory of Open Access Journals (Sweden)

    Seyedjamal Zolhavarieh

    2014-01-01

    Full Text Available Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

  5. A review of subsequence time series clustering.

    Science.gov (United States)

    Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

    2014-01-01

    Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

  6. A Review of Subsequence Time Series Clustering

    Science.gov (United States)

    Teh, Ying Wah

    2014-01-01

    Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

  7. Bayesian Decision Theoretical Framework for Clustering

    Science.gov (United States)

    Chen, Mo

    2011-01-01

    In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…

  8. Time series clustering in large data sets

    Directory of Open Access Journals (Sweden)

    Jiří Fejfar

    2011-01-01

    Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

  9. Trade (Marketing): Occupational Cluster Series-6.

    Science.gov (United States)

    Miller, David H., Comp.; Moore, Allen B., Comp.

    This compilation of ERIC abstracts dealing with trade is the sixth in a series that identifies research and instructional materials in selected occupational clusters. Fifty-seven documents were identified by means of computer searches of "Research in Education" from 1967 to December 1972. Instructions on how to use ERIC reference…

  10. Koopman Operator Framework for Time Series Modeling and Analysis

    Science.gov (United States)

    Surana, Amit

    2018-01-01

    We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

  11. Message Passing Framework for Globally Interconnected Clusters

    International Nuclear Information System (INIS)

    Hafeez, M; Riaz, N; Asghar, S; Malik, U A; Rehman, A

    2011-01-01

    In prevailing technology trends it is apparent that the network requirements and technologies will advance in future. Therefore the need of High Performance Computing (HPC) based implementation for interconnecting clusters is comprehensible for scalability of clusters. Grid computing provides global infrastructure of interconnecting clusters consisting of dispersed computing resources over Internet. On the other hand the leading model for HPC programming is Message Passing Interface (MPI). As compared to Grid computing, MPI is better suited for solving most of the complex computational problems. MPI itself is restricted to a single cluster. It does not support message passing over the internet to use the computing resources of different clusters in an optimal way. We propose a model that provides message passing capabilities between parallel applications over the internet. The proposed model is based on Architecture for Java Universal Message Passing (A-JUMP) framework and Enterprise Service Bus (ESB) named as High Performance Computing Bus. The HPC Bus is built using ActiveMQ. HPC Bus is responsible for communication and message passing in an asynchronous manner. Asynchronous mode of communication offers an assurance for message delivery as well as a fault tolerance mechanism for message passing. The idea presented in this paper effectively utilizes wide-area intercluster networks. It also provides scheduling, dynamic resource discovery and allocation, and sub-clustering of resources for different jobs. Performance analysis and comparison study of the proposed framework with P2P-MPI are also presented in this paper.

  12. A Dynamic Fuzzy Cluster Algorithm for Time Series

    Directory of Open Access Journals (Sweden)

    Min Ji

    2013-01-01

    clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.

  13. Bulgarian clusters under development: Political framework and results

    Directory of Open Access Journals (Sweden)

    Bankova Yovka

    2011-01-01

    Full Text Available The idea of clusters is not new but nowadays clusters are in a highlight again. Through cluster policies the countries aim at raising their national competitiveness. The paper deals with two objectives - discussion and evaluation of the strategic framework for clusters in Bulgaria and an analysis of the state of Bulgarian clusters. The paper presents briefly general issues concerning the national competitiveness and clusters as being one of the possible instruments to achieve a sustainable competitiveness. The practice of the policy in the EU in the field of clusters is the basis for conclusions about the role of the governments. The second part deals with the strategic framework for the cluster initiatives in Bulgaria and with a selection of indicators about the SMEs and clusters in the country. On this basis a conclusion about the development stage of Bulgarian clusters is derived.

  14. On clustering fMRI time series

    DEFF Research Database (Denmark)

    Goutte, Cyril; Toft, Peter Aundal; Rostrup, E.

    1999-01-01

    Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do...

  15. Segmentation of Nonstationary Time Series with Geometric Clustering

    DEFF Research Database (Denmark)

    Bocharov, Alexei; Thiesson, Bo

    2013-01-01

    We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...... from data, where clustering is used to propose one single split candidate at each split level. We use the class of ART time series models to serve as illustration, but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range of time-series models that go...

  16. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

  17. Clustering for Generating Framework Top-Level Views

    DEFF Research Database (Denmark)

    Schäfer, Thorsten; Aracic, Ivica; Merz, Matthias

    2007-01-01

    To use a framework, developers need to understand its building blocks. In this paper, we present a clustering technique that employs usage data from framework instantiations as examples to produce an overview of a framework's main building blocks as seen from a user's perspective. The evaluation...... of the approach by two case studies shows that the automatically generated building blocks are similar to a manually defined overview created by framework experts, even in cases where only few framework instantiations are available....

  18. Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Simmhan, Yogesh; Noor, Muhammad Usman

    2013-10-09

    Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

  19. CORECLUSTER: A Degeneracy Based Graph Clustering Framework

    OpenAIRE

    Giatsidis , Christos; Malliaros , Fragkiskos; Thilikos , Dimitrios M. ,; Vazirgiannis , Michalis

    2014-01-01

    International audience; Graph clustering or community detection constitutes an important task forinvestigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such asspectral methods, typically suffer from high time and space complexity. In thisarticle, we present \\textsc{CoreCluster}, an efficient graph clusteringframework based on the concept of graph degeneracy, that can be used along withany known graph clusteri...

  20. Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

    Science.gov (United States)

    Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

    2010-09-01

    A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

  1. Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

    Science.gov (United States)

    Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

    2014-11-01

    Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  2. 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.

  3. 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

  4. Clustering Multivariate Time Series Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Shima Ghassempour

    2014-03-01

    Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

  5. Spectral embedded clustering: a framework for in-sample and out-of-sample spectral clustering.

    Science.gov (United States)

    Nie, Feiping; Zeng, Zinan; Tsang, Ivor W; Xu, Dong; Zhang, Changshui

    2011-11-01

    Spectral clustering (SC) methods have been successfully applied to many real-world applications. The success of these SC methods is largely based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label. However, such an assumption might not always hold on high-dimensional data. When the data do not exhibit a clear low-dimensional manifold structure (e.g., high-dimensional and sparse data), the clustering performance of SC will be degraded and become even worse than K -means clustering. In this paper, motivated by the observation that the true cluster assignment matrix for high-dimensional data can be always embedded in a linear space spanned by the data, we propose the spectral embedded clustering (SEC) framework, in which a linearity regularization is explicitly added into the objective function of SC methods. More importantly, the proposed SEC framework can naturally deal with out-of-sample data. We also present a new Laplacian matrix constructed from a local regression of each pattern and incorporate it into our SEC framework to capture both local and global discriminative information for clustering. Comprehensive experiments on eight real-world high-dimensional datasets demonstrate the effectiveness and advantages of our SEC framework over existing SC methods and K-means-based clustering methods. Our SEC framework significantly outperforms SC using the Nyström algorithm on unseen data.

  6. Fisher information framework for time series modeling

    Science.gov (United States)

    Venkatesan, R. C.; Plastino, A.

    2017-08-01

    A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of (i) the probability density function of the coefficients of the working hypothesis and (ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the working hypothesis, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: (i) the Mackey-Glass delay-differential equation, (ii) one ECG signal from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and (iii) one ECG signal from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.

  7. Myofascial trigger points in cluster headache patients: a case series

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    Rico-Villademoros Fernando

    2008-12-01

    Full Text Available Abstract Active myofascial trigger points (MTrPs have been found to contribute to chronic tension-type headache and migraine. The purpose of this case series was to examine if active trigger points (TrPs provoking cluster-type referred pain could be found in cluster headache patients and, if so, to evaluate the effectiveness of active TrPs anaesthetic injections both in the acute and preventive headache's treatment. Twelve patients, 4 experiencing episodic and 8 chronic cluster headache, were studied. TrPs were found in all of them. Abortive infiltrations could be done in 2 episodic and 4 chronic patients, and preemptive infiltrations could be done in 2 episodic and 5 chronic patients, both kind of interventions being successful in 5 (83.3% and in 6 (85.7% of the cases respectively. When combined with prophylactic drug therapy, injections were associated with significant improvement in 7 of the 8 chronic cluster patients. Our data suggest that peripheral sensitization may play a role in cluster headache pathophysiology and that first neuron afferent blockade can be useful in cluster headache management.

  8. A Differentiation Framework for Maritime Clusters: Comparisons across Europe

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    Paulo Neto

    2013-09-01

    Full Text Available The purpose of this paper is to point out some of the main characteristics and critical factors for success that can substantiate the proposal of a differentiation framework for maritime clusters. We conduct a benchmarking analysis intended to distinguish the most relevant aspects which can or should be observed in these types of clusters, applied to the following countries: Spain (Basque Country, Germany (Lander of Schleswig-Holstein, the Netherlands and Norway. The differentiation factors involve agglomeration economies and endogenous conditions derived from geographic proximity, essential for lowering transaction costs, strengthening the leverage of public/private cooperation through centres of maritime excellence, at the same time providing an adequate local environment that favours positive interactions between the different maritime industries and actors. The main results arising from this article are presented through a reconceptualisation of Porter’s Diamond framework for diagnosing the competitiveness of maritime clusters.

  9. iterClust: a statistical framework for iterative clustering analysis.

    Science.gov (United States)

    Ding, Hongxu; Wang, Wanxin; Califano, Andrea

    2018-03-22

    In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2. Here we present iterClust, an iterative clustering framework, which can separate more pronounced differences (e.g. A and B) in starting iterations, followed by relatively subtle differences (e.g. B1 and B2), providing a comprehensive clustering trajectory. iterClust is implemented as a Bioconductor R package. andrea.califano@columbia.edu, hd2326@columbia.edu. Supplementary information is available at Bioinformatics online.

  10. Time series clustering analysis of health-promoting behavior

    Science.gov (United States)

    Yang, Chi-Ta; Hung, Yu-Shiang; Deng, Guang-Feng

    2013-10-01

    Health promotion must be emphasized to achieve the World Health Organization goal of health for all. Since the global population is aging rapidly, ComCare elder health-promoting service was developed by the Taiwan Institute for Information Industry in 2011. Based on the Pender health promotion model, ComCare service offers five categories of health-promoting functions to address the everyday needs of seniors: nutrition management, social support, exercise management, health responsibility, stress management. To assess the overall ComCare service and to improve understanding of the health-promoting behavior of elders, this study analyzed health-promoting behavioral data automatically collected by the ComCare monitoring system. In the 30638 session records collected for 249 elders from January, 2012 to March, 2013, behavior patterns were identified by fuzzy c-mean time series clustering algorithm combined with autocorrelation-based representation schemes. The analysis showed that time series data for elder health-promoting behavior can be classified into four different clusters. Each type reveals different health-promoting needs, frequencies, function numbers and behaviors. The data analysis result can assist policymakers, health-care providers, and experts in medicine, public health, nursing and psychology and has been provided to Taiwan National Health Insurance Administration to assess the elder health-promoting behavior.

  11. JTSA: an open source framework for time series abstractions.

    Science.gov (United States)

    Sacchi, Lucia; Capozzi, Davide; Bellazzi, Riccardo; Larizza, Cristiana

    2015-10-01

    The evaluation of the clinical status of a patient is frequently based on the temporal evolution of some parameters, making the detection of temporal patterns a priority in data analysis. Temporal abstraction (TA) is a methodology widely used in medical reasoning for summarizing and abstracting longitudinal data. This paper describes JTSA (Java Time Series Abstractor), a framework including a library of algorithms for time series preprocessing and abstraction and an engine to execute a workflow for temporal data processing. The JTSA framework is grounded on a comprehensive ontology that models temporal data processing both from the data storage and the abstraction computation perspective. The JTSA framework is designed to allow users to build their own analysis workflows by combining different algorithms. Thanks to the modular structure of a workflow, simple to highly complex patterns can be detected. The JTSA framework has been developed in Java 1.7 and is distributed under GPL as a jar file. JTSA provides: a collection of algorithms to perform temporal abstraction and preprocessing of time series, a framework for defining and executing data analysis workflows based on these algorithms, and a GUI for workflow prototyping and testing. The whole JTSA project relies on a formal model of the data types and of the algorithms included in the library. This model is the basis for the design and implementation of the software application. Taking into account this formalized structure, the user can easily extend the JTSA framework by adding new algorithms. Results are shown in the context of the EU project MOSAIC to extract relevant patterns from data coming related to the long term monitoring of diabetic patients. The proof that JTSA is a versatile tool to be adapted to different needs is given by its possible uses, both as a standalone tool for data summarization and as a module to be embedded into other architectures to select specific phenotypes based on TAs in a large

  12. The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

    KAUST Repository

    Euá n, Carolina; Ombao, Hernando; Ortega, Joaquí n

    2018-01-01

    We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms

  13. Trend analysis using non-stationary time series clustering based on the finite element method

    OpenAIRE

    Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

    2014-01-01

    In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods ...

  14. Simulating star clusters with the AMUSE software framework. I. Dependence of cluster lifetimes on model assumptions and cluster dissolution modes

    International Nuclear Information System (INIS)

    Whitehead, Alfred J.; McMillan, Stephen L. W.; Vesperini, Enrico; Portegies Zwart, Simon

    2013-01-01

    We perform a series of simulations of evolving star clusters using the Astrophysical Multipurpose Software Environment (AMUSE), a new community-based multi-physics simulation package, and compare our results to existing work. These simulations model a star cluster beginning with a King model distribution and a selection of power-law initial mass functions and contain a tidal cutoff. They are evolved using collisional stellar dynamics and include mass loss due to stellar evolution. After studying and understanding that the differences between AMUSE results and results from previous studies are understood, we explored the variation in cluster lifetimes due to the random realization noise introduced by transforming a King model to specific initial conditions. This random realization noise can affect the lifetime of a simulated star cluster by up to 30%. Two modes of star cluster dissolution were identified: a mass evolution curve that contains a runaway cluster dissolution with a sudden loss of mass, and a dissolution mode that does not contain this feature. We refer to these dissolution modes as 'dynamical' and 'relaxation' dominated, respectively. For Salpeter-like initial mass functions, we determined the boundary between these two modes in terms of the dynamical and relaxation timescales.

  15. A Software Data Transport Framework for Trigger Applications on Clusters

    CERN Document Server

    Steinbeck, T M; Tilsner, H; Steinbeck, Timm M.; Lindenstruth, Volker; Tilsner, Heinz

    2003-01-01

    In the future ALICE heavy ion experiment at CERN's Large Hadron Collider input data rates of up to 25 GB/s have to be handled by the High Level Trigger (HLT) system, which has to scale them down to at most 1.25 GB/s before being written to permanent storage. The HLT system that is being designed to cope with these data rates consists of a large PC cluster, up to the order of a 1000 nodes, connected by a fast network. For the software that will run on these nodes a flexible data transport and distribution software framework has been developed. This framework consists of a set of separate components, that can be connected via a common interface, allowing to construct different configurations for the HLT, that are even changeable at runtime. To ensure a fault-tolerant operation of the HLT, the framework includes a basic fail-over mechanism that will be further expanded in the future, utilizing the runtime reconnection feature of the framework's component interface. First performance tests show very promising res...

  16. Born series for (2 cluster) → (2 cluster) scattering of two, three, and four particle Schroedinger operators

    International Nuclear Information System (INIS)

    Hagedorn, G.A.

    1979-01-01

    We investigate elastic and inelastic (2 cluster)→(2 cluster)scattering for classes of two, three, and four body Schroedinger operators H=H 0 +ΣVij. Formulas are derived for those generalized eigenfunctions of H which correspond asymptotically in the past to two freely moving clusters. With these eigenfunctions, we establish a formula for the (2 cluster)→(2 cluster) T-matrix and prove the convergence of a Born series for the T-matrix at high energy. (orig.) [de

  17. Coherence-based Time Series Clustering for Brain Connectivity Visualization

    KAUST Repository

    Euan, Carolina

    2017-11-19

    We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels that display high level of coordination as measured by

  18. Coherence-based Time Series Clustering for Brain Connectivity Visualization

    KAUST Repository

    Euan, Carolina; Sun, Ying; Ombao, Hernando

    2017-01-01

    We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels that display high level of coordination as measured by

  19. A framework using cluster-based hybrid network architecture for collaborative virtual surgery.

    Science.gov (United States)

    Qin, Jing; Choi, Kup-Sze; Poon, Wai-Sang; Heng, Pheng-Ann

    2009-12-01

    Research on collaborative virtual environments (CVEs) opens the opportunity for simulating the cooperative work in surgical operations. It is however a challenging task to implement a high performance collaborative surgical simulation system because of the difficulty in maintaining state consistency with minimum network latencies, especially when sophisticated deformable models and haptics are involved. In this paper, an integrated framework using cluster-based hybrid network architecture is proposed to support collaborative virtual surgery. Multicast transmission is employed to transmit updated information among participants in order to reduce network latencies, while system consistency is maintained by an administrative server. Reliable multicast is implemented using distributed message acknowledgment based on cluster cooperation and sliding window technique. The robustness of the framework is guaranteed by the failure detection chain which enables smooth transition when participants join and leave the collaboration, including normal and involuntary leaving. Communication overhead is further reduced by implementing a number of management approaches such as computational policies and collaborative mechanisms. The feasibility of the proposed framework is demonstrated by successfully extending an existing standalone orthopedic surgery trainer into a collaborative simulation system. A series of experiments have been conducted to evaluate the system performance. The results demonstrate that the proposed framework is capable of supporting collaborative surgical simulation.

  20. Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

    Directory of Open Access Journals (Sweden)

    M. A. Porta-Garcia

    2018-01-01

    Full Text Available Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.

  1. jClustering, an open framework for the development of 4D clustering algorithms.

    Directory of Open Access Journals (Sweden)

    José María Mateos-Pérez

    Full Text Available We present jClustering, an open framework for the design of clustering algorithms in dynamic medical imaging. We developed this tool because of the difficulty involved in manually segmenting dynamic PET images and the lack of availability of source code for published segmentation algorithms. Providing an easily extensible open tool encourages publication of source code to facilitate the process of comparing algorithms and provide interested third parties with the opportunity to review code. The internal structure of the framework allows an external developer to implement new algorithms easily and quickly, focusing only on the particulars of the method being implemented and not on image data handling and preprocessing. This tool has been coded in Java and is presented as an ImageJ plugin in order to take advantage of all the functionalities offered by this imaging analysis platform. Both binary packages and source code have been published, the latter under a free software license (GNU General Public License to allow modification if necessary.

  2. Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

    Science.gov (United States)

    Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

    2018-01-01

    Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

  3. Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

    Science.gov (United States)

    Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

    2018-01-01

    Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

  4. Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering

    Directory of Open Access Journals (Sweden)

    Markatou Marianthi

    2011-01-01

    Full Text Available Abstract Background The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation, but the signaling mechanisms between irradiated and non-irradiated bystander cells are not fully understood. In this study, we measured a time-series of gene expression after α-particle irradiation and applied the Feature Based Partitioning around medoids Algorithm (FBPA, a new clustering method suitable for sparse time series, to identify signaling modules that act in concert in the response to direct irradiation and bystander signaling. We compared our results with those of an alternate clustering method, Short Time series Expression Miner (STEM. Results While computational evaluations of both clustering results were similar, FBPA provided more biological insight. After irradiation, gene clusters were enriched for signal transduction, cell cycle/cell death and inflammation/immunity processes; but only FBPA separated clusters by function. In bystanders, gene clusters were enriched for cell communication/motility, signal transduction and inflammation processes; but biological functions did not separate as clearly with either clustering method as they did in irradiated samples. Network analysis confirmed p53 and NF-κB transcription factor-regulated gene clusters in irradiated and bystander cells and suggested novel regulators, such as KDM5B/JARID1B (lysine (K-specific demethylase 5B and HDACs (histone deacetylases, which could epigenetically coordinate gene expression after irradiation. Conclusions In this study, we have shown that a new time series clustering method, FBPA, can provide new leads to the mechanisms regulating the dynamic cellular response to radiation. The findings implicate epigenetic control of gene expression in addition to transcription factor networks.

  5. The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

    KAUST Repository

    Euán, Carolina

    2018-04-12

    We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms. The extent of similarity between a pair of time series is measured using the total variation distance between their estimated spectral densities. At each step of the algorithm, every time two clusters merge, a new spectral density is estimated using the whole information present in both clusters, which is representative of all the series in the new cluster. The method is implemented in an R package HSMClust. We present two applications of the HSM method, one to data coming from wave-height measurements in oceanography and the other to electroencefalogram (EEG) data.

  6. Trend analysis using non-stationary time series clustering based on the finite element method

    Science.gov (United States)

    Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

    2014-05-01

    In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.

  7. A cluster merging method for time series microarray with production values.

    Science.gov (United States)

    Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

    2014-09-01

    A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

  8. Complex open-framework germanate built by 8-coordinated Ge 10 clusters

    KAUST Repository

    Yue, Huijuan; Peskov, Maxim; Sun, Junliang; Zou, Xiaodong

    2012-01-01

    cluster building units can be concluded. The framework of SU-67 is based on an elaborate topological pattern of connected Ge 10 clusters forming intersecting 10- and 11-ring channels and has a low framework density (12.4 Ge atoms per 1000 ̊ 3). We have

  9. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  10. Functional clustering of time series gene expression data by Granger causality

    Science.gov (United States)

    2012-01-01

    Background A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them. PMID:23107425

  11. Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

    Science.gov (United States)

    Hallac, David; Vare, Sagar; Boyd, Stephen; Leskovec, Jure

    2018-01-01

    Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. PMID:29770257

  12. A perturbative clustering hyper-heuristic framework for the Danish railway system

    DEFF Research Database (Denmark)

    M. Pour, Shahrzad; Rasmussen, Kourosh Marjani; Burke, Edmund K.

    , we propose a perturbative clustering hyper-heuristic framework. The framework improves an initial solution by reassigning outliers (those tasks that are far away) to a better cluster choice at each iteration while taking balanced crews workloads into account. The framework introduces five lowlevel...... heuristics and employs an adaptive choice function as a robust learning mechanism. The results of adaptive clustering hyper-heuristic are compared with two exact and heuristic assignment algorithms from the literature and with the random hyper-heuristic framework on 12 datasets. In comparison with the exact...... formulation, the proposed framework could obtain promising results and solved the data instances up to 5000 number of tasks. In comparison with heuristic assignment and the random hyper-heuristic, the framework yielded approximately 11%, 27% and 10%,13% mprovement on total distance and the maximum distance...

  13. Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

    Science.gov (United States)

    Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

    2013-02-01

    Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

  14. Central San Juan caldera cluster: Regional volcanic framework

    Science.gov (United States)

    Lipman, Peter W.

    2000-01-01

    Eruption of at least 8800 km3 of dacitic-rhyolitic magma as 9 major ash-slow sheets (individually 150-5000 km3) was accompanied by recurrent caldera subsidence between 28.3 and about 26.5 Ma in the central San Juan Mountains, Colorado. Voluminous andesitic-decitic lavas and breccias were erupted from central volcanoes prior to the ash-flow eruptions, and similar lava eruptions continued within and adjacent to the calderas during the period of explosive volcanism, making the central San Juan caldera cluster an exceptional site for study of caldera-related volcanic processes. Exposed calderas vary in size from 10 to 75 km in maximum diameter, the largest calderas being associated with the most voluminous eruptions. After collapse of the giant La Garita caldera during eruption if the Fish Canyon Tuff at 17.6 Ma, seven additional explosive eruptions and calderas formed inside the La Garita depression within about 1 m.y. Because of the nested geometry, maximum loci of recurrently overlapping collapse events are inferred to have subsided as much as 10-17 km, far deeper than the roof of the composite subvolcanic batholith defined by gravity data, which represents solidified caldera-related magma bodies. Erosional dissection to depths of as much as 1.5 km, although insufficient to reach the subvolcanic batholith, has exposed diverse features of intracaldera ash-flow tuff and interleaved caldera-collapse landslide deposits that accumulated to multikilometer thickness within concurrently subsiding caldera structures. The calderas display a variety of postcollapse resurgent uplift structures, and caldera-forming events produced complex fault geometries that localized late mineralization, including the epithermal base- and precious-metal veins of the well-known Creede mining district. Most of the central San Juan calderas have been deeply eroded, and their identification is dependent on detailed geologic mapping. In contrast, the primary volcanic morphology of the

  15. Thermodynamics of Pore Filling Metal Clusters in Metal Organic Frameworks: Pd in UiO-66

    DEFF Research Database (Denmark)

    Vilhelmsen, Lasse; Sholl, David S.

    2012-01-01

    Metal organic frameworks (MOFs) have experimentally been demonstrated to be capable of supporting isolated transition-metal clusters, but the stability of these clusters with respect to aggregation is unclear. In this letter we use a genetic algorithm together with density functional theory...... calculations to predict the structure of Pd clusters in UiO-66. The cluster sizes examined are far larger than those in any previous modeling studies of metal clusters in MOFs and allow us to test the hypothesis that the physically separated cavities in UiO-66 could stabilize isolated Pd clusters. Our...... calculations show that Pd clusters in UiO-66 are, at best, metastable and will aggregate into connected pore filling structures at equilibrium....

  16. Forecasting Jakarta composite index (IHSG) based on chen fuzzy time series and firefly clustering algorithm

    Science.gov (United States)

    Ningrum, R. W.; Surarso, B.; Farikhin; Safarudin, Y. M.

    2018-03-01

    This paper proposes the combination of Firefly Algorithm (FA) and Chen Fuzzy Time Series Forecasting. Most of the existing fuzzy forecasting methods based on fuzzy time series use the static length of intervals. Therefore, we apply an artificial intelligence, i.e., Firefly Algorithm (FA) to set non-stationary length of intervals for each cluster on Chen Method. The method is evaluated by applying on the Jakarta Composite Index (IHSG) and compare with classical Chen Fuzzy Time Series Forecasting. Its performance verified through simulation using Matlab.

  17. Reliable, Memory Speed Storage for Cluster Computing Frameworks

    Science.gov (United States)

    2014-06-16

    data, the data needs to be written to a storage system. Nectar [24] also uses lineage for a specific framework (DryadLINQ) with the goal of saving space... Nectar [24] also uses the concept of lineage, but it does so only for a specific pro- gramming framework (DryadLINQ [44]), and in the con- text of a...traditional, replicated file system. Nectar is a data reuse system for DryadLINQ queries whose goals are to save space and to avoid redundant

  18. A time-series approach for clustering farms based on slaughterhouse health aberration data.

    Science.gov (United States)

    Hulsegge, B; de Greef, K H

    2018-05-01

    A large amount of data is collected routinely in meat inspection in pig slaughterhouses. A time series clustering approach is presented and applied that groups farms based on similar statistical characteristics of meat inspection data over time. A three step characteristic-based clustering approach was used from the idea that the data contain more info than the incidence figures. A stratified subset containing 511,645 pigs was derived as a study set from 3.5 years of meat inspection data. The monthly averages of incidence of pleuritis and of pneumonia of 44 Dutch farms (delivering 5149 batches to 2 pig slaughterhouses) were subjected to 1) derivation of farm level data characteristics 2) factor analysis and 3) clustering into groups of farms. The characteristic-based clustering was able to cluster farms for both lung aberrations. Three groups of data characteristics were informative, describing incidence, time pattern and degree of autocorrelation. The consistency of clustering similar farms was confirmed by repetition of the analysis in a larger dataset. The robustness of the clustering was tested on a substantially extended dataset. This confirmed the earlier results, three data distribution aspects make up the majority of distinction between groups of farms and in these groups (clusters) the majority of the farms was allocated comparable to the earlier allocation (75% and 62% for pleuritis and pneumonia, respectively). The difference between pleuritis and pneumonia in their seasonal dependency was confirmed, supporting the biological relevance of the clustering. Comparison of the identified clusters of statistically comparable farms can be used to detect farm level risk factors causing the health aberrations beyond comparison on disease incidence and trend alone. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. A novel water quality data analysis framework based on time-series data mining.

    Science.gov (United States)

    Deng, Weihui; Wang, Guoyin

    2017-07-01

    The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Unbinned likelihood maximisation framework for neutrino clustering in Python

    Energy Technology Data Exchange (ETDEWEB)

    Coenders, Stefan [Technische Universitaet Muenchen, Boltzmannstr. 2, 85748 Garching (Germany)

    2016-07-01

    Albeit having detected an astrophysical neutrino flux with IceCube, sources of astrophysical neutrinos remain hidden up to now. A detection of a neutrino point source is a smoking gun for hadronic processes and acceleration of cosmic rays. The search for neutrino sources has many degrees of freedom, for example steady versus transient, point-like versus extended sources, et cetera. Here, we introduce a Python framework designed for unbinned likelihood maximisations as used in searches for neutrino point sources by IceCube. Implementing source scenarios in a modular way, likelihood searches on various kinds can be implemented in a user-friendly way, without sacrificing speed and memory management.

  1. Estimating the parameters of globular cluster M 30 (NGC 7099) from time-series photometry

    DEFF Research Database (Denmark)

    Kains, N.; Bramich, D.M.; Figuera Jaimes, R.

    2013-01-01

    Aims. We present the analysis of 26 nights of V and I time-series observations from 2011 and 2012 of the globular cluster M 30 (NGC 7099). We used our data to search for variable stars in this cluster and refine the periods of known variables; we then used our variable star light curves to derive...... values for the cluster's parameters. Methods. We used difference image analysis to reduce our data to obtain high-precision light curves of variable stars. We then estimated the cluster parameters by performing a Fourier decomposition of the light curves of RR Lyrae stars for which a good period estimate...... stars to derive cluster parameters using empirical relations. We find a cluster metallicity [Fe/H]ZW =-2.01 ± 0.04, or [Fe/H]UVES =-2.11 ± 0.06, and a distance of 8.32 ± 0.20 kpc (using RR0 variables), 8.10 kpc (using one RR1 variable), and 8.35 ± 0.42 kpc (using our SX Phoenicis star detection in M 30...

  2. Fourier Magnitude-Based Privacy-Preserving Clustering on Time-Series Data

    Science.gov (United States)

    Kim, Hea-Suk; Moon, Yang-Sae

    Privacy-preserving clustering (PPC in short) is important in publishing sensitive time-series data. Previous PPC solutions, however, have a problem of not preserving distance orders or incurring privacy breach. To solve this problem, we propose a new PPC approach that exploits Fourier magnitudes of time-series. Our magnitude-based method does not cause privacy breach even though its techniques or related parameters are publicly revealed. Using magnitudes only, however, incurs the distance order problem, and we thus present magnitude selection strategies to preserve as many Euclidean distance orders as possible. Through extensive experiments, we showcase the superiority of our magnitude-based approach.

  3. Clustering of 1p-shell nuclei in the framework of the shell model

    International Nuclear Information System (INIS)

    Kwasniewicz, E.

    1991-01-01

    The two- and three-fragment clustering of the 1p-shell nuclei has been studied in the framework of the shell model. The absolute probabilities of the required types of clustering in a given nucleus have been obtained by projecting its realistic shell-model wavefunction onto the suitable subspace of the orthonormal, completely antisymmetric two- or three-cluster states. With the aid of these data the selectivity in population of final states produced in multinucleon transfer reactions has been discussed. This problem has also been considered in the approach where the exchange of nucleons between clusters has been neglected. This has enabled to demonstrate the role of the complete antisymmetrization in predicting the intensities of states populated in multinucleon transfer reactions. The compact theory of the multinucleon one- and two-cluster spectroscopic amplitudes has been formulated. The examples of studying the nuclear structure and reactions with the aid of these spectroscopic amplitudes have been presented. (author)

  4. Vector Nonlinear Time-Series Analysis of Gamma-Ray Burst Datasets on Heterogeneous Clusters

    Directory of Open Access Journals (Sweden)

    Ioana Banicescu

    2005-01-01

    Full Text Available The simultaneous analysis of a number of related datasets using a single statistical model is an important problem in statistical computing. A parameterized statistical model is to be fitted on multiple datasets and tested for goodness of fit within a fixed analytical framework. Definitive conclusions are hopefully achieved by analyzing the datasets together. This paper proposes a strategy for the efficient execution of this type of analysis on heterogeneous clusters. Based on partitioning processors into groups for efficient communications and a dynamic loop scheduling approach for load balancing, the strategy addresses the variability of the computational loads of the datasets, as well as the unpredictable irregularities of the cluster environment. Results from preliminary tests of using this strategy to fit gamma-ray burst time profiles with vector functional coefficient autoregressive models on 64 processors of a general purpose Linux cluster demonstrate the effectiveness of the strategy.

  5. ENTREPRENEURIAL ACTIVITY IN ROMANIA – A TIME SERIES CLUSTERING ANALYSIS AT THE NUTS3 LEVEL

    Directory of Open Access Journals (Sweden)

    Sipos-Gug Sebastian

    2013-07-01

    Full Text Available Entrepreneurship is an active field of research, having known a major increase in interest and publication levels in the last years (Landström et al., 2012. Within this field recently there has been an increasing interest in understanding why some regions seem to have a significantly higher entrepreneurship activity compared to others. In line with this research field, we would like to investigate the differences in entrepreneurial activity among the Romanian counties (NUTS 3 regions. While the classical research paradigm in this field is to conduct a temporally stationary analysis, we choose to use a time series clustering analysis to better understanding the dynamics of entrepreneurial activity between counties. Our analysis showed that if we use the total number of new privately owned companies that are founded each year in the last decade (2002-2012 we can distinguish between 5 clusters, one with high total entrepreneurial activity (18 counties, one with above average activity (8 counties, two clusters with average and slightly below average activity (total of 18 counties and one cluster with low and declining activity (2 counties. If we are interested in the entrepreneurial activity rate, that is the number of new privately owned companies founded each year adjusted by the population of the respective county, we obtain 4 clusters, one with a very high entrepreneurial rate (1 county, one with average rate (10 counties, and two clusters with below average entrepreneurial rate (total of 31 counties. In conclusion, our research shows that Romania is far from being a homogeneous geographical area in respect to entrepreneurial activity. Depending on what we are interested in, it can be divided in 5 or 4 clusters of counties, which behave differently as a function of time. Further research should be focused on explaining these regional differences, on studying the high performance clusters and trying to improve the low performing ones.

  6. Clustering of financial time series with application to index and enhanced index tracking portfolio

    Science.gov (United States)

    Dose, Christian; Cincotti, Silvano

    2005-09-01

    A stochastic-optimization technique based on time series cluster analysis is described for index tracking and enhanced index tracking problems. Our methodology solves the problem in two steps, i.e., by first selecting a subset of stocks and then setting the weight of each stock as a result of an optimization process (asset allocation). Present formulation takes into account constraints on the number of stocks and on the fraction of capital invested in each of them, whilst not including transaction costs. Computational results based on clustering selection are compared to those of random techniques and show the importance of clustering in noise reduction and robust forecasting applications, in particular for enhanced index tracking.

  7. Clustering Gene Expression Time Series with Coregionalization: Speed propagation of ALS

    OpenAIRE

    Rahman, Muhammad Arifur; Heath, Paul R.; Lawrence, Neil D.

    2018-01-01

    Clustering of gene expression time series gives insight into which genes may be coregulated, allowing us to discern the activity of pathways in a given microarray experiment. Of particular interest is how a given group of genes varies with different model conditions or genetic background. Amyotrophic lateral sclerosis (ALS), an irreversible diverse neurodegenerative disorder showed consistent phenotypic differences and the disease progression is heterogeneous with significant variability. Thi...

  8. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.

    Science.gov (United States)

    Hensman, James; Lawrence, Neil D; Rattray, Magnus

    2013-08-20

    Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications. We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications. The hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.

  9. Clustering gene expression time series data using an infinite Gaussian process mixture model.

    Science.gov (United States)

    McDowell, Ian C; Manandhar, Dinesh; Vockley, Christopher M; Schmid, Amy K; Reddy, Timothy E; Engelhardt, Barbara E

    2018-01-01

    Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

  10. Clustering gene expression time series data using an infinite Gaussian process mixture model.

    Directory of Open Access Journals (Sweden)

    Ian C McDowell

    2018-01-01

    Full Text Available Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP, which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

  11. [Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis].

    Science.gov (United States)

    Luo, Yi; Zhang, Tao; Li, Xiao-song

    2016-05-01

    To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.

  12. FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN

    Directory of Open Access Journals (Sweden)

    M R Sumathi

    2017-04-01

    Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.

  13. A Cluster-Based Framework for the Security of Medical Sensor Environments

    Science.gov (United States)

    Klaoudatou, Eleni; Konstantinou, Elisavet; Kambourakis, Georgios; Gritzalis, Stefanos

    The adoption of Wireless Sensor Networks (WSNs) in the healthcare sector poses many security issues, mainly because medical information is considered particularly sensitive. The security mechanisms employed are expected to be more efficient in terms of energy consumption and scalability in order to cope with the constrained capabilities of WSNs and patients’ mobility. Towards this goal, cluster-based medical WSNs can substantially improve efficiency and scalability. In this context, we have proposed a general framework for cluster-based medical environments on top of which security mechanisms can rely. This framework fully covers the varying needs of both in-hospital environments and environments formed ad hoc for medical emergencies. In this paper, we further elaborate on the security of our proposed solution. We specifically focus on key establishment mechanisms and investigate the group key agreement protocols that can best fit in our framework.

  14. A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures

    Directory of Open Access Journals (Sweden)

    Shaoyi Liang

    2017-09-01

    Full Text Available Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC. Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency.

  15. Determining open cluster membership. A Bayesian framework for quantitative member classification

    Science.gov (United States)

    Stott, Jonathan J.

    2018-01-01

    Aims: My goal is to develop a quantitative algorithm for assessing open cluster membership probabilities. The algorithm is designed to work with single-epoch observations. In its simplest form, only one set of program images and one set of reference images are required. Methods: The algorithm is based on a two-stage joint astrometric and photometric assessment of cluster membership probabilities. The probabilities were computed within a Bayesian framework using any available prior information. Where possible, the algorithm emphasizes simplicity over mathematical sophistication. Results: The algorithm was implemented and tested against three observational fields using published survey data. M 67 and NGC 654 were selected as cluster examples while a third, cluster-free, field was used for the final test data set. The algorithm shows good quantitative agreement with the existing surveys and has a false-positive rate significantly lower than the astrometric or photometric methods used individually.

  16. A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2017-11-01

    Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

  17. Complex open-framework germanate built by 8-coordinated Ge 10 clusters

    KAUST Repository

    Yue, Huijuan

    2012-11-19

    A novel open-framework germanate |(C 5H 14N 2) 2(C 5H 12N 2) 0.5(H 2O) 2.5|[Ge 12.5O 26(OH) 2] with three-dimensional 10- and 11-ring channels, denoted as SU-67, has been synthesized under hydrothermal conditions using 2-methylpiperazine (MPP) as the structure-directing agent (SDA). The synthesis is intimately related to that of JLG-5, a tubular germanate built from Ge 7 clusters. The influences of synthesis parameters are discussed. A strong influence of the hydrofluoric acid quantity on the resulting cluster building units can be concluded. The framework of SU-67 is based on an elaborate topological pattern of connected Ge 10 clusters forming intersecting 10- and 11-ring channels and has a low framework density (12.4 Ge atoms per 1000 ̊ 3). We have discovered that the topology of SU-67 is a new 8-connected nce-8-I4 1/acd net. Strong hydrogen bonding among the organic SDAs, water molecules, and Ge 10 clusters resulted in helical networks in SU-67. © 2012 American Chemical Society.

  18. Implementation of Automatic Clustering Algorithm and Fuzzy Time Series in Motorcycle Sales Forecasting

    Science.gov (United States)

    Rasim; Junaeti, E.; Wirantika, R.

    2018-01-01

    Accurate forecasting for the sale of a product depends on the forecasting method used. The purpose of this research is to build motorcycle sales forecasting application using Fuzzy Time Series method combined with interval determination using automatic clustering algorithm. Forecasting is done using the sales data of motorcycle sales in the last ten years. Then the error rate of forecasting is measured using Means Percentage Error (MPE) and Means Absolute Percentage Error (MAPE). The results of forecasting in the one-year period obtained in this study are included in good accuracy.

  19. Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.

    Science.gov (United States)

    Nguyen, Hien D; Ullmann, Jeremy F P; McLachlan, Geoffrey J; Voleti, Venkatakaushik; Li, Wenze; Hillman, Elizabeth M C; Reutens, David C; Janke, Andrew L

    2018-02-01

    Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.

  20. Increasing Power by Sharing Information from Genetic Background and Treatment in Clustering of Gene Expression Time Series

    OpenAIRE

    Sura Zaki Alrashid; Muhammad Arifur Rahman; Nabeel H Al-Aaraji; Neil D Lawrence; Paul R Heath

    2018-01-01

    Clustering of gene expression time series gives insight into which genes may be co-regulated, allowing us to discern the activity of pathways in a given microarray experiment. Of particular interest is how a given group of genes varies with different conditions or genetic background. This paper develops
a new clustering method that allows each cluster to be parameterised according to whether the behaviour of the genes across conditions is correlated or anti-correlated. By specifying correlati...

  1. Catalytic dehydrogenation of alcohol over solid-state molybdenum sulfide clusters with an octahedral metal framework

    Energy Technology Data Exchange (ETDEWEB)

    Kamiguchi, Satoshi, E-mail: kamigu@riken.jp [Advanced Catalysis Research Group, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako City, Saitama 351-0198 (Japan); Organometallic Chemistry Laboratory, RIKEN, 2-1 Hirosawa, Wako City, Saitama 351-0198 (Japan); Okumura, Kazu [School of Advanced Engineering, Kogakuin University, Nakano-machi, Hachioji City, Tokyo 192-0015 (Japan); Nagashima, Sayoko; Chihara, Teiji [Graduate School of Science and Engineering, Saitama University, Shimo-Okubo, Sakura-ku, Saitama City, Saitama 338-8570 (Japan)

    2015-12-15

    Graphical abstract: - Highlights: • Solid-state molybdenum sulfide clusters catalyzed the dehydrogenation of alcohol. • The dehydrogenation proceeded without the addition of any oxidants. • The catalytic activity developed when the cluster was activated at 300–500 °C in H{sub 2}. • The Lewis-acidic molybdenum atom and basic sulfur ligand were catalytically active. • The clusters function as bifunctional acid–base catalysts. - Abstract: Solid-state molybdenum sulfide clusters with an octahedral metal framework, the superconducting Chevrel phases, are applied to catalysis. A copper salt of a nonstoichiometric sulfur-deficient cluster, Cu{sub x}Mo{sub 6}S{sub 8–δ} (x = 2.94 and δ ≈ 0.3), is stored in air for more than 90 days. When the oxygenated cluster is thermally activated in a hydrogen stream above 300 °C, catalytic activity for the dehydrogenation of primary alcohols to aldehydes and secondary alcohols to ketones develops. The addition of pyridine or benzoic acid decreases the dehydrogenation activity, indicating that both a Lewis-acidic coordinatively unsaturated molybdenum atom and a basic sulfur ligand synergistically act as the catalytic active sites.

  2. TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

    Science.gov (United States)

    Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

    2017-12-01

    Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at

  3. Manipulating Light with Transition Metal Clusters, Organic Dyes, and Metal Organic Frameworks

    Energy Technology Data Exchange (ETDEWEB)

    Ogut, Serdar [Univ. of Illinois, Chicago, IL (United States)

    2017-09-11

    The primary goals of our research program is to develop and apply state-of-the-art first-principles methods to predict electronic and optical properties of three systems of significant scientific and technological interest: transition metal clusters, organic dyes, and metal-organic frameworks. These systems offer great opportunities to manipulate light for a wide ranging list of energy-related scientific problems and applications. During this grant period, we focused our investigations on the development, implementation, and benchmarking of many-body Green’s function methods (GW approximation and the Bethe-Salpeter equation) to examine excited-state properties of transition metal/transition-metal-oxide clusters and organic molecules that comprise the building blocks of dyes and metal-organic frameworks.

  4. A framework for assessing frequency domain causality in physiological time series with instantaneous effects.

    Science.gov (United States)

    Faes, Luca; Erla, Silvia; Porta, Alberto; Nollo, Giandomenico

    2013-08-28

    We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the known directed coherence (DC) and partial DC measures. The new measures are illustrated in theoretical examples showing that they reduce to the known measures in the absence of instantaneous causality, and describe peculiar aspects of directional interaction among multiple series when instantaneous causality is non-negligible. Then, the issue of estimating eMVAR models from time-series data is faced, proposing two approaches for model identification and discussing problems related to the underlying model assumptions. Finally, applications of the framework on cardiovascular variability series and multichannel EEG recordings are presented, showing how it allows one to highlight patterns of frequency domain causality consistent with well-interpretable physiological interaction mechanisms.

  5. DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

    OpenAIRE

    Kim, Hanjoo; Park, Jaehong; Jang, Jaehee; Yoon, Sungroh

    2016-01-01

    The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operation...

  6. Framework methodology for increased energy efficiency and renewable feedstock integration in industrial clusters

    International Nuclear Information System (INIS)

    Hackl, Roman; Harvey, Simon

    2013-01-01

    Highlights: • Framework methodology for energy efficiency of process plants and total sites. • Identification of suitable biorefinery based on host site future energy systems. • Case study results show large energy savings of site wide heat integration. • Case study on refrigeration systems: 15% shaft work savings potential. • Case study on biorefinery integration: utility savings potential of up to 37%. - Abstract: Energy intensive industries, such as the bulk chemical industry, are facing major challenges and adopting strategies to face these challenges. This paper investigates options for clusters of chemical process plants to decrease their energy and emission footprints. There is a wide range of technologies and process integration opportunities available for achieving these objectives, including (i) decreasing fossil fuel and electricity demand by increasing heat integration within individual processes and across the total cluster site; (ii) replacing fossil feedstocks with renewables and biorefinery integration with the existing cluster; (iii) increasing external utilization of excess process heat wherever possible. This paper presents an overview of the use of process integration methods for development of chemical clusters. Process simulation, pinch analysis, Total Site Analysis (TSA) and exergy concepts are combined in a holistic approach to identify opportunities to improve energy efficiency and integrate renewable feedstocks within such clusters. The methodology is illustrated by application to a chemical cluster in Stenungsund on the West Coast of Sweden consisting of five different companies operating six process plants. The paper emphasizes and quantifies the gains that can be made by adopting a total site approach for targeting energy efficiency measures within the cluster and when investigating integration opportunities for advanced biorefinery concepts compared to restricting the analysis to the individual constituent plants. The

  7. Design and synthesis of polyoxometalate-framework materials from cluster precursors

    Science.gov (United States)

    Vilà-Nadal, Laia; Cronin, Leroy

    2017-10-01

    Inorganic oxide materials are used in semiconductor electronics, ion exchange, catalysis, coatings, gas sensors and as separation materials. Although their synthesis is well understood, the scope for new materials is reduced because of the stability limits imposed by high-temperature processing and top-down synthetic approaches. In this Review, we describe the derivatization of polyoxometalate (POM) clusters, which enables their assembly into a range of frameworks by use of organic or inorganic linkers. Additionally, bottom-up synthetic approaches can be used to make metal oxide framework materials, and the features of the molecular POM precursors are retained in these structures. Highly robust all-inorganic frameworks can be made using metal-ion linkers, which combine molecular synthetic control without the need for organic components. The resulting frameworks have high stability, and high catalytic, photochemical and electrochemical activity. Conceptually, these inorganic oxide materials bridge the gap between zeolites and metal-organic frameworks (MOFs) and establish a new class of all-inorganic POM frameworks that can be designed using topological and reactivity principles similar to MOFs.

  8. Automated Inventory and Monitoring of the ALICE HLT Cluster Resources with the SysMES Framework

    International Nuclear Information System (INIS)

    Ulrich, J; Lara, C; Böttger, S; Kebschull, U; Haaland, Ø; Röhrich, D

    2012-01-01

    The High-Level-Trigger (HLT) cluster of the ALICE experiment is a computer cluster with about 200 nodes and 20 infrastructure machines. In its current state, the cluster consists of nearly 10 different configurations of nodes in terms of installed hardware, software and network structure. In such a heterogeneous environment with a distributed application, information about the actual configuration of the nodes is needed to automatically distribute and adjust the application accordingly. An inventory database provides a unified interface to such information. To be useful, the data in the inventory has to be up to date, complete and consistent. Manual maintenance of such databases is error-prone and data tends to become outdated. The inventory module of the ALICE HLT cluster overcomes these drawbacks by automatically updating the actual state periodically and, in contrast to existing solutions, it allows the definition of a target state for each node. A target state can simply be a fully operational state, i.e. a state without malfunctions, or a dedicated configuration of the node. The target state is then compared to the actual state to detect deviations and malfunctions which could induce severe problems when running the application. The inventory module of the ALICE HLT cluster has been integrated into the monitoring and management framework SysMES in order to use existing functionality like transactionality and monitoring infrastructure. Additionally, SysMES allows to solve detected problems automatically via its rule-system. To describe the heterogeneous environment with all its specifics, like custom hardware, the inventory module uses an object-oriented model which is based on the Common Information Model. The inventory module provides an automatically updated actual state of the cluster, detects discrepancies between the actual and the target state and is able to solve detected problems automatically. This contribution presents the current implementation

  9. From Stable ZnO and GaN Clusters to Novel Double Bubbles and Frameworks

    Directory of Open Access Journals (Sweden)

    Matthew R. Farrow

    2014-05-01

    Full Text Available A bottom up approach is employed in the design of novel materials: first, gas-phase “double bubble” clusters are constructed from high symmetry, Th, 24 and 96 atom, single bubbles of ZnO and GaN. These are used to construct bulk frameworks. Upon geometry optimization—minimisation of energies and forces computed using density functional theory—the symmetry of the double bubble clusters is reduced to either C1 or C2, and the average bond lengths for the outer bubbles are 1.9 Å, whereas the average bonds for the inner bubble are larger for ZnO than for GaN; 2.0 Å and 1.9 Å, respectively. A careful analysis of the bond distributions reveals that the inter-bubble bonds are bi-modal, and that there is a greater distortion for ZnO. Similar bond distributions are found for the corresponding frameworks. The distortion of the ZnO double bubble is found to be related to the increased flexibility of the outer bubble when composed of ZnO rather than GaN, which is reflected in their bulk moduli. The energetics suggest that (ZnO12@(GaN48 is more stable both in gas phase and bulk frameworks than (ZnO12@(ZnO48 and (GaN12@(GaN48. Formation enthalpies are similar to those found for carbon fullerenes.

  10. A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.

    Science.gov (United States)

    Liu, Zitao; Hauskrecht, Milos

    2015-01-01

    Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs' spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets.

  11. Optimizing Availability of a Framework in Series Configuration Utilizing Markov Model and Monte Carlo Simulation Techniques

    Directory of Open Access Journals (Sweden)

    Mansoor Ahmed Siddiqui

    2017-06-01

    Full Text Available This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.

  12. Ligand combination strategy for the preparation of novel low-dimensional and open-framework metal cluster materials

    Science.gov (United States)

    Anokhina, Ekaterina V.

    Low-dimensional and open-framework materials containing transition metals have a wide range of applications in redox catalysis, solid-state batteries, and electronic and magnetic devices. This dissertation reports on research carried out with the goal to develop a strategy for the preparation of low-dimensional and open-framework materials using octahedral metal clusters as building blocks. Our approach takes its roots from crystal engineering principles where the desired framework topologies are achieved through building block design. The key idea of this work is to induce directional bonding preferences in the cluster units using a combination of ligands with a large difference in charge density. This investigation led to the preparation and characterization of a new family of niobium oxychloride cluster compounds with original structure types exhibiting 1ow-dimensional or open-framework character. Most of these materials have framework topologies unprecedented in compounds containing octahedral clusters. Comparative analysis of their structural features indicates that the novel cluster connectivity patterns in these systems are the result of complex interplay between the effects of anisotropic ligand arrangement in the cluster unit and optimization of ligand-counterion electrostatic interactions. The important role played by these factors sets niobium oxychloride systems apart from cluster compounds with one ligand type or statistical ligand distribution where the main structure-determining factor is the total number of ligands. These results provide a blueprint for expanding the ligand combination strategy to other transition metal cluster systems and for the future rational design of cluster-based materials.

  13. Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework.

    Science.gov (United States)

    Herrera, Jose L; Del-Blanco, Carlos R; Garcia, Narciso

    2018-07-01

    There has been a significant increase in the availability of 3D players and displays in the last years. Nonetheless, the amount of 3D content has not experimented an increment of such magnitude. To alleviate this problem, many algorithms for converting images and videos from 2D to 3D have been proposed. Here, we present an automatic learning-based 2D-3D image conversion approach, based on the key hypothesis that color images with similar structure likely present a similar depth structure. The presented algorithm estimates the depth of a color query image using the prior knowledge provided by a repository of color + depth images. The algorithm clusters this database attending to their structural similarity, and then creates a representative of each color-depth image cluster that will be used as prior depth map. The selection of the appropriate prior depth map corresponding to one given color query image is accomplished by comparing the structural similarity in the color domain between the query image and the database. The comparison is based on a K-Nearest Neighbor framework that uses a learning procedure to build an adaptive combination of image feature descriptors. The best correspondences determine the cluster, and in turn the associated prior depth map. Finally, this prior estimation is enhanced through a segmentation-guided filtering that obtains the final depth map estimation. This approach has been tested using two publicly available databases, and compared with several state-of-the-art algorithms in order to prove its efficiency.

  14. Challenge Online Time Series Clustering For Demand Response A Theory to Break the ‘Curse of Dimensionality'

    Energy Technology Data Exchange (ETDEWEB)

    Pal, Ranjan [Univ. of Southern California, Los Angeles, CA (United States); Chelmis, Charalampos [Univ. of Southern California, Los Angeles, CA (United States); Aman, Saima [Univ. of Southern California, Los Angeles, CA (United States); Frincu, Marc [Univ. of Southern California, Los Angeles, CA (United States); Prasanna, Viktor [Univ. of Southern California, Los Angeles, CA (United States)

    2015-07-15

    The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring considerable computational resources and efficient algorithms. To overcome this Big Data challenge, data clustering techniques have been proposed. Current approaches however do not scale in the face of the “increasing dimensionality” problem where a cluster point is represented by the entire customer consumption time series. To overcome this aspect we first rethink the way cluster points are created and designed, and then design an efficient online clustering technique for demand response (DR) in order to analyze high volume, high dimensional energy consumption time series data at scale, and on the fly. Our online algorithm is randomized in nature, and provides optimal performance guarantees in a computationally efficient manner. Unlike prior work we (i) study the consumption properties of the whole population simultaneously rather than developing individual models for each customer separately, claiming it to be a ‘killer’ approach that breaks the “curse of dimensionality” in online time series clustering, and (ii) provide tight performance guarantees in theory to validate our approach. Our insights are driven by the field of sociology, where collective behavior often emerges as the result of individual patterns and lifestyles.

  15. A novel series of isoreticular metal organic frameworks: Realizing metastable structures by liquid phase epitaxy

    KAUST Repository

    Liu, Jinxuan

    2012-12-04

    A novel class of metal organic frameworks (MOFs) has been synthesized from Cu-acetate and dicarboxylic acids using liquid phase epitaxy. The SURMOF-2 isoreticular series exhibits P4 symmetry, for the longest linker a channel-size of 3 3 nm2 is obtained, one of the largest values reported for any MOF so far. High quality, ab-initio electronic structure calculations confirm the stability of a regular packing of (Cu++) 2-carboxylate paddle-wheel planes with P4 symmetry and reveal, that the SURMOF-2 structures are in fact metastable, with a fairly large activation barrier for the transition to the bulk MOF-2 structures exhibiting a lower, twofold (P2 or C2) symmetry. The theoretical calculations also allow identifying the mechanism for the low-temperature epitaxial growth process and to explain, why a synthesis of this highly interesting, new class of high-symmetry, metastable MOFs is not possible using the conventional solvothermal process.

  16. A novel series of isoreticular metal organic frameworks: Realizing metastable structures by liquid phase epitaxy

    KAUST Repository

    Liu, Jinxuan; Lukose, Binit; Shekhah, Osama; Arslan, Hasan Kemal; Weidler, Peter; Gliemann, Hartmut; Brä se, Stefan; Grosjean, Sylvain; Godt, Adelheid; Feng, Xinliang; Mü llen, Klaus; Magdau, Ioan-Bogdan; Heine, Thomas; Wö ll, Christof

    2012-01-01

    A novel class of metal organic frameworks (MOFs) has been synthesized from Cu-acetate and dicarboxylic acids using liquid phase epitaxy. The SURMOF-2 isoreticular series exhibits P4 symmetry, for the longest linker a channel-size of 3 3 nm2 is obtained, one of the largest values reported for any MOF so far. High quality, ab-initio electronic structure calculations confirm the stability of a regular packing of (Cu++) 2-carboxylate paddle-wheel planes with P4 symmetry and reveal, that the SURMOF-2 structures are in fact metastable, with a fairly large activation barrier for the transition to the bulk MOF-2 structures exhibiting a lower, twofold (P2 or C2) symmetry. The theoretical calculations also allow identifying the mechanism for the low-temperature epitaxial growth process and to explain, why a synthesis of this highly interesting, new class of high-symmetry, metastable MOFs is not possible using the conventional solvothermal process.

  17. Metal-Organic Framework of Lanthanoid Dinuclear Clusters Undergoes Slow Magnetic Relaxation

    Directory of Open Access Journals (Sweden)

    Hikaru Iwami

    2017-01-01

    Full Text Available Lanthanoid metal-organic frameworks (Ln-MOFs can adopt a variety of new structures due to the large coordination numbers of Ln metal ions, and Ln-MOFs are expected to show new luminescence and magnetic properties due to the localized f electrons. In particular, some Ln metal ions, such as Dy(III and Tb(III ions, work as isolated quantum magnets when they have magnetic anisotropy. In this work, using 4,4′,4″-s-triazine-2,4,6-triyl-tribenzoic acid (H3TATB as a ligand, two new Ln-MOFs, [Dy(TATB(DMF2] (1 and [Tb(TATB(DMF2] (2, were obtained. The Ln-MOFs contain Ln dinuclear clusters as secondary building units, and 1 underwent slow magnetic relaxation similar to single-molecule magnets.

  18. Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R

    Directory of Open Access Journals (Sweden)

    Michael Hahsler

    2017-02-01

    Full Text Available In recent years, data streams have become an increasingly important area of research for the computer science, database and statistics communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non-stationary data generating process. Common data mining tasks associated with data streams include clustering, classification and frequent pattern mining. New algorithms for these types of data are proposed regularly and it is important to evaluate them thoroughly under standardized conditions. In this paper we introduce stream, a research tool that includes modeling and simulating data streams as well as an extensible framework for implementing, interfacing and experimenting with algorithms for various data stream mining tasks. The main advantage of stream is that it seamlessly integrates with the large existing infrastructure provided by R. In addition to data handling, plotting and easy scripting capabilities, R also provides many existing algorithms and enables users to interface code written in many programming languages popular among data mining researchers (e.g., C/C++, Java and Python. In this paper we describe the architecture of stream and focus on its use for data stream clustering research. stream was implemented with extensibility in mind and will be extended in the future to cover additional data stream mining tasks like classification and frequent pattern mining.

  19. TIME-SERIES PHOTOMETRY OF GLOBULAR CLUSTERS: M62 (NGC 6266), THE MOST RR LYRAE-RICH GLOBULAR CLUSTER IN THE GALAXY?

    International Nuclear Information System (INIS)

    Contreras, R.; Catelan, M.; Smith, H. A.; Kuehn, C. A.; Pritzl, B. J.; Borissova, J.

    2010-01-01

    We present new time-series CCD photometry, in the B and V bands, for the moderately metal-rich ([Fe/H] ≅ -1.3) Galactic globular cluster M62 (NGC 6266). The present data set is the largest obtained so far for this cluster and consists of 168 images per filter, obtained with the Warsaw 1.3 m telescope at the Las Campanas Observatory and the 1.3 m telescope of the Cerro Tololo Inter-American Observatory, in two separate runs over the time span of 3 months. The procedure adopted to detect the variable stars was the optimal image subtraction method (ISIS v2.2), as implemented by Alard. The photometry was performed using both ISIS and Stetson's DAOPHOT/ALLFRAME package. We have identified 245 variable stars in the cluster fields that have been analyzed so far, of which 179 are new discoveries. Of these variables, 133 are fundamental mode RR Lyrae stars (RRab), 76 are first overtone (RRc) pulsators, 4 are type II Cepheids, 25 are long-period variables (LPVs), 1 is an eclipsing binary, and 6 are not yet well classified. Such a large number of RR Lyrae stars places M62 among the top two most RR Lyrae-rich (in the sense of total number of RR Lyrae stars present) globular clusters known in the Galaxy, second only to M3 (NGC 5272) with a total of 230 known RR Lyrae stars. Since this study covers most but not all of the cluster area, it is not unlikely that M62 is in fact the most RR Lyrae-rich globular cluster in the Galaxy. In like vein, thanks to the time coverage of our data sets, we were also able to detect the largest sample of LPVs known so far in a Galactic globular cluster. We analyze a variety of Oosterhoff type indicators for the cluster, including mean periods, period distribution, Bailey diagrams, and Fourier decomposition parameters (as well as the physical parameters derived therefrom). All of these indicators clearly show that M62 is an Oosterhoff type I system. This is in good agreement with the moderately high metallicity of the cluster, in spite of its

  20. Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.

    Science.gov (United States)

    Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru

    2010-12-01

    The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

  1. A Modularized Efficient Framework for Non-Markov Time Series Estimation

    Science.gov (United States)

    Schamberg, Gabriel; Ba, Demba; Coleman, Todd P.

    2018-06-01

    We present a compartmentalized approach to finding the maximum a-posteriori (MAP) estimate of a latent time series that obeys a dynamic stochastic model and is observed through noisy measurements. We specifically consider modern signal processing problems with non-Markov signal dynamics (e.g. group sparsity) and/or non-Gaussian measurement models (e.g. point process observation models used in neuroscience). Through the use of auxiliary variables in the MAP estimation problem, we show that a consensus formulation of the alternating direction method of multipliers (ADMM) enables iteratively computing separate estimates based on the likelihood and prior and subsequently "averaging" them in an appropriate sense using a Kalman smoother. As such, this can be applied to a broad class of problem settings and only requires modular adjustments when interchanging various aspects of the statistical model. Under broad log-concavity assumptions, we show that the separate estimation problems are convex optimization problems and that the iterative algorithm converges to the MAP estimate. As such, this framework can capture non-Markov latent time series models and non-Gaussian measurement models. We provide example applications involving (i) group-sparsity priors, within the context of electrophysiologic specrotemporal estimation, and (ii) non-Gaussian measurement models, within the context of dynamic analyses of learning with neural spiking and behavioral observations.

  2. A novel model for Time-Series Data Clustering Based on piecewise SVD and BIRCH for Stock Data Analysis on Hadoop Platform

    Directory of Open Access Journals (Sweden)

    Ibgtc Bowala

    2017-06-01

    Full Text Available With the rapid growth of financial markets, analyzers are paying more attention on predictions. Stock data are time series data, with huge amounts. Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative. There are various statistical models for forecasting time series data, but accurate clusters are a pre-requirement. Clustering analysis for time series data is one of the main methods for mining time series data for many other analysis processes. However, general clustering algorithms cannot perform clustering for time series data because series data has a special structure and a high dimensionality has highly co-related values due to high noise level. A novel model for time series clustering is presented using BIRCH, based on piecewise SVD, leading to a novel dimension reduction approach. Highly co-related features are handled using SVD with a novel approach for dimensionality reduction in order to keep co-related behavior optimal and then use BIRCH for clustering. The algorithm is a novel model that can handle massive time series data. Finally, this new model is successfully applied to real stock time series data of Yahoo finance with satisfactory results.

  3. Research on classified real-time flood forecasting framework based on K-means cluster and rough set.

    Science.gov (United States)

    Xu, Wei; Peng, Yong

    2015-01-01

    This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.

  4. Increasing Power by Sharing Information from Genetic Background and Treatment in Clustering of Gene Expression Time Series

    Directory of Open Access Journals (Sweden)

    Sura Zaki Alrashid

    2018-02-01

    Full Text Available Clustering of gene expression time series gives insight into which genes may be co-regulated, allowing us to discern the activity of pathways in a given microarray experiment. Of particular interest is how a given group of genes varies with different conditions or genetic background. This paper develops
a new clustering method that allows each cluster to be parameterised according to whether the behaviour of the genes across conditions is correlated or anti-correlated. By specifying correlation between such genes,more information is gain within the cluster about how the genes interrelate. Amyotrophic lateral sclerosis (ALS is an irreversible neurodegenerative disorder that kills the motor neurons and results in death within 2 to 3 years from the symptom onset. Speed of progression for different patients are heterogeneous with significant variability. The SOD1G93A transgenic mice from different backgrounds (129Sv and C57 showed consistent phenotypic differences for disease progression. A hierarchy of Gaussian isused processes to model condition-specific and gene-specific temporal co-variances. This study demonstrated about finding some significant gene expression profiles and clusters of associated or co-regulated gene expressions together from four groups of data (SOD1G93A and Ntg from 129Sv and C57 backgrounds. Our study shows the effectiveness of sharing information between replicates and different model conditions when modelling gene expression time series. Further gene enrichment score analysis and ontology pathway analysis of some specified clusters for a particular group may lead toward identifying features underlying the differential speed of disease progression.

  5. SERA Scenarios of Early Market Fuel Cell Electric Vehicle Introductions: Modeling Framework, Regional Markets, and Station Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Bush, B. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Melaina, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Penev, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Daniel, W. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2013-09-01

    This report describes the development and analysis of detailed temporal and spatial scenarios for early market hydrogen fueling infrastructure clustering and fuel cell electric vehicle rollout using the Scenario Evaluation, Regionalization and Analysis (SERA) model. The report provides an overview of the SERA scenario development framework and discusses the approach used to develop the nationwidescenario.

  6. A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

    Science.gov (United States)

    Ngan, Chun-Kit

    2013-01-01

    Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

  7. Short-term memory and critical clusterization in brain neurons spike series

    Science.gov (United States)

    Bershadskii, A.; Dremencov, E.; Yadid, G.

    2003-06-01

    A new phenomenon: critical clusterization, is observed in the neuron firing of a genetically defined rat model of depression. The critical clusterization is studied using a multiscaling analysis of the data obtained from the neurons belonging to the Red Nucleus area of the depressive brains. It is suggested that this critical phenomenon can be partially responsible for the observed ill behavior of the depressive brains: loss of short-term motor memory and slow motor reaction.

  8. TIME-SERIES SPECTROSCOPY OF TWO CANDIDATE DOUBLE DEGENERATES IN THE OPEN CLUSTER NGC 6633

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Kurtis A.; Chakraborty, Subho [Department of Physics and Astrophysics, Texas A and M University-Commerce, P.O. Box 3011, Commerce, TX, 75429 (United States); Serna-Grey, Donald [Department of Astronomy, University of Washington, Box 351580, Seattle, WA, 98195 (United States); Gianninas, A.; Canton, Paul A., E-mail: Kurtis.Williams@tamuc.edu [Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, 440 W. Brooks St., Norman, OK, 73019 (United States)

    2015-12-15

    SNe Ia are heavily used tools in precision cosmology, yet we still are not certain what the progenitor systems are. General plausibility arguments suggest there is potential for identifying double degenerate SN Ia progenitors in intermediate-age open star clusters. We present time-resolved high-resolution spectroscopy of two white dwarfs (WDs) in the field of the open cluster NGC 6633 that had previously been identified as candidate double degenerates in the cluster. However, three hours of continuous observations of each candidate failed to detect any significant radial velocity variations at the ≳10 km s{sup −1} level, making it highly unlikely that either WD is a double degenerate that will merge within a Hubble Time. The WD LAWDS NGC 6633 4 has a radial velocity inconsistent with cluster membership at the 2.5σ level, while the radial velocity of LAWDS NGC 6633 7 is consistent with cluster membership. We conservatively conclude that LAWDS 7 is a viable massive double degenerate candidate, though unlikely to be a Type Ia progenitor. Astrometric data from GAIA will likely be needed to determine if either WD is truly a cluster member.

  9. An annual framework for clustering-based pricing for an electricity retailer

    International Nuclear Information System (INIS)

    Mahmoudi-Kohan, N.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.

    2010-01-01

    In the competitive environment, it is necessary for a retailer to increase his/her profit as much as possible. There are few researches focused on the subjects related to the retailer and the retail market. In addition, those researches have mostly focused on the participation of the retailer in the wholesale market. In order to determine the optimal selling price, the knowledge of how and when consumers use electricity is essential to the retailer. This type of information can be found in load profiles of customers. In this paper, an annual framework for optimal price offering by a retailer is proposed which is based on clustering technique. For this purpose, load profiles of customers are used as their consumption patterns. Also, a profit function is defined as the objective of optimization problem based on the load profile considering conditional value at risk (CVaR) for risk modeling. Also, a new acceptance function is proposed to overcome drawbacks of the traditional ones. The objective function is a mixed-integer nonlinear problem which is solved by GAMS software. (author)

  10. Theories of behaviour change synthesised into a set of theoretical groupings: introducing a thematic series on the theoretical domains framework.

    Science.gov (United States)

    Francis, Jill J; O'Connor, Denise; Curran, Janet

    2012-04-24

    Behaviour change is key to increasing the uptake of evidence into healthcare practice. Designing behaviour-change interventions first requires problem analysis, ideally informed by theory. Yet the large number of partly overlapping theories of behaviour makes it difficult to select the most appropriate theory. The need for an overarching theoretical framework of behaviour change was addressed in research in which 128 explanatory constructs from 33 theories of behaviour were identified and grouped. The resulting Theoretical Domains Framework (TDF) appears to be a helpful basis for investigating implementation problems. Research groups in several countries have conducted TDF-based studies. It seems timely to bring together the experience of these teams in a thematic series to demonstrate further applications and to report key developments. This overview article describes the TDF, provides a brief critique of the framework, and introduces this thematic series.In a brief review to assess the extent of TDF-based research, we identified 133 papers that cite the framework. Of these, 17 used the TDF as the basis for empirical studies to explore health professionals' behaviour. The identified papers provide evidence of the impact of the TDF on implementation research. Two major strengths of the framework are its theoretical coverage and its capacity to elicit beliefs that could signify key mediators of behaviour change. The TDF provides a useful conceptual basis for assessing implementation problems, designing interventions to enhance healthcare practice, and understanding behaviour-change processes. We discuss limitations and research challenges and introduce papers in this series.

  11. Series-NonUniform Rational B-Spline (S-NURBS) model: a geometrical interpolation framework for chaotic data.

    Science.gov (United States)

    Shao, Chenxi; Liu, Qingqing; Wang, Tingting; Yin, Peifeng; Wang, Binghong

    2013-09-01

    Time series is widely exploited to study the innate character of the complex chaotic system. Existing chaotic models are weak in modeling accuracy because of adopting either error minimization strategy or an acceptable error to end the modeling process. Instead, interpolation can be very useful for solving differential equations with a small modeling error, but it is also very difficult to deal with arbitrary-dimensional series. In this paper, geometric theory is considered to reduce the modeling error, and a high-precision framework called Series-NonUniform Rational B-Spline (S-NURBS) model is developed to deal with arbitrary-dimensional series. The capability of the interpolation framework is proved in the validation part. Besides, we verify its reliability by interpolating Musa dataset. The main improvement of the proposed framework is that we are able to reduce the interpolation error by properly adjusting weights series step by step if more information is given. Meanwhile, these experiments also demonstrate that studying the physical system from a geometric perspective is feasible.

  12. A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

    Directory of Open Access Journals (Sweden)

    Ozge Cagcag Yolcu

    2013-01-01

    Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

  13. What Factors Constitute Structures of Clustering Creative Industries? Incorporating New Institutional Economics and New Economic Sociology into A Conceptual Framework

    Directory of Open Access Journals (Sweden)

    Poppy Ismalina

    2012-11-01

    Full Text Available Creative industries tend to cluster in specific places and the reasons for this phenomenon can be a multiplicity of elements linked mainly to culture, creativity, innovation and local development. In the international literature, it is pretty well recognized that creativity is frequently characterized by the agglomeration of firms so that creative industries are not homogeneously distributed across the territory but they are concentrated in the space. Three theories are becoming the dominant theoretical perspectives in agglomeration economies theory and they are increasingly being applied in industrial clusters analysis to study the effect of clustering industries. The theories are Marshall’s theoretical principles of localization economies, Schmitz’s collective efficiency and Porter’s five-diamond approach. However, those have adequately theorized neither the institutionalization process through which change takes place nor the socio-economic context of the institutional formations of clustering creative industries. This text begins by reviewing three main theories to more fully articulate institutionalization processes of an economic institution. Specifically, this paper incorporates new institutional economics (NIE and new economic sociology (NES to explain the processes associated with creating institutional practices within clustering creative industries. Both streams of institutional theory constitute that economic organizations are socially constructed. Next, this text proposes the framework that depicts the socio-economic context better and more directly addresses the dynamics of enacting, embedding and changing organizational features and processes within clustering creative industries. Some pertinent definitions are offered to be used in a conceptual framework of research about how economic institutions like clustering creative industries constitute their structures.

  14. The Application of Clustering Techniques to Citation Data. Research Reports Series B No. 6.

    Science.gov (United States)

    Arms, William Y.; Arms, Caroline

    This report describes research carried out as part of the Design of Information Systems in the Social Sciences (DISISS) project. Cluster analysis techniques were applied to a machine readable file of bibliographic data in the form of cited journal titles in order to identify groupings which could be used to structure bibliographic files. Practical…

  15. [Ti8Zr2O12(COO16] Cluster: An Ideal Inorganic Building Unit for Photoactive Metal–Organic Frameworks

    Directory of Open Access Journals (Sweden)

    Shuai Yuan

    2017-11-01

    Full Text Available Metal–organic frameworks (MOFs based on Ti-oxo clusters (Ti-MOFs represent a naturally self-assembled superlattice of TiO2 nanoparticles separated by designable organic linkers as antenna chromophores, epitomizing a promising platform for solar energy conversion. However, despite the vast, diverse, and well-developed Ti-cluster chemistry, only a scarce number of Ti-MOFs have been documented. The synthetic conditions of most Ti-based clusters are incompatible with those required for MOF crystallization, which has severely limited the development of Ti-MOFs. This challenge has been met herein by the discovery of the [Ti8Zr2O12­(COO16] cluster as a nearly ideal building unit for photoactive MOFs. A family of isoreticular photoactive MOFs were assembled, and their orbital alignments were fine-tuned by rational functionalization of organic linkers under computational guidance. These MOFs demonstrate high porosity, excellent chemical stability, tunable photoresponse, and good activity toward photocatalytic hydrogen evolution reactions. The discovery of the [Ti8Zr2O12­(COO16] cluster and the facile construction of photoactive MOFs from this cluster shall pave the way for the development of future Ti-MOF-based photocatalysts.

  16. [Ti8Zr2O12(COO)16] Cluster: An Ideal Inorganic Building Unit for Photoactive Metal-Organic Frameworks.

    Science.gov (United States)

    Yuan, Shuai; Qin, Jun-Sheng; Xu, Hai-Qun; Su, Jie; Rossi, Daniel; Chen, Yuanping; Zhang, Liangliang; Lollar, Christina; Wang, Qi; Jiang, Hai-Long; Son, Dong Hee; Xu, Hongyi; Huang, Zhehao; Zou, Xiaodong; Zhou, Hong-Cai

    2018-01-24

    Metal-organic frameworks (MOFs) based on Ti-oxo clusters (Ti-MOFs) represent a naturally self-assembled superlattice of TiO 2 nanoparticles separated by designable organic linkers as antenna chromophores, epitomizing a promising platform for solar energy conversion. However, despite the vast, diverse, and well-developed Ti-cluster chemistry, only a scarce number of Ti-MOFs have been documented. The synthetic conditions of most Ti-based clusters are incompatible with those required for MOF crystallization, which has severely limited the development of Ti-MOFs. This challenge has been met herein by the discovery of the [Ti 8 Zr 2 O 12 (COO) 16 ] cluster as a nearly ideal building unit for photoactive MOFs. A family of isoreticular photoactive MOFs were assembled, and their orbital alignments were fine-tuned by rational functionalization of organic linkers under computational guidance. These MOFs demonstrate high porosity, excellent chemical stability, tunable photoresponse, and good activity toward photocatalytic hydrogen evolution reactions. The discovery of the [Ti 8 Zr 2 O 12 (COO) 16 ] cluster and the facile construction of photoactive MOFs from this cluster shall pave the way for the development of future Ti-MOF-based photocatalysts.

  17. Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data.

    Science.gov (United States)

    Vera, J Fernando; Macías, Rodrigo

    2017-06-01

    One of the main problems in cluster analysis is that of determining the number of groups in the data. In general, the approach taken depends on the cluster method used. For K-means, some of the most widely employed criteria are formulated in terms of the decomposition of the total point scatter, regarding a two-mode data set of N points in p dimensions, which are optimally arranged into K classes. This paper addresses the formulation of criteria to determine the number of clusters, in the general situation in which the available information for clustering is a one-mode [Formula: see text] dissimilarity matrix describing the objects. In this framework, p and the coordinates of points are usually unknown, and the application of criteria originally formulated for two-mode data sets is dependent on their possible reformulation in the one-mode situation. The decomposition of the variability of the clustered objects is proposed in terms of the corresponding block-shaped partition of the dissimilarity matrix. Within-block and between-block dispersion values for the partitioned dissimilarity matrix are derived, and variance-based criteria are subsequently formulated in order to determine the number of groups in the data. A Monte Carlo experiment was carried out to study the performance of the proposed criteria. For simulated clustered points in p dimensions, greater efficiency in recovering the number of clusters is obtained when the criteria are calculated from the related Euclidean distances instead of the known two-mode data set, in general, for unequal-sized clusters and for low dimensionality situations. For simulated dissimilarity data sets, the proposed criteria always outperform the results obtained when these criteria are calculated from their original formulation, using dissimilarities instead of distances.

  18. IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

    Science.gov (United States)

    Chang, Hsien-Tsung; Mishra, Nilamadhab; Lin, Chung-Chih

    2015-01-01

    The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.

  19. IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

    Science.gov (United States)

    Chang, Hsien-Tsung; Mishra, Nilamadhab; Lin, Chung-Chih

    2015-01-01

    The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions. PMID:26600156

  20. IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

    Directory of Open Access Journals (Sweden)

    Hsien-Tsung Chang

    Full Text Available The current rapid growth of Internet of Things (IoT in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.

  1. Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

    Directory of Open Access Journals (Sweden)

    Yaolong Li

    2017-01-01

    Full Text Available By focusing on the issue of rolling element bearing (REB performance degradation assessment (PDA, a solution based on variational mode decomposition (VMD and Gath-Geva clustering time series segmentation (GGCTSS has been proposed. VMD is a new decomposition method. Since it is different from the recursive decomposition method, for example, empirical mode decomposition (EMD, local mean decomposition (LMD, and local characteristic-scale decomposition (LCD, VMD needs a priori parameters. In this paper, we will propose a method to optimize the parameters in VMD, namely, the number of decomposition modes and moderate bandwidth constraint, based on genetic algorithm. Executing VMD with the acquired parameters, the BLIMFs are obtained. By taking the envelope of the BLIMFs, the sensitive BLIMFs are selected. And then we take the amplitude of the defect frequency (ADF as a degradative feature. To get the performance degradation assessment, we are going to use the method called Gath-Geva clustering time series segmentation. Afterwards, the method is carried out by two pieces of run-to-failure data. The results indicate that the extracted feature could depict the process of degradation precisely.

  2. Case series: toxicity from 25B-NBOMe--a cluster of N-bomb cases.

    Science.gov (United States)

    Gee, Paul; Schep, Leo J; Jensen, Berit P; Moore, Grant; Barrington, Stuart

    2016-01-01

    Background A new class of hallucinogens called NBOMes has emerged. This class includes analogues 25I-NBOMe, 25C-NBOMe and 25B-NBOMe. Case reports and judicial seizures indicate that 25I-NBOMe and 25C-NBOMe are more prevalently abused. There have been a few confirmed reports of 25B-NBOMe use or toxicity. Report Observational case series. This report describes a series of 10 patients who suffered adverse effects from 25B-NBOMe. Hallucinations and violent agitation predominate along with serotonergic/stimulant signs such as mydriasis, tachycardia, hypertension and hyperthermia. The majority (7/10) required sedation with benzodiazepines. Analytical method 25B-NBOMe concentrations in plasma and urine were quantified in all patients using a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Peak plasma levels were measured between 0.7-10.1 ng/ml. Discussion The NBOMes are desired by users because of their hallucinogenic and stimulant effects. They are often sold as LSD or synthetic LSD. Reported cases of 25B- NBOMe toxicity are reviewed and compared to our series. Seizures and one pharmacological death have been described but neither were observed in our series. Based on our experience with cases of mild to moderate toxicity, we suggest that management should be supportive and focused on preventing further (self) harm. High doses of benzodiazepines may be required to control agitation. Patients who develop significant hyperthermia need to be actively managed. Conclusions Effects from 25B-NBOMe in our series were similar to previous individual case reports. The clinical features were also similar to effects from other analogues in the class (25I-NBOMe, 25C-NBOMe). Violent agitation frequently present along with signs of serotonergic stimulation. Hyperthermia, rhabdomyolysis and kidney injury were also observed.

  3. clusters

    Indian Academy of Sciences (India)

    2017-09-27

    Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...

  4. clusters

    Indian Academy of Sciences (India)

    environmental as well as technical problems during fuel gas utilization. ... adsorption on some alloys of Pd, namely PdAu, PdAg ... ried out on small neutral and charged Au24,26,27, Cu,28 ... study of Zanti et al.29 on Pdn (n = 1–9) clusters.

  5. A Framework Based on Sustainability, Open Innovation, and Value Cocreation Paradigms—A Case in an Italian Maritime Cluster

    Directory of Open Access Journals (Sweden)

    Daniela Rupo

    2018-03-01

    Full Text Available The paper deals with a case study in an Italian maritime cluster seen through a multiple paradigms framework, based on Sustainability (SUS, Open Innovation (OI, and Value Co-creation (VCc. The proposed theoretical framework helps to interpret a true phenomenon consisting of the design of a new product with a prototype created in a network of multiple actors. The approach adopted stems in part from recent writings in qualitative research methodology and is quite apt in this context considering the qualitative, confirmatory nature of this work. The prototype named “TESEO I” was realized through open innovation aimed at sustainability, not only directed at environmental aspects but synergistically with value cocreation, which emerged from interaction among the actors, while also including social and economic aspects. The work concludes with a discussion of theoretical implications related to the proposed framework and the results that emerged from the case study, with both referring to sustainability, open innovation, and value cocreation.

  6. Measuring Group Synchrony: A Cluster-Phase Method for Analyzing Multivariate Movement Time-Series

    Directory of Open Access Journals (Sweden)

    Michael eRichardson

    2012-10-01

    Full Text Available A new method for assessing group synchrony is introduced as being potentially useful for objectively determining degree of group cohesiveness or entitativity. The cluster-phase method of Frank and Richardson (2010 was used to analyze movement data from the rocking chair movements of six-member groups who rocked their chairs while seated in a circle facing the center. In some trials group members had no information about others’ movements (their eyes were shut or they had their eyes open and gazed at a marker in the center of the group. As predicted, the group level synchrony measure was able to distinguish between situations where synchrony would have been possible and situations where it would be impossible. Moreover, other aspects of the analysis illustrated how the cluster phase measures can be used to determine the type of patterning of group synchrony, and, when integrated with multi-level modeling, can be used to examine individual-level differences in synchrony and dyadic level synchrony as well.

  7. hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.

    Science.gov (United States)

    Fulcher, Ben D; Jones, Nick S

    2017-11-22

    Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  8. A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.

    Science.gov (United States)

    Lara, Juan A; Lizcano, David; Pérez, Aurora; Valente, Juan P

    2014-10-01

    There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG). Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Solvent-mediated secondary building units (SBUs) diversification in a series of MnII-based metal-organic frameworks (MOFs)

    Science.gov (United States)

    Niu, Yan-Fei; Cui, Li-Ting; Han, Jie; Zhao, Xiao-Li

    2016-09-01

    The role of auxiliary solvents in the formation of MOFs has been investigated for a series of MnII-based framework systems. Reactions of 4,4‧,4″-nitrilotribenzoic acid (H3L) with MnII through varying auxiliary solvents of the medium resulted in the formation of diversified multinuclear MnII subunits in four new coordination polymers: [Mn3(L)(HCOO)3(DEF)3] (1), [Mn3(L)2(EtOH)2]·DMF (2), [Mn5(L)4(H2O)2]·2(H2NMe2)+·4DMF·2H2O (3), and [Mn3(L)2(py)4(H2O)]·H2O (4) (H3L=4,4‧,4‧-nitrilotribenzoic acid, DMF=dimethylformamide, DEF=N,N-diethylformamide, py=pyridine). These four compounds were fully characterized by single-crystal X-ray diffraction, showing interesting SBUs variations. For compound 1, it displays a (3,6)-connected kgd net with wheel [Mn6] cluster serving as SBU, whereas in 2, the sequence of Mn3(COO)9(EtOH)2 is repeated by inversion centers located between Mn1 and Mn3 to form an infinite Mn-carboxylate chain, which are further interlinked by L3- ligands to form a 3D architecture. In 3, the pentanuclear Mn5(CO2)12 clusters are interlinked to form a layer, which are further pillared by L3- to generate a 3D network. Compound 4 has a (3,6)-connected network in which the SBU is a linear trimeric Mn3(COO)6(py)4(H2O) cluster. In addition, the thermal stabilities, X-ray powder diffraction of all the compounds were studied, photoluminescence behaviors of compounds 1, 3 and 4 are discussed.

  10. A KST framework for correlation network construction from time series signals

    Science.gov (United States)

    Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

    2018-04-01

    A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

  11. Chromium: A Stress-Processing Framework for Interactive Rendering on Clusters

    International Nuclear Information System (INIS)

    Humphreys, G.; Houston, M.; Ng, Y.-R.; Frank, R.; Ahern, S.; Kirchner, P.D.; Klosowski, J.T.

    2002-01-01

    We describe Chromium, a system for manipulating streams of graphics API commands on clusters of workstations. Chromium's stream filters can be arranged to create sort-first and sort-last parallel graphics architectures that, in many cases, support the same applications while using only commodity graphics accelerators. In addition, these stream filters can be extended programmatically, allowing the user to customize the stream transformations performed by nodes in a cluster. Because our stream processing mechanism is completely general, any cluster-parallel rendering algorithm can be either implemented on top of or embedded in Chromium. In this paper, we give examples of real-world applications that use Chromium to achieve good scalability on clusters of workstations, and describe other potential uses of this stream processing technology. By completely abstracting the underlying graphics architecture, network topology, and API command processing semantics, we allow a variety of applications to run in different environments

  12. Utilizing an ANP framework for prioritizing effective criteria on industrial clusters' formation

    Directory of Open Access Journals (Sweden)

    Jalal Haghighat Monfared

    2012-04-01

    Full Text Available Clustering plays an important role on developing industries, since business units can take advantage of many existing industries for trouble shooting or sharing their experiences to increase efficiency. One of the primary concerns for developing clustering is to identify and remove important barriers. In this paper, we gather experts' feedbacks on forming clustering in Iran's industries and, using analytical network process, we prioritize the important factors and provide some necessary guidelines to develop clustering. The results of this paper indicate that the existence of a supplier network is the most important factor, followed by the existence of competition between operational units, existence of high-risk investors, existence of suitable infrastructures. There are also other less important criteria including the existence of flexibility, suitable technology and competition, governmental regularities, social background, trust, etc.

  13. Cluster formation in precompound nuclei in the time-dependent framework

    Science.gov (United States)

    Schuetrumpf, B.; Nazarewicz, W.

    2017-12-01

    Background: Modern applications of nuclear time-dependent density functional theory (TDDFT) are often capable of providing quantitative description of heavy ion reactions. However, the structures of precompound (preequilibrium, prefission) states produced in heavy ion reactions are difficult to assess theoretically in TDDFT as the single-particle density alone is a weak indicator of shell structure and cluster states. Purpose: We employ the time-dependent nucleon localization function (NLF) to reveal the structure of precompound states in nuclear reactions involving light and medium-mass ions. We primarily focus on spin saturated systems with N =Z . Furthermore, we study reactions with oxygen and carbon ions, for which some experimental evidence for α clustering in precompound states exists. Method: We utilize the symmetry-free TDDFT approach with the Skyrme energy density functional UNEDF1 and compute the time-dependent NLFs to describe 16O + 16O,40Ca + 16O, 40Ca + 40Ca, and O,1816 + 12C collisions at energies above the Coulomb barrier. Results: We show that NLFs reveal a variety of time-dependent modes involving cluster structures. For instance, the 16O + 16O collision results in a vibrational mode of a quasimolecular α - 12C - 12C-α state. For heavier ions, a variety of cluster configurations are predicted. For the collision of O,1816 + 12C, we showed that the precompound system has a tendency to form α clusters. This result supports the experimental findings that the presence of cluster structures in the projectile and target nuclei gives rise to strong entrance channel effects and enhanced α emission. Conclusion: The time-dependent nucleon localization measure is a very good indicator of cluster structures in complex precompound states formed in heavy-ion fusion reactions. The localization reveals the presence of collective vibrations involving cluster structures, which dominate the initial dynamics of the fusing system.

  14. METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS.

    Science.gov (United States)

    Tignor, Nicole; Wang, Pei; Genes, Nicholas; Rogers, Linda; Hershman, Steven G; Scott, Erick R; Zweig, Micol; Yvonne Chan, Yu-Feng; Schadt, Eric E

    2017-01-01

    In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important

  15. Stepped-wedge cluster randomised controlled trials: a generic framework including parallel and multiple-level designs.

    Science.gov (United States)

    Hemming, Karla; Lilford, Richard; Girling, Alan J

    2015-01-30

    Stepped-wedge cluster randomised trials (SW-CRTs) are being used with increasing frequency in health service evaluation. Conventionally, these studies are cross-sectional in design with equally spaced steps, with an equal number of clusters randomised at each step and data collected at each and every step. Here we introduce several variations on this design and consider implications for power. One modification we consider is the incomplete cross-sectional SW-CRT, where the number of clusters varies at each step or where at some steps, for example, implementation or transition periods, data are not collected. We show that the parallel CRT with staggered but balanced randomisation can be considered a special case of the incomplete SW-CRT. As too can the parallel CRT with baseline measures. And we extend these designs to allow for multiple layers of clustering, for example, wards within a hospital. Building on results for complete designs, power and detectable difference are derived using a Wald test and obtaining the variance-covariance matrix of the treatment effect assuming a generalised linear mixed model. These variations are illustrated by several real examples. We recommend that whilst the impact of transition periods on power is likely to be small, where they are a feature of the design they should be incorporated. We also show examples in which the power of a SW-CRT increases as the intra-cluster correlation (ICC) increases and demonstrate that the impact of the ICC is likely to be smaller in a SW-CRT compared with a parallel CRT, especially where there are multiple levels of clustering. Finally, through this unified framework, the efficiency of the SW-CRT and the parallel CRT can be compared. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

  16. ECONOMETRIC APPROACH OF HETEROSKEDASTICITY ON FINANCIAL TIME SERIES IN A GENERAL FRAMEWORK

    Directory of Open Access Journals (Sweden)

    FELICIA RAMONA BIRĂU

    2012-12-01

    Full Text Available The aim of this paper is to provide an overview of the diagnostic tests for detecting heteroskedasticity on financial time series. In financial econometrics, heteroskedasticity is generally associated with cross sectional data but can also be identified modeling time series data. The presence of heteroscedasticity in financial time series can be caused by certain specific factors, like a model misspecification, inadequate data transformation or as a result of certain outliers. Heteroskedasticity arise when the homoskedasticity assumption is violated. Testing for the presence of heteroskedasticity in financial time is performed by applying diagnostic test, such as : Breusch-Pagan LM test, White’s test, Glesjer LM test, Harvey-Godfrey LM test, Park LM test and Goldfeld-Quand test.

  17. Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2012-01-01

    Full Text Available Voice biometrics has a long history in biosecurity applications such as verification and identification based on characteristics of the human voice. The other application called voice classification which has its important role in grouping unlabelled voice samples, however, has not been widely studied in research. Lately voice classification is found useful in phone monitoring, classifying speakers’ gender, ethnicity and emotion states, and so forth. In this paper, a collection of computational algorithms are proposed to support voice classification; the algorithms are a combination of hierarchical clustering, dynamic time wrap transform, discrete wavelet transform, and decision tree. The proposed algorithms are relatively more transparent and interpretable than the existing ones, though many techniques such as Artificial Neural Networks, Support Vector Machine, and Hidden Markov Model (which inherently function like a black box have been applied for voice verification and voice identification. Two datasets, one that is generated synthetically and the other one empirically collected from past voice recognition experiment, are used to verify and demonstrate the effectiveness of our proposed voice classification algorithm.

  18. Framework for Optimizing Cluster Selection using Geo-assisted Movement Prediction

    DEFF Research Database (Denmark)

    Kristensen, Thomas Sander; Madsen, Jacob Theilgaard; Pedersen, Michael Sølvkjær

    2012-01-01

    Due to the availability of satellite- and radio-based location systems in most new devices, it is possible to use geographical location of a node for network management and communication protocol optimization. It is a common belief that usage of location information can bring performance benefits...... movement prediction and inaccurate location estimation on its performance. The proposed algorithm is compared with two reference algorithms: when a considered node associates with either the first discovered cluster or the nearest cluster. Evaluation shows significant performance benefits in terms...

  19. A Unified Framework for Estimating Minimum Detectable Effects for Comparative Short Interrupted Time Series Designs

    Science.gov (United States)

    Price, Cristofer; Unlu, Fatih

    2014-01-01

    The Comparative Short Interrupted Time Series (C-SITS) design is a frequently employed quasi-experimental method, in which the pre- and post-intervention changes observed in the outcome levels of a treatment group is compared with those of a comparison group where the difference between the former and the latter is attributed to the treatment. The…

  20. Half-lives for proton emission, alpha decay, cluster radioactivity, and cold fission processes calculated in a unified theoretical framework

    International Nuclear Information System (INIS)

    Duarte, S.B.; Tavares, O.A.P.; Guzman, F.; Dimarco, A.; Garcia, F.; Goncalves, M.

    2002-01-01

    Half-life values of spontaneous nuclear decay processes are presented in the framework of the Effective Liquid Drop Model (ELDM) using the combination of varying mass asymmetry shape description for the mass transfer with Werner-Wheeler's inertia coefficient V MAS /WW. The calculated half-lives of ground-state to ground-state transitions for the proton emission, alpha decay, cluster radioactivity, and cold fission processes are compared with experimental data. Results have shown that the ELDM is a very efficient model to describe these different decay processes in a same, unified theoretical framework. A Table listing the predicted half-life values, τ c is presented for all possible cases of spontaneous nuclear break-up such that -7.30 10 τ c [S] 10 (τ/τ c ) > -17.0, where τ is the total half-life of the parent nucleus. (author)

  1. Entrapment of metal clusters in metal-organic framework channels by extended hooks anchored at open metal sites.

    Science.gov (United States)

    Zheng, Shou-Tian; Zhao, Xiang; Lau, Samuel; Fuhr, Addis; Feng, Pingyun; Bu, Xianhui

    2013-07-17

    Reported here are the new concept of utilizing open metal sites (OMSs) for architectural pore design and its practical implementation. Specifically, it is shown here that OMSs can be used to run extended hooks (isonicotinates in this work) from the framework walls to the channel centers to effect the capture of single metal ions or clusters, with the concurrent partitioning of the large channel spaces into multiple domains, alteration of the host-guest charge relationship and associated guest-exchange properties, and transfer of OMSs from the walls to the channel centers. The concept of the extended hook, demonstrated here in the multicomponent dual-metal and dual-ligand system, should be generally applicable to a range of framework types.

  2. A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

    Science.gov (United States)

    Bao, Wei; Yue, Jun; Rao, Yulei

    2017-01-01

    The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

  3. A deep learning framework for financial time series using stacked autoencoders and long-short term memory

    Science.gov (United States)

    Bao, Wei; Rao, Yulei

    2017-01-01

    The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance. PMID:28708865

  4. A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

    Directory of Open Access Journals (Sweden)

    Wei Bao

    Full Text Available The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT, stacked autoencoders (SAEs and long-short term memory (LSTM are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

  5. A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

    2011-01-01

    comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we...

  6. Conceptual Framework for the National Pilot Project on Livestock and the Environment, Livestock Series Report 2

    OpenAIRE

    Aziz Bouzaher; Stanley R. Johnson; Shannon Neibergs; Ron Jones; Larry Beran; Larry Frarey; Larry M. Hauck

    1993-01-01

    Assessing the effects of alternative policies that regulate nonpoint pollution from concentrated animal feeding operations (CAFOs) requires insight into the interactions of livestock production practices, waste management technologies, and their impacts on the environment. CAFOs have been identified as a source of nutrient loadings that impair ground and surface water quality, and they can emit intense odor that impairs air quality. This report describes the conceptual framework and the integ...

  7. Pattern-Based Development of Enterprise Systems: from Conceptual Framework to Series of Implementations

    Directory of Open Access Journals (Sweden)

    Sergey V. Zykov

    2013-04-01

    Full Text Available Building enterprise software is a dramatic challenge due to data size, complexity and rapid growth of the both in time. The issue becomes even more dramatic when it gets to integrating heterogeneous applications. Therewith, a uniform approach is required, which combines formal models and CASE tools. The methodology is based on extracting common ERP module level patterns and applying them to series of heterogeneous implementations. The approach includes a lifecycle model, which extends conventional spiral model by formal data representation/management models and DSL-based "low-level" CASE tools supporting the formalisms. The methodology has been successfully implemented as a series of portal-based ERP systems in ITERA oil-and-gas corporation, and in a number of trading/banking enterprise applications for other enterprises. Semantic network-based airline dispatch system, and a 6D-model-driven nuclear power plant construction support system are currently in progress.

  8. Bridging Zirconia Nodes within a Metal–Organic Framework via Catalytic Ni-Hydroxo Clusters to Form Heterobimetallic Nanowires

    International Nuclear Information System (INIS)

    Platero-Prats, Ana E.; League, Aaron B.; Bernales, Varinia; Ye, Jingyun; Gallington, Leighanne C.

    2017-01-01

    Metal-organic frameworks (MOFs), with their well-ordered pore networks and tunable surface chemistries, offer a versatile platform for preparing well-defined nanostructures wherein functionality such as catalysis can be incorporated. Notably, atomic layer deposition (ALD) in MOFs has recently emerged as a versatile approach to functionalize MOF surfaces with a wide variety of catalytic metal-oxo species. Understanding the structure of newly deposited species and how they are tethered within the MOF is critical to understanding how these components couple to govern the active material properties. By combining local and long-range structure probes, including X-ray absorption spectroscopy, pair distribution function analysis and difference envelope density analysis, with electron microscopy imag-ing and computational modeling, we resolve the precise atomic structure of metal-oxo species deposited in the MOF NU-1000 through ALD. These analyses demonstrate that deposition of NiO x H y clusters occurs selectively within the smallest pores of NU-1000, between the zirconia nodes, serving to connect these nodes along the c-direction to yield hetero-bimetallic metal-oxo nanowires. Finally, this bridging motif perturbs the NU-1000 framework structure, drawing the zirconia nodes closer together, and also underlies the sintering-resistance of these clusters during the hydrogenation of light olefins.

  9. Methane Oxidation to Methanol Catalyzed by Cu-Oxo Clusters Stabilized in NU-1000 Metal-Organic Framework.

    Science.gov (United States)

    Ikuno, Takaaki; Zheng, Jian; Vjunov, Aleksei; Sanchez-Sanchez, Maricruz; Ortuño, Manuel A; Pahls, Dale R; Fulton, John L; Camaioni, Donald M; Li, Zhanyong; Ray, Debmalya; Mehdi, B Layla; Browning, Nigel D; Farha, Omar K; Hupp, Joseph T; Cramer, Christopher J; Gagliardi, Laura; Lercher, Johannes A

    2017-08-02

    Copper oxide clusters synthesized via atomic layer deposition on the nodes of the metal-organic framework (MOF) NU-1000 are active for oxidation of methane to methanol under mild reaction conditions. Analysis of chemical reactivity, in situ X-ray absorption spectroscopy, and density functional theory calculations are used to determine structure/activity relations in the Cu-NU-1000 catalytic system. The Cu-loaded MOF contained Cu-oxo clusters of a few Cu atoms. The Cu was present under ambient conditions as a mixture of ∼15% Cu + and ∼85% Cu 2+ . The oxidation of methane on Cu-NU-1000 was accompanied by the reduction of 9% of the Cu in the catalyst from Cu 2+ to Cu + . The products, methanol, dimethyl ether, and CO 2 , were desorbed with the passage of 10% water/He at 135 °C, giving a carbon selectivity for methane to methanol of 45-60%. Cu oxo clusters stabilized in NU-1000 provide an active, first generation MOF-based, selective methane oxidation catalyst.

  10. Training nurses in a competency framework to support adults with epilepsy and intellectual disability: the EpAID cluster RCT.

    Science.gov (United States)

    Ring, Howard; Howlett, James; Pennington, Mark; Smith, Christopher; Redley, Marcus; Murphy, Caroline; Hook, Roxanne; Platt, Adam; Gilbert, Nakita; Jones, Elizabeth; Kelly, Joanna; Pullen, Angela; Mander, Adrian; Donaldson, Cam; Rowe, Simon; Wason, James; Irvine, Fiona

    2018-02-01

    People with an intellectual (learning) disability (ID) and epilepsy have an increased seizure frequency, higher frequencies of multiple antiepileptic drug (AED) use and side effects, higher treatment costs, higher mortality rates and more behavioural problems than the rest of the population with epilepsy. The introduction of nurse-led care may lead to improvements in outcome for those with an ID and epilepsy; however, this has not been tested in a definitive clinical trial. To determine whether or not ID nurses, using a competency framework developed to optimise nurse management of epilepsy in people with an ID, can cost-effectively improve clinical and quality-of-life outcomes in the management of epilepsy compared with treatment as usual. Cluster-randomised two-arm trial. Community-based secondary care delivered by members of community ID teams. Participants were adults aged 18-65 years with an ID and epilepsy under the care of a community ID team and had had at least one seizure in the 6 months before the trial. The experimental intervention was the Learning Disability Epilepsy Specialist Nurse Competency Framework. This provides guidelines describing a structure and goals to support the delivery of epilepsy care and management by ID-trained nurses. The primary outcome was the seizure severity scale from the Epilepsy and Learning Disabilities Quality of Life questionnaire. Measures of mood, behaviour, AED side effects and carer strain were also collected. A cost-utility analysis was undertaken along with a qualitative examination of carers' views of participants' epilepsy management. In total, 312 individuals were recruited into the study from 17 research clusters. Using an intention-to-treat analysis controlling for baseline individual-level and cluster-level variables there was no significant difference in seizure severity score between the two arms. Altogether, 238 complete cases were included in the non-imputed primary analysis. Analyses of the secondary

  11. CCD time-series photometry of the globular cluster NGC 5053: RR Lyrae, Blue Stragglers and SX Phoenicis stars revisited

    Science.gov (United States)

    Arellano Ferro, A.; Giridhar, Sunetra; Bramich, D. M.

    2010-02-01

    We report the results of CCD V, r and I time-series photometry of the globular cluster NGC 5053. New times of maximum light are given for the eight known RR Lyrae stars in the field of our images, and their periods are revised. Their V light curves were Fourier decomposed to estimate their physical parameters. A discussion on the accuracy of the Fourier-based iron abundances, temperatures, masses and radii is given. New periods are found for the five known SX Phe stars, and a critical discussion of their secular period changes is offered. The mean iron abundance for the RR Lyrae stars is found to be [Fe/H] ~ -1.97 +/- 0.16 and lower values are not supported by the present analysis. The absolute magnitude calibrations of the RR Lyrae stars yield an average true distance modulus of 16.12 +/- 0.04 or a distance of 16.7 +/- 0.3 kpc. Comparison of the observational colour magnitude diagram (CMD) with theoretical isochrones indicates an age of 12.5 +/- 2.0 Gyr for the cluster. A careful identification of all reported blue stragglers (BS) and their V, I magnitudes leads to the conclusion that BS12, BS22, BS23 and BS24 are not BS. On the other hand, three new BS are reported. Variability was found in seven BS, very likely of the SX Phe type in five of them, and in one red giant star. The new SX Phe stars follow established Period-Luminosity relationships and indicate a distance in agreement with the distance from the RR Lyrae stars. Based on observations collected at the Indian Astrophysical Observatory, Hanle, India. E-mail: armando@astroscu.unam.mx (AAF); giridhar@iiap.res.in (SG); dan.bramich@hotmail.co.uk (DMB)

  12. Control rod cluster drop time anomaly Guandong nuclear power station (Daya bay) and Electricite de France nuclear power stations (1450 MWe N4 Series)

    International Nuclear Information System (INIS)

    Olivera, J.J.; Naury, S.; Tricot, N.; Tran Dai, P.; Gama, J.M.

    1996-01-01

    The anomaly of control rod cluster drop time revealed at Guandong Nuclear Power Station in Daya Bay and in the Chooz B1 pilot unit for the N4 series, led to the replacement of the M1 type control rod cluster guide tubes with 1300 MWe PWR type guide tubes, adapted to the geometry of the Guandong reactors and the 1450 MWe reactors of the N4 series. The comparison of the drop times obtained with the 1300 MWe type control rod cluster guide 1300 MWe type control rod cluster guide tubes gave satisfactory results. These met the safety criterion for N4 series control rod cluster drop times (2.15 under hot shutdown conditions). The drop time tests which will be carried out in middle of and at the end of cycle 1 of Chooz B1 should make it possible to finally validate the solution already successfully implemented at Guandong. However, this anomaly has revealed the limits of representativeness of the experimental test loops with regard to the real reactor configuration. In view of this, it has been deemed necessary to ask Electricite de France to pursue its analysis both on the understanding of the phenomena which led to this anomaly and on the limits of the representativeness of the experimental test loops. (authors)

  13. A Survey Work on Optimization Techniques Utilizing Map Reduce Framework in Hadoop Cluster

    OpenAIRE

    Bibhudutta Jena; Mahendra Kumar Gourisaria; Siddharth Swarup Rautaray; Manjusha Pandey

    2017-01-01

    Data is one of the most important and vital aspect of different activities in today's world. Therefore vast amount of data is generated in each and every second. A rapid growth of data in recent time in different domains required an intelligent data analysis tool that would be helpful to satisfy the need to analysis a huge amount of data. Map Reduce framework is basically designed to process large amount of data and to support effective decision making. It consists of ...

  14. A high-speed DAQ framework for future high-level trigger and event building clusters

    International Nuclear Information System (INIS)

    Caselle, M.; Perez, L.E. Ardila; Balzer, M.; Dritschler, T.; Kopmann, A.; Mohr, H.; Rota, L.; Vogelgesang, M.; Weber, M.

    2017-01-01

    Modern data acquisition and trigger systems require a throughput of several GB/s and latencies of the order of microseconds. To satisfy such requirements, a heterogeneous readout system based on FPGA readout cards and GPU-based computing nodes coupled by InfiniBand has been developed. The incoming data from the back-end electronics is delivered directly into the internal memory of GPUs through a dedicated peer-to-peer PCIe communication. High performance DMA engines have been developed for direct communication between FPGAs and GPUs using 'DirectGMA (AMD)' and 'GPUDirect (NVIDIA)' technologies. The proposed infrastructure is a candidate for future generations of event building clusters, high-level trigger filter farms and low-level trigger system. In this paper the heterogeneous FPGA-GPU architecture will be presented and its performance be discussed.

  15. International Network Performance and Security Testing Based on Distributed Abyss Storage Cluster and Draft of Data Lake Framework

    Directory of Open Access Journals (Sweden)

    ByungRae Cha

    2018-01-01

    Full Text Available The megatrends and Industry 4.0 in ICT (Information Communication & Technology are concentrated in IoT (Internet of Things, BigData, CPS (Cyber Physical System, and AI (Artificial Intelligence. These megatrends do not operate independently, and mass storage technology is essential as large computing technology is needed in the background to support them. In order to evaluate the performance of high-capacity storage based on open source Ceph, we carry out the network performance test of Abyss storage with domestic and overseas sites using KOREN (Korea Advanced Research Network. And storage media and network bonding are tested to evaluate the performance of the storage itself. Additionally, the security test is demonstrated by Cuckoo sandbox and Yara malware detection among Abyss storage cluster and oversea sites. Lastly, we have proposed the draft design of Data Lake framework in order to solve garbage dump problem.

  16. Weighted Clustering

    DEFF Research Database (Denmark)

    Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

    2012-01-01

    We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...

  17. A series of novel lanthanide carboxyphosphonates with a 3D framework structure: synthesis, structure, and luminescent and magnetic properties.

    Science.gov (United States)

    Chen, Kai; Dong, Da-Peng; Sun, Zhen-Gang; Jiao, Cheng-Qi; Li, Chao; Wang, Cheng-Lin; Zhu, Yan-Yu; Zhao, Yan; Zhu, Jiang; Sun, Shou-Hui; Zheng, Ming-Jing; Tian, Hui; Chu, Wei

    2012-08-28

    By introduction of 1,4-benzenedicarboxylic acid as the second organic ligand, a series of novel lanthanide carboxyphosphonates with a 3D framework structure, namely, [Ln(3)(H(2)L)(HL)(2)(bdc)(2)(H(2)O)]·7H(2)O (Ln = La (1), Ce (2), Pr (3), Nd (4), Sm (5), Eu (6), Gd (7), Tb (8); H(3)L = H(2)O(3)PCH(2)NC(5)H(9)COOH; H(2)bdc = HOOCC(6)H(4)COOH) have been synthesized under hydrothermal conditions. Compounds are isostructural and feature a 3D framework in which Ln(III) polyhedra are interconnected by bridging {CPO(3)} tetrahedra into 2D inorganic layers parallel to the ab plane. The organic groups of H(2)L(-) are grafted on the two sides of the layer. These layers are further cross-linked by the bdc(2-) ligands from one layer to the Ln atoms from the other into a pillared-layered architecture with one-dimensional channel system along the a axis. The thermal stability of compounds has been investigated. Luminescent properties of compounds , and the magnetic properties of compound have also been studied.

  18. Half-lives for proton emission, alpha decay, cluster radioactivity, and cold fission processes calculated in a unified theoretical framework

    Energy Technology Data Exchange (ETDEWEB)

    Duarte, S.B.; Tavares, O.A.P.; Guzman, F.; Dimarco, A. [Centro Brasileiro de Pesquisas Fisicas (CBPF), Rio de Janeiro, RJ (Brazil); Garcia, F. [Sao Paulo Univ., SP (Brazil). Inst. de Fisica; Universidade Estadual de Santa Cruz, Ilheus, BA (Brazil). Dept. de Ciencias Exatas e Tecnologicas; Rodriguez, O. [Sao Paulo Univ., SP (Brazil). Inst. de Fisica; Instituto Superior de Ciencias e Tecnologia Nucleares, La Habana (Cuba); Goncalves, M. [Instituto de Radioprotecao e Dosimetria (IRD), Rio de Janeiro, RJ (Brazil)

    2002-01-01

    Half-life values of spontaneous nuclear decay processes are presented in the framework of the Effective Liquid Drop Model (ELDM) using the combination of varying mass asymmetry shape description for the mass transfer with Werner-Wheeler's inertia coefficient V{sub MAS}/WW. The calculated half-lives of ground-state to ground-state transitions for the proton emission, alpha decay, cluster radioactivity, and cold fission processes are compared with experimental data. Results have shown that the ELDM is a very efficient model to describe these different decay processes in a same, unified theoretical framework. A Table listing the predicted half-life values, {tau}{sub c} is presented for all possible cases of spontaneous nuclear break-up such that -7.30 <{approx_equal} log{sub 10} {tau}{sub c} [S] <{approx_equal} 27.50 and log {sub 10}({tau}/{tau}{sub c}) > -17.0, where {tau} is the total half-life of the parent nucleus. (author)

  19. An organizational framework and strategic implementation for system-level change to enhance research-based practice: QUERI Series

    Directory of Open Access Journals (Sweden)

    Mittman Brian S

    2008-05-01

    Full Text Available Abstract Background The continuing gap between available evidence and current practice in health care reinforces the need for more effective solutions, in particular related to organizational context. Considerable advances have been made within the U.S. Veterans Health Administration (VA in systematically implementing evidence into practice. These advances have been achieved through a system-level program focused on collaboration and partnerships among policy makers, clinicians, and researchers. The Quality Enhancement Research Initiative (QUERI was created to generate research-driven initiatives that directly enhance health care quality within the VA and, simultaneously, contribute to the field of implementation science. This paradigm-shifting effort provided a natural laboratory for exploring organizational change processes. This article describes the underlying change framework and implementation strategy used to operationalize QUERI. Strategic approach to organizational change QUERI used an evidence-based organizational framework focused on three contextual elements: 1 cultural norms and values, in this case related to the role of health services researchers in evidence-based quality improvement; 2 capacity, in this case among researchers and key partners to engage in implementation research; 3 and supportive infrastructures to reinforce expectations for change and to sustain new behaviors as part of the norm. As part of a QUERI Series in Implementation Science, this article describes the framework's application in an innovative integration of health services research, policy, and clinical care delivery. Conclusion QUERI's experience and success provide a case study in organizational change. It demonstrates that progress requires a strategic, systems-based effort. QUERI's evidence-based initiative involved a deliberate cultural shift, requiring ongoing commitment in multiple forms and at multiple levels. VA's commitment to QUERI came in the

  20. An analytical framework for extracting hydrological information from time series of small reservoirs in a semi-arid region

    Science.gov (United States)

    Annor, Frank; van de Giesen, Nick; Bogaard, Thom; Eilander, Dirk

    2013-04-01

    small reservoirs in the Upper East Region of Ghana. Reservoirs without obvious large seepage losses (field survey) were selected. To verify this, stable water isotopic samples are collected from groundwater upstream and downstream from the reservoir. By looking at possible enrichment of downstream groundwater, a good estimate of seepage can be made in addition to estimates on evaporation. We estimated the evaporative losses and compared those with field measurements using eddy correlation measurements. Lastly, we determined the cumulative surface runoff curves for the small reservoirs .We will present this analytical framework for extracting hydrological information from time series of small reservoirs and show the first results for our study region of northern Ghana.

  1. Cluster imaging of multi-brain networks (CIMBN: a general framework for hyperscanning and modeling a group of interacting brains

    Directory of Open Access Journals (Sweden)

    Lian eDuan

    2015-07-01

    Full Text Available Studying the neural basis of human social interactions is a key topic in the field of social neuroscience. Brain imaging studies in this field usually focus on the neural correlates of the social interactions between two participants. However, as the participant number further increases, even by a small amount, great difficulties raise. One challenge is how to concurrently scan all the interacting brains with high ecological validity, especially for a large number of participants. The other challenge is how to effectively model the complex group interaction behaviors emerging from the intricate neural information exchange among a group of socially organized people. Confronting these challenges, we propose a new approach called Cluster Imaging of Multi-brain Networks (CIMBN. CIMBN consists of two parts. The first part is a cluster imaging technique with high ecological validity based on multiple functional near-infrared spectroscopy (fNIRS systems. Using this technique, we can easily extend the simultaneous imaging capacity of social neuroscience studies up to dozens of participants. The second part of CIMBN is a multi-brain network (MBN modeling method based on graph theory. By taking each brain as a network node and the relationship between any two brains as a network edge, one can construct a network model for a group of interacting brains. The emergent group social behaviors can then be studied using the network’s properties, such as its topological structure and information exchange efficiency. Although there is still much work to do, as a general framework for hyperscanning and modeling a group of interacting brains, CIMBN can provide new insights into the neural correlates of group social interactions, and advance social neuroscience and social psychology.

  2. The GOLM-database standard- a framework for time-series data management based on free software

    Science.gov (United States)

    Eichler, M.; Francke, T.; Kneis, D.; Reusser, D.

    2009-04-01

    Monitoring and modelling projects usually involve time series data originating from different sources. Often, file formats, temporal resolution and meta-data documentation rarely adhere to a common standard. As a result, much effort is spent on converting, harmonizing, merging, checking, resampling and reformatting these data. Moreover, in work groups or during the course of time, these tasks tend to be carried out redundantly and repeatedly, especially when new data becomes available. The resulting duplication of data in various formats strains additional ressources. We propose a database structure and complementary scripts for facilitating these tasks. The GOLM- (General Observation and Location Management) framework allows for import and storage of time series data of different type while assisting in meta-data documentation, plausibility checking and harmonization. The imported data can be visually inspected and its coverage among locations and variables may be visualized. Supplementing scripts provide options for data export for selected stations and variables and resampling of the data to the desired temporal resolution. These tools can, for example, be used for generating model input files or reports. Since GOLM fully supports network access, the system can be used efficiently by distributed working groups accessing the same data over the internet. GOLM's database structure and the complementary scripts can easily be customized to specific needs. Any involved software such as MySQL, R, PHP, OpenOffice as well as the scripts for building and using the data base, including documentation, are free for download. GOLM was developed out of the practical requirements of the OPAQUE-project. It has been tested and further refined in the ERANET-CRUE and SESAM projects, all of which used GOLM to manage meteorological, hydrological and/or water quality data.

  3. Evolution of an adenine-copper cluster to a highly porous cuboidal framework: solution-phase ripening and gas-adsorption properties.

    Science.gov (United States)

    Venkatesh, V; Pachfule, Pradip; Banerjee, Rahul; Verma, Sandeep

    2014-09-15

    The synthesis and directed evolution of a tetranuclear copper cluster, supported by 8-mercapto-N9-propyladenine ligand, to a highly porous three-dimensional cubic framework in the solid state is reported. The structure of this porous framework was unambiguously characterized by X-ray crystallography. The framework contains about 62 % solvent-accessible void; the presence of a free exocyclic amino group in the porous framework facilitates reversible adsorption of gas and solvent molecules. Oriented growth of framework in solution was also tracked by force and scanning electron microscopy studies, leading to identification of an intriguing ripening process, over a period of 30 days, which also revealed formation of cuboidal aggregates in solution. The elemental composition of these cuboidal aggregates was ascertained by EDAX analysis. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Silver-induced reconstruction of an adeninate-based metal-organic framework for encapsulation of luminescent adenine-stabilized silver clusters.

    Science.gov (United States)

    Jonckheere, Dries; Coutino-Gonzalez, Eduardo; Baekelant, Wouter; Bueken, Bart; Reinsch, Helge; Stassen, Ivo; Fenwick, Oliver; Richard, Fanny; Samorì, Paolo; Ameloot, Rob; Hofkens, Johan; Roeffaers, Maarten B J; De Vos, Dirk E

    2016-05-21

    Bright luminescent silver-adenine species were successfully stabilized in the pores of the MOF-69A (zinc biphenyldicarboxylate) metal-organic framework, starting from the intrinsically blue luminescent bio-MOF-1 (zinc adeninate 4,4'-biphenyldicarboxylate). Bio-MOF-1 is transformed to the MOF-69A framework by selectively leaching structural adenine linkers from the original framework using silver nitrate solutions in aqueous ethanol. Simultaneously, bright blue-green luminescent silver-adenine clusters are formed inside the pores of the recrystallized MOF-69A matrix in high local concentrations. The structural transition and concurrent changes in optical properties were characterized using a range of structural, physicochemical and spectroscopic techniques (steady-state and time-resolved luminescence, quantum yield determination, fluorescence microscopy). The presented results open new avenues for exploring the use of MOFs containing luminescent silver clusters for solid-state lighting and sensor applications.

  5. OLYMPUS: an automated hybrid clustering method in time series gene expression. Case study: host response after Influenza A (H1N1) infection.

    Science.gov (United States)

    Dimitrakopoulou, Konstantina; Vrahatis, Aristidis G; Wilk, Esther; Tsakalidis, Athanasios K; Bezerianos, Anastasios

    2013-09-01

    The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes. We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection. The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web (http://biosignal.med.upatras.gr/wordpress/biosignal/). Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  6. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering

    Science.gov (United States)

    Elangasinghe, M. A.; Singhal, N.; Dirks, K. N.; Salmond, J. A.; Samarasinghe, S.

    2014-09-01

    This paper uses artificial neural networks (ANN), combined with k-means clustering, to understand the complex time series of PM10 and PM2.5 concentrations at a coastal location of New Zealand based on data from a single site. Out of available meteorological parameters from the network (wind speed, wind direction, solar radiation, temperature, relative humidity), key factors governing the pattern of the time series concentrations were identified through input sensitivity analysis performed on the trained neural network model. The transport pathways of particulate matter under these key meteorological parameters were further analysed through bivariate concentration polar plots and k-means clustering techniques. The analysis shows that the external sources such as marine aerosols and local sources such as traffic and biomass burning contribute equally to the particulate matter concentrations at the study site. These results are in agreement with the results of receptor modelling by the Auckland Council based on Positive Matrix Factorization (PMF). Our findings also show that contrasting concentration-wind speed relationships exist between marine aerosols and local traffic sources resulting in very noisy and seemingly large random PM10 concentrations. The inclusion of cluster rankings as an input parameter to the ANN model showed a statistically significant (p advanced air dispersion models.

  7. Solvent-mediated secondary building units (SBUs) diversification in a series of Mn{sup II}-based metal-organic frameworks (MOFs)

    Energy Technology Data Exchange (ETDEWEB)

    Niu, Yan-Fei; Cui, Li-Ting [Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Department of Chemistry, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062 (China); Han, Jie, E-mail: chan@ouhk.edu.hk [School of Science & Technology, The Open University of Hong Kong, Kowloon, Hong Kong Special Administrative Region (Hong Kong); Zhao, Xiao-Li, E-mail: xlzhao@chem.ecnu.edu.cn [Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Department of Chemistry, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062 (China); State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002 (China)

    2016-09-15

    The role of auxiliary solvents in the formation of MOFs has been investigated for a series of Mn{sup II}-based framework systems. Reactions of 4,4′,4″-nitrilotribenzoic acid (H{sub 3}L) with Mn{sup II} through varying auxiliary solvents of the medium resulted in the formation of diversified multinuclear Mn{sup II} subunits in four new coordination polymers: [Mn{sub 3}(L)(HCOO){sub 3}(DEF){sub 3}] (1), [Mn{sub 3}(L){sub 2}(EtOH){sub 2}]·DMF (2), [Mn{sub 5}(L){sub 4}(H{sub 2}O){sub 2}]·2(H{sub 2}NMe{sub 2}){sup +}·4DMF·2H{sub 2}O (3), and [Mn{sub 3}(L){sub 2}(py){sub 4}(H{sub 2}O)]·H{sub 2}O (4) (H{sub 3}L=4,4′,4′-nitrilotribenzoic acid, DMF=dimethylformamide, DEF=N,N-diethylformamide, py=pyridine). These four compounds were fully characterized by single-crystal X-ray diffraction, showing interesting SBUs variations. For compound 1, it displays a (3,6)-connected kgd net with wheel [Mn{sub 6}] cluster serving as SBU, whereas in 2, the sequence of Mn{sub 3}(COO){sub 9}(EtOH){sub 2} is repeated by inversion centers located between Mn1 and Mn3 to form an infinite Mn-carboxylate chain, which are further interlinked by L{sup 3−} ligands to form a 3D architecture. In 3, the pentanuclear Mn{sub 5}(CO{sub 2}){sub 12} clusters are interlinked to form a layer, which are further pillared by L{sup 3−} to generate a 3D network. Compound 4 has a (3,6)-connected network in which the SBU is a linear trimeric Mn{sub 3}(COO){sub 6}(py){sub 4}(H{sub 2}O) cluster. In addition, the thermal stabilities, X-ray powder diffraction of all the compounds were studied, photoluminescence behaviors of compounds 1, 3 and 4 are discussed. - Graphical abstract: Supramolecular assembly of C{sub 3}-symmetric ligand 4,4′,4″-nitrilotribenzoic acid (H{sub 3}L) with Mn{sup II} through varying auxiliary solvents of the medium resulted in the formation of diversified multinuclear Mn{sup II} subunits in four new coordination polymers. The results exhibit the structures of Mn-SBUs in these

  8. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

    Science.gov (United States)

    Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

    2013-03-01

    Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

  9. Cluster-cluster clustering

    International Nuclear Information System (INIS)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

    1985-01-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

  10. Extending the Compositional Range of Nanocasting in the Oxozirconium Cluster-Based Metal–Organic Framework NU-1000—A Comparative Structural Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Wenyang [Department; Wang, Zhao [Department; Malonzo, Camille D. [Department; Webber, Thomas E. [Department; Platero-Prats, Ana E. [X-ray; Sotomayor, Francisco [Quantachrome Instruments, 1900 Corporate; Vermeulen, Nicolaas A. [Department; Wang, Timothy C. [Department; Hupp, Joseph T. [Department; Farha, Omar K. [Department; Department; Penn, R. Lee [Department; Chapman, Karena W. [X-ray; Thommes, Matthias [Quantachrome Instruments, 1900 Corporate; Stein, Andreas [Department

    2018-02-08

    The process of nanocasting in metal-organic frameworks (MOFs) is a versatile approach to modify these porous materials by introducing supporting scaffolds. The nanocast scaffolds can stabilize metal-oxo clusters in MOFs at high temperatures and modulate their chemical environments. Here we demonstrate a range of nanocasting approaches in the MOF NU-1000, which contains hexanuclear oxozirconium clusters (denoted as Zr6 clusters) that are suitable for modification with other metals. We developed methods for introducing SiO2, TiO2, polymeric, and carbon scaffolds into the NU-1000 structure. The responses of NU-1000 towards different scaffold precursors were studied, including the effects on morphology, precursor distribution, and porosity after nanocasting. Upon removal of organic linkers in the MOF by calcination/pyrolysis at 500 °C or above, the Zr6 clusters remained accessible and maintained their Lewis acidity in SiO2 nanocast samples, whereas additional treatment was necessary for Zr6 clusters to become accessible in carbon nanocast samples. Aggregation of Zr6 clusters was largely prevented with SiO2 or carbon scaffolds even after thermal treatment at 500 °C or above. In the case of titania nanocasting, NU- 1000 crystals underwent a pseudomorphic transformation, in which Zr6 clusters reacted with titania to form small oxaggregates of a Zr/Ti mixed oxide with a local structure resembling that of ZrTi2O6. The ability to maintain high densities of discrete Lewis acidic Zr6 clusters on SiO2 or carbon supports at high temperatures provides a starting point for designing new thermally stable catalysts.

  11. Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology

    Directory of Open Access Journals (Sweden)

    Huimin Liu

    2018-04-01

    Full Text Available Land Surface Temperature (LST is a critical component to understand the impact of urbanization on the urban thermal environment. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization. This paper presents a workflow to extract the spatio-temporal pattern of LST through time series clustering by focusing on the LST of Wuhan, China, from 2002 to 2017 with a 3-year time interval with 8-day MODerate-resolution Imaging Spectroradiometer (MODIS satellite image products. The Latent pattern of LST (LLST generated by non-parametric Multi-Task Gaussian Process Modeling (MTGP and the Multi-Scale Shape Index (MSSI which characterizes the morphology of LLST are coupled for pattern recognition. Specifically, spatio-temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of k -means and the Back-Propagation neural networks (BP-Net. The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. For spatio-temporal pattern recognition, LLSTs and MSSIs of the 6 years are regarded as geo-referenced time series. Multiple algorithms including traditional k -means with Euclidean Distance (ED, shape-based k -means with the constrained Dynamic Time Warping ( c DTW distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA centroid computation method ( k - c DBA and k -shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k - c DBA as the optimal time series clustering algorithm for our study. The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST

  12. A general framework to test gravity using galaxy clusters - I. Modelling the dynamical mass of haloes in f(R) gravity

    Science.gov (United States)

    Mitchell, Myles A.; He, Jian-hua; Arnold, Christian; Li, Baojiu

    2018-06-01

    We propose a new framework for testing gravity using cluster observations, which aims to provide an unbiased constraint on modified gravity models from Sunyaev-Zel'dovich (SZ) and X-ray cluster counts and the cluster gas fraction, among other possible observables. Focusing on a popular f(R) model of gravity, we propose a novel procedure to recalibrate mass scaling relations from Λ cold dark matter (ΛCDM) to f(R) gravity for SZ and X-ray cluster observables. We find that the complicated modified gravity effects can be simply modelled as a dependence on a combination of the background scalar field and redshift, fR(z)/(1 + z), regardless of the f(R) model parameter. By employing a large suite of N-body simulations, we demonstrate that a theoretically derived tanh fitting formula is in excellent agreement with the dynamical mass enhancement of dark matter haloes for a large range of background field parameters and redshifts. Our framework is sufficiently flexible to allow for tests of other models and inclusion of further observables, and the one-parameter description of the dynamical mass enhancement can have important implications on the theoretical modelling of observables and on practical tests of gravity.

  13. piRNA analysis framework from small RNA-Seq data by a novel cluster prediction tool - PILFER.

    Science.gov (United States)

    Ray, Rishav; Pandey, Priyanka

    2017-12-19

    With the increasing number of studies focusing on PIWI-interacting RNA (piRNAs), it is now pertinent to develop efficient tools dedicated towards piRNA analysis. We have developed a novel cluster prediction tool called PILFER (PIrna cLuster FindER), which can accurately predict piRNA clusters from small RNA sequencing data. PILFER is an open source, easy to use tool, and can be executed even on a personal computer with minimum resources. It uses a sliding-window mechanism by integrating the expression of the reads along with the spatial information to predict the piRNA clusters. We have additionally defined a piRNA analysis pipeline incorporating PILFER to detect and annotate piRNAs and their clusters from raw small RNA sequencing data and implemented it on publicly available data from healthy germline and somatic tissues. We compared PILFER with other existing piRNA cluster prediction tools and found it to be statistically more accurate and superior in many aspects such as the robustness of PILFER clusters is higher and memory efficiency is more. Overall, PILFER provides a fast and accurate solution to piRNA cluster prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. The Recovery Framework in the BRRD and its Effectiveness, NORDIC & EUROPEAN COMPANY LAW Working Paper Series No. 15‐04

    DEFF Research Database (Denmark)

    Hu, Chenchen

    2015-01-01

    The EU has made great endeavors in establishing a bank resolution procedure after the global financial crisis (GFC). In April 2014 the directive on establishing a framework for the recovery and resolution of credit institutions and investment firms (referred to as The Bank Recovery and Resolution...... recovery framework, including the intra-group support, recovery and resolution planning, and the early intervention in the wake of bank failures, such as Lehman Brothers fall. It aims at assessing to which extent the new recovery framework enhances the resilience of banks and facilitates orderly resolution...... Directive, the BRRD) was finally adopted, which set up the basic framework for the resolution regime in the EU. The BRRD includes a three-step resolution framework: recovery and resolution planning, the early intervention, and the resolution. This article analyses the effectiveness of the pre-resolution...

  15. Anomalous properties of technetium clusters

    International Nuclear Information System (INIS)

    Kryuchkov, S.V.

    1985-01-01

    On the basis of critical evaluation of literature data in the field of chemistry of technetium cluster compounds with ligands of a weak field a conclusion is made on specific, ''anomalous'' properties of technetium cluster complexes which consist in an increased ability of the given element to the formation of a series of binuclear and multinuclear clusters, similar in composition and structure and easily transforming in each other. The majority of technetium clusters unlike similar compounds of other elements are paramagnetic with one unpaired electron on ''metallic'' MO of loosening type. All theoretical conceptions known today on the electronic structure of technetium clusters are considered. It is pointed out, that the best results in the explanation of ''anomalous'' properties of technetium clusters can be obtained in the framework of nonempirical methods of self-consistent field taking into account configuration interactions. It is also shown, that certain properties of technetium clusters can be explained on the basis of qualitative model of Coulomb repulsion of metal atoms in clusters. The conclusion is made, that technetium position in the Periodic table, as well as recently detected technetium property to the decrease of effective charge on its atoms during M-M bond formation promote a high ability of the element to cluster formation both with weak field ligands and with strong field one

  16. Multiobjective optimization of the inspection intervals of a nuclear safety system: A clustering-based framework for reducing the Pareto Front

    International Nuclear Information System (INIS)

    Zio, E.; Bazzo, R.

    2010-01-01

    In this paper, a framework is developed for identifying a limited number of representative solutions of a multiobjective optimization problem concerning the inspection intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are first clustered into 'families', which are then synthetically represented by a 'head of the family' solution. Three clustering methods are analyzed. Level Diagrams are then used to represent, analyse and interpret the Pareto Fronts reduced to their head-of-the-family solutions. Two decision situations are considered: without or with decision maker preferences, the latter implying the introduction of a scoring system to rank the solutions with respect to the different objectives: a fuzzy preference assignment is then employed to this purpose. The results of the application of the framework of analysis to the problem of optimizing the inspection intervals of a nuclear power plant safety system show that the clustering-based reduction maintains the Pareto Front shape and relevant characteristics, while making it easier for the decision maker to select the final solution.

  17. The Nature and Nurture of Giftedness: A New Framework for Understanding Gifted Education. Education & Psychology of the Gifted Series

    Science.gov (United States)

    Dai, David Yun

    2010-01-01

    With unprecedented scope and vision, Dr. Dai systematically redefines giftedness and proposes a new framework for the field of gifted education. He identifies nine essential tensions, revolving around three core questions: What do we know about the respective roles of natural ability, environment and experiences, and personal effort in talent…

  18. The Reading Turn-Around: A Five Part Framework for Differentiated Instruction. Practitioners Bookshelf, Language & Literacy Series

    Science.gov (United States)

    Jones, Stephanie; Clarke, Lane; Enriquez, Grace

    2009-01-01

    This book demonstrates a five-part framework for teachers, reading specialists, and literacy coaches who want to help their least engaged students become powerful readers. Merging theory and practice, the guide offers successful strategies to reach your "struggling" learners. The authors show how you can "turn-around" your instructional practice,…

  19. Calculations of non-adiabatic couplings within equation-of-motion coupled-cluster framework: Theory, implementation, and validation against multi-reference methods

    Science.gov (United States)

    Faraji, Shirin; Matsika, Spiridoula; Krylov, Anna I.

    2018-01-01

    We report an implementation of non-adiabatic coupling (NAC) forces within the equation-of-motion coupled-cluster with single and double excitations (EOM-CCSD) framework via the summed-state approach. Using illustrative examples, we compare NAC forces computed with EOM-CCSD and multi-reference (MR) wave functions (for selected cases, we also consider configuration interaction singles). In addition to the magnitude of the NAC vectors, we analyze their direction, which is important for the calculations of the rate of non-adiabatic transitions. Our benchmark set comprises three doublet radical-cations (hexatriene, cyclohexadiene, and uracil), neutral uracil, and sodium-doped ammonia clusters. When the characters of the states agree among different methods, we observe good agreement between the respective NAC vectors, both in the Franck-Condon region and away. In the cases of large discrepancies between the methods, the disagreement can be attributed to the difference in the states' character, which, in some cases, is very sensitive to electron correlation, both within single-reference and multi-reference frameworks. The numeric results confirm that the accuracy of NAC vectors depends critically on the quality of the underlying wave functions. Within their domain of applicability, EOM-CC methods provide a viable alternative to MR approaches.

  20. Study on high-resolution sequence stratigraphy framework of uranium-hosting rock series in Qianjiadian sag

    International Nuclear Information System (INIS)

    Chen Fanghong; Zhang Mingyu

    2005-01-01

    The ore-hosting Yaojia Formation is composed of a set of braided stream medium-fine grained sediments. Guided by the basic theory of high-resolution sequence stratigraphy, and based on the core observation, the analysis of chemical composition of rocks, and data of natural potential logging and apparent resistivity logging, the authors have set up the high-resolution sequence stratigraphy framework of the ore-hosting Yaojia Formation, and discussed the relation of the stratigraphic structure of the middle cycle, as well as the paleotopography, the micro-facies to the formation of uranium deposit. (authors)

  1. An Investigation Into the Culture and Social Actors Representation in Summit Series ELT Textbooks Within van Leeuwen’s 1996 Framework

    Directory of Open Access Journals (Sweden)

    Nasser Rashidi

    2015-03-01

    Full Text Available The current study aims at identifying particular ways through which social actors are represented in Summit Series ELT textbooks. It examines cultural load in the textbooks within critical discourse analysis framework, in this case van Leeuwen’s framework. Particularly, the study attempts to explore if values, norms, and roles are culture/context-bound. Results of the analyses showed that among discursive features, Inclusion, Genericization, and Indetermination were used more than Exclusion, Specification, and Determination. Activation was more observed than Passivation, and Categorization had an important function in the representation of some of the social actors along with Assimilation and Impersonalization. The analysis also indicated the impartiality toward the representation of social actors. Moral, social, and personal values were the most disseminated values, while social morality and traditions had the highest occurrence. However, a few discriminative cases were found regarding gender roles. The researchers proposed that Summit Series were less grounded in cultural assumptions/biases. This impartiality eases language learning by keeping learners away from misunderstanding and incomprehensibility.

  2. Equation-of-motion coupled cluster perturbation theory revisited

    DEFF Research Database (Denmark)

    Eriksen, Janus Juul; Jørgensen, Poul; Olsen, Jeppe

    2014-01-01

    The equation-of-motion coupled cluster (EOM-CC) framework has been used for deriving a novel series of perturbative corrections to the coupled cluster singles and doubles energy that formally con- verges towards the full configuration interaction energy limit. The series is based on a Møller-Ples......-Plesset partitioning of the Hamiltonian and thus size extensive at any order in the perturbation, thereby rem- edying the major deficiency inherent to previous perturbation series based on the EOM-CC ansatz. © 2014 AIP Publishing LLC. [http://dx.doi.org/10.1063/1.4873138]...

  3. Topological evolution and photoluminescent properties of a series of divalent zinc-based metal–organic frameworks tuned via ancillary ligating spacers

    International Nuclear Information System (INIS)

    Lian, Xiao-Min; Zhao, Wen; Zhao, Xiao-Li

    2013-01-01

    The combination of divalent zinc ions, 4-(4-carboxybenzamido)benzoic acid and exo-bidendate bipyridine ligands gave rise to a series of new MOFs: [ZnL(bipy)]·DMF·H 2 O (1), [ZnL(bpe)]·1.5H 2 O (2), [ZnL(bpa)]·4H 2 O (3) and [ZnL(bpp)]·1.75H 2 O (4) (MOF=metal-organic framework, bipy=4,4′-bipyridine, bpe=trans-1,2-bis(4-pyridyl)ethylene, bpa=1,2-bis(4-pyridinyl)ethane, bpp=1,3-bis(4-pyridinyl)propane, H 2 L=4,4′-(carbonylimino)dibenzoic acid). Fine tune over the topology of the MOFs was achieved via systematically varying the geometric length of the second ligating bipyridine ligands. Single-crystal X-ray analysis reveals that complex 1 has a triply interpenetrated three-dimensional (3D) framework with elongated primitive cubic topology, whereas isostructural complexes 2 and 3 each possesses a 6-fold interpenetrated diamondiod 3D framework. Further expansion of the length of the bipyridine ligand to bpp leads to the formation of 4, which features an interesting entangled architecture of 2D→3D parallel polycatenation. In addition, the thermogravimetric analyses and solid-state photoluminescent properties of the selected complexes are investigated. - Graphical abstract: The incorporation of exo-bidendate bipyridine spacers into the Zn–H 2 L system has yielded a series of new MOFs exhibiting topological evolution from 3-fold interpenetration to 6-fold interpenetration and 2D→3D parallel polycatenation. Highlights: ► The effect of the pyridyl-based spacers on the formation of MOFs was explored. ► Fine tune over the topology of the MOFs was achieved. ► An interesting structure of 2D→3D parallel polycatenation is reported

  4. An Architectural Based Framework for the Distributed Collection, Analysis and Query from Inhomogeneous Time Series Data Sets and Wearables for Biofeedback Applications

    Directory of Open Access Journals (Sweden)

    James Lee

    2017-02-01

    Full Text Available The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”. This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming.

  5. An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

    Directory of Open Access Journals (Sweden)

    Dongdong Yang

    2015-02-01

    Full Text Available Synthetic aperture radar (SAR image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous nonlocal mean (NLM filter is introduced in this study to suppress the multiplicative speckle noise in SAR image. Furthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the searching window are projected into a lower dimensional subspace by principal component analysis (PCA. Thus, the nonlocal mean filter is implemented in the subspace. Afterwards, a multi-objective clustering algorithm is proposed using the principals of artificial immune system (AIS and kernel-induced distance measures. The multi-objective clustering has been shown to discover the data distribution with different characteristics and the kernel methods can improve its robustness to noise and outliers. Experiments demonstrate that the proposed method is able to partition the SAR image robustly and accurately than the conventional approaches.

  6. Time Series Analysis Using Geometric Template Matching.

    Science.gov (United States)

    Frank, Jordan; Mannor, Shie; Pineau, Joelle; Precup, Doina

    2013-03-01

    We present a novel framework for analyzing univariate time series data. At the heart of the approach is a versatile algorithm for measuring the similarity of two segments of time series called geometric template matching (GeTeM). First, we use GeTeM to compute a similarity measure for clustering and nearest-neighbor classification. Next, we present a semi-supervised learning algorithm that uses the similarity measure with hierarchical clustering in order to improve classification performance when unlabeled training data are available. Finally, we present a boosting framework called TDEBOOST, which uses an ensemble of GeTeM classifiers. TDEBOOST augments the traditional boosting approach with an additional step in which the features used as inputs to the classifier are adapted at each step to improve the training error. We empirically evaluate the proposed approaches on several datasets, such as accelerometer data collected from wearable sensors and ECG data.

  7. Tectonosedimentary framework of Upper Cretaceous -Neogene series in the Gulf of Tunis inferred from subsurface data: implications for petroleum exploration

    Science.gov (United States)

    Dhraief, Wissem; Dhahri, Ferid; Chalwati, Imen; Boukadi, Noureddine

    2017-04-01

    The objective and the main contribution of this issue are dedicated to using subsurface data to delineate a basin beneath the Gulf of Tunis and its neighbouring areas, and to investigate the potential of this area in terms of hydrocarbon resources. Available well data provided information about the subsurface geology beneath the Gulf of Tunis. 2D seismic data allowed delineation of the basin shape, strata geometries, and some potential promising subsurface structures in terms of hydrocarbon accumulation. Together with lithostratigraphic data obtained from drilled wells, seismic data permitted the construction of isochron and isobath maps of Upper Cretaceous-Neogene strata. Structural and lithostratigraphic interpretations indicate that the area is tectonically complex, and they highlight the tectonic control of strata deposition during the Cretaceous and Neogene. Tectonic activity related to the geodynamic evolution of the northern African margin appears to have been responsible for several thickness and facies variations, and to have played a significant role in the establishment and evolution of petroleum systems in northeastern Tunisia. As for petroleum systems in the basin, the Cretaceous series of the Bahloul, Mouelha and Fahdene formations are acknowledged to be the main source rocks. In addition, potential reservoirs (Fractured Abiod and Bou Dabbous carbonated formations) sealed by shaly and marly formations (Haria and Souar formations respectively) show favourable geometries of trap structures (anticlines, tilted blocks, unconformities, etc.) which make this area adequate for hydrocarbon accumulations.

  8. Bridging Zirconia Nodes within a Metal–Organic Framework via Catalytic Ni-Hydroxo Clusters to Form Heterobimetallic Nanowires

    Energy Technology Data Exchange (ETDEWEB)

    Platero-Prats, Ana E.; League, Aaron; Bernales Candia, Sandra Varinia; Ye, Jingyun; Gallington, Leighanne C.; Vjunov, Aleksei; Schweitzer, Neil; Li, Zhanyong; Zheng, Jian; Mehdi, Beata L.; Stevens, Andrew J.; Dohnalkova, Alice; Balasubramanian, Mahalingam; Farha, Omar; Hupp, Joseph; Browning, Nigel D.; Fulton, John L.; Camaioni, Donald M.; Lercher, Johannes A.; Truhlar, Donald G.; Gagliardi, Laura; Cramer, Christopher; Chapman, Karena W.

    2017-07-24

    Metal-organic frameworks (MOFs), with their well-ordered pore networks and tunable surface chemistries, offer a versatile platform for preparing well-defined nanostructures wherein functionality such as catalysis can be incorporated. We resolved the atomic structure of Ni-oxo species deposited in the MOF NU-1000 through atomic layer deposition using local and long-range structure probes, including X-ray absorption spectroscopy, pair distribution function analysis and difference envelope density analysis, with electron microscopy imaging and computational modeling.

  9. Two series of reactant's ratio-dependent lanthanide organic frameworks derived from nicotinic acid N-oxide and oxalate: synthesis, crystal structures and luminescence properties.

    Science.gov (United States)

    Yu, Yanyan; Zhang, Lijuan; Zhou, Yunshan; Zuhra, Zareen

    2015-03-14

    Two series of lanthanide(III)–organic frameworks with the molecular formula [Ln2(NNO)2(OX)2(H2O)4]n (Ln = Eu 1, Tb 2, Sm 3, Dy 4, Gd 5) and [Ln2(NNO)4(OX)(H2O)2]n (Ln = Eu 6, Tb 7, Sm 8, Dy 9, Gd 10) were synthesized successfully under the same hydrothermal conditions with nicotinic N-oxide (HNNO) and oxalic acid (H2OX) as the mixed ligands merely through varying the molar ratio of the reactants. The compounds were characterized by IR, elemental analysis, UV, TG-DTA and powder X-ray diffraction (XRD). X-ray single-crystal diffraction analyses of compounds 1 and 7 selected as representatives and powder XRD analysis of the compounds revealed that both the series of compounds feature three-dimensional (3-D) open frameworks, and crystallize in the triclinic P1 space group while with different unit cell parameters. In compound 1, pairs of Eu(3+) ions and pairs of NNO(−) ligands connect with each other alternately to form a 1-D infinite Eu-NNO double chain, the adjacent 1-D double-chains are then joined together through OX(2−) ligands leading to a 2D layer, the 2-D layers are further ‘pillared’ by OX(2−) ligands resulting in a 3-D framework. In compound 7, the 1-D Tb-NNO infinite chain and its 2-D layer are formed in an almost similar fashion to that in compound 1. The difference between the structures of the two compounds 1 and 7 is that the adjacent 2-D layers in compound 7 are further connected by NNO(−) ligands resulting in a 3-D framework. The photoluminescence properties and energy transfer mechanism of the compounds were studied systematically. The energy level of the lowest triplet states of the HNNO ligand (23148 cm(−1)) was determined based on the phosphorescence spectrum of compound 5 at 77 K. The (5)D0 (Eu(3+)) and (5)D4 (Tb(3+)) emission lifetimes are 0.46 ms, 0.83 ms, 0.69 ms and 0.89 ms and overall quantum yields are 1.03%, 3.29%, 2.58% and 3.78% for the compounds 1, 2, 6 and 7, respectively.

  10. A framework for consistent estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from MODIS time-series data

    DEFF Research Database (Denmark)

    Xiao, Zhiqiang; Liang, Shunlin; Wang, Jindi

    2015-01-01

    -series MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. If the reflectance data showed snow-free areas, an ensemble Kalman filter (EnKF) technique was used to estimate leaf area index (LAI) for a two-layer canopy reflectance model (ACRM) by combining predictions from a phenology...... model and the MODIS surface reflectance data. The estimated LAI values were then input into the ACRM to calculate the surface albedo and the fraction of absorbed photosynthetically active radiation (FAPAR). For snow-covered areas, the surface albedo was calculated as the underlying vegetation canopy...... albedo plus the weighted distance between the underlying vegetation canopy albedo and the albedo over deep snow. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43...

  11. Using a time-series statistical framework to quantify trends and abrupt change in US corn, soybean, and wheat yields from 1970-2016

    Science.gov (United States)

    Zhang, J.; Ives, A. R.; Turner, M. G.; Kucharik, C. J.

    2017-12-01

    Previous studies have identified global agricultural regions where "stagnation" of long-term crop yield increases has occurred. These studies have used a variety of simple statistical methods that often ignore important aspects of time series regression modeling. These methods can lead to differing and contradictory results, which creates uncertainty regarding food security given rapid global population growth. Here, we present a new statistical framework incorporating time series-based algorithms into standard regression models to quantify spatiotemporal yield trends of US maize, soybean, and winter wheat from 1970-2016. Our primary goal was to quantify spatial differences in yield trends for these three crops using USDA county level data. This information was used to identify regions experiencing the largest changes in the rate of yield increases over time, and to determine whether abrupt shifts in the rate of yield increases have occurred. Although crop yields continue to increase in most maize-, soybean-, and winter wheat-growing areas, yield increases have stagnated in some key agricultural regions during the most recent 15 to 16 years: some maize-growing areas, except for the northern Great Plains, have shown a significant trend towards smaller annual yield increases for maize; soybean has maintained an consistent long-term yield gains in the Northern Great Plains, the Midwest, and southeast US, but has experienced a shift to smaller annual increases in other regions; winter wheat maintained a moderate annual increase in eastern South Dakota and eastern US locations, but showed a decline in the magnitude of annual increases across the central Great Plains and western US regions. Our results suggest that there were abrupt shifts in the rate of annual yield increases in a variety of US regions among the three crops. The framework presented here can be broadly applied to additional yield trend analyses for different crops and regions of the Earth.

  12. Co2 and Co3 Mixed Cluster Secondary Building Unit Approach toward a Three-Dimensional Metal-Organic Framework with Permanent Porosity

    Directory of Open Access Journals (Sweden)

    Meng-Yao Chao

    2018-03-01

    Full Text Available Large and permanent porosity is the primary concern when designing metal-organic frameworks (MOFs for specific applications, such as catalysis and drug delivery. In this article, we report a MOF Co11(BTB6(NO34(DEF2(H2O14 (1, H3BTB = 1,3,5-tris(4-carboxyphenylbenzene; DEF = N,N-diethylformamide via a mixed cluster secondary building unit (SBU approach. MOF 1 is sustained by a rare combination of a linear trinuclear Co3 and two types of dinuclear Co2 SBUs in a 1:2:2 ratio. These SBUs are bridged by BTB ligands to yield a three-dimensional (3D non-interpenetrated MOF as a result of the less effective packing due to the geometrically contrasting SBUs. The guest-free framework of 1 has an estimated density of 0.469 g cm−3 and exhibits a potential solvent accessible void of 69.6% of the total cell volume. The activated sample of 1 exhibits an estimated Brunauer-Emmett-Teller (BET surface area of 155 m2 g−1 and is capable of CO2 uptake of 58.61 cm3 g−1 (2.63 mmol g−1, 11.6 wt % at standard temperature and pressure in a reversible manner at 195 K, showcasing its permanent porosity.

  13. Topological evolution and photoluminescent properties of a series of divalent zinc-based metal–organic frameworks tuned via ancillary ligating spacers

    Energy Technology Data Exchange (ETDEWEB)

    Lian, Xiao-Min; Zhao, Wen [Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Department of Chemistry, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062 (China); Zhao, Xiao-Li, E-mail: xlzhao@chem.ecnu.edu.cn [Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Department of Chemistry, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062 (China)

    2013-04-15

    The combination of divalent zinc ions, 4-(4-carboxybenzamido)benzoic acid and exo-bidendate bipyridine ligands gave rise to a series of new MOFs: [ZnL(bipy)]·DMF·H{sub 2}O (1), [ZnL(bpe)]·1.5H{sub 2}O (2), [ZnL(bpa)]·4H{sub 2}O (3) and [ZnL(bpp)]·1.75H{sub 2}O (4) (MOF=metal-organic framework, bipy=4,4′-bipyridine, bpe=trans-1,2-bis(4-pyridyl)ethylene, bpa=1,2-bis(4-pyridinyl)ethane, bpp=1,3-bis(4-pyridinyl)propane, H{sub 2}L=4,4′-(carbonylimino)dibenzoic acid). Fine tune over the topology of the MOFs was achieved via systematically varying the geometric length of the second ligating bipyridine ligands. Single-crystal X-ray analysis reveals that complex 1 has a triply interpenetrated three-dimensional (3D) framework with elongated primitive cubic topology, whereas isostructural complexes 2 and 3 each possesses a 6-fold interpenetrated diamondiod 3D framework. Further expansion of the length of the bipyridine ligand to bpp leads to the formation of 4, which features an interesting entangled architecture of 2D→3D parallel polycatenation. In addition, the thermogravimetric analyses and solid-state photoluminescent properties of the selected complexes are investigated. - Graphical abstract: The incorporation of exo-bidendate bipyridine spacers into the Zn–H{sub 2}L system has yielded a series of new MOFs exhibiting topological evolution from 3-fold interpenetration to 6-fold interpenetration and 2D→3D parallel polycatenation. Highlights: ► The effect of the pyridyl-based spacers on the formation of MOFs was explored. ► Fine tune over the topology of the MOFs was achieved. ► An interesting structure of 2D→3D parallel polycatenation is reported.

  14. Spin-orbit couplings within the equation-of-motion coupled-cluster framework: Theory, implementation, and benchmark calculations

    Energy Technology Data Exchange (ETDEWEB)

    Epifanovsky, Evgeny [Department of Chemistry, University of Southern California, Los Angeles, California 90089-0482 (United States); Department of Chemistry, University of California, Berkeley, California 94720 (United States); Q-Chem Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588 (United States); Klein, Kerstin; Gauss, Jürgen [Institut für Physikalische Chemie, Universität Mainz, D-55099 Mainz (Germany); Stopkowicz, Stella [Department of Chemistry, Centre for Theoretical and Computational Chemistry, University of Oslo, N-0315 Oslo (Norway); Krylov, Anna I. [Department of Chemistry, University of Southern California, Los Angeles, California 90089-0482 (United States)

    2015-08-14

    We present a formalism and an implementation for calculating spin-orbit couplings (SOCs) within the EOM-CCSD (equation-of-motion coupled-cluster with single and double substitutions) approach. The following variants of EOM-CCSD are considered: EOM-CCSD for excitation energies (EOM-EE-CCSD), EOM-CCSD with spin-flip (EOM-SF-CCSD), EOM-CCSD for ionization potentials (EOM-IP-CCSD) and electron attachment (EOM-EA-CCSD). We employ a perturbative approach in which the SOCs are computed as matrix elements of the respective part of the Breit-Pauli Hamiltonian using zeroth-order non-relativistic wave functions. We follow the expectation-value approach rather than the response-theory formulation for property calculations. Both the full two-electron treatment and the mean-field approximation (a partial account of the two-electron contributions) have been implemented and benchmarked using several small molecules containing elements up to the fourth row of the periodic table. The benchmark results show the excellent performance of the perturbative treatment and the mean-field approximation. When used with an appropriate basis set, the errors with respect to experiment are below 5% for the considered examples. The findings regarding basis-set requirements are in agreement with previous studies. The impact of different correlation treatment in zeroth-order wave functions is analyzed. Overall, the EOM-IP-CCSD, EOM-EA-CCSD, EOM-EE-CCSD, and EOM-SF-CCSD wave functions yield SOCs that agree well with each other (and with the experimental values when available). Using an EOM-CCSD approach that provides a more balanced description of the target states yields more accurate results.

  15. Data Clustering

    Science.gov (United States)

    Wagstaff, Kiri L.

    2012-03-01

    clustering, in which some partial information about item assignments or other components of the resulting output are already known and must be accommodated by the solution. Some algorithms seek a partition of the data set into distinct clusters, while others build a hierarchy of nested clusters that can capture taxonomic relationships. Some produce a single optimal solution, while others construct a probabilistic model of cluster membership. More formally, clustering algorithms operate on a data set X composed of items represented by one or more features (dimensions). These could include physical location, such as right ascension and declination, as well as other properties such as brightness, color, temporal change, size, texture, and so on. Let D be the number of dimensions used to represent each item, xi ∈ RD. The clustering goal is to produce an organization P of the items in X that optimizes an objective function f : P -> R, which quantifies the quality of solution P. Often f is defined so as to maximize similarity within a cluster and minimize similarity between clusters. To that end, many algorithms make use of a measure d : X x X -> R of the distance between two items. A partitioning algorithm produces a set of clusters P = {c1, . . . , ck} such that the clusters are nonoverlapping (c_i intersected with c_j = empty set, i != j) subsets of the data set (Union_i c_i=X). Hierarchical algorithms produce a series of partitions P = {p1, . . . , pn }. For a complete hierarchy, the number of partitions n’= n, the number of items in the data set; the top partition is a single cluster containing all items, and the bottom partition contains n clusters, each containing a single item. For model-based clustering, each cluster c_j is represented by a model m_j , such as the cluster center or a Gaussian distribution. The wide array of available clustering algorithms may seem bewildering, and covering all of them is beyond the scope of this chapter. Choosing among them for a

  16. A cluster-randomized controlled knowledge translation feasibility study in Alberta community pharmacies using the PARiHS framework: study protocol.

    Science.gov (United States)

    Rosenthal, Meagen M; Tsuyuki, Ross T; Houle, Sherilyn Kd

    2015-01-01

    Despite evidence of benefit for pharmacist involvement in chronic disease management, the provision of these services in community pharmacy has been suboptimal. The Promoting Action on Research Implementation in Health Services (PARiHS) framework suggests that for knowledge translation to be effective, there must be evidence of benefit, a context conducive to implementation, and facilitation to support uptake. We hypothesize that while the evidence and context components of this framework are satisfied, that uptake into practice has been insufficient because of a lack of facilitation. This protocol describes the rationale and methods of a feasibility study to test a facilitated pharmacy practice intervention based on the PARiHS framework, to assist community pharmacists in increasing the number of formal and documented medication management services completed for patients with diabetes, dyslipidemia, and hypertension. A cluster-randomized before-after design will compare ten pharmacies from within a single organization, with the unit of randomization being the pharmacy. Pharmacies will be randomized to facilitated intervention based on the PARiHS framework or usual practice. The Alberta Context Tool will be used to establish the context of practice in each pharmacy. Pharmacies randomized to the intervention will receive task-focused facilitation from an external facilitator, with the goal of developing alternative team processes to allow the greater provision of medication management services for patients with diabetes, hypertension, and dyslipidemia. The primary outcome will be a process evaluation of the needs of community pharmacies to provide more clinical services, the acceptability and uptake of modifications made, and the willingness of pharmacies to participate. Secondary outcomes will include the change in the number of formal and documented medication management services in the aforementioned chronic conditions provided 6 months before, versus after, the

  17. Use of the challenge point framework to guide motor learning of stepping reactions for improved balance control in people with stroke: a case series.

    Science.gov (United States)

    Pollock, Courtney L; Boyd, Lara A; Hunt, Michael A; Garland, S Jayne

    2014-04-01

    Stepping reactions are important for walking balance and community-level mobility. Stepping reactions of people with stroke are characterized by slow reaction times, poor coordination of motor responses, and low amplitude of movements, which may contribute to their decreased ability to recover their balance when challenged. An important aspect of rehabilitation of mobility after stroke is optimizing the motor learning associated with retraining effective stepping reactions. The Challenge Point Framework (CPF) is a model that can be used to promote motor learning through manipulation of conditions of practice to modify task difficulty, that is, the interaction of the skill of the learner and the difficulty of the task to be learned. This case series illustrates how the retraining of multidirectional stepping reactions may be informed by the CPF to improve balance function in people with stroke. Four people (53-68 years of age) with chronic stroke (>1 year) and mild to moderate motor recovery received 4 weeks of multidirectional stepping reaction retraining. Important tenets of motor learning were optimized for each person during retraining in accordance with the CPF. Participants demonstrated improved community-level walking balance, as determined with the Community Balance and Mobility Scale. These improvements were evident 1 year later. Aspects of balance-related self-efficacy and movement kinematics also showed improvements during the course of the intervention. The application of CPF motor learning principles in the retraining of stepping reactions to improve community-level walking balance in people with chronic stroke appears to be promising. The CPF provides a plausible theoretical framework for the progression of functional task training in neurorehabilitation.

  18. Clustering of near clusters versus cluster compactness

    International Nuclear Information System (INIS)

    Yu Gao; Yipeng Jing

    1989-01-01

    The clustering properties of near Zwicky clusters are studied by using the two-point angular correlation function. The angular correlation functions for compact and medium compact clusters, for open clusters, and for all near Zwicky clusters are estimated. The results show much stronger clustering for compact and medium compact clusters than for open clusters, and that open clusters have nearly the same clustering strength as galaxies. A detailed study of the compactness-dependence of correlation function strength is worth investigating. (author)

  19. Validating clustering of molecular dynamics simulations using polymer models

    Directory of Open Access Journals (Sweden)

    Phillips Joshua L

    2011-11-01

    Full Text Available Abstract Background Molecular dynamics (MD simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our

  20. Structural variations and photoluminescent properties of a series of metal-organic frameworks constructed from 5-(4-carboxybenzoylamino)-isophthalic acid

    International Nuclear Information System (INIS)

    Zhao, Wen; Zhang, Li-Juan; Zhao, Xiao-Li

    2013-01-01

    Five new metal-organic frameworks (MOFs) with 5-(4-carboxybenzoylamino)-isophthalic acid (H 3 L), namely, [Cd 9 L 6 (DMA) 6 ]·4DMA (1), [Cd 3 L 2 (H 2 O) 9 ]·4H 2 O (2), [LaL(H 2 O) 4 ]·2H 2 O (3), [CeL(H 2 O) 4 ]·H 2 O (4) and [Tb(HL)(H 2 L)(H 2 O) 3 ]·5H 2 O (5) (DMA=N,N-dimethylacetamide), have been synthesized. Complex 1 shows a three-dimensional architecture generated from linkage of Cd–O chains via L 3− ligands. Minor variations in synthetic conditions of 1 afforded 2, which features an interesting 2D→3D catenation architecture containing helical chains. Complexes 3 and 4 are isostructural and each feature a two-dimensional architecture constructed from the linkage of L 3− with Ln 3+ . Complex 5 displays a chain-like structure, of which the most interesting feature is the existence of free carboxylic acid (–COOH) group which may confer unique functionality. Moreover, the investigations of the thermal stability, powder X-ray diffractions and solid-state photoluminescent properties for these crystalline materials have been carried out. - Graphical Abstract: Solvothermal reactions of tricarboxylate ligand H 3 L with Cd 2+ /Ln 3+ has yielded a series of new MOFs containing interesting structural motifs. - Highlights: • A tricarboxylate ligand whose coordinating functionalities are not symmetry equivalent is employed to construct MOFs. • Complex 2 features an interesting 2D→3D catenation architecture containing helical chains. • Complex 3 feature chain-like structure containing free – COOH group, which may confer unique functionality. • Photoluminescent properties and thermal behaviors for 1–5 have been reported

  1. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  2. Symmetry and topology code of the cluster self-assembly of framework MT structures of alumophosphates AlPO4(H2O)2 (metavariscite and variscite) and Al2(PO4)2(H2O)3 (APC)

    Science.gov (United States)

    Ilyushin, G. D.; Blatov, V. A.

    2017-03-01

    The supramolecular chemistry of alumophosphates, which form framework 3D MT structures from polyhedral AlO4(H2O)2 clusters with octahedral O coordination (of M polyhedra) and PO4 and AlO4 with tetrahedral O coordination (of T polyhedra), is considered. A combinatorial-topological modeling of the formation of possible types of linear (six types) and ring (two types) tetrapolyhedral cluster precursors M2T2 from MT monomers is carried out. Different versions of chain formation from linked (MT)2 rings (six types) are considered. The model, which has a universal character, has been used to simulate the cluster selfassembly of the crystal structure of AlPO4(H2O)2 minerals (metavariscite, m-VAR, and variscite, VAR) and zeolite [Al2(PO4)2(H2O)2] · H2O (APC). A tetrapolyhedral linear precursor is established for m-VAR and a ring precursor (MT)2 is established for VAR and APC. The symmetry and topology code of the processes of crystal structure self-assembly from cluster precursors is completely reconstructed. The functional role of the O-H···O hydrogen bonds is considered for the first time. The cluster self-assembly model explains the specific features of the morphogenesis of single crystals: m-VAR prisms, flattened VAR octahedra, and needleshaped APC square-base prisms.

  3. Multivariate Spatio-Temporal Clustering: A Framework for Integrating Disparate Data to Understand Network Representativeness and Scaling Up Sparse Ecosystem Measurements

    Science.gov (United States)

    Hoffman, F. M.; Kumar, J.; Maddalena, D. M.; Langford, Z.; Hargrove, W. W.

    2014-12-01

    Disparate in situ and remote sensing time series data are being collected to understand the structure and function of ecosystems and how they may be affected by climate change. However, resource and logistical constraints limit the frequency and extent of observations, particularly in the harsh environments of the arctic and the tropics, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent variability at desired scales. These regions host large areas of potentially vulnerable ecosystems that are poorly represented in Earth system models (ESMs), motivating two new field campaigns, called Next Generation Ecosystem Experiments (NGEE) for the Arctic and Tropics, funded by the U.S. Department of Energy. Multivariate Spatio-Temporal Clustering (MSTC) provides a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. We applied MSTC to down-scaled general circulation model results and data for the State of Alaska at a 4 km2 resolution to define maps of ecoregions for the present (2000-2009) and future (2090-2099), showing how combinations of 37 bioclimatic characteristics are distributed and how they may shift in the future. Optimal representative sampling locations were identified on present and future ecoregion maps, and representativeness maps for candidate sampling locations were produced. We also applied MSTC to remotely sensed LiDAR measurements and multi-spectral imagery from the WorldView-2 satellite at a resolution of about 5 m2 within the Barrow Environmental Observatory (BEO) in Alaska. At this resolution, polygonal ground features—such as centers, edges, rims, and troughs—can be distinguished. Using these remote sensing data, we up-scaled vegetation distribution data collected on these polygonal ground features to a large area of the BEO to provide distributions of plant functional types that can

  4. Mixed-Initiative Clustering

    Science.gov (United States)

    Huang, Yifen

    2010-01-01

    Mixed-initiative clustering is a task where a user and a machine work collaboratively to analyze a large set of documents. We hypothesize that a user and a machine can both learn better clustering models through enriched communication and interactive learning from each other. The first contribution or this thesis is providing a framework of…

  5. A study on the redox, spectroscopic, and photophysical characteristics of a series of octahedral hexamolybdenum(ii) clusters: [{Mo6X8}Y6]2- (X, Y = Cl, Br, or I).

    Science.gov (United States)

    Akagi, Soichiro; Fujii, Sho; Kitamura, Noboru

    2018-01-23

    We report a systematic study on the redox, spectroscopic, and photophysical properties of a series of [{Mo 6 X 8 }Y 6 ] 2- (X, Y = Cl, Br, or I. 1-9). All of the [{Mo 6 X 8 }Y 6 ] 2- clusters show intense and long-lived phosphorescence in both CH 3 CN and crystalline phases at 298 K. We found that the emission quantum yields (Φ em ) of 1-9 increase in the sequences X = Cl Br Br Br < Cl for given {Mo 6 X 8 } 4+ -core clusters. The present data demonstrate that arbitrary combinations of X and Y in [{Mo 6 X 8 }Y 6 ] 2- could tune τ em and Φ em in the ranges of 85-300 μs and 0.09-0.47, respectively. Both capping (X) and terminal ligand (Y) effects on the photophysical properties of the clusters are discussed on the basis of the energy gap (i.e., emission energy) dependence of the nonradiative decay rate constant.

  6. An Alternative Framework for Time Series Decomposition and Forecastingand its Relevance for Portfolio Choice – A Comparative Study of the Indian Consumer Durable and Small Cap Sectors

    OpenAIRE

    SEN, Jaydip; DATTA CHAUDHURI, Tamal

    2016-01-01

    Abstract. One of the challenging research problems in the domain of time series analysis and forecasting is making efficient and robust prediction of stock market prices. With rapid development and evolution of sophisticated algorithms and with the availability of extremely fast computing platforms, it has now become possible to effectively extract, store, process and analyze high volume stock market time series data. Complex algorithms for forecasting are now available for speedy execution o...

  7. Spatial cluster modelling

    CERN Document Server

    Lawson, Andrew B

    2002-01-01

    Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

  8. Using Social Marketing Theory as a Framework for Understanding and Increasing HPV Vaccine Series Completion Among Hispanic Adolescents: A Qualitative Study.

    Science.gov (United States)

    Roncancio, Angelica M; Ward, Kristy K; Carmack, Chakema C; Muñoz, Becky T; Cano, Miguel A; Cribbs, Felicity

    2017-02-01

    HPV vaccine series completion rates among adolescent Hispanic females and males (~39 and 21 %, respectively) are far below the Healthy People 80 % coverage goal. Completion of the 3-dose vaccine series is critical to reducing the incidence of HPV-associated cancers. This formative study applies social marketing theory to assess the needs and preferences of Hispanic mothers in order to guide the development of interventions to increase HPV vaccine completion. We conducted 51 in-depth interviews with Hispanic mothers of adolescents to identify the key concepts of social marketing theory (i.e., the four P's: product, price, place and promotion). Results suggest that a desire complete the vaccine series, vaccine reminders and preventing illnesses and protecting their children against illnesses and HPV all influence vaccination (product). The majority of Completed mothers did not experience barriers that prevented vaccine series completion and Initiated mothers perceived a lack of health insurance and the cost of the vaccine as potential barriers. Informational barriers were prevalent across both market segments (price). Clinics are important locations for deciding to complete the vaccine series (place). They are the preferred sources to obtain information about the HPV vaccine thus making them ideal locations to deliver intervention messages, followed by television, the child's school and brochures (promotion). Increasing HPV vaccine coverage among Hispanic adolescents will reduce the rates of HPV-associated cancers and the cervical cancer health disparity among Hispanic women. This research can inform the development of an intervention to increase HPV vaccine series completion in this population.

  9. Cluster fusion algorithm: application to Lennard-Jones clusters

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2006-01-01

    paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

  10. Cluster fusion algorithm: application to Lennard-Jones clusters

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2008-01-01

    paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

  11. PREFACE: Nuclear Cluster Conference; Cluster'07

    Science.gov (United States)

    Freer, Martin

    2008-05-01

    The Cluster Conference is a long-running conference series dating back to the 1960's, the first being initiated by Wildermuth in Bochum, Germany, in 1969. The most recent meeting was held in Nara, Japan, in 2003, and in 2007 the 9th Cluster Conference was held in Stratford-upon-Avon, UK. As the name suggests the town of Stratford lies upon the River Avon, and shortly before the conference, due to unprecedented rainfall in the area (approximately 10 cm within half a day), lay in the River Avon! Stratford is the birthplace of the `Bard of Avon' William Shakespeare, and this formed an intriguing conference backdrop. The meeting was attended by some 90 delegates and the programme contained 65 70 oral presentations, and was opened by a historical perspective presented by Professor Brink (Oxford) and closed by Professor Horiuchi (RCNP) with an overview of the conference and future perspectives. In between, the conference covered aspects of clustering in exotic nuclei (both neutron and proton-rich), molecular structures in which valence neutrons are exchanged between cluster cores, condensates in nuclei, neutron-clusters, superheavy nuclei, clusters in nuclear astrophysical processes and exotic cluster decays such as 2p and ternary cluster decay. The field of nuclear clustering has become strongly influenced by the physics of radioactive beam facilities (reflected in the programme), and by the excitement that clustering may have an important impact on the structure of nuclei at the neutron drip-line. It was clear that since Nara the field had progressed substantially and that new themes had emerged and others had crystallized. Two particular topics resonated strongly condensates and nuclear molecules. These topics are thus likely to be central in the next cluster conference which will be held in 2011 in the Hungarian city of Debrechen. Martin Freer Participants and Cluster'07

  12. Chiral Silver-Lanthanide Metal-Organic Frameworks Comprised of One-Dimensional Triple Right-Handed Helical Chains Based on [Ln7(μ3-OH)8]13+ Clusters.

    Science.gov (United States)

    Guo, Yan; Zhang, Lijuan; Muhammad, Nadeem; Xu, Yan; Zhou, Yunshan; Tang, Fang; Yang, Shaowei

    2018-02-05

    Three new isostructural chiral silver-lanthanide heterometal-organic frameworks [Ag 3 Ln 7 (μ 3 -OH) 8 (bpdc) 6 (NO 3 ) 3 (H 2 O) 6 ](NO 3 )·2H 2 O [Ln = Eu (1), Tb (2, Sm (3); H 2 bpdc = 2,2'-bipyridine-3,3'-dicarboxylic acid] based on heptanuclear lanthanide clusters [Ln 7 (μ 3 -OH) 8 ] 13+ comprised of one-dimensional triple right-handed helical chains were hydrothermally synthesized. Various means such as UV-vis spectroscopy, IR spectroscopy, elemental analysis, powder X-ray diffraction, and thermogravimetric/differential thermal analysis were used to characterize the compounds, wherein compound 3 was crystallographically characterized. In the structure of compound 3, eight μ 3 -OH - groups link seven Sm 3+ ions, forming a heptanuclear cluster, [Sm 7 (μ 3 -OH) 8 ] 13+ , and the adjacent [Sm 7 (μ 3 -OH) 8 ] 13+ clusters are linked by the carboxylic groups of bpdc 2- ligands, leading to the formation of a one-dimensional triple right-handed helical chain. The adjacent triple right-handed helical chains are further joined together by coordinating the pyridyl N atoms of the bpdc 2- ligands with Ag + , resulting in a chiral three-dimensional silver(I)-lanthanide(III) heterometal-organic framework with one-dimensional channels wherein NO 3 - anions and crystal lattice H 2 O molecules are trapped. The compounds were studied systematically with respect to their photoluminescence properties and energy-transfer mechanism, and it was found that H 2 bpdc (the energy level for the triplet states of the ligand H 2 bpdc is 21505 cm -1 ) can sensitize Eu 3+ luminescence more effectively than Tb 3+ and Sm 3+ luminescence because of effective energy transfer from bpdc 2- to Eu 3+ under excitation in compound 1.

  13. Radio investigations of clusters of galaxies

    International Nuclear Information System (INIS)

    Valentijn, E.A.

    1978-01-01

    This thesis contains a number of papers of the series entitled, A Westerbork Survey of Rich Clusters of Galaxies. The primary aim was to study the radio characteristics of cluster galaxies and especially the question whether their ''radio-activity'' is influenced by their location inside a cluster. It is enquired whether the presence of an intra-cluster medium (ICM), or the typical cluster evolution or cluster dynamical processes can give rise to radio-observable effects on the behaviour of cluster galaxies. 610 MHz WSRT observations of the Coma cluster (and radio observations of the Hercules supercluster) are presented. Extended radio sources in Abell clusters are then described. (Auth.)

  14. How are small endohedral silicon clusters stabilized?

    Science.gov (United States)

    Avaltroni, Fabrice; Steinmann, Stephan N; Corminboeuf, Clémence

    2012-11-21

    Clusters in the (Be, B, C)@Si(n)((0,1,2+)) (n = 6-10) series, isoelectronic to Si(n)(2-), present multiple symmetric structures, including rings, cages and open structures, which the doping atom stabilizes using contrasting bonding mechanisms. The most striking feature of these clusters is the absence of electron transfer (for Be) or even the inversion (for B and C) in comparison to classic endohedral metallofullerenes (e.g. from the outer frameworks towards the enclosed atom). The relatively small cavity of the highly symmetric Si(8) cubic cage benefits more strongly from the encapsulation of a boron atom than from the insertion of a too large beryllium atom. Overall, the maximization of multicenter-type bonding, as visualized by the Localized Orbital Locator (LOL), is the key to the stabilization of the small Si(n) cages. Boron offers the best balance between size, electronegativity and delocalized bonding pattern when compared to beryllium and carbon.

  15. Investigating cluster formation in adsorption of CO2, CH4, and Ar in zeolites and metal organic frameworks at subcritical temperatures

    NARCIS (Netherlands)

    Krishna, R.; van Baten, J.M.

    2010-01-01

    The critical temperatures, T-c, of CO2, CH4, and Ar are 304 K, 191 K, and 151 K, respectively. This paper highlights some unusual characteristics of adsorption and diffusion of these molecules in microporous structures such as zeolites and metal organic frameworks at temperatures T < T-c. Published

  16. Implementing and evaluating a regional strategy to improve testing rates in VA patients at risk for HIV, utilizing the QUERI process as a guiding framework: QUERI Series

    OpenAIRE

    Goetz, MB; Bowman, C; Hoang, T; Anaya, H; Osborn, T; Gifford, AL; Asch, SM

    2008-01-01

    Abstract Background We describe how we used the framework of the U.S. Department of Veterans Affairs (VA) Quality Enhancement Research Initiative (QUERI) to develop a program to improve rates of diagnostic testing for the Human Immunodeficiency Virus (HIV). This venture was prompted by the observation by the CDC that 25% of HIV-infected patients do not know their diagnosis – a point of substantial importance to the VA, which is the largest provider of HIV care in the United States. Methods Fo...

  17. Interpretable Categorization of Heterogeneous Time Series Data

    Science.gov (United States)

    Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

    2017-01-01

    We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

  18. Multi-Optimisation Consensus Clustering

    Science.gov (United States)

    Li, Jian; Swift, Stephen; Liu, Xiaohui

    Ensemble Clustering has been developed to provide an alternative way of obtaining more stable and accurate clustering results. It aims to avoid the biases of individual clustering algorithms. However, it is still a challenge to develop an efficient and robust method for Ensemble Clustering. Based on an existing ensemble clustering method, Consensus Clustering (CC), this paper introduces an advanced Consensus Clustering algorithm called Multi-Optimisation Consensus Clustering (MOCC), which utilises an optimised Agreement Separation criterion and a Multi-Optimisation framework to improve the performance of CC. Fifteen different data sets are used for evaluating the performance of MOCC. The results reveal that MOCC can generate more accurate clustering results than the original CC algorithm.

  19. Cluster headache

    Science.gov (United States)

    Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... Doctors do not know exactly what causes cluster headaches. They ... (chemical in the body released during an allergic response) or ...

  20. Mathematical classification and clustering

    CERN Document Server

    Mirkin, Boris

    1996-01-01

    I am very happy to have this opportunity to present the work of Boris Mirkin, a distinguished Russian scholar in the areas of data analysis and decision making methodologies. The monograph is devoted entirely to clustering, a discipline dispersed through many theoretical and application areas, from mathematical statistics and combina­ torial optimization to biology, sociology and organizational structures. It compiles an immense amount of research done to date, including many original Russian de­ velopments never presented to the international community before (for instance, cluster-by-cluster versions of the K-Means method in Chapter 4 or uniform par­ titioning in Chapter 5). The author's approach, approximation clustering, allows him both to systematize a great part of the discipline and to develop many in­ novative methods in the framework of optimization problems. The optimization methods considered are proved to be meaningful in the contexts of data analysis and clustering. The material presented in ...

  1. A Time Series Forecasting Method

    Directory of Open Access Journals (Sweden)

    Wang Zhao-Yu

    2017-01-01

    Full Text Available This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

  2. Self-assembly of polyoxometalate clusters into a 3-D heterometallic framework via covalent bonding: synthesis, structure and characterization of Na4[Nd8(dipic)12(H2O)9][Mo8O26].8H2O

    International Nuclear Information System (INIS)

    Shen Enhong; Lue Jian; Li Yangguang; Wang Enbo; Hu Changwen; Xu Lin

    2004-01-01

    An unprecedented hybrid solid obtained by self-assembly of octamolybdate clusters into a three-dimensional alkali metal modified neodymium-organic heterometallic framework is described. Crystal data: monoclinic, space group P21/n, a=21.868(4)A, b=13.039(3)A, c=22.479(5)A, β=98.90(3) o ; V=6332(2)A3; Z=2, R (final)=0.0474. The data were collected on a Rigaku R-AXIS RAPID IP diffractometer at 293K using graphite-monochromated MoKα radiation (λ=0.71073A) and oscillation scans technique in the range of 1.98 deg. θ27.48 deg

  3. Tectonosedimentary framework of Upper Cretaceous –Neogene series in the Gulf of Tunis inferred from subsurface data: implications for petroleum exploration

    Directory of Open Access Journals (Sweden)

    Dhraief Wissem

    2017-04-01

    Full Text Available The objective and the main contribution of this issue are dedicated to using subsurface data to delineate a basin beneath the Gulf of Tunis and its neighbouring areas, and to investigate the potential of this area in terms of hydrocarbon resources. Available well data provided information about the subsurface geology beneath the Gulf of Tunis. 2D seismic data allowed delineation of the basin shape, strata geometries, and some potential promising subsurface structures in terms of hydrocarbon accumulation. Together with lithostratigraphic data obtained from drilled wells, seismic data permitted the construction of isochron and isobath maps of Upper Cretaceous-Neogene strata. Structural and lithostratigraphic interpretations indicate that the area is tectonically complex, and they highlight the tectonic control of strata deposition during the Cretaceous and Neogene. Tectonic activity related to the geodynamic evolution of the northern African margin appears to have been responsible for several thickness and facies variations, and to have played a significant role in the establishment and evolution of petroleum systems in northeastern Tunisia. As for petroleum systems in the basin, the Cretaceous series of the Bahloul, Mouelha and Fahdene formations are acknowledged to be the main source rocks. In addition, potential reservoirs (Fractured Abiod and Bou Dabbous carbonated formations sealed by shaly and marly formations (Haria and Souar formations respectively show favourable geometries of trap structures (anticlines, tilted blocks, unconformities, etc. which make this area adequate for hydrocarbon accumulations.

  4. Platinum triangles in the Pt/Al framework of the intermetallic REPt6Al3 (RE = Ce-Nd, Sm, Gd, Tb) series

    International Nuclear Information System (INIS)

    Eustermann, Fabian; Stegemann, Frank; Renner, Konstantin; Janka, Oliver

    2017-01-01

    The compounds of the REPt 6 Al 3 series (RE = Ce-Nd, Sm, Gd, Tb) were obtained by reaction of the elements via arc-melting. They were characterized by powder and single-crystal X-ray diffraction (NdPt 6 Al 3 : wR = 0.0432, 759 F 2 values, 33 variables) as well as by magnetic susceptibility measurements. The isostructural compounds crystallize with a new structure type in the trigonal crystal system with space group R anti 3c, twelve formula units in the unit cell, and lattice parameters of a = 752-755 and c = 3882-3945 pm. The crystal structure can be described by different slabs stacked along [001]. One layer features Pt 3 triangles, centering the cavities of a flat honeycomb RE layer that are arranged in a..ABCA ' B ' C ' .. sequence. The other layer consists of condensed hexagonal [Pt 6 Al 6 ] prisms, centered by Pt atoms, separating the before mentioned slabs. Magnetic measurements revealed that all rare-earth atoms are in the trivalent oxidation state, however, due to the low lanthanoide content magnetic ordering phenomena were observed only at low temperatures [SmPt 6 Al 3 : T C = 5.0(1) K; GdPt 6 Al 3 : T C = 7.3(1) K; TbPt 6 Al 3 : T N = 3.6(1) K]. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  5. Fourier series

    CERN Document Server

    Tolstov, Georgi P

    1962-01-01

    Richard A. Silverman's series of translations of outstanding Russian textbooks and monographs is well-known to people in the fields of mathematics, physics, and engineering. The present book is another excellent text from this series, a valuable addition to the English-language literature on Fourier series.This edition is organized into nine well-defined chapters: Trigonometric Fourier Series, Orthogonal Systems, Convergence of Trigonometric Fourier Series, Trigonometric Series with Decreasing Coefficients, Operations on Fourier Series, Summation of Trigonometric Fourier Series, Double Fourie

  6. Platinum triangles in the Pt/Al framework of the intermetallic REPt{sub 6}Al{sub 3} (RE = Ce-Nd, Sm, Gd, Tb) series

    Energy Technology Data Exchange (ETDEWEB)

    Eustermann, Fabian; Stegemann, Frank; Renner, Konstantin [Institut fuer Anorganische und Analytische Chemie, Westfaelische Wilhelms-Universitaet Muenster (Germany); Janka, Oliver [Institut fuer Anorganische und Analytische Chemie, Westfaelische Wilhelms-Universitaet Muenster (Germany); Institut fuer Chemie, Carl von Ossietzky Universitaet Oldenburg (Germany)

    2017-12-13

    The compounds of the REPt{sub 6}Al{sub 3} series (RE = Ce-Nd, Sm, Gd, Tb) were obtained by reaction of the elements via arc-melting. They were characterized by powder and single-crystal X-ray diffraction (NdPt{sub 6}Al{sub 3}: wR = 0.0432, 759 F{sup 2} values, 33 variables) as well as by magnetic susceptibility measurements. The isostructural compounds crystallize with a new structure type in the trigonal crystal system with space group R anti 3c, twelve formula units in the unit cell, and lattice parameters of a = 752-755 and c = 3882-3945 pm. The crystal structure can be described by different slabs stacked along [001]. One layer features Pt{sub 3} triangles, centering the cavities of a flat honeycomb RE layer that are arranged in a..ABCA{sup '}B{sup '}C{sup '}.. sequence. The other layer consists of condensed hexagonal [Pt{sub 6}Al{sub 6}] prisms, centered by Pt atoms, separating the before mentioned slabs. Magnetic measurements revealed that all rare-earth atoms are in the trivalent oxidation state, however, due to the low lanthanoide content magnetic ordering phenomena were observed only at low temperatures [SmPt{sub 6}Al{sub 3}: T{sub C} = 5.0(1) K; GdPt{sub 6}Al{sub 3}: T{sub C} = 7.3(1) K; TbPt{sub 6}Al{sub 3}: T{sub N} = 3.6(1) K]. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  7. Series of edge-sharing bi-triangle Ln4 clusters with a μ4-NO3- bridge: syntheses, structures, luminescence, and the SMM behavior of the Dy4 analogue.

    Science.gov (United States)

    Zou, Hua-Hong; Wang, Rong; Chen, Zi-Lu; Liu, Dong-Cheng; Liang, Fu-Pei

    2014-02-14

    A series of Ln4 clusters, [Ln4L2(μ3-OH)2(μ4-NO3)(NO3)4(OCH3)(H2O)]·xMeCN·yMeOH (Ln = Gd (1), Tb (2), Dy (3), Ho (4), Er (5), Yb (6), L = 2-{[2-(2-hydroxy-ethoxy)-ethylimino]-methyl}-6-methoxyphenol), have been synthesized by the reaction of Ln(NO)3 and a Schiff-base ligand formed in situ. The six complexes display similar structures, with an overall metal core comprising two edge-sharing triangular Ln3 units linked by a μ4-NO3(-) bridge. The luminescence spectrum of complex 2 shows the characteristic emission of the Tb(III) ions. The magnetic susceptibility studies reveal that the Ln(III) ions are very weakly interacting in all six compounds. Frequency dependence of the ac-susceptibility was found for 3, suggesting a typical single-molecule magnet (SMM) behavior with an anisotropic barrier of 28 K.

  8. Implementing and evaluating a regional strategy to improve testing rates in VA patients at risk for HIV, utilizing the QUERI process as a guiding framework: QUERI Series.

    Science.gov (United States)

    Goetz, Matthew B; Bowman, Candice; Hoang, Tuyen; Anaya, Henry; Osborn, Teresa; Gifford, Allen L; Asch, Steven M

    2008-03-19

    We describe how we used the framework of the U.S. Department of Veterans Affairs (VA) Quality Enhancement Research Initiative (QUERI) to develop a program to improve rates of diagnostic testing for the Human Immunodeficiency Virus (HIV). This venture was prompted by the observation by the CDC that 25% of HIV-infected patients do not know their diagnosis - a point of substantial importance to the VA, which is the largest provider of HIV care in the United States. Following the QUERI steps (or process), we evaluated: 1) whether undiagnosed HIV infection is a high-risk, high-volume clinical issue within the VA, 2) whether there are evidence-based recommendations for HIV testing, 3) whether there are gaps in the performance of VA HIV testing, and 4) the barriers and facilitators to improving current practice in the VA.Based on our findings, we developed and initiated a QUERI step 4/phase 1 pilot project using the precepts of the Chronic Care Model. Our improvement strategy relies upon electronic clinical reminders to provide decision support; audit/feedback as a clinical information system, and appropriate changes in delivery system design. These activities are complemented by academic detailing and social marketing interventions to achieve provider activation. Our preliminary formative evaluation indicates the need to ensure leadership and team buy-in, address facility-specific barriers, refine the reminder, and address factors that contribute to inter-clinic variances in HIV testing rates. Preliminary unadjusted data from the first seven months of our program show 3-5 fold increases in the proportion of at-risk patients who are offered HIV testing at the VA sites (stations) where the pilot project has been undertaken; no change was seen at control stations. This project demonstrates the early success of the application of the QUERI process to the development of a program to improve HIV testing rates. Preliminary unadjusted results show that the coordinated use of

  9. Implementing and evaluating a regional strategy to improve testing rates in VA patients at risk for HIV, utilizing the QUERI process as a guiding framework: QUERI Series

    Directory of Open Access Journals (Sweden)

    Osborn Teresa

    2008-03-01

    Full Text Available Abstract Background We describe how we used the framework of the U.S. Department of Veterans Affairs (VA Quality Enhancement Research Initiative (QUERI to develop a program to improve rates of diagnostic testing for the Human Immunodeficiency Virus (HIV. This venture was prompted by the observation by the CDC that 25% of HIV-infected patients do not know their diagnosis – a point of substantial importance to the VA, which is the largest provider of HIV care in the United States. Methods Following the QUERI steps (or process, we evaluated: 1 whether undiagnosed HIV infection is a high-risk, high-volume clinical issue within the VA, 2 whether there are evidence-based recommendations for HIV testing, 3 whether there are gaps in the performance of VA HIV testing, and 4 the barriers and facilitators to improving current practice in the VA. Based on our findings, we developed and initiated a QUERI step 4/phase 1 pilot project using the precepts of the Chronic Care Model. Our improvement strategy relies upon electronic clinical reminders to provide decision support; audit/feedback as a clinical information system, and appropriate changes in delivery system design. These activities are complemented by academic detailing and social marketing interventions to achieve provider activation. Results Our preliminary formative evaluation indicates the need to ensure leadership and team buy-in, address facility-specific barriers, refine the reminder, and address factors that contribute to inter-clinic variances in HIV testing rates. Preliminary unadjusted data from the first seven months of our program show 3–5 fold increases in the proportion of at-risk patients who are offered HIV testing at the VA sites (stations where the pilot project has been undertaken; no change was seen at control stations. Discussion This project demonstrates the early success of the application of the QUERI process to the development of a program to improve HIV testing rates

  10. Cluster management.

    Science.gov (United States)

    Katz, R

    1992-11-01

    Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

  11. Isotopic clusters

    International Nuclear Information System (INIS)

    Geraedts, J.M.P.

    1983-01-01

    Spectra of isotopically mixed clusters (dimers of SF 6 ) are calculated as well as transition frequencies. The result leads to speculations about the suitability of the laser-cluster fragmentation process for isotope separation. (Auth.)

  12. Cluster Headache

    Science.gov (United States)

    ... a role. Unlike migraine and tension headache, cluster headache generally isn't associated with triggers, such as foods, hormonal changes or stress. Once a cluster period begins, however, drinking alcohol ...

  13. Cluster Headache

    OpenAIRE

    Pearce, Iris

    1985-01-01

    Cluster headache is the most severe primary headache with recurrent pain attacks described as worse than giving birth. The aim of this paper was to make an overview of current knowledge on cluster headache with a focus on pathophysiology and treatment. This paper presents hypotheses of cluster headache pathophysiology, current treatment options and possible future therapy approaches. For years, the hypothalamus was regarded as the key structure in cluster headache, but is now thought to be pa...

  14. Categorias Cluster

    OpenAIRE

    Queiroz, Dayane Andrade

    2015-01-01

    Neste trabalho apresentamos as categorias cluster, que foram introduzidas por Aslak Bakke Buan, Robert Marsh, Markus Reineke, Idun Reiten e Gordana Todorov, com o objetivo de categoriíicar as algebras cluster criadas em 2002 por Sergey Fomin e Andrei Zelevinsky. Os autores acima, em [4], mostraram que existe uma estreita relação entre algebras cluster e categorias cluster para quivers cujo grafo subjacente é um diagrama de Dynkin. Para isto desenvolveram uma teoria tilting na estrutura triang...

  15. Meaningful Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Calapristi, Augustin J.; Crow, Vernon L.; Hetzler, Elizabeth G.; Turner, Alan E.

    2004-05-26

    We present an approach to the disambiguation of cluster labels that capitalizes on the notion of semantic similarity to assign WordNet senses to cluster labels. The approach provides interesting insights on how document clustering can provide the basis for developing a novel approach to word sense disambiguation.

  16. Horticultural cluster

    OpenAIRE

    SHERSTIUK S.V.; POSYLAYEVA K.I.

    2013-01-01

    In the article there are the theoretical and methodological approaches to the nature and existence of the cluster. The cluster differences from other kinds of cooperative and integration associations. Was develop by scientific-practical recommendations for forming a competitive horticultur cluster.

  17. Cluster Matters

    DEFF Research Database (Denmark)

    Gulati, Mukesh; Lund-Thomsen, Peter; Suresh, Sangeetha

    2018-01-01

    sell their products successfully in international markets, but there is also an increasingly large consumer base within India. Indeed, Indian industrial clusters have contributed to a substantial part of this growth process, and there are several hundred registered clusters within the country...... of this handbook, which focuses on the role of CSR in MSMEs. Hence we contribute to the literature on CSR in industrial clusters and specifically CSR in Indian industrial clusters by investigating the drivers of CSR in India’s industrial clusters....

  18. Infinite series

    CERN Document Server

    Hirschman, Isidore Isaac

    2014-01-01

    This text for advanced undergraduate and graduate students presents a rigorous approach that also emphasizes applications. Encompassing more than the usual amount of material on the problems of computation with series, the treatment offers many applications, including those related to the theory of special functions. Numerous problems appear throughout the book.The first chapter introduces the elementary theory of infinite series, followed by a relatively complete exposition of the basic properties of Taylor series and Fourier series. Additional subjects include series of functions and the app

  19. Percolation with multiple giant clusters

    International Nuclear Information System (INIS)

    Ben-Naim, E; Krapivsky, P L

    2005-01-01

    We study mean-field percolation with freezing. Specifically, we consider cluster formation via two competing processes: irreversible aggregation and freezing. We find that when the freezing rate exceeds a certain threshold, the percolation transition is suppressed. Below this threshold, the system undergoes a series of percolation transitions with multiple giant clusters ('gels') formed. Giant clusters are not self-averaging as their total number and their sizes fluctuate from realization to realization. The size distribution F k , of frozen clusters of size k, has a universal tail, F k ∼ k -3 . We propose freezing as a practical mechanism for controlling the gel size. (letter to the editor)

  20. I Cluster geografici

    Directory of Open Access Journals (Sweden)

    Maurizio Rosina

    2010-03-01

    Full Text Available Geographic ClustersOver the past decade, public alphanumeric database have been growing at exceptional rate. Most of data can be georeferenced, so that is possible gaining new knowledge from such databases. The contribution of this paper is two-fold. We first present a model of geographic clusters, which uses only geographic and functionally data properties. The model is useful to process huge amount of public/government data, even daily upgrading. After that, we merge the model into the framework GEOPOI (GEOcoding Points Of Interest, and show some graphic map results.

  1. I Cluster geografici

    Directory of Open Access Journals (Sweden)

    Maurizio Rosina

    2010-03-01

    Full Text Available Geographic Clusters Over the past decade, public alphanumeric database have been growing at exceptional rate. Most of data can be georeferenced, so that is possible gaining new knowledge from such databases. The contribution of this paper is two-fold. We first present a model of geographic clusters, which uses only geographic and functionally data properties. The model is useful to process huge amount of public/government data, even daily upgrading. After that, we merge the model into the framework GEOPOI (GEOcoding Points Of Interest, and show some graphic map results.

  2. From Networks to Time Series

    Science.gov (United States)

    Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

    2012-10-01

    In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

  3. Cluster bomb ocular injuries.

    Science.gov (United States)

    Mansour, Ahmad M; Hamade, Haya; Ghaddar, Ayman; Mokadem, Ahmad Samih; El Hajj Ali, Mohamad; Awwad, Shady

    2012-01-01

    To present the visual outcomes and ocular sequelae of victims of cluster bombs. This retrospective, multicenter case series of ocular injury due to cluster bombs was conducted for 3 years after the war in South Lebanon (July 2006). Data were gathered from the reports to the Information Management System for Mine Action. There were 308 victims of clusters bombs; 36 individuals were killed, of which 2 received ocular lacerations and; 272 individuals were injured with 18 receiving ocular injury. These 18 surviving individuals were assessed by the authors. Ocular injury occurred in 6.5% (20/308) of cluster bomb victims. Trauma to multiple organs occurred in 12 of 18 cases (67%) with ocular injury. Ocular findings included corneal or scleral lacerations (16 eyes), corneal foreign bodies (9 eyes), corneal decompensation (2 eyes), ruptured cataract (6 eyes), and intravitreal foreign bodies (10 eyes). The corneas of one patient had extreme attenuation of the endothelium. Ocular injury occurred in 6.5% of cluster bomb victims and 67% of the patients with ocular injury sustained trauma to multiple organs. Visual morbidity in civilians is an additional reason for a global ban on the use of cluster bombs.

  4. QCS: a system for querying, clustering and summarizing documents.

    Energy Technology Data Exchange (ETDEWEB)

    Dunlavy, Daniel M.; Schlesinger, Judith D. (Center for Computing Sciences, Bowie, MD); O' Leary, Dianne P. (University of Maryland, College Park, MD); Conroy, John M. (Center for Computing Sciences, Bowie, MD)

    2006-10-01

    Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel hybrid information retrieval system--the Query, Cluster, Summarize (QCS) system--which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of components in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test sets from the Document Understanding Conferences (DUC) along with the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence 'trimming', and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design

  5. QCS : a system for querying, clustering, and summarizing documents.

    Energy Technology Data Exchange (ETDEWEB)

    Dunlavy, Daniel M.

    2006-08-01

    Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel hybrid information retrieval system--the Query, Cluster, Summarize (QCS) system--which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of components in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test sets from the Document Understanding Conferences (DUC) along with the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence ''trimming'', and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of

  6. Cluster evolution

    International Nuclear Information System (INIS)

    Schaeffer, R.

    1987-01-01

    The galaxy and cluster luminosity functions are constructed from a model of the mass distribution based on hierarchical clustering at an epoch where the matter distribution is non-linear. These luminosity functions are seen to reproduce the present distribution of objects as can be inferred from the observations. They can be used to deduce the redshift dependence of the cluster distribution and to extrapolate the observations towards the past. The predicted evolution of the cluster distribution is quite strong, although somewhat less rapid than predicted by the linear theory

  7. Laser ionization of molecular clusters

    International Nuclear Information System (INIS)

    Desai, S.; Feigerle, C.S.

    1995-01-01

    Multiphoton ionization coupled with mass spectrometry was used to investigate molecular cluster distributions. Three examples will be discussed in this presentation. First, in studies of neat nitric oxide clusters, (NO) m , an interesting odd-even intensity alternation was observed and will be discussed in terms of electron-pairing considerations. In a separate study, the binary clusters comprising nitric oxide and methane preferentially form a stoichiometric cluster made up of repeating units of (NO) 2 CH 4 . These presumably represent a particularly strongly bound open-quotes van der Waalsclose quotes subunit. Finally, in similar studies of neat carbon disulfide clusters, (CS 2 ) m , additional photon absorption after the two-photon ionization step stimulates a series of intracluster ion-molecular reactions leading to formation of S m + and (CS) m + polymers, as well as intermediate species such as S m + (CS 2 ). This molecular cluster analogue of open-quotes laser snowclose quotes will be described in detail

  8. Exotic cluster structures on

    CERN Document Server

    Gekhtman, M; Vainshtein, A

    2017-01-01

    This is the second paper in the series of papers dedicated to the study of natural cluster structures in the rings of regular functions on simple complex Lie groups and Poisson-Lie structures compatible with these cluster structures. According to our main conjecture, each class in the Belavin-Drinfeld classification of Poisson-Lie structures on \\mathcal{G} corresponds to a cluster structure in \\mathcal{O}(\\mathcal{G}). The authors have shown before that this conjecture holds for any \\mathcal{G} in the case of the standard Poisson-Lie structure and for all Belavin-Drinfeld classes in SL_n, n<5. In this paper the authors establish it for the Cremmer-Gervais Poisson-Lie structure on SL_n, which is the least similar to the standard one.

  9. Multiple Clustering Views via Constrained Projections

    DEFF Research Database (Denmark)

    Dang, Xuan-Hong; Assent, Ira; Bailey, James

    2012-01-01

    Clustering, the grouping of data based on mutual similarity, is often used as one of principal tools to analyze and understand data. Unfortunately, most conventional techniques aim at finding only a single clustering over the data. For many practical applications, especially those being described...... in high dimensional data, it is common to see that the data can be grouped into different yet meaningful ways. This gives rise to the recently emerging research area of discovering alternative clusterings. In this preliminary work, we propose a novel framework to generate multiple clustering views....... The framework relies on a constrained data projection approach by which we ensure that a novel alternative clustering being found is not only qualitatively strong but also distinctively different from a reference clustering solution. We demonstrate the potential of the proposed framework using both synthetic...

  10. Hydrothermal synthesis, structure, and optical properties of two nanosized Ln{sub 26} rate at CO{sub 3} (Ln=Dy and Tb) cluster-based lanthanide-transition-metal-organic frameworks (Ln MOFs)

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Yu; Huang, Lian; Miao, Hao; Wan, Hong Xiang; Mei, Hua; Liu, Ying [State Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing Tech University (China); Xu, Yan [State Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing Tech University (China); State Key Laboratory of Coordination Chemistry, Nanjing Tech University (China)

    2015-02-16

    Two Ln{sub 26} rate at CO{sub 3} (Ln=Dy and Tb) cluster-based lanthanide-transition-metal-organic frameworks (Ln MOFs) formulated as [Dy{sub 26}Cu{sub 3}(Nic){sub 24}(CH{sub 3}COO){sub 8}(CO{sub 3}){sub 11}(OH){sub 26}(H{sub 2}O){sub 14}]Cl . 3 H{sub 2}O (1; HNic=nicotinic acid) and [Tb{sub 26}NaAg{sub 3}(Nic){sub 27}(CH{sub 3}COO){sub 6}(CO{sub 3}){sub 11}(OH){sub 26}Cl(H{sub 2}O){sub 15}] . 7.5 H{sub 2}O (2) have been successfully synthesized by hydrothermal methods and characterized by IR, thermogravimetric analysis (TGA), elemental analysis, and single X-ray diffraction. Compound 1 crystallizes in the monoclinic space group Cc with a=35.775(12) Aa, b=33.346(11) Aa, c=24.424(8) Aa, β=93.993(5) , V=29065(16) Aa{sup 3}, whereas 2 crystallizes in the triclinic space group P anti 1 with a=20.4929(19) Aa, b=24.671(2) Aa, c=29.727(3) Aa, α=81.9990(10) , β=88.0830(10) , γ=89.9940(10) , V=14875(2) Aa{sup 3}. Structural analysis indicates the framework of 1 is a 3D perovskite-like structure constructed out of CO{sub 3} rate at Dy{sub 26} building units and Cu{sup +} centers by means of nicotinic acid ligand bridging. In 2, however, nanosized CO{sub 3} rate at Tb{sub 26} units and [Ag{sub 3}Cl]{sup 2+} centers are connected by Nic{sup -} bridges to give rise to a 2D structure. It is worth mentioning that this kind of 4d-4f cluster-based MOF is quite rare as most of the reported analogous compounds are 3d-4f ones. Additionally, the solid-state emission spectra of pure compound 2 at room temperature suggest an efficient energy transfer from the ligand Nic{sup -} to Tb{sup 3+} ions, which we called the ''antenna effect''. Compound 2 shows a good two-photon absorption (TPA) with a TPA coefficient of 0.06947 cm GM{sup -1} (1 GM = 10{sup -50} cm{sup 4} s photon{sup -1}), which indicates that compound 2 might be a good choice for third-order nonlinear optical materials. (copyright 2015 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  11. Direct growth of metal-organic frameworks thin film arrays on glassy carbon electrode based on rapid conversion step mediated by copper clusters and hydroxide nanotubes for fabrication of a high performance non-enzymatic glucose sensing platform.

    Science.gov (United States)

    Shahrokhian, Saeed; Khaki Sanati, Elnaz; Hosseini, Hadi

    2018-07-30

    The direct growth of self-supported metal-organic frameworks (MOFs) thin film can be considered as an effective strategy for fabrication of the advanced modified electrodes in sensors and biosensor applications. However, most of the fabricated MOFs-based sensors suffer from some drawbacks such as time consuming for synthesis of MOF and electrode making, need of a binder or an additive layer, need of expensive equipment and use of hazardous solvents. Here, a novel free-standing MOFs-based modified electrode was fabricated by the rapid direct growth of MOFs on the surface of the glassy carbon electrode (GCE). In this method, direct growth of MOFs was occurred by the formation of vertically aligned arrays of Cu clusters and Cu(OH) 2 nanotubes, which can act as both mediator and positioning fixing factor for the rapid formation of self-supported MOFs on GCE surface. The effect of both chemically and electrochemically formed Cu(OH) 2 nanotubes on the morphological and electrochemical performance of the prepared MOFs were investigated. Due to the unique properties of the prepared MOFs thin film electrode such as uniform and vertically aligned structure, excellent stability, high electroactive surface area, and good availability to analyte and electrolyte diffusion, it was directly used as the electrode material for non-enzymatic electrocatalytic oxidation of glucose. Moreover, the potential utility of this sensing platform for the analytical determination of glucose concentration was evaluated by the amperometry technique. The results proved that the self-supported MOFs thin film on GCE is a promising electrode material for fabricating and designing non-enzymatic glucose sensors. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Hyperreal Numbers for Infinite Divergent Series

    OpenAIRE

    Bartlett, Jonathan

    2018-01-01

    Treating divergent series properly has been an ongoing issue in mathematics. However, many of the problems in divergent series stem from the fact that divergent series were discovered prior to having a number system which could handle them. The infinities that resulted from divergent series led to contradictions within the real number system, but these contradictions are largely alleviated with the hyperreal number system. Hyperreal numbers provide a framework for dealing with divergent serie...

  13. Projected coupled cluster theory.

    Science.gov (United States)

    Qiu, Yiheng; Henderson, Thomas M; Zhao, Jinmo; Scuseria, Gustavo E

    2017-08-14

    Coupled cluster theory is the method of choice for weakly correlated systems. But in the strongly correlated regime, it faces a symmetry dilemma, where it either completely fails to describe the system or has to artificially break certain symmetries. On the other hand, projected Hartree-Fock theory captures the essential physics of many kinds of strong correlations via symmetry breaking and restoration. In this work, we combine and try to retain the merits of these two methods by applying symmetry projection to broken symmetry coupled cluster wave functions. The non-orthogonal nature of states resulting from the application of symmetry projection operators furnishes particle-hole excitations to all orders, thus creating an obstacle for the exact evaluation of overlaps. Here we provide a solution via a disentanglement framework theory that can be approximated rigorously and systematically. Results of projected coupled cluster theory are presented for molecules and the Hubbard model, showing that spin projection significantly improves unrestricted coupled cluster theory while restoring good quantum numbers. The energy of projected coupled cluster theory reduces to the unprojected one in the thermodynamic limit, albeit at a much slower rate than projected Hartree-Fock.

  14. Clustering of Sun Exposure Measurements

    OpenAIRE

    Have, Anna Szynkowiak; Larsen, Jan; Hansen, Lars Kai; Philipsen, Peter Alshede; Thieden, Elisabeth; Wulf, Hans Christian

    2002-01-01

    In a medically motivated Sun-exposure study, questionnaires concerning Sun-habits were collected from a number of subjects together with UV radiation measurements. This paper focuses on identifying clusters in the heterogeneous set of data for the purpose of understanding possible relations between Sun-habits exposure and eventually assessing the risk of skin cancer. A general probabilistic framework originally developed for text and Web mining is demonstrated to be useful for clustering of b...

  15. What Qualifies as a Cluster Theory?

    DEFF Research Database (Denmark)

    Maskell, Peter; Kebir, Leïla

    2005-01-01

    This paper investigates the theoretical backgrounds of the `cluster' and proposes a framework aiming at drawing the contour of cluster theory.The profundity of the notion of `clusters' is arguably conditional on the coherence of four fundamental issues associated with the concept: 1) the economic...... and social benefits that may accrue to firms when clustering or co-locating (the existence argument); 2) the diseconomiesencountered when clustering exceeds certain geographical and sectoral thresholds (the extension argument); 3) the advantages obtained by exploiting intra-cluster synergies rather engaging...... in external interaction (the exchange argument); and, finally, 4) the possible erosionof economies and onset of diseconomies over the lifecycle of the cluster (the exhaustion argument).Each of these four issues is examined in terms of three relevant major theoretical frameworks that can be brought to bear...

  16. Chart Series

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Centers for Medicare and Medicaid Services (CMS) offers several different Chart Series with data on beneficiary health status, spending, operations, and quality...

  17. Clustering Dycom

    KAUST Repository

    Minku, Leandro L.

    2017-10-06

    Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom\\'s predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom\\'s CC subsets.

  18. Clustering analysis

    International Nuclear Information System (INIS)

    Romli

    1997-01-01

    Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K -mean method ' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods

  19. Cluster analysis

    CERN Document Server

    Everitt, Brian S; Leese, Morven; Stahl, Daniel

    2011-01-01

    Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

  20. Cluster editing

    DEFF Research Database (Denmark)

    Böcker, S.; Baumbach, Jan

    2013-01-01

    . The problem has been the inspiration for numerous algorithms in bioinformatics, aiming at clustering entities such as genes, proteins, phenotypes, or patients. In this paper, we review exact and heuristic methods that have been proposed for the Cluster Editing problem, and also applications......The Cluster Editing problem asks to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. Although the problem has been proven NP-complete several times, it has nevertheless attracted much research both from the theoretical and the applied side...

  1. Clustering of Sun Exposure Measurements

    DEFF Research Database (Denmark)

    Have, Anna Szynkowiak; Larsen, Jan; Hansen, Lars Kai

    2002-01-01

    In a medically motivated Sun-exposure study, questionnaires concerning Sun-habits were collected from a number of subjects together with UV radiation measurements. This paper focuses on identifying clusters in the heterogeneous set of data for the purpose of understanding possible relations between...... Sun-habits exposure and eventually assessing the risk of skin cancer. A general probabilistic framework originally developed for text and Web mining is demonstrated to be useful for clustering of behavioral data. The framework combines principal component subspace projection with probabilistic...

  2. 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...

  3. Lanthanide metal-organic frameworks

    International Nuclear Information System (INIS)

    Cheng, Peng

    2015-01-01

    This book contains the following nine chapters: lanthanide metal-organic frameworks: syntheses, properties, and potential applications (Stephen Fordham, Xuan Wang, Mathieu Bosch, Hong-Cai Zhou); 2. chiral lanthanide metal-organic frameworks (Weisheng Liu, Xiaoliang Tang); 3. Porous lanthanide metal-organic frameworks for gas storage and separation (Bin Li, Banglin Chen); 4. Luminescent lanthanide metal-organic frameworks (Xue-Zhi Song, Shu-Yan Song, Hong-Jie Zhang); 5. Metal-organic frameworks based on lanthanide clusters (Lian Chen, Feilong Jiang, Kang Zhou, Mingyan Wu, Maochun Hong); 6. metal-organic frameworks with d-f cyanide bridges: structural diversity, bonding regime, and magnetism (Marilena Ferbinteanu, Fanica Cimpoesu, Stefania Tanase); 7. transition-lanthanide heterometal-organic frameworks: synthesis, structures, and properties (Wei Shi, Ke Liu, Peng Cheng); 8: MOFs of uranium and the actinides (Juan Su, Jiesheng Chen); 9. Nanostructured and/or nanoscale lanthanide metal-organic frameworks (Zhonghao Zhang, Zhiping Zheng).

  4. Lanthanide metal-organic frameworks

    Energy Technology Data Exchange (ETDEWEB)

    Cheng, Peng (ed.) [Nankai Univ., Tianjin (China). Dept. of Chemistry

    2015-03-01

    This book contains the following nine chapters: lanthanide metal-organic frameworks: syntheses, properties, and potential applications (Stephen Fordham, Xuan Wang, Mathieu Bosch, Hong-Cai Zhou); 2. chiral lanthanide metal-organic frameworks (Weisheng Liu, Xiaoliang Tang); 3. Porous lanthanide metal-organic frameworks for gas storage and separation (Bin Li, Banglin Chen); 4. Luminescent lanthanide metal-organic frameworks (Xue-Zhi Song, Shu-Yan Song, Hong-Jie Zhang); 5. Metal-organic frameworks based on lanthanide clusters (Lian Chen, Feilong Jiang, Kang Zhou, Mingyan Wu, Maochun Hong); 6. metal-organic frameworks with d-f cyanide bridges: structural diversity, bonding regime, and magnetism (Marilena Ferbinteanu, Fanica Cimpoesu, Stefania Tanase); 7. transition-lanthanide heterometal-organic frameworks: synthesis, structures, and properties (Wei Shi, Ke Liu, Peng Cheng); 8: MOFs of uranium and the actinides (Juan Su, Jiesheng Chen); 9. Nanostructured and/or nanoscale lanthanide metal-organic frameworks (Zhonghao Zhang, Zhiping Zheng).

  5. Occupational Clusters.

    Science.gov (United States)

    Pottawattamie County School System, Council Bluffs, IA.

    The 15 occupational clusters (transportation, fine arts and humanities, communications and media, personal service occupations, construction, hospitality and recreation, health occupations, marine science occupations, consumer and homemaking-related occupations, agribusiness and natural resources, environment, public service, business and office…

  6. Fuzzy Clustering

    DEFF Research Database (Denmark)

    Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan

    2000-01-01

    A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...

  7. Cluster generator

    Science.gov (United States)

    Donchev, Todor I [Urbana, IL; Petrov, Ivan G [Champaign, IL

    2011-05-31

    Described herein is an apparatus and a method for producing atom clusters based on a gas discharge within a hollow cathode. The hollow cathode includes one or more walls. The one or more walls define a sputtering chamber within the hollow cathode and include a material to be sputtered. A hollow anode is positioned at an end of the sputtering chamber, and atom clusters are formed when a gas discharge is generated between the hollow anode and the hollow cathode.

  8. Cluster Bulleticity

    OpenAIRE

    Massey, Richard; Kitching, Thomas; Nagai, Daisuke

    2010-01-01

    The unique properties of dark matter are revealed during collisions between clusters of galaxies, such as the bullet cluster (1E 0657−56) and baby bullet (MACS J0025−12). These systems provide evidence for an additional, invisible mass in the separation between the distributions of their total mass, measured via gravitational lensing, and their ordinary ‘baryonic’ matter, measured via its X-ray emission. Unfortunately, the information available from these systems is limited by their rarity. C...

  9. Cluster headache

    OpenAIRE

    Leroux, Elizabeth; Ducros, Anne

    2008-01-01

    Abstract Cluster headache (CH) is a primary headache disease characterized by recurrent short-lasting attacks (15 to 180 minutes) of excruciating unilateral periorbital pain accompanied by ipsilateral autonomic signs (lacrimation, nasal congestion, ptosis, miosis, lid edema, redness of the eye). It affects young adults, predominantly males. Prevalence is estimated at 0.5–1.0/1,000. CH has a circannual and circadian periodicity, attacks being clustered (hence the name) in bouts that can occur ...

  10. Extracting biologically significant patterns from short time series gene expression data

    Directory of Open Access Journals (Sweden)

    McGinnis Thomas

    2009-08-01

    Full Text Available Abstract Background Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult. Results We developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, ASTRO and MiMeSR, are inspired by the rank order preserving framework and the minimum mean squared residue approach, respectively. However, ASTRO and MiMeSR differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO annotations and chromatin immunoprecipitation (ChIP-chip data. Conclusion Our approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at http://www.benoslab.pitt.edu/astro/.

  11. Combining cluster number counts and galaxy clustering

    Energy Technology Data Exchange (ETDEWEB)

    Lacasa, Fabien; Rosenfeld, Rogerio, E-mail: fabien@ift.unesp.br, E-mail: rosenfel@ift.unesp.br [ICTP South American Institute for Fundamental Research, Instituto de Física Teórica, Universidade Estadual Paulista, São Paulo (Brazil)

    2016-08-01

    The abundance of clusters and the clustering of galaxies are two of the important cosmological probes for current and future large scale surveys of galaxies, such as the Dark Energy Survey. In order to combine them one has to account for the fact that they are not independent quantities, since they probe the same density field. It is important to develop a good understanding of their correlation in order to extract parameter constraints. We present a detailed modelling of the joint covariance matrix between cluster number counts and the galaxy angular power spectrum. We employ the framework of the halo model complemented by a Halo Occupation Distribution model (HOD). We demonstrate the importance of accounting for non-Gaussianity to produce accurate covariance predictions. Indeed, we show that the non-Gaussian covariance becomes dominant at small scales, low redshifts or high cluster masses. We discuss in particular the case of the super-sample covariance (SSC), including the effects of galaxy shot-noise, halo second order bias and non-local bias. We demonstrate that the SSC obeys mathematical inequalities and positivity. Using the joint covariance matrix and a Fisher matrix methodology, we examine the prospects of combining these two probes to constrain cosmological and HOD parameters. We find that the combination indeed results in noticeably better constraints, with improvements of order 20% on cosmological parameters compared to the best single probe, and even greater improvement on HOD parameters, with reduction of error bars by a factor 1.4-4.8. This happens in particular because the cross-covariance introduces a synergy between the probes on small scales. We conclude that accounting for non-Gaussian effects is required for the joint analysis of these observables in galaxy surveys.

  12. How to detect trap cluster systems?

    International Nuclear Information System (INIS)

    Mandowski, Arkadiusz

    2008-01-01

    Spatially correlated traps and recombination centres (trap-recombination centre pairs and larger clusters) are responsible for many anomalous phenomena that are difficult to explain in the framework of both classical models, i.e. model of localized transitions (LT) and the simple trap model (STM), even with a number of discrete energy levels. However, these 'anomalous' effects may provide a good platform for identifying trap cluster systems. This paper considers selected cluster-type effects, mainly relating to an anomalous dependence of TL on absorbed dose in the system of isolated clusters (ICs). Some consequences for interacting cluster (IAC) systems, involving both localized and delocalized transitions occurring simultaneously, are also discussed

  13. Case Series

    African Journals Online (AJOL)

    calciphylaxis is prevention through rigorous control of phosphate and calcium balance. We here present two ... The authors declared no conflict of interest. Introduction. Calciphylaxis is a rare but serious disorder .... were reported to resolve the calciphylaxis lesions in a chronic renal failure patient [20]. In a series of five.

  14. Fourier Series

    Indian Academy of Sciences (India)

    polynomials are dense in the class of continuous functions! The body of literature dealing with Fourier series has reached epic proportions over the last two centuries. We have only given the readers an outline of the topic in this article. For the full length episode we refer the reader to the monumental treatise of. A Zygmund.

  15. Case series

    African Journals Online (AJOL)

    abp

    13 oct. 2017 ... This is an Open Access article distributed under the terms of the Creative Commons Attribution ... Bifocal leg fractures pose many challenges for the surgeon due to .... Dans notre serie, le taux d'infection est reste dans un.

  16. Fourier Series

    Indian Academy of Sciences (India)

    The theory of Fourier series deals with periodic functions. By a periodic ..... including Dirichlet, Riemann and Cantor occupied themselves with the problem of ... to converge only on a set which is negligible in a certain sense (Le. of measure ...

  17. case series

    African Journals Online (AJOL)

    Administrator

    Key words: Case report, case series, concept analysis, research design. African Health Sciences 2012; (4): 557 - 562 http://dx.doi.org/10.4314/ahs.v12i4.25. PO Box 17666 .... According to the latest version of the Dictionary of. Epidemiology ...

  18. Cluster structure of light nuclei

    Science.gov (United States)

    Iachello, Francesco

    2018-02-01

    Matter and charge densities of kα structures with k=2 (8Be), k=3 (12C) and k=4 (16O) calculated within the framework of the algebraic cluster model (ACM) are briefly reviewed and explicitly displayed. Their parameters are determined from a comparison with electron scattering data.

  19. Study of the sup 3 H( sup 3 H, 2n) sup 4 He and sup 3 He( sup 3 He, 2p) sup 4 He reactions in the framework of three-cluster microscopic model

    CERN Document Server

    Vasilevsky, V S; Arickx, F; Broeckhove, J

    2002-01-01

    The reactions sup 3 H( sup 3 H, 2n) sup 4 He and sup 3 He( sup 3 He, 2p) sup 4 He are investigated within a fully microscopic cluster model featuring a three-cluster exit channel. A Hyperspherical Harmonics basis is used to describe the three-cluster continuum. The resulting astrophysical s-factor of both reactions is in good agreement with experimental data. Analysis of the low-energy scattering parameters reveals no evidence for a hidden resonance state would increase the cross-section of the reactions, and would help to resolve the solar neutrino problem.

  20. Exposures series

    OpenAIRE

    Stimson, Blake

    2011-01-01

    Reaktion Books’ Exposures series, edited by Peter Hamilton and Mark Haworth-Booth, is comprised of 13 volumes and counting, each less than 200 pages with 80 high-quality illustrations in color and black and white. Currently available titles include Photography and Australia, Photography and Spirit, Photography and Cinema, Photography and Literature, Photography and Flight, Photography and Egypt, Photography and Science, Photography and Africa, Photography and Italy, Photography and the USA, P...

  1. Clustering Dycom

    KAUST Repository

    Minku, Leandro L.; Hou, Siqing

    2017-01-01

    baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number

  2. Electricity Consumption Clustering Using Smart Meter Data

    Directory of Open Access Journals (Sweden)

    Alexander Tureczek

    2018-04-01

    Full Text Available Electricity smart meter consumption data is enabling utilities to analyze consumption information at unprecedented granularity. Much focus has been directed towards consumption clustering for diversifying tariffs; through modern clustering methods, cluster analyses have been performed. However, the clusters developed exhibit a large variation with resulting shadow clusters, making it impossible to truly identify the individual clusters. Using clearly defined dwelling types, this paper will present methods to improve clustering by harvesting inherent structure from the smart meter data. This paper clusters domestic electricity consumption using smart meter data from the Danish city of Esbjerg. Methods from time series analysis and wavelets are applied to enable the K-Means clustering method to account for autocorrelation in data and thereby improve the clustering performance. The results show the importance of data knowledge and we identify sub-clusters of consumption within the dwelling types and enable K-Means to produce satisfactory clustering by accounting for a temporal component. Furthermore our study shows that careful preprocessing of the data to account for intrinsic structure enables better clustering performance by the K-Means method.

  3. PHP frameworks

    OpenAIRE

    Srša, Aljaž

    2016-01-01

    The thesis presents one of the four most popular PHP web frameworks: Laravel, Symfony, CodeIgniter and CakePHP. These frameworks are compared with each other according to the four criteria, which can help with the selection of a framework. These criteria are size of the community, quality of official support, comprehensibility of framework’s documentation and implementation of functionalities in individual frameworks, which are automatic code generation, routing, object-relational mapping and...

  4. Effect of primordial non-Gaussianities on galaxy clusters scaling relations

    Science.gov (United States)

    Trindade, A. M. M.; da Silva, Antonio

    2017-07-01

    Galaxy clusters are a valuable source of cosmological information. Their formation and evolution depends on the underlying cosmology and on the statistical nature of the primordial density fluctuations. Here we investigate the impact of primordial non-Gaussianities (PNG) on the scaling properties of galaxy clusters. We performed a series of hydrodynamic N-body simulations featuring adiabatic gas physics and different levels of non-Gaussianity within the Λ cold dark matter framework. We focus on the T-M, S-M, Y-M and YX-M scalings relating the total cluster mass with temperature, entropy and Sunyaev-Zeld'ovich integrated pressure that reflect the thermodynamic state of the intracluster medium. Our results show that PNG have an impact on cluster scalings laws. The scalings mass power-law indexes are almost unaffected by the existence of PNG, but the amplitude and redshift evolution of their normalizations are clearly affected. Changes in the Y-M and YX-M normalizations are as high as 22 per cent and 16 per cent when fNL varies from -500 to 500, respectively. Results are consistent with the view that positive/negative fNL affect cluster profiles due to an increase/decrease of cluster concentrations. At low values of fNL, as suggested by present Planck constraints on a scale invariant fNL, the impact on the scaling normalizations is only a few per cent. However, if fNL varies with scale, PNG may have larger amplitudes at clusters scales; thus, our results suggest that PNG should be taken into account when cluster data are used to infer or forecast cosmological parameters from existing or future cluster surveys.

  5. A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting

    International Nuclear Information System (INIS)

    Azimi, R.; Ghayekhloo, M.; Ghofrani, M.

    2016-01-01

    Highlights: • A novel clustering approach is proposed based on the data transformation approach. • A novel cluster selection method based on correlation analysis is presented. • The proposed hybrid clustering approach leads to deep learning for MLPNN. • A hybrid forecasting method is developed to predict solar radiations. • The evaluation results show superior performance of the proposed forecasting model. - Abstract: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a Transformation based K-means algorithm, named TB K-means, to increase the forecast accuracy. The proposed clustering method is a combination of a new initialization technique, K-means algorithm and a new gradual data transformation approach. Unlike the other K-means based clustering methods which are not capable of providing a fixed and definitive answer due to the selection of different cluster centroids for each run, the proposed clustering provides constant results for different runs of the algorithm. The proposed clustering is combined with a time-series analysis, a novel cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to develop the hybrid solar radiation forecasting method for different time horizons (1 h ahead, 2 h ahead, …, 48 h ahead). The performance of the proposed TB K-means clustering is evaluated using several different datasets and compared with different variants of K-means algorithm. Solar datasets with different solar radiation characteristics are also used to determine the accuracy and processing speed of the developed forecasting method with the proposed TB K-means and other clustering techniques. The results of direct comparison with other well-established forecasting models demonstrate the superior performance of the proposed hybrid forecasting method. Furthermore, a comparative analysis with the benchmark solar

  6. Cluster growing process and a sequence of magic numbers

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2003-01-01

    demonstrate that in this way all known global minimum structures of the Lennard-Jones (LJ) clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence for the clusters of noble gas atoms......We present a new theoretical framework for modeling the cluster growing process. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system, and absorbing its energy at each step, we find cluster growing paths up to the cluster sizes of more than 100 atoms. We...

  7. A possibilistic approach to clustering

    Science.gov (United States)

    Krishnapuram, Raghu; Keller, James M.

    1993-01-01

    Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

  8. Cluster forcing

    DEFF Research Database (Denmark)

    Christensen, Thomas Budde

    The cluster theory attributed to Michael Porter has significantly influenced industrial policies in countries across Europe and North America since the beginning of the 1990s. Institutions such as the EU, OECD and the World Bank and governments in countries such as the UK, France, The Netherlands...... or management. Both the Accelerate Wales and the Accelerate Cluster programmes target this issue by trying to establish networks between companies that can be used to supply knowledge from research institutions to manufacturing companies. The paper concludes that public sector interventions can make...... businesses. The universities were not considered by the participating companies to be important parts of the local business environment and inputs from universities did not appear to be an important source to access knowledge about new product development or new techniques in production, distribution...

  9. Regional Innovation Clusters

    Data.gov (United States)

    Small Business Administration — The Regional Innovation Clusters serve a diverse group of sectors and geographies. Three of the initial pilot clusters, termed Advanced Defense Technology clusters,...

  10. Cluster analysis

    OpenAIRE

    Mucha, Hans-Joachim; Sofyan, Hizir

    2000-01-01

    As an explorative technique, duster analysis provides a description or a reduction in the dimension of the data. It classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of many variables. Its aim is to construct groups in such a way that the profiles of objects in the same groups are relatively homogenous whereas the profiles of objects in different groups are relatively heterogeneous. Clustering is distinct from classification techniques, ...

  11. ClusterControl: a web interface for distributing and monitoring bioinformatics applications on a Linux cluster.

    Science.gov (United States)

    Stocker, Gernot; Rieder, Dietmar; Trajanoski, Zlatko

    2004-03-22

    ClusterControl is a web interface to simplify distributing and monitoring bioinformatics applications on Linux cluster systems. We have developed a modular concept that enables integration of command line oriented program into the application framework of ClusterControl. The systems facilitate integration of different applications accessed through one interface and executed on a distributed cluster system. The package is based on freely available technologies like Apache as web server, PHP as server-side scripting language and OpenPBS as queuing system and is available free of charge for academic and non-profit institutions. http://genome.tugraz.at/Software/ClusterControl

  12. Forward-backward multiplicity correlations and the clusterization

    International Nuclear Information System (INIS)

    Kostenko, B.F.; Musul'manbekov, Zh.Zh.

    1990-01-01

    An analysis of the forward-backward multiplicity correlations for pp- and p-barp-collisions has been fulfilled in the framework of the statistical cluster model. Connection between the strength of correlations and sizes of clusters is investigated. The dependence of masses and sizes of clusters on the energy of colliding hadrons is obtained. 15 refs.; 9 figs.; 1 tab

  13. Case Series.

    Science.gov (United States)

    Vetrayan, Jayachandran; Othman, Suhana; Victor Paulraj, Smily Jesu Priya

    2017-01-01

    To assess the effectiveness and feasibility of behavioral sleep intervention for medicated children with ADHD. Six medicated children (five boys, one girl; aged 6-12 years) with ADHD participated in a 4-week sleep intervention program. The main behavioral strategies used were Faded Bedtime With Response Cost (FBRC) and positive reinforcement. Within a case-series design, objective measure (Sleep Disturbance Scale for Children [SDSC]) and subjective measure (sleep diaries) were used to record changes in children's sleep. For all six children, significant decrease was found in the severity of children's sleep problems (based on SDSC data). Bedtime resistance and mean sleep onset latency were reduced following the 4-week intervention program according to sleep diaries data. Gains were generally maintained at the follow-up. Parents perceived the intervention as being helpful. Based on the initial data, this intervention shows promise as an effective and feasible treatment.

  14. Photoabsorption of small sodium and magnesium clusters

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2004-01-01

    We predict the strong enhancement in the photoabsorption of small Mg clusters in the region of 4-5 eV due to the resonant excitation of the plasmon oscillations of cluster electrons. The photoabsorption spectra for neutral Mg clusters consisting of up to N=11 atoms have been calculated using it ab...... initio framework based on the time dependent density functional theory (TDDFT). The nature of predicted resonances has been elucidated by comparison of the results of the it ab initio calculations with the results of the classical Mie theory. The splitting of the plasmon resonances caused by the cluster...

  15. Optical response of small magnesium clusters

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2004-01-01

    We predict strong enhancement in the photoabsorption of small Mg clusters in the region of 4–5 eV due to the resonant excitation of the plasmon oscillations of cluster electrons. Photoabsorption spectra for neutral Mg clusters consisting of up to N = 11 atoms have been calculated using an ab initio...... framework based on the time-dependent density functional theory (TDDFT). The nature of predicted resonances has been elucidated by comparison of the results of the an ab initio calculations with the results of the classical Mie theory. The splitting of the plasmon resonances caused by the cluster...

  16. The clustered nucleus-cluster structures in stable and unstable nuclei

    International Nuclear Information System (INIS)

    Freer, Martin

    2007-01-01

    The subject of clustering has a lineage which runs throughout the history of nuclear physics. Its attraction is the simplification of the often uncorrelated behaviour of independent particles to organized and coherent quasi-crystalline structures. In this review the ideas behind the development of clustering in light nuclei are investigated, mostly from the stand-point of the harmonic oscillator framework. This allows a unifying description of alpha-conjugate and neutron-rich nuclei, alike. More sophisticated models of clusters are explored, such as antisymmetrized molecular dynamics. A number of contemporary topics in clustering are touched upon; the 3α-cluster state in 12 C, nuclear molecules and clustering at the drip-line. Finally, an understanding of the 12 C+ 12 C resonances in 24 Mg, within the framework of the theoretical ideas developed in the review, is presented

  17. Clustering in Hilbert simplex geometry

    KAUST Repository

    Nielsen, Frank

    2017-04-03

    Clustering categorical distributions in the probability simplex is a fundamental primitive often met in applications dealing with histograms or mixtures of multinomials. Traditionally, the differential-geometric structure of the probability simplex has been used either by (i) setting the Riemannian metric tensor to the Fisher information matrix of the categorical distributions, or (ii) defining the information-geometric structure induced by a smooth dissimilarity measure, called a divergence. In this paper, we introduce a novel computationally-friendly non-Riemannian framework for modeling the probability simplex: Hilbert simplex geometry. We discuss the pros and cons of those three statistical modelings, and compare them experimentally for clustering tasks.

  18. An Ecological Analysis of the Effects of Deviant Peer Clustering on Sexual Promiscuity, Problem Behavior, and Childbearing from Early Adolescence to Adulthood: An Enhancement of the Life History Framework

    Science.gov (United States)

    Dishion, Thomas J.; Ha, Thao; Veronneau, Marie-Helene

    2012-01-01

    The authors propose that peer relationships should be included in a life history perspective on adolescent problem behavior. Longitudinal analyses were used to examine deviant peer clustering as the mediating link between attenuated family ties, peer marginalization, and social disadvantage in early adolescence and sexual promiscuity in middle…

  19. An ecological analysis of the effects of deviant peer clustering on sexual promiscuity, problem behavior, and childbearing from early adolescence to adulthood: an enhancement of the life history framework.

    Science.gov (United States)

    Dishion, Thomas J; Ha, Thao; Véronneau, Marie-Hélène

    2012-05-01

    The authors propose that peer relationships should be included in a life history perspective on adolescent problem behavior. Longitudinal analyses were used to examine deviant peer clustering as the mediating link between attenuated family ties, peer marginalization, and social disadvantage in early adolescence and sexual promiscuity in middle adolescence and childbearing by early adulthood. Specifically, 998 youths, along with their families, were assessed at age 11 years and periodically through age 24 years. Structural equation modeling revealed that the peer-enhanced life history model provided a good fit to the longitudinal data, with deviant peer clustering strongly predicting adolescent sexual promiscuity and other correlated problem behaviors. Sexual promiscuity, as expected, also strongly predicted the number of children by ages 22-24 years. Consistent with a life history perspective, family social disadvantage directly predicted deviant peer clustering and number of children in early adulthood, controlling for all other variables in the model. These data suggest that deviant peer clustering is a core dimension of a fast life history strategy, with strong links to sexual activity and childbearing. The implications of these findings are discussed with respect to the need to integrate an evolutionary-based model of self-organized peer groups in developmental and intervention science.

  20. An Ecological Analysis of the Effects of Deviant Peer Clustering on Sexual Promiscuity, Problem Behavior, and Childbearing From Early Adolescence to Adulthood: An Enhancement of the Life History Framework

    NARCIS (Netherlands)

    Dishion, T.J.; Ha, P.T.; Veronneau, M.H.

    2012-01-01

    The authors propose that peer relationships should be included in a life history perspective on adolescent problem behavior. Longitudinal analyses were used to examine deviant peer clustering as the mediating link between attenuated family ties, peer marginalization, and social disadvantage in early

  1. Time series analysis of temporal networks

    Science.gov (United States)

    Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

    2016-01-01

    A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

  2. DCP Series

    Directory of Open Access Journals (Sweden)

    Philip Stearns

    2011-06-01

    Full Text Available Photo essay. A collection of Images produced by intentionally corrupting the circuitry of a Kodak DC280 2 MP digitalcamera. By rewiring the electronics of a digital camera, glitched images are produced in a manner that parallels chemically processing unexposed film or photographic paper to produce photographic images without exposure to light. The DCP Series of Digital Images are direct visualizations of data generated by a digital camera as it takes a picture. Electronic processes associated with the normal operations of the camera, which are usually taken for granted, are revealed through an act of intervention. The camera is turned inside­out through complexes of short­circuits, selected by the artist, transforming the camera from a picture taking device to a data capturing device that renders raw data (electronic signals as images. In essence, these images are snap­shots of electronic signals dancing through the camera's circuits, manually rerouted, written directly to the on­board memory device. Rather than seeing images of the world through a lens, we catch a glimpse of what the camera sees when it is forced to peer inside its own mind.

  3. Nuclear clustering - a cluster core model study

    International Nuclear Information System (INIS)

    Paul Selvi, G.; Nandhini, N.; Balasubramaniam, M.

    2015-01-01

    Nuclear clustering, similar to other clustering phenomenon in nature is a much warranted study, since it would help us in understanding the nature of binding of the nucleons inside the nucleus, closed shell behaviour when the system is highly deformed, dynamics and structure at extremes. Several models account for the clustering phenomenon of nuclei. We present in this work, a cluster core model study of nuclear clustering in light mass nuclei

  4. Structure and clusters of light unstable nuclei

    International Nuclear Information System (INIS)

    En'yo, Yoshiko

    2010-01-01

    As it is known, cluster structures are often observed in light nuclei. In the recent evolution of unstable nuclear research (on nuclei having unbalanced number of neutron and proton) further new types of clusters are coming to be revealed. In this report, structures of light unstable nuclei and some of the theoretical models to describe them are reviewed. The following topics are picked up. 1. Cluster structure and theoretical models, 2. Cluster structure of unstable nuclei (low excited state). 3. Cluster structure of neutron excess beryllium isotopes. 4. Cluster gas like state in C isotope. 5. Dineutron structure of He isotopes. Numbers of strange nuclear structures of light nuclei are illustrated. Antisymmetrized molecular dynamics (AMD) is the recently developed theoretical framework which has been successfully used in heavy ion reactions and nuclear structure studies. Successful application of AMD to the isotopes of Be, B and C are illustrated. (S. Funahashi)

  5. Vector dark energy and high-z massive clusters

    Science.gov (United States)

    Carlesi, Edoardo; Knebe, Alexander; Yepes, Gustavo; Gottlöber, Stefan; Jiménez, Jose Beltrán.; Maroto, Antonio L.

    2011-12-01

    The detection of extremely massive clusters at z > 1 such as SPT-CL J0546-5345, SPT-CL J2106-5844 and XMMU J2235.3-2557 has been considered by some authors as a challenge to the standard Λ cold dark matter cosmology. In fact, assuming Gaussian initial conditions, the theoretical expectation of detecting such objects is as low as ≤1 per cent. In this paper we discuss the probability of the existence of such objects in the light of the vector dark energy paradigm, showing by means of a series of N-body simulations that chances of detection are substantially enhanced in this non-standard framework.

  6. Decoding divergent series in nonparaxial optics.

    Science.gov (United States)

    Borghi, Riccardo; Gori, Franco; Guattari, Giorgio; Santarsiero, Massimo

    2011-03-15

    A theoretical analysis aimed at investigating the divergent character of perturbative series involved in the study of free-space nonparaxial propagation of vectorial optical beams is proposed. Our analysis predicts a factorial divergence for such series and provides a theoretical framework within which the results of recently published numerical experiments concerning nonparaxial propagation of vectorial Gaussian beams find a meaningful interpretation in terms of the decoding operated on such series by the Weniger transformation.

  7. Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

    OpenAIRE

    Qing Ye; Hao Pan; Changhua Liu

    2015-01-01

    A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA)...

  8. Optimizing the Performance of Data Analytics Frameworks

    NARCIS (Netherlands)

    Ghit, B.I.

    2017-01-01

    Data analytics frameworks enable users to process large datasets while hiding the complexity of scaling out their computations on large clusters of thousands of machines. Such frameworks parallelize the computations, distribute the data, and tolerate server failures by deploying their own runtime

  9. Cluster headache

    Directory of Open Access Journals (Sweden)

    Ducros Anne

    2008-07-01

    Full Text Available Abstract Cluster headache (CH is a primary headache disease characterized by recurrent short-lasting attacks (15 to 180 minutes of excruciating unilateral periorbital pain accompanied by ipsilateral autonomic signs (lacrimation, nasal congestion, ptosis, miosis, lid edema, redness of the eye. It affects young adults, predominantly males. Prevalence is estimated at 0.5–1.0/1,000. CH has a circannual and circadian periodicity, attacks being clustered (hence the name in bouts that can occur during specific months of the year. Alcohol is the only dietary trigger of CH, strong odors (mainly solvents and cigarette smoke and napping may also trigger CH attacks. During bouts, attacks may happen at precise hours, especially during the night. During the attacks, patients tend to be restless. CH may be episodic or chronic, depending on the presence of remission periods. CH is associated with trigeminovascular activation and neuroendocrine and vegetative disturbances, however, the precise cautive mechanisms remain unknown. Involvement of the hypothalamus (a structure regulating endocrine function and sleep-wake rhythms has been confirmed, explaining, at least in part, the cyclic aspects of CH. The disease is familial in about 10% of cases. Genetic factors play a role in CH susceptibility, and a causative role has been suggested for the hypocretin receptor gene. Diagnosis is clinical. Differential diagnoses include other primary headache diseases such as migraine, paroxysmal hemicrania and SUNCT syndrome. At present, there is no curative treatment. There are efficient treatments to shorten the painful attacks (acute treatments and to reduce the number of daily attacks (prophylactic treatments. Acute treatment is based on subcutaneous administration of sumatriptan and high-flow oxygen. Verapamil, lithium, methysergide, prednisone, greater occipital nerve blocks and topiramate may be used for prophylaxis. In refractory cases, deep-brain stimulation of the

  10. Clustering Coefficients for Correlation Networks.

    Science.gov (United States)

    Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

    2018-01-01

    correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.

  11. Clustering Coefficients for Correlation Networks

    Directory of Open Access Journals (Sweden)

    Naoki Masuda

    2018-03-01

    were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.

  12. Clustering Coefficients for Correlation Networks

    Science.gov (United States)

    Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

    2018-01-01

    correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties. PMID:29599714

  13. Fusion process of Lennard-Jones clusters: global minima and magic numbers formation

    DEFF Research Database (Denmark)

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2004-01-01

    We present a new theoretical framework for modeling the fusion process of Lennard–Jones (LJ) clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing paths up to the cluster size of 150 atoms...

  14. A PROOF Analysis Framework

    International Nuclear Information System (INIS)

    González Caballero, I; Cuesta Noriega, A; Rodríguez Marrero, A; Fernández del Castillo, E

    2012-01-01

    The analysis of the complex LHC data usually follows a standard path that aims at minimizing not only the amount of data but also the number of observables used. After a number of steps of slimming and skimming the data, the remaining few terabytes of ROOT files hold a selection of the events and a flat structure for the variables needed that can be more easily inspected and traversed in the final stages of the analysis. PROOF arises at this point as an efficient mechanism to distribute the analysis load by taking advantage of all the cores in modern CPUs through PROOF Lite, or by using PROOF Cluster or PROOF on Demand tools to build dynamic PROOF cluster on computing facilities with spare CPUs. However using PROOF at the level required for a serious analysis introduces some difficulties that may scare new adopters. We have developed the PROOF Analysis Framework (PAF) to facilitate the development of new analysis by uniformly exposing the PROOF related configurations across technologies and by taking care of the routine tasks as much as possible. We describe the details of the PAF implementation as well as how we succeeded in engaging a group of CMS physicists to use PAF as their daily analysis framework.

  15. Magnetic properties of free alkali and transition metal clusters

    International Nuclear Information System (INIS)

    Heer, W. de; Milani, P.; Chatelain, A.

    1991-01-01

    The Stern-Gerlach deflections of small alkali clusters (N<6) and iron clusters (10< N<500) show that the paramagnetic alkali clusters always have a nondeflecting component, while the iron clusters always deflect in the high field direction. Both of these effects appear to be related to spin relaxation however in the case of alkali clusters it is shown that they are in fact caused by avoided level crossing in the Zeeman diagram. For alkali clusters the relatively weak couplings cause reduced magnetic moments where levels cross. For iron clusters however the total spin is strongly coupled to the molecular framework. Consequently this coupling is responsible for avoided level crossing which ultimately cause the total energy of the cluster to decrease with increasing magnetic field so that the iron clusters will deflect in one direction when introduced in an inhomogeneous magnetic field. Experiment and theory are discussed for both cases. (orig.)

  16. Dynamic Trajectory Extraction from Stereo Vision Using Fuzzy Clustering

    Science.gov (United States)

    Onishi, Masaki; Yoda, Ikushi

    In recent years, many human tracking researches have been proposed in order to analyze human dynamic trajectory. These researches are general technology applicable to various fields, such as customer purchase analysis in a shopping environment and safety control in a (railroad) crossing. In this paper, we present a new approach for tracking human positions by stereo image. We use the framework of two-stepped clustering with k-means method and fuzzy clustering to detect human regions. In the initial clustering, k-means method makes middle clusters from objective features extracted by stereo vision at high speed. In the last clustering, c-means fuzzy method cluster middle clusters based on attributes into human regions. Our proposed method can be correctly clustered by expressing ambiguity using fuzzy clustering, even when many people are close to each other. The validity of our technique was evaluated with the experiment of trajectories extraction of doctors and nurses in an emergency room of a hospital.

  17. An introduction to cluster science

    CERN Document Server

    Dinh, Phuong Mai; Suraud, Eric

    2013-01-01

    Filling the need for a solid textbook, this short primer in cluster science is ideal for a one-semester lecture for advanced undergraduate students. It is based on a series of lectures given by the well-established and recognized authors for the past ten years. The book covers both the basics of the domain as well as up-to-date developments. It can be divided roughly into two parts. The first three chapters introduce basic concepts of cluster science. Chapter 1 provides a general introduction, complemented by chapter 2 on experimental and chapter 3 on theoretical aspects. The second half of the book is devoted to a systematic presentation of free cluster properties, and to a thorough discussion of the impact of clusters in other domains of science. These explicitly worked-out links between cluster physics and other research areas are unique both in terms of fundamental aspects and of applications, and cannot be found elsewhere in the literature. Also suitable for researchers outside of the field looking for...

  18. The Centaurus cluster of galaxies. II. The bimodal-velocity structure

    International Nuclear Information System (INIS)

    Lucey, J.R.; Currie, M.J.; Dickens, R.J.

    1985-09-01

    This is the second paper in a series that describes an extensive study of the Centaurus cluster of galaxies. The paper concerns the bimodal velocity distribution of the galaxies in the cluster. The likely location of the two main cluster components is discussed. The data strongly favours the hypothesis that the two components lie within the same cluster. (UK)

  19. Brightest Cluster Galaxies in REXCESS Clusters

    Science.gov (United States)

    Haarsma, Deborah B.; Leisman, L.; Bruch, S.; Donahue, M.

    2009-01-01

    Most galaxy clusters contain a Brightest Cluster Galaxy (BCG) which is larger than the other cluster ellipticals and has a more extended profile. In the hierarchical model, the BCG forms through many galaxy mergers in the crowded center of the cluster, and thus its properties give insight into the assembly of the cluster as a whole. In this project, we are working with the Representative XMM-Newton Cluster Structure Survey (REXCESS) team (Boehringer et al 2007) to study BCGs in 33 X-ray luminous galaxy clusters, 0.055 < z < 0.183. We are imaging the BCGs in R band at the Southern Observatory for Astrophysical Research (SOAR) in Chile. In this poster, we discuss our methods and give preliminary measurements of the BCG magnitudes, morphology, and stellar mass. We compare these BCG properties with the properties of their host clusters, particularly of the X-ray emitting gas.

  20. Partitional clustering algorithms

    CERN Document Server

    2015-01-01

    This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...

  1. Monitoring in a grid cluster

    International Nuclear Information System (INIS)

    Crooks, David; Mitchell, Mark; Roy, Gareth; Skipsey, Samuel Cadellin; Britton, David; Purdie, Stuart

    2014-01-01

    The monitoring of a grid cluster (or of any piece of reasonably scaled IT infrastructure) is a key element in the robust and consistent running of that site. There are several factors which are important to the selection of a useful monitoring framework, which include ease of use, reliability, data input and output. It is critical that data can be drawn from different instrumentation packages and collected in the framework to allow for a uniform view of the running of a site. It is also very useful to allow different views and transformations of this data to allow its manipulation for different purposes, perhaps unknown at the initial time of installation. In this context, we present the findings of an investigation of the Graphite monitoring framework and its use at the ScotGrid Glasgow site. In particular, we examine the messaging system used by the framework and means to extract data from different tools, including the existing framework Ganglia which is in use at many sites, in addition to adapting and parsing data streams from external monitoring frameworks and websites.

  2. An open-framework bimetallic chalcogenide structure K3Rb3Zn4Sn3Se13 built on a unique [Zn4Sn3Se16]12- cluster: synthesis, crystal structure, ion exchange and optical properties

    International Nuclear Information System (INIS)

    Wu Min; Su Weiping; Jasutkar, Niren; Huang, Xiaoying; Li Jing

    2005-01-01

    Single crystals of K 3 Rb 3 Zn 4 Sn 3 Se 13 were synthesized by solvothermal method. The building block in this structure is a [Zn 4 Sn 3 Se 16 ] 12- cluster which consists of four ZnSe 4 and three SnSe 4 tetrahedra connected through corner-sharing of Se atoms. The 3D network contains intersecting channels running parallel to the crystallographic [2 1 1], [1-1-1] and [12-1] directions. The disordered K + and Rb + cations reside in these channels. Ion exchange of Cs + with disordered Rb + /K + ions in the structure showed a partial replacement of 15.8%. Optical diffuse reflectance experiments were carried out and gave a sharp absorption edge at 2.6 eV

  3. Exact WKB analysis and cluster algebras

    International Nuclear Information System (INIS)

    Iwaki, Kohei; Nakanishi, Tomoki

    2014-01-01

    We develop the mutation theory in the exact WKB analysis using the framework of cluster algebras. Under a continuous deformation of the potential of the Schrödinger equation on a compact Riemann surface, the Stokes graph may change the topology. We call this phenomenon the mutation of Stokes graphs. Along the mutation of Stokes graphs, the Voros symbols, which are monodromy data of the equation, also mutate due to the Stokes phenomenon. We show that the Voros symbols mutate as variables of a cluster algebra with surface realization. As an application, we obtain the identities of Stokes automorphisms associated with periods of cluster algebras. The paper also includes an extensive introduction of the exact WKB analysis and the surface realization of cluster algebras for nonexperts. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Cluster algebras in mathematical physics’. (paper)

  4. Stable Chimeras and Independently Synchronizable Clusters

    Science.gov (United States)

    Cho, Young Sul; Nishikawa, Takashi; Motter, Adilson E.

    2017-08-01

    Cluster synchronization is a phenomenon in which a network self-organizes into a pattern of synchronized sets. It has been shown that diverse patterns of stable cluster synchronization can be captured by symmetries of the network. Here, we establish a theoretical basis to divide an arbitrary pattern of symmetry clusters into independently synchronizable cluster sets, in which the synchronization stability of the individual clusters in each set is decoupled from that in all the other sets. Using this framework, we suggest a new approach to find permanently stable chimera states by capturing two or more symmetry clusters—at least one stable and one unstable—that compose the entire fully symmetric network.

  5. Study of aluminum-doped silicon clusters

    International Nuclear Information System (INIS)

    Zhan Shichang; Li Baoxing; Yang Jiansong

    2007-01-01

    Using full-muffin-tin-orbital molecular-dynamics (FP-LMTO-MD) method, we have investigated the effect of aluminum heteroatoms on the geometric structures and bond characteristics of Si n (n=5-10) clusters in detail. It is found that the geometric framework of the ground state structures for Si n (n=5-10) clusters change to some extent upon the substitution of Al atoms in some Si atoms. The effect of aluminum doping on the silicon clusters depends on the geometric structures of Si n (n=5-10) clusters. In particular, the calculations suggest that the aluminum doping would improve the bond strength of some Si-Si bonds in the mixed Si n - m Al m clusters

  6. Unsupervised land cover change detection: meaningful sequential time series analysis

    CSIR Research Space (South Africa)

    Salmon, BP

    2011-06-01

    Full Text Available An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short...

  7. Diversity among galaxy clusters

    International Nuclear Information System (INIS)

    Struble, M.F.; Rood, H.J.

    1988-01-01

    The classification of galaxy clusters is discussed. Consideration is given to the classification scheme of Abell (1950's), Zwicky (1950's), Morgan, Matthews, and Schmidt (1964), and Morgan-Bautz (1970). Galaxies can be classified based on morphology, chemical composition, spatial distribution, and motion. The correlation between a galaxy's environment and morphology is examined. The classification scheme of Rood-Sastry (1971), which is based on clusters's morphology and galaxy population, is described. The six types of clusters they define include: (1) a cD-cluster dominated by a single large galaxy, (2) a cluster dominated by a binary, (3) a core-halo cluster, (4) a cluster dominated by several bright galaxies, (5) a cluster appearing flattened, and (6) an irregularly shaped cluster. Attention is also given to the evolution of cluster structures, which is related to initial density and cluster motion

  8. Coronal Mass Ejection Data Clustering and Visualization of Decision Trees

    Science.gov (United States)

    Ma, Ruizhe; Angryk, Rafal A.; Riley, Pete; Filali Boubrahimi, Soukaina

    2018-05-01

    Coronal mass ejections (CMEs) can be categorized as either “magnetic clouds” (MCs) or non-MCs. Features such as a large magnetic field, low plasma-beta, and low proton temperature suggest that a CME event is also an MC event; however, so far there is neither a definitive method nor an automatic process to distinguish the two. Human labeling is time-consuming, and results can fluctuate owing to the imprecise definition of such events. In this study, we approach the problem of MC and non-MC distinction from a time series data analysis perspective and show how clustering can shed some light on this problem. Although many algorithms exist for traditional data clustering in the Euclidean space, they are not well suited for time series data. Problems such as inadequate distance measure, inaccurate cluster center description, and lack of intuitive cluster representations need to be addressed for effective time series clustering. Our data analysis in this work is twofold: clustering and visualization. For clustering we compared the results from the popular hierarchical agglomerative clustering technique to a distance density clustering heuristic we developed previously for time series data clustering. In both cases, dynamic time warping will be used for similarity measure. For classification as well as visualization, we use decision trees to aggregate single-dimensional clustering results to form a multidimensional time series decision tree, with averaged time series to present each decision. In this study, we achieved modest accuracy and, more importantly, an intuitive interpretation of how different parameters contribute to an MC event.

  9. A framework for classification of prokaryotic protein kinases.

    Directory of Open Access Journals (Sweden)

    Nidhi Tyagi

    Full Text Available BACKGROUND: Overwhelming majority of the Serine/Threonine protein kinases identified by gleaning archaeal and eubacterial genomes could not be classified into any of the well known Hanks and Hunter subfamilies of protein kinases. This is owing to the development of Hanks and Hunter classification scheme based on eukaryotic protein kinases which are highly divergent from their prokaryotic homologues. A large dataset of prokaryotic Serine/Threonine protein kinases recognized from genomes of prokaryotes have been used to develop a classification framework for prokaryotic Ser/Thr protein kinases. METHODOLOGY/PRINCIPAL FINDINGS: We have used traditional sequence alignment and phylogenetic approaches and clustered the prokaryotic kinases which represent 72 subfamilies with at least 4 members in each. Such a clustering enables classification of prokaryotic Ser/Thr kinases and it can be used as a framework to classify newly identified prokaryotic Ser/Thr kinases. After series of searches in a comprehensive sequence database we recognized that 38 subfamilies of prokaryotic protein kinases are associated to a specific taxonomic level. For example 4, 6 and 3 subfamilies have been identified that are currently specific to phylum proteobacteria, cyanobacteria and actinobacteria respectively. Similarly subfamilies which are specific to an order, sub-order, class, family and genus have also been identified. In addition to these, we also identify organism-diverse subfamilies. Members of these clusters are from organisms of different taxonomic levels, such as archaea, bacteria, eukaryotes and viruses. CONCLUSION/SIGNIFICANCE: Interestingly, occurrence of several taxonomic level specific subfamilies of prokaryotic kinases contrasts with classification of eukaryotic protein kinases in which most of the popular subfamilies of eukaryotic protein kinases occur diversely in several eukaryotes. Many prokaryotic Ser/Thr kinases exhibit a wide variety of modular

  10. What Makes Clusters Decline?

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    2015-01-01

    Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark. The longit...... but being quick to withdraw in times of crisis....

  11. Clustering of correlated networks

    OpenAIRE

    Dorogovtsev, S. N.

    2003-01-01

    We obtain the clustering coefficient, the degree-dependent local clustering, and the mean clustering of networks with arbitrary correlations between the degrees of the nearest-neighbor vertices. The resulting formulas allow one to determine the nature of the clustering of a network.

  12. Increasing retention in care of HIV-positive women in PMTCT services through continuous quality improvement-breakthrough (CQI-BTS) series in primary and secondary health care facilities in Nigeria: a cluster randomized controlled trial. The Lafiyan Jikin Mata Study.

    Science.gov (United States)

    Oyeledun, Bolanle; Oronsaye, Frank; Oyelade, Taiwo; Becquet, Renaud; Odoh, Deborah; Anyaike, Chukwuma; Ogirima, Francis; Ameh, Bernice; Ajibola, Abiola; Osibo, Bamidele; Imarhiagbe, Collins; Abutu, Inedu

    2014-11-01

    Rates of retention in care of HIV-positive pregnant women in care programs in Nigeria remain generally poor with rates around 40% reported for specific programs. Poor quality of services in health facilities and long waiting times are among the critical factors militating against retention of these women in care. The aim of the interventions in this study is to assess whether a continuous quality improvement intervention using a Breakthrough Series approach in local district hospitals and primary health care clinics will lead to improved retention of HIV-positive women and mothers. A cluster randomized controlled trial with 32 health facilities randomized to receive a continuous quality improvement/Breakthrough Series intervention or not. The care protocol for HIV-infected pregnant women and mothers is the same in all sites. The quality improvement intervention started 4 months before enrollment of individual HIV-infected pregnant women and initially focused on reducing waiting times for women and also ensuring that antiretroviral drugs are dispensed on the same day as clinic attendance. The primary outcome measure is retention of HIV-positive mothers in care at 6 months postpartum. Results of this trial will inform whether quality improvement interventions are an effective means of improving retention in prevention of mother-to-child transmission of HIV programs and will also guide where health system interventions should focus to improve the quality of care for HIV-positive women. This will benefit policymakers and program managers as they seek to improve retention rates in HIV care programs.

  13. Relevant Subspace Clustering

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

    2009-01-01

    Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....

  14. Clustering by Partitioning around Medoids using Distance-Based ...

    African Journals Online (AJOL)

    OLUWASOGO

    outperforms both the Euclidean and Manhattan distance metrics in certain situations. KEYWORDS: PAM ... version of a dataset, compare the quality of clusters obtained from the Euclidean .... B. Theoretical Framework and Methodology.

  15. Algorithms of maximum likelihood data clustering with applications

    Science.gov (United States)

    Giada, Lorenzo; Marsili, Matteo

    2002-12-01

    We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.

  16. THE SWIFT AGN AND CLUSTER SURVEY. II. CLUSTER CONFIRMATION WITH SDSS DATA

    International Nuclear Information System (INIS)

    Griffin, Rhiannon D.; Dai, Xinyu; Kochanek, Christopher S.; Bregman, Joel N.

    2016-01-01

    We study 203 (of 442) Swift AGN and Cluster Survey extended X-ray sources located in the SDSS DR8 footprint to search for galaxy over-densities in three-dimensional space using SDSS galaxy photometric redshifts and positions near the Swift cluster candidates. We find 104 Swift clusters with a >3σ galaxy over-density. The remaining targets are potentially located at higher redshifts and require deeper optical follow-up observations for confirmation as galaxy clusters. We present a series of cluster properties including the redshift, brightest cluster galaxy (BCG) magnitude, BCG-to-X-ray center offset, optical richness, and X-ray luminosity. We also detect red sequences in ∼85% of the 104 confirmed clusters. The X-ray luminosity and optical richness for the SDSS confirmed Swift clusters are correlated and follow previously established relations. The distribution of the separations between the X-ray centroids and the most likely BCG is also consistent with expectation. We compare the observed redshift distribution of the sample with a theoretical model, and find that our sample is complete for z ≲ 0.3 and is still 80% complete up to z ≃ 0.4, consistent with the SDSS survey depth. These analysis results suggest that our Swift cluster selection algorithm has yielded a statistically well-defined cluster sample for further study of cluster evolution and cosmology. We also match our SDSS confirmed Swift clusters to existing cluster catalogs, and find 42, 23, and 1 matches in optical, X-ray, and Sunyaev–Zel’dovich catalogs, respectively, and so the majority of these clusters are new detections

  17. Cluster ion beam facilities

    International Nuclear Information System (INIS)

    Popok, V.N.; Prasalovich, S.V.; Odzhaev, V.B.; Campbell, E.E.B.

    2001-01-01

    A brief state-of-the-art review in the field of cluster-surface interactions is presented. Ionised cluster beams could become a powerful and versatile tool for the modification and processing of surfaces as an alternative to ion implantation and ion assisted deposition. The main effects of cluster-surface collisions and possible applications of cluster ion beams are discussed. The outlooks of the Cluster Implantation and Deposition Apparatus (CIDA) being developed in Guteborg University are shown

  18. Tuning Properties in Silver Clusters

    KAUST Repository

    Joshi, Chakra Prasad

    2015-07-09

    The properties of Ag nanoclusters are not as well understood as those of their more precious Au cousins. However, a recent surge in the exploration of strategies to tune the physicochemical characteristics of Ag clusters addresses this imbalance, leading to new insights into their optical, luminescence, crystal habit, metal-core, ligand-shell and environmental properties. In this Perspective, we provide an overview of the latest strategies along with a brief introduction of the theoretical framework necessary to understand the properties of silver nanoclusters and the basis for their tuning. The advances in cluster research and the future prospects presented in this Perspective will eventually guide the next large systematic study of nanoclusters, resulting in a single collection of data similar to the periodic table of elements.

  19. Tuning Properties in Silver Clusters

    KAUST Repository

    Joshi, Chakra Prasad; Bootharaju, Megalamane Siddaramappa; Bakr, Osman

    2015-01-01

    The properties of Ag nanoclusters are not as well understood as those of their more precious Au cousins. However, a recent surge in the exploration of strategies to tune the physicochemical characteristics of Ag clusters addresses this imbalance, leading to new insights into their optical, luminescence, crystal habit, metal-core, ligand-shell and environmental properties. In this Perspective, we provide an overview of the latest strategies along with a brief introduction of the theoretical framework necessary to understand the properties of silver nanoclusters and the basis for their tuning. The advances in cluster research and the future prospects presented in this Perspective will eventually guide the next large systematic study of nanoclusters, resulting in a single collection of data similar to the periodic table of elements.

  20. Coupled Cluster Theory for Large Molecules

    DEFF Research Database (Denmark)

    Baudin, Pablo

    This thesis describes the development of local approximations to coupled cluster (CC) theory for large molecules. Two different methods are presented, the divide–expand–consolidate scheme (DEC), for the calculation of ground state energies, and a local framework denoted LoFEx, for the calculation...

  1. 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...

  2. Variable dimensionality and framework found in a series of quaternary zinc selenites, A2Zn3(SeO3)4·xH2O (A = Na, Rb, and Cs; 0≤x≤1) and Cs2Zn2(SeO3)3·2H2O

    International Nuclear Information System (INIS)

    Lü, Minfeng; Jo, Hongil; Oh, Seung-Jin; Ok, Kang Min

    2017-01-01

    Five new alkali metal zinc selenites, A 2 Zn 3 (SeO 3 ) 4 ·xH 2 O (A = Na, Rb, and Cs; 0≤x≤1) and Cs 2 Zn 2 (SeO 3 ) 3 ·2H 2 O have been synthesized by heating a mixture of ZnO, SeO 2 and A 2 CO 3 (A = Na, Rb, and Cs), and characterized by X-ray diffraction (XRD) and spectroscopic analyses techniques. All of the reported materials revealed a rich structural chemistry with different frameworks and connection modes of Zn 2+ . While Rb 2 Zn 3 (SeO 3 ) 4 and Cs 2 Zn 3 (SeO 3 ) 4 ·H 2 O revealed three-dimensional frameworks consisting of isolated ZnO 4 tetrahedra and SeO 3 polyhedra, Na 2 Zn 3 (SeO 3 ) 4 , Cs 2 Zn 3 (SeO 3 ) 4 , and Cs 2 Zn 2 (SeO 3 ) 3 ·2H 2 O contained two-dimensional [Zn 3 (SeO 3 ) 4 ] 2- layers. Specifically, whereas isolated ZnO 4 tetrahedra and SeO 3 polyhedra are arranged into two-dimensional [Zn 3 (SeO 3 ) 4 ] 2- layers in two cesium compounds, circular [Zn 3 O 10 ] 14- chains and SeO 3 linkers are formed in two-dimensional [Zn 3 (SeO 3 ) 4 ] 2- layers in Na 2 Zn 3 (SeO 3 ) 4 . Close structural examinations suggest that the size of alkali metal is significant in determining the framework geometry as well as connection modes of transition metal cations. - Graphical abstract: Variable dimensions and frameworks were found in a series of quaternary zinc selenites, A 2 Zn 3 (SeO 3 ) 4 (A = Na, Rb and Cs). - Highlights: • Five novel quaternary zinc selenites are synthesized. • All the selenites with different structures contain polarizable d 10 and lone pair cations. • The size of alkali metal cations is significant in determining the framework geometry.

  3. Investigation of clustering in sets of analytical data

    International Nuclear Information System (INIS)

    Kajfosz, J.

    1993-04-01

    Foundation of the statistical method of cluster analysis are briefly presented and its usefulness for the examination and evaluation of analytical data obtained from series of samples investigated by PIXE, PIGE or other methods is discussed. A simple program for fast examination of dissimilarities between samples within an investigated series is described. Useful information on clustering for several hundreds of samples can be obtained with minimal time and storage requirements. (author). 5 refs, 10 figs

  4. Investigation of clustering in sets of analytical data

    Energy Technology Data Exchange (ETDEWEB)

    Kajfosz, J [Institute of Nuclear Physics, Cracow (Poland)

    1993-04-01

    Foundation of the statistical method of cluster analysis are briefly presented and its usefulness for the examination and evaluation of analytical data obtained from series of samples investigated by PIXE, PIGE or other methods is discussed. A simple program for fast examination of dissimilarities between samples within an investigated series is described. Useful information on clustering for several hundreds of samples can be obtained with minimal time and storage requirements. (author). 5 refs, 10 figs.

  5. Construction Cluster Volume I [Wood Structural Framing].

    Science.gov (United States)

    Pennsylvania State Dept. of Justice, Harrisburg. Bureau of Correction.

    The document is the first of a series, to be integrated with a G.E.D. program, containing instructional materials at the basic skills level for the construction cluster. It focuses on wood structural framing and contains 20 units: (1) occupational information; (2) blueprint reading; (3) using leveling instruments and laying out building lines; (4)…

  6. Cluster Approach to Network Interaction in Pedagogical University

    Science.gov (United States)

    Chekaleva, Nadezhda V.; Makarova, Natalia S.; Drobotenko, Yulia B.

    2016-01-01

    The study presented in the article is devoted to the analysis of theory and practice of network interaction within the framework of education clusters. Education clusters are considered to be a novel form of network interaction in pedagogical education in Russia. The aim of the article is to show the advantages and disadvantages of the cluster…

  7. Result diversification based on query-specific cluster ranking

    NARCIS (Netherlands)

    He, J.; Meij, E.; de Rijke, M.

    2011-01-01

    Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification

  8. Result Diversification Based on Query-Specific Cluster Ranking

    NARCIS (Netherlands)

    J. He (Jiyin); E. Meij; M. de Rijke (Maarten)

    2011-01-01

    htmlabstractResult diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking,

  9. Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

    Science.gov (United States)

    Hwang, Heungsun; Dillon, William R.

    2010-01-01

    A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…

  10. Exactly soluble models for surface partition of large clusters

    International Nuclear Information System (INIS)

    Bugaev, K.A.; Bugaev, K.A.; Elliott, J.B.

    2007-01-01

    The surface partition of large clusters is studied analytically within a framework of the 'Hills and Dales Model'. Three formulations are solved exactly by using the Laplace-Fourier transformation method. In the limit of small amplitude deformations, the 'Hills and Dales Model' gives the upper and lower bounds for the surface entropy coefficient of large clusters. The found surface entropy coefficients are compared with those of large clusters within the 2- and 3-dimensional Ising models

  11. Management of cluster headache

    DEFF Research Database (Denmark)

    Tfelt-Hansen, Peer C; Jensen, Rigmor H

    2012-01-01

    The prevalence of cluster headache is 0.1% and cluster headache is often not diagnosed or misdiagnosed as migraine or sinusitis. In cluster headache there is often a considerable diagnostic delay - an average of 7 years in a population-based survey. Cluster headache is characterized by very severe...... or severe orbital or periorbital pain with a duration of 15-180 minutes. The cluster headache attacks are accompanied by characteristic associated unilateral symptoms such as tearing, nasal congestion and/or rhinorrhoea, eyelid oedema, miosis and/or ptosis. In addition, there is a sense of restlessness...... and agitation. Patients may have up to eight attacks per day. Episodic cluster headache (ECH) occurs in clusters of weeks to months duration, whereas chronic cluster headache (CCH) attacks occur for more than 1 year without remissions. Management of cluster headache is divided into acute attack treatment...

  12. Symmetries of cluster configurations

    International Nuclear Information System (INIS)

    Kramer, P.

    1975-01-01

    A deeper understanding of clustering phenomena in nuclei must encompass at least two interrelated aspects of the subject: (A) Given a system of A nucleons with two-body interactions, what are the relevant and persistent modes of clustering involved. What is the nature of the correlated nucleon groups which form the clusters, and what is their mutual interaction. (B) Given the cluster modes and their interaction, what systematic patterns of nuclear structure and reactions emerge from it. Are there, for example, families of states which share the same ''cluster parents''. Which cluster modes are compatible or exclude each other. What quantum numbers could characterize cluster configurations. There is no doubt that we can learn a good deal from the experimentalists who have discovered many of the features relevant to aspect (B). Symmetries specific to cluster configurations which can throw some light on both aspects of clustering are discussed

  13. Structural properties of gold clusters at different temperatures

    CSIR Research Space (South Africa)

    Mahladisa, MA

    2005-09-01

    Full Text Available A series of gold clusters consisting of aggregates of from 13 to 147 atoms was studied using the Sutton-Chen type many-body potential in molecular dynamics simulations. The properties of these clusters at temperatures from 10 K to 1000 K were...

  14. Career Education: The Leisure Occupations Cluster. Information Series No. 86.

    Science.gov (United States)

    Verhoven, Peter J.; Vinton, Dennis A.

    The guide is intended to supplement career education curricula with information about leisure occupations (recreation, hospitality, and tourism). It traces the growth and significance of leisure occupations with regard to the scientific, economic, and social advances which have motivated more Americans than ever before to seek leisure…

  15. Summation of series

    CERN Document Server

    Jolley, LB W

    2004-01-01

    Over 1,100 common series, all grouped for easy reference. Arranged by category, these series include arithmetical and geometrical progressions, powers and products of natural numbers, figurate and polygonal numbers, inverse natural numbers, exponential and logarithmic series, binomials, simple inverse products, factorials, trigonometrical and hyperbolic expansions, and additional series. 1961 edition.

  16. Hierarchical modeling of cluster size in wildlife surveys

    Science.gov (United States)

    Royle, J. Andrew

    2008-01-01

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

  17. A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

    Directory of Open Access Journals (Sweden)

    F. Xiao

    2018-04-01

    Full Text Available In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

  18. A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

    Science.gov (United States)

    Xiao, F.

    2018-04-01

    In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

  19. Foundations of Sequence-to-Sequence Modeling for Time Series

    OpenAIRE

    Kuznetsov, Vitaly; Mariet, Zelda

    2018-01-01

    The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

  20. A Framework for Evaluating Stay Detection Approaches

    Directory of Open Access Journals (Sweden)

    Cornelia Schneider

    2017-10-01

    Full Text Available In recent years, sensors of mobile devices are increasingly used in the research field of Active and Assisted Living (AAL, in particular, for movement analysis. Questions, such as where users typically stay (and for how long, where they have been or where they will most likely be going to, are of utmost importance for implementing smart AAL services. Due to the plethora of application scenarios and varying requirements, the challenge is the identification of an appropriate stay detection approach. Thus, this paper presents a comprehensive framework covering the entire process from data acquisition, pre-processing, parameterization to evaluation so that it can be applied to evaluate various stay detection methods. Additionally, ground truth data as well as application field data are used within the framework. The framework has been validated with three different spatio-temporal clustering approaches (time-based/incremental clustering, extended density based clustering, and a mixed method approach. Using the framework with ground truth data and data from the AAL field, it can be concluded that the time-based/incremental clustering approach is most suitable for this type of AAL applications. Furthermore, using two different datasets has proven successful as it provides additional data for selecting the appropriate method. Finally, the way the framework is designed it might be applied to other domains such as transportation, mobility, or tourism by adapting the pre-selection criteria.

  1. Cluster Decline and Resilience

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark, 1963......-2011. Our longitudinal study reveals that technological lock-in and exit of key firms have contributed to impairment of the cluster’s resilience in adapting to disruptions. Entrepreneurship has a positive effect on cluster resilience, while multinational companies have contradicting effects by bringing...... in new resources to the cluster but being quick to withdraw in times of crisis....

  2. Comprehensive cluster analysis with Transitivity Clustering.

    Science.gov (United States)

    Wittkop, Tobias; Emig, Dorothea; Truss, Anke; Albrecht, Mario; Böcker, Sebastian; Baumbach, Jan

    2011-03-01

    Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

  3. Large-Scale Multi-Dimensional Document Clustering on GPU Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Mueller, Frank [North Carolina State University; Zhang, Yongpeng [ORNL; Potok, Thomas E [ORNL

    2010-01-01

    Document clustering plays an important role in data mining systems. Recently, a flocking-based document clustering algorithm has been proposed to solve the problem through simulation resembling the flocking behavior of birds in nature. This method is superior to other clustering algorithms, including k-means, in the sense that the outcome is not sensitive to the initial state. One limitation of this approach is that the algorithmic complexity is inherently quadratic in the number of documents. As a result, execution time becomes a bottleneck with large number of documents. In this paper, we assess the benefits of exploiting the computational power of Beowulf-like clusters equipped with contemporary Graphics Processing Units (GPUs) as a means to significantly reduce the runtime of flocking-based document clustering. Our framework scales up to over one million documents processed simultaneously in a sixteennode GPU cluster. Results are also compared to a four-node cluster with higher-end GPUs. On these clusters, we observe 30X-50X speedups, which demonstrates the potential of GPU clusters to efficiently solve massive data mining problems. Such speedups combined with the scalability potential and accelerator-based parallelization are unique in the domain of document-based data mining, to the best of our knowledge.

  4. LMC clusters: young

    International Nuclear Information System (INIS)

    Freeman, K.C.

    1980-01-01

    The young globular clusters of the LMC have ages of 10 7 -10 8 y. Their masses and structure are similar to those of the smaller galactic globular clusters. Their stellar mass functions (in the mass range 6 solar masses to 1.2 solar masses) vary greatly from cluster to cluster, although the clusters are similar in total mass, age, structure and chemical composition. It would be very interesting to know why these clusters are forming now in the LMC and not in the Galaxy. The author considers the 'young globular' or 'blue populous' clusters of the LMC. The ages of these objects are 10 7 to 10 8 y, and their masses are 10 4 to 10 5 solar masses, so they are populous enough to be really useful for studying the evolution of massive stars. The author concentrates on the structure and stellar content of these young clusters. (Auth.)

  5. Star clusters and associations

    International Nuclear Information System (INIS)

    Ruprecht, J.; Palous, J.

    1983-01-01

    All 33 papers presented at the symposium were inputted to INIS. They dealt with open clusters, globular clusters, stellar associations and moving groups, and local kinematics and galactic structures. (E.S.)

  6. Cluster beam injection

    International Nuclear Information System (INIS)

    Bottiglioni, F.; Coutant, J.; Fois, M.

    1978-01-01

    Areas of possible applications of cluster injection are discussed. The deposition inside the plasma of molecules, issued from the dissociation of the injected clusters, has been computed. Some empirical scaling laws for the penetration are given

  7. Improved multi-objective clustering algorithm using particle swarm optimization.

    Directory of Open Access Journals (Sweden)

    Congcong Gong

    Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

  8. Improved multi-objective clustering algorithm using particle swarm optimization.

    Science.gov (United States)

    Gong, Congcong; Chen, Haisong; He, Weixiong; Zhang, Zhanliang

    2017-01-01

    Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

  9. Evaluating Clustering in Subspace Projections of High Dimensional Data

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Günnemann, Stephan; Assent, Ira

    2009-01-01

    Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation...... and comparison between these paradigms on a common basis. Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects...... of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention. In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering...

  10. Clustering at high redshifts

    International Nuclear Information System (INIS)

    Shaver, P.A.

    1986-01-01

    Evidence for clustering of and with high-redshift QSOs is discussed. QSOs of different redshifts show no clustering, but QSOs of similar redshifts appear to be clustered on a scale comparable to that of galaxies at the present epoch. In addition, spectroscopic studies of close pairs of QSOs indicate that QSOs are surrounded by a relatively high density of absorbing matter, possibly clusters of galaxies

  11. Metal–organic framework membranes: from synthesis to separation application

    KAUST Repository

    Qiu, Shilun; Xue, Ming; Zhu, Guangshan

    2014-01-01

    Metal-organic framework (MOF) materials, which are constructed from metal ions or metal ion clusters and bridging organic linkers, exhibit regular crystalline lattices with relatively well-defined pore structures and interesting properties. As a new

  12. Cluster Physics with Merging Galaxy Clusters

    Directory of Open Access Journals (Sweden)

    Sandor M. Molnar

    2016-02-01

    Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

  13. Size selected metal clusters

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. The Optical Absorption Spectra of Small Silver Clusters (5-11) ... Soft Landing and Fragmentation of Small Clusters Deposited in Noble-Gas Films. Harbich, W.; Fedrigo, S.; Buttet, J. Phys. Rev. B 1998, 58, 7428. CO combustion on supported gold clusters. Arenz M ...

  14. The Durban Auto Cluster

    DEFF Research Database (Denmark)

    Lorentzen, Jochen; Robbins, Glen; Barnes, Justin

    2004-01-01

    The paper describes the formation of the Durban Auto Cluster in the context of trade liberalization. It argues that the improvement of operational competitiveness of firms in the cluster is prominently due to joint action. It tests this proposition by comparing the gains from cluster activities...

  15. Marketing research cluster analysis

    Directory of Open Access Journals (Sweden)

    Marić Nebojša

    2002-01-01

    Full Text Available One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

  16. Marketing research cluster analysis

    OpenAIRE

    Marić Nebojša

    2002-01-01

    One area of applications of cluster analysis in marketing is identification of groups of cities and towns with similar demographic profiles. This paper considers main aspects of cluster analysis by an example of clustering 12 cities with the use of Minitab software.

  17. Minimalist's linux cluster

    International Nuclear Information System (INIS)

    Choi, Chang-Yeong; Kim, Jeong-Hyun; Kim, Seyong

    2004-01-01

    Using barebone PC components and NIC's, we construct a linux cluster which has 2-dimensional mesh structure. This cluster has smaller footprint, is less expensive, and use less power compared to conventional linux cluster. Here, we report our experience in building such a machine and discuss our current lattice project on the machine

  18. Range-clustering queries

    NARCIS (Netherlands)

    Abrahamsen, M.; de Berg, M.T.; Buchin, K.A.; Mehr, M.; Mehrabi, A.D.

    2017-01-01

    In a geometric k -clustering problem the goal is to partition a set of points in R d into k subsets such that a certain cost function of the clustering is minimized. We present data structures for orthogonal range-clustering queries on a point set S : given a query box Q and an integer k>2 , compute

  19. Cosmology with cluster surveys

    Indian Academy of Sciences (India)

    Abstract. Surveys of clusters of galaxies provide us with a powerful probe of the den- sity and nature of the dark energy. The red-shift distribution of detected clusters is highly sensitive to the dark energy equation of state parameter w. Upcoming Sunyaev–. Zel'dovich (SZ) surveys would provide us large yields of clusters to ...

  20. Inferring interdependencies from short time series

    Indian Academy of Sciences (India)

    Abstract. Complex networks provide an invaluable framework for the study of interlinked dynamical systems. In many cases, such networks are constructed from observed time series by first estimating the ...... does not quantify causal relations (unlike IOTA, or .... Africa_map_regions.svg, which is under public domain.

  1. ADVANCED CLUSTER BASED IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    D. Kesavaraja

    2011-11-01

    Full Text Available This paper presents efficient and portable implementations of a useful image segmentation technique which makes use of the faster and a variant of the conventional connected components algorithm which we call parallel Components. In the Modern world majority of the doctors are need image segmentation as the service for various purposes and also they expect this system is run faster and secure. Usually Image segmentation Algorithms are not working faster. In spite of several ongoing researches in Conventional Segmentation and its Algorithms might not be able to run faster. So we propose a cluster computing environment for parallel image Segmentation to provide faster result. This paper is the real time implementation of Distributed Image Segmentation in Clustering of Nodes. We demonstrate the effectiveness and feasibility of our method on a set of Medical CT Scan Images. Our general framework is a single address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient cluster process which uses a novel approach for parallel merging. Our experimental results are consistent with the theoretical analysis and practical results. It provides the faster execution time for segmentation, when compared with Conventional method. Our test data is different CT scan images from the Medical database. More efficient implementations of Image Segmentation will likely result in even faster execution times.

  2. Derivation of Mayer Series from Canonical Ensemble

    International Nuclear Information System (INIS)

    Wang Xian-Zhi

    2016-01-01

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

  3. Derivation of Mayer Series from Canonical Ensemble

    Science.gov (United States)

    Wang, Xian-Zhi

    2016-02-01

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

  4. Cluster analysis for applications

    CERN Document Server

    Anderberg, Michael R

    1973-01-01

    Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis.Comprised of 10 chapters, this book begins with an introduction to the subject o

  5. A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

    OpenAIRE

    Weiwei Lin; Wentai Wu; James Z. Wang

    2016-01-01

    Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual ...

  6. Industrial Clusters and Social Upgrading in Developing Countries

    DEFF Research Database (Denmark)

    Pyke, Frank; Lund-Thomsen, Peter

    In this article, we explore the relationship between industrial clusters and social upgrading in developing countries. Our article focuses on the hitherto little-considered influence of the economic and regulatory environment on the social upgrading of a cluster and on its governance system....... In doing so, we develop an analytical framework that seeks to explain how the enabling environment and different actors in cluster governance can either facilitate and/or hinder the process of social upgrading in cluster settings in developing countries. Finally, the conclusion outlines our main findings...

  7. Clusters in nuclei

    CERN Document Server

    Following the pioneering discovery of alpha clustering and of molecular resonances, the field of nuclear clustering is today one of those domains of heavy-ion nuclear physics that faces the greatest challenges, yet also contains the greatest opportunities. After many summer schools and workshops, in particular over the last decade, the community of nuclear molecular physicists has decided to collaborate in producing a comprehensive collection of lectures and tutorial reviews covering the field. This third volume follows the successful Lect. Notes Phys. 818 (Vol. 1) and 848 (Vol. 2), and comprises six extensive lectures covering the following topics:  - Gamma Rays and Molecular Structure - Faddeev Equation Approach for Three Cluster Nuclear Reactions - Tomography of the Cluster Structure of Light Nuclei Via Relativistic Dissociation - Clustering Effects Within the Dinuclear Model : From Light to Hyper-heavy Molecules in Dynamical Mean-field Approach - Clusterization in Ternary Fission - Clusters in Light N...

  8. Clusters and how to make it work : Cluster Strategy Toolkit

    NARCIS (Netherlands)

    Manickam, Anu; van Berkel, Karel

    2014-01-01

    Clusters are the magic answer to regional economic development. Firms in clusters are more innovative; cluster policy dominates EU policy; ‘top-sectors’ and excellence are the choice of national policy makers; clusters are ‘in’. But, clusters are complex, clusters are ‘messy’; there is no clear

  9. Dividing traffic cluster into parts by signal control

    Science.gov (United States)

    Nagatani, Takashi

    2018-02-01

    When a cluster of vehicles with various speeds moves through the series of signals, the cluster breaks down by stopping at signals and results in smaller groups of vehicles. We present the nonlinear-map model of the motion of vehicles controlled by the signals. We study the breakup of a cluster of vehicles through the series of signals. The cluster of vehicles is divided into various groups by controlling the cycle time of signals. The vehicles within each group move with the same mean velocity. The breakup of the traffic cluster depends highly on the signal control. The dependence of dividing on both cycle time and vehicular speed is clarified. Also, we investigate the effect of the irregular interval between signals on dividing.

  10. Geometric Series via Probability

    Science.gov (United States)

    Tesman, Barry

    2012-01-01

    Infinite series is a challenging topic in the undergraduate mathematics curriculum for many students. In fact, there is a vast literature in mathematics education research on convergence issues. One of the most important types of infinite series is the geometric series. Their beauty lies in the fact that they can be evaluated explicitly and that…

  11. On the series

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... http://www.ias.ac.in/article/fulltext/pmsc/115/04/0371-0381. Keywords. Inverse binomial series; hypergeometric series; polylogarithms; integral representations. Abstract. In this paper we investigate the series ∑ k = 1 ∞ ( 3 k k ) − 1 k − n x k . Obtaining some integral representations of them, we evaluated the ...

  12. Size estimates of nobel gas clusters by Rayleigh scattering experiments

    Institute of Scientific and Technical Information of China (English)

    Pinpin Zhu (朱频频); Guoquan Ni (倪国权); Zhizhan Xu (徐至展)

    2003-01-01

    Noble gases (argon, krypton, and xenon) are puffed into vacuum through a nozzle to produce clusters for studying laser-cluster interactions. Good estimates of the average size of the argon, krypton and xenon clusters are made by carrying out a series of Rayleigh scattering experiments. In the experiments, we have found that the scattered signal intensity varied greatly with the opening area of the pulsed valve. A new method is put forward to choose the appropriate scattered signal and measure the size of Kr cluster.

  13. Search for diquark clustering in baryons

    International Nuclear Information System (INIS)

    Fleck, S.; Silvestre-Brac, B.; Richard, J.M.

    1988-03-01

    In the framework of the non-relativistic quark model, we examine to which extent baryons consist of a quark bound to a localized cluster of two quarks simulating a diquark. We consider ground states and orbital excitations for various flavour combinations. A striking clustering shows up sometimes especially for the leading Regge trajectory of the nucleon and single flavoured baryons or for the ground state of baryons bearing two heavy flavours. This is, however, far from being a general pattern and there are clear differences between the three-quark description of baryons and the quark-diquark model

  14. Violence, Democracy and Education: An Analytical Framework. LCSHD Paper Series.

    Science.gov (United States)

    Salmi, Jamil

    How can the triumph of Western liberalism be reconciled with the pictures of chaos, war, crime, terror, and poverty which continue to appear in the daily news? Does violence coexist, in a significant fashion, with capitalism and democracy? What role does education play in this context? In addressing these questions, this paper presents a framework…

  15. Clusters in nonsmooth oscillator networks

    Science.gov (United States)

    Nicks, Rachel; Chambon, Lucie; Coombes, Stephen

    2018-03-01

    For coupled oscillator networks with Laplacian coupling, the master stability function (MSF) has proven a particularly powerful tool for assessing the stability of the synchronous state. Using tools from group theory, this approach has recently been extended to treat more general cluster states. However, the MSF and its generalizations require the determination of a set of Floquet multipliers from variational equations obtained by linearization around a periodic orbit. Since closed form solutions for periodic orbits are invariably hard to come by, the framework is often explored using numerical techniques. Here, we show that further insight into network dynamics can be obtained by focusing on piecewise linear (PWL) oscillator models. Not only do these allow for the explicit construction of periodic orbits, their variational analysis can also be explicitly performed. The price for adopting such nonsmooth systems is that many of the notions from smooth dynamical systems, and in particular linear stability, need to be modified to take into account possible jumps in the components of Jacobians. This is naturally accommodated with the use of saltation matrices. By augmenting the variational approach for studying smooth dynamical systems with such matrices we show that, for a wide variety of networks that have been used as models of biological systems, cluster states can be explicitly investigated. By way of illustration, we analyze an integrate-and-fire network model with event-driven synaptic coupling as well as a diffusively coupled network built from planar PWL nodes, including a reduction of the popular Morris-Lecar neuron model. We use these examples to emphasize that the stability of network cluster states can depend as much on the choice of single node dynamics as it does on the form of network structural connectivity. Importantly, the procedure that we present here, for understanding cluster synchronization in networks, is valid for a wide variety of systems in

  16. Allan deviation analysis of financial return series

    Science.gov (United States)

    Hernández-Pérez, R.

    2012-05-01

    We perform a scaling analysis for the return series of different financial assets applying the Allan deviation (ADEV), which is used in the time and frequency metrology to characterize quantitatively the stability of frequency standards since it has demonstrated to be a robust quantity to analyze fluctuations of non-stationary time series for different observation intervals. The data used are opening price daily series for assets from different markets during a time span of around ten years. We found that the ADEV results for the return series at short scales resemble those expected for an uncorrelated series, consistent with the efficient market hypothesis. On the other hand, the ADEV results for absolute return series for short scales (first one or two decades) decrease following approximately a scaling relation up to a point that is different for almost each asset, after which the ADEV deviates from scaling, which suggests that the presence of clustering, long-range dependence and non-stationarity signatures in the series drive the results for large observation intervals.

  17. Agricultural Clusters in the Netherlands

    NARCIS (Netherlands)

    Schouten, M.A.; Heijman, W.J.M.

    2012-01-01

    Michael Porter was the first to use the term cluster in an economic context. He introduced the term in The Competitive Advantage of Nations (1990). The term cluster is also known as business cluster, industry cluster, competitive cluster or Porterian cluster. This article aims at determining and

  18. Open source clustering software.

    Science.gov (United States)

    de Hoon, M J L; Imoto, S; Nolan, J; Miyano, S

    2004-06-12

    We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C. The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code. The complete source code is available at http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster. Alternatively, Algorithm::Cluster can be downloaded from CPAN, while Pycluster is also available as part of the Biopython distribution.

  19. Ananke: temporal clustering reveals ecological dynamics of microbial communities

    Directory of Open Access Journals (Sweden)

    Michael W. Hall

    2017-09-01

    Full Text Available Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.

  20. A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

    Directory of Open Access Journals (Sweden)

    Jiuqi Han

    2018-04-01

    Full Text Available Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods.

  1. ZEND FRAMEWORK

    Directory of Open Access Journals (Sweden)

    Lupasc Adrian

    2013-12-01

    Full Text Available In this paper we present Zend Architecture, which is an open source technology for developing web applications and services, based on object-oriented components, and the Model-View-Controller architectural pattern, also known as MVC, which is the fundament of this architecture. The MVC presentation emphasises its main characteristics, such as facilitating the components reuse by dividing the application into distinct interconnected modules, tasks distribution in the process of developing an application, the MVC life cycle and also the essential features of the components in which it separates the application: model, view, controller. The controller coordinates the models and views and it’s responsible with manipulating the user events through the corresponding actions. The model contains application rules, respectively the scripts that implement the database manipulation. The third component, the view represents the controllers interface with the user or the way it displays the response to the event triggered by the user. Another aspect treated in this paper consists in highlighting the Zend architecture advantages and disadvantages. Among the framework advantages, we can enumerate good code organization, due to its delimitation into three sections, presentation, logic and data access, and dividing the code into components, which facilitates the code reuse and testing. Other advantages are the open-source license and the support for multiple database systems. The main disadvantages are represented by its size and complexity, that makes it hard to understand for a beginner programmer, the resources it needs etc. The last section of the paper presents a comparison between Zend and other PHP architectures, like Symphony, CakePHP and CodeIgniter, which includes their essential features and points out their similarities and differences, based on the unique functions that set them apart from others. The main thing that distinguishes ZF from the

  2. Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients.

    Science.gov (United States)

    Steimer, Andreas; Müller, Michael; Schindler, Kaspar

    2017-05-01

    During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield a markedly lower posterior probability for the seizure related clusters, when compared to the resection of random or empty collections. Conversely, for 4 out of 5 Engel class III/IV patients who still suffer from postsurgical seizures, performance of the actually resected collection is not significantly better than performances displayed by random or empty collections. As the number of possible collections ranges into billions and more, this is a substantial contribution to a problem that today is still solved by visual EEG inspection. Apart from epilepsy research, our clustering methodology is also of general interest for the analysis of multivariate time series and as a generative model for temporally evolving functional networks in the neurosciences and beyond. Hum Brain Mapp 38:2509-2531, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  3. Applying Diffusion Theory to Electronic Publishing: A Conceptual Framework for Examining Issues and Outcomes.

    Science.gov (United States)

    Hahn, Karla L.; Schoch, Natalie A.

    1997-01-01

    Electronic publishing can best be understood as a cluster of related innovations which can be incorporated in different combinations. The innovation cluster is defined, and a framework is provided to characterize several recent publishing ventures demonstrating how the framework facilitates comparison and evaluation of individual implementations…

  4. Electron: Cluster interactions

    International Nuclear Information System (INIS)

    Scheidemann, A.A.; Knight, W.D.

    1994-02-01

    Beam depletion spectroscopy has been used to measure absolute total inelastic electron-sodium cluster collision cross sections in the energy range from E ∼ 0.1 to E ∼ 6 eV. The investigation focused on the closed shell clusters Na 8 , Na 20 , Na 40 . The measured cross sections show an increase for the lowest collision energies where electron attachment is the primary scattering channel. The electron attachment cross section can be understood in terms of Langevin scattering, connecting this measurement with the polarizability of the cluster. For energies above the dissociation energy the measured electron-cluster cross section is energy independent, thus defining an electron-cluster interaction range. This interaction range increases with the cluster size

  5. Clustering high dimensional data

    DEFF Research Database (Denmark)

    Assent, Ira

    2012-01-01

    High-dimensional data, i.e., data described by a large number of attributes, pose specific challenges to clustering. The so-called ‘curse of dimensionality’, coined originally to describe the general increase in complexity of various computational problems as dimensionality increases, is known...... to render traditional clustering algorithms ineffective. The curse of dimensionality, among other effects, means that with increasing number of dimensions, a loss of meaningful differentiation between similar and dissimilar objects is observed. As high-dimensional objects appear almost alike, new approaches...... for clustering are required. Consequently, recent research has focused on developing techniques and clustering algorithms specifically for high-dimensional data. Still, open research issues remain. Clustering is a data mining task devoted to the automatic grouping of data based on mutual similarity. Each cluster...

  6. Cluster Ions and Atmospheric Processes

    Science.gov (United States)

    D'Auria, R.; Turco, R. P.

    We investigate the properties and possible roles of naturally occurring ions under at- mospheric conditions. Among other things, the formation of stable charged molecular clusters represents the initial stages of aerosol nucleation [e.g., Keesee and Castle- man, 1982], while the conversion of vapor to aggregates is the first step in certain atmospheric phase transitions [e.g. Hamill and Turco, 2000]. We analyze the stability and size distributions of common ionic clusters by solving the differential equations describing their growth and loss. The necessary reaction rate coefficients are deter- mined using kinetic and thermodynamic data. The latter are derived from direct labo- ratory measurements of equilibrium constants, from the classical charged liquid drop model applied to large aggregates (i.e., the Thomson model [Thomson, 1906]), and from quantum mechanical calculations of the thermodynamic potentials associated with the cluster structures. This approach allows us to characterize molecular clusters across the entire size range from true molecular species to larger aggregates exhibiting macroscopic behavior [D'Auria, 2001]. Cluster systems discussed in this talk include the proton hydrates (PHs) and nitrate-water and nitrate-nitric acid series [D'Auria and Turco, 2001]. These ions have frequently been detected in the stratosphere and tropo- sphere [e.g., Arnold et al., 1977; Viggiano and Arnold, 1981]. We show how the pro- posed hybrid cluster model can be extended to a wide range of ion systems, including non-proton hydrates (NPHs), mixed-ligand clusters such as nitrate-water-nitric acid and sulfate-sulfuric acid-water, as well as more exotic species containing ammonia, pyridine and other organic compounds found on ions [e.g., Eisele, 1988; Tanner and Eisele, 1991]. References: Arnold, F., D. Krankowsky and K. H. Marien, First mass spectrometric measurements of posi- tive ions in the stratosphere, Nature, 267, 30-32, 1977. D'Auria, R., A study of ionic

  7. Detecting nonlinear structure in time series

    International Nuclear Information System (INIS)

    Theiler, J.

    1991-01-01

    We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of ''surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs

  8. Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

    KAUST Repository

    Ben Ghorbel, Mahdi

    2012-04-01

    Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

  9. Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

    KAUST Repository

    Ben Ghorbel, Mahdi; Nam, Haewoon; Alouini, Mohamed-Slim

    2012-01-01

    Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

  10. Static dipole polarizabilities of Scn (n ≤ 15) clusters

    International Nuclear Information System (INIS)

    Xi-Bo, Li; Jiang-Shan, Luo; Wei-Dong, Wu; Yong-Jian, Tang; Hong-Yan, Wang; Yun-Dong, Guo

    2009-01-01

    The static dipole polarizabilities of scandium clusters with up to 15 atoms are determined by using the numerically finite field method in the framework of density functional theory. The electronic effects on the polarizabilities are investigated for the scandium clusters. We examine a large highest occupied molecular orbital — the lowest occupied molecular orbital (HOMO–LUMO) gap of a scandium cluster usually corresponds to a large dipole moment. The static polarizability per atom decreases slowly and exhibits local minimum with increasing cluster size. The polarizability anisotropy and the ratio of mean static polarizability to the HOMO–LUMO gap can also reflect the cluster stability. The polarizability of the scandium cluster is partially related to the HOMO–LUMO gap and is also dependent on geometrical characteristics. A strong correlation between the polarizability and ionization energy is observed. (atomic and molecular physics)

  11. Fourier Series Formalization in ACL2(r

    Directory of Open Access Journals (Sweden)

    Cuong K. Chau

    2015-09-01

    Full Text Available We formalize some basic properties of Fourier series in the logic of ACL2(r, which is a variant of ACL2 that supports reasoning about the real and complex numbers by way of non-standard analysis. More specifically, we extend a framework for formally evaluating definite integrals of real-valued, continuous functions using the Second Fundamental Theorem of Calculus. Our extended framework is also applied to functions containing free arguments. Using this framework, we are able to prove the orthogonality relationships between trigonometric functions, which are the essential properties in Fourier series analysis. The sum rule for definite integrals of indexed sums is also formalized by applying the extended framework along with the First Fundamental Theorem of Calculus and the sum rule for differentiation. The Fourier coefficient formulas of periodic functions are then formalized from the orthogonality relations and the sum rule for integration. Consequently, the uniqueness of Fourier sums is a straightforward corollary. We also present our formalization of the sum rule for definite integrals of infinite series in ACL2(r. Part of this task is to prove the Dini Uniform Convergence Theorem and the continuity of a limit function under certain conditions. A key technique in our proofs of these theorems is to apply the overspill principle from non-standard analysis.

  12. Substructure in clusters of galaxies

    International Nuclear Information System (INIS)

    Fitchett, M.J.

    1988-01-01

    Optical observations suggesting the existence of substructure in clusters of galaxies are examined. Models of cluster formation and methods used to detect substructure in clusters are reviewed. Consideration is given to classification schemes based on a departure of bright cluster galaxies from a spherically symmetric distribution, evidence for statistically significant substructure, and various types of substructure, including velocity, spatial, and spatial-velocity substructure. The substructure observed in the galaxy distribution in clusters is discussed, focusing on observations from general cluster samples, the Virgo cluster, the Hydra cluster, Centaurus, the Coma cluster, and the Cancer cluster. 88 refs

  13. Clustered data acquisition for the CMS experiment

    International Nuclear Information System (INIS)

    Gutleber, J.; Antchev, G.; Cano, E.; Csilling, A.; Cittolin, S.; Gigi, D.; Gras, P.; Jacobs, C.; Meijers, F.; Meschi, E.; Oh, A.; Orsini, L.; Pollet, L.; Racz, A.; Samyn, D.; Schwick, C.; Zangrando, L.; Erhan, S.; Gulmini, M.; Sphicas, P.; Ninane, A.

    2001-01-01

    Powerful mainstream computing equipment and the advent of affordable multi-Gigabit communication technology allow us to tackle data acquisition problems with clusters of inexpensive computers. Such networks typically incorporate heterogeneous platforms, real-time partitions and custom devices. Therefore, one must strive for a software infrastructure that efficiently combines the nodes to a single, unified resource for the user. Overall requirements for such middleware are high efficiency and configuration flexibility. Intelligent I/O (I 2 O) is an industry specification that defines a uniform messaging format and executing model for processor-enabled communication equipment. Mapping this concept to a distributed computing environment and encapsulating the details of the specification into an application-programming framework allow us to provide run-time support for cluster operation. The authors give a brief overview of a framework, XDAQ that we designed and implemented at CERN for the Compact Muon Solenoid experiment's prototype data acquisition system

  14. Nuclear cluster states

    International Nuclear Information System (INIS)

    Rae, W.D.M.; Merchant, A.C.

    1993-01-01

    We review clustering in light nuclei including molecular resonances in heavy ion reactions. In particular we study the systematics, paying special attention to the relationships between cluster states and superdeformed configurations. We emphasise the selection rules which govern the formation and decay of cluster states. We review some recent experimental results from Daresbury and elsewhere. In particular we report on the evidence for a 7-α chain state in 28 Si in experiments recently performed at the NSF, Daresbury. Finally we begin to address theoretically the important question of the lifetimes of cluster states as deduced from the experimental energy widths of the resonances. (Author)

  15. 15th Cluster workshop

    CERN Document Server

    Laakso, Harri; Escoubet, C. Philippe; The Cluster Active Archive : Studying the Earth’s Space Plasma Environment

    2010-01-01

    Since the year 2000 the ESA Cluster mission has been investigating the small-scale structures and processes of the Earth's plasma environment, such as those involved in the interaction between the solar wind and the magnetospheric plasma, in global magnetotail dynamics, in cross-tail currents, and in the formation and dynamics of the neutral line and of plasmoids. This book contains presentations made at the 15th Cluster workshop held in March 2008. It also presents several articles about the Cluster Active Archive and its datasets, a few overview papers on the Cluster mission, and articles reporting on scientific findings on the solar wind, the magnetosheath, the magnetopause and the magnetotail.

  16. Clusters in simple fluids

    International Nuclear Information System (INIS)

    Sator, N.

    2003-01-01

    This article concerns the correspondence between thermodynamics and the morphology of simple fluids in terms of clusters. Definitions of clusters providing a geometric interpretation of the liquid-gas phase transition are reviewed with an eye to establishing their physical relevance. The author emphasizes their main features and basic hypotheses, and shows how these definitions lead to a recent approach based on self-bound clusters. Although theoretical, this tutorial review is also addressed to readers interested in experimental aspects of clustering in simple fluids

  17. Amine reactivity with charged sulfuric acid clusters

    Directory of Open Access Journals (Sweden)

    B. R. Bzdek

    2011-08-01

    Full Text Available The distribution of charged species produced by electrospray of an ammonium sulfate solution in both positive and negative polarities is examined using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS. Positively-charged ammonium bisulfate cluster composition differs significantly from negatively-charged cluster composition. For positively-charged clusters all sulfuric acid is neutralized to bisulfate, whereas for negatively-charged clusters the degree of sulfuric acid neutralization is cluster size-dependent. With increasing cluster size (and, therefore, a decreasing role of charge, both positively- and negatively-charged cluster compositions converge toward ammonium bisulfate. The reactivity of negatively-charged sulfuric acid-ammonia clusters with dimethylamine and ammonia is also investigated by FTICR-MS. Two series of negatively-charged clusters are investigated: [(HSO4(H2SO4x] and [(NH4x(HSO4x+1(H2SO43]. Dimethylamine substitution for ammonia in [(NH4 x(HSO4 x+1(H2SO43] clusters is nearly collision-limited, and subsequent addition of dimethylamine to neutralize H2SO4 to bisulfate is within one order of magnitude of the substitution rate. Dimethylamine addition to [(HSO4 (H2SO4 x] clusters is either not observed or very slow. The results of this study indicate that amine chemistry will be evident and important only in large ambient negative ions (>m/z 400, whereas amine chemistry may be evident in small ambient positive ions. Addition of ammonia to unneutralized clusters occurs at a rate that is ~2–3 orders of magnitude slower than incorporation of dimethylamine either by substitution or addition

  18. Alpha-clustering in dilute nucleonic sea

    International Nuclear Information System (INIS)

    Tohsaki, Akihiro

    1999-01-01

    α-clusters are expected to come out here and there in nucleonic sea owing to energetic benefit as its density is diluted. We propose a precise treatment to elucidate α-clusterized process in nucleonic sea after the breakdown of the uniformness. In order to do this, an infinite number of nucleons are considered by taking account of both the Pauli exclusion principle and effective internucleon forces. This method is called a microscopic approach, which has been successful in an α-cluster structure in light nuclei. In particular, we shed light on overcoming difficulties in a static model within the microscopic framework. This improvement is verified by using the empirical value in Weizaecker's mass formula. (author)

  19. Series Transmission Line Transformer

    Science.gov (United States)

    Buckles, Robert A.; Booth, Rex; Yen, Boris T.

    2004-06-29

    A series transmission line transformer is set forth which includes two or more of impedance matched sets of at least two transmissions lines such as shielded cables, connected in parallel at one end ans series at the other in a cascading fashion. The cables are wound about a magnetic core. The series transmission line transformer (STLT) which can provide for higher impedance ratios and bandwidths, which is scalable, and which is of simpler design and construction.

  20. Series expansions without diagrams

    International Nuclear Information System (INIS)

    Bhanot, G.; Creutz, M.; Horvath, I.; Lacki, J.; Weckel, J.

    1994-01-01

    We discuss the use of recursive enumeration schemes to obtain low- and high-temperature series expansions for discrete statistical systems. Using linear combinations of generalized helical lattices, the method is competitive with diagrammatic approaches and is easily generalizable. We illustrate the approach using Ising and Potts models. We present low-temperature series results in up to five dimensions and high-temperature series in three dimensions. The method is general and can be applied to any discrete model

  1. Structure and Mobility of Metal Clusters in MOFs: Au, Pd, and AuPd Clusters in MOF-74

    DEFF Research Database (Denmark)

    Vilhelmsen, Lasse; Walton, Krista S.; Sholl, David S.

    2012-01-01

    is just as important for nanocluster adsorption as open Zn or Mg metal sites. Using the large number of clusters generated by the GA, we developed a systematic method for predicting the mobility of adsorbed clusters. Through the investigation of diffusion paths a relationship between the cluster......Understanding the adsorption and mobility of metal–organic framework (MOF)-supported metal nanoclusters is critical to the development of these catalytic materials. We present the first theoretical investigation of Au-, Pd-, and AuPd-supported clusters in a MOF, namely MOF-74. We combine density...... functional theory (DFT) calculations with a genetic algorithm (GA) to reliably predict the structure of the adsorbed clusters. This approach allows comparison of hundreds of adsorbed configurations for each cluster. From the investigation of Au8, Pd8, and Au4Pd4 we find that the organic part of the MOF...

  2. Impact of metal and anion substitutions on the hydrogen storage properties of M-BTT metal-organic frameworks.

    Science.gov (United States)

    Sumida, Kenji; Stück, David; Mino, Lorenzo; Chai, Jeng-Da; Bloch, Eric D; Zavorotynska, Olena; Murray, Leslie J; Dincă, Mircea; Chavan, Sachin; Bordiga, Silvia; Head-Gordon, Martin; Long, Jeffrey R

    2013-01-23

    Microporous metal-organic frameworks are a class of materials being vigorously investigated for mobile hydrogen storage applications. For high-pressure storage at ambient temperatures, the M(3)[(M(4)Cl)(3)(BTT)(8)](2) (M-BTT; BTT(3-) = 1,3,5-benzenetristetrazolate) series of frameworks are of particular interest due to the high density of exposed metal cation sites on the pore surface. These sites give enhanced zero-coverage isosteric heats of adsorption (Q(st)) approaching the optimal value for ambient storage applications. However, the Q(st) parameter provides only a limited insight into the thermodynamics of the individual adsorption sites, the tuning of which is paramount for optimizing the storage performance. Here, we begin by performing variable-temperature infrared spectroscopy studies of Mn-, Fe-, and Cu-BTT, allowing the thermodynamics of H(2) adsorption to be probed experimentally. This is complemented by a detailed DFT study, in which molecular fragments representing the metal clusters within the extended solid are simulated to obtain a more thorough description of the structural and thermodynamic aspects of H(2) adsorption at the strongest binding sites. Then, the effect of substitutions at the metal cluster (metal ion and anion within the tetranuclear cluster) is discussed, showing that the configuration of this unit indeed plays an important role in determining the affinity of the framework toward H(2). Interestingly, the theoretical study has identified that the Zn-based analogs would be expected to facilitate enhanced adsorption profiles over the compounds synthesized experimentally, highlighting the importance of a combined experimental and theoretical approach to the design and synthesis of new frameworks for H(2) storage applications.

  3. Deep brain stimulation for cluster headache

    DEFF Research Database (Denmark)

    Grover, Patrick J; Pereira, Erlick A C; Green, Alexander L

    2009-01-01

    Cluster headache is a severely debilitating disorder that can remain unrelieved by current pharmacotherapy. Alongside ablative neurosurgical procedures, neuromodulatory treatments of deep brain stimulation (DBS) and occipital nerve simulation have emerged in the last few years as effective...... treatments for medically refractory cluster headaches. Pioneers in the field have sought to publish guidelines for neurosurgical treatment; however, only small case series with limited long-term follow-up have been published. Controversy remains over which surgical treatments are best and in which...... circumstances to intervene. Here we review current data on neurosurgical interventions for chronic cluster headache focusing upon DBS and occipital nerve stimulation, and discuss the indications for and putative mechanisms of DBS including translational insights from functional neuroimaging, diffusion weighted...

  4. Chemical graph-theoretic cluster expansions

    International Nuclear Information System (INIS)

    Klein, D.J.

    1986-01-01

    A general computationally amenable chemico-graph-theoretic cluster expansion method is suggested as a paradigm for incorporation of chemical structure concepts in a systematic manner. The cluster expansion approach is presented in a formalism general enough to cover a variety of empirical, semiempirical, and even ab initio applications. Formally such approaches for the utilization of chemical structure-related concepts may be viewed as discrete analogues of Taylor series expansions. The efficacy of the chemical structure concepts then is simply bound up in the rate of convergence of the cluster expansions. In many empirical applications, e.g., boiling points, chromatographic separation coefficients, and biological activities, this rate of convergence has been observed to be quite rapid. More note will be made here of quantum chemical applications. Relations to questions concerning size extensivity of energies and size consistency of wave functions are addressed

  5. Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

    Directory of Open Access Journals (Sweden)

    Ujjwal Maulik

    Full Text Available Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request.sarkar@labri.fr.

  6. Metal organic frameworks for gas storage

    KAUST Repository

    Alezi, Dalal

    2016-06-09

    Embodiments provide a method of storing a compound using a metal organic framework (MOF). The method includes contacting one or more MOFs with a fluid and sorbing one or more compounds, such as O2 and CH4. O2 and CH4 can be sorbed simultaneously or in series. The metal organic framework can be an M-soc-MOF, wherein M can include aluminum, iron, gallium, indium, vanadium, chromium, titanium, or scandium.

  7. Bayesian Nonparametric Clustering for Positive Definite Matrices.

    Science.gov (United States)

    Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos

    2016-05-01

    Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.

  8. Egocentric daily activity recognition via multitask clustering.

    Science.gov (United States)

    Yan, Yan; Ricci, Elisa; Liu, Gaowen; Sebe, Nicu

    2015-10-01

    Recognizing human activities from videos is a fundamental research problem in computer vision. Recently, there has been a growing interest in analyzing human behavior from data collected with wearable cameras. First-person cameras continuously record several hours of their wearers' life. To cope with this vast amount of unlabeled and heterogeneous data, novel algorithmic solutions are required. In this paper, we propose a multitask clustering framework for activity of daily living analysis from visual data gathered from wearable cameras. Our intuition is that, even if the data are not annotated, it is possible to exploit the fact that the tasks of recognizing everyday activities of multiple individuals are related, since typically people perform the same actions in similar environments, e.g., people working in an office often read and write documents). In our framework, rather than clustering data from different users separately, we propose to look for clustering partitions which are coherent among related tasks. In particular, two novel multitask clustering algorithms, derived from a common optimization problem, are introduced. Our experimental evaluation, conducted both on synthetic data and on publicly available first-person vision data sets, shows that the proposed approach outperforms several single-task and multitask learning methods.

  9. Lifting to cluster-tilting objects in higher cluster categories

    OpenAIRE

    Liu, Pin

    2008-01-01

    In this note, we consider the $d$-cluster-tilted algebras, the endomorphism algebras of $d$-cluster-tilting objects in $d$-cluster categories. We show that a tilting module over such an algebra lifts to a $d$-cluster-tilting object in this $d$-cluster category.

  10. MCdevelop - a universal framework for Stochastic Simulations

    Science.gov (United States)

    Slawinska, M.; Jadach, S.

    2011-03-01

    We present MCdevelop, a universal computer framework for developing and exploiting the wide class of Stochastic Simulations (SS) software. This powerful universal SS software development tool has been derived from a series of scientific projects for precision calculations in high energy physics (HEP), which feature a wide range of functionality in the SS software needed for advanced precision Quantum Field Theory calculations for the past LEP experiments and for the ongoing LHC experiments at CERN, Geneva. MCdevelop is a "spin-off" product of HEP to be exploited in other areas, while it will still serve to develop new SS software for HEP experiments. Typically SS involve independent generation of large sets of random "events", often requiring considerable CPU power. Since SS jobs usually do not share memory it makes them easy to parallelize. The efficient development, testing and running in parallel SS software requires a convenient framework to develop software source code, deploy and monitor batch jobs, merge and analyse results from multiple parallel jobs, even before the production runs are terminated. Throughout the years of development of stochastic simulations for HEP, a sophisticated framework featuring all the above mentioned functionality has been implemented. MCdevelop represents its latest version, written mostly in C++ (GNU compiler gcc). It uses Autotools to build binaries (optionally managed within the KDevelop 3.5.3 Integrated Development Environment (IDE)). It uses the open-source ROOT package for histogramming, graphics and the mechanism of persistency for the C++ objects. MCdevelop helps to run multiple parallel jobs on any computer cluster with NQS-type batch system. Program summaryProgram title:MCdevelop Catalogue identifier: AEHW_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHW_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http

  11. Simple description of cluster radioactivity

    International Nuclear Information System (INIS)

    Tavares, O.A.P.; Medeiros, E.L.

    2012-05-01

    The partial half-life of radioactive decay of nuclei by the emission of fragments heavier than the alpha particle, such as the emission of carbon, oxygen, neon, magnesium, and silicon isotopes from translead nuclei (known as cluster radioactivity), is re-evaluated in the framework of a semiempirical, one-parameter model based on the quantum mechanical tunneling mechanism through a potential barrier where the Coulomb, centrifugal, and overlapping contributions to the barrier are considered within the spherical nucleus approximation. This treatment has shown not only very adequate to t all the existing half-life data, but also to give more reliable half-life predictions for new, yet unmeasured cases of spontaneous emission of massive nuclear fragments both from heavy and intermediate-mass parent nuclei as well. (author)

  12. PROSPECTS OF THE REGIONAL INTEGRATION POLICY BASED ON CLUSTER FORMATION

    Directory of Open Access Journals (Sweden)

    Elena Tsepilova

    2018-01-01

    Full Text Available The purpose of this article is to develop the theoretical foundations of regional integration policy and to determine its prospects on the basis of cluster formation. The authors use such research methods as systematization, comparative and complex analysis, synthesis, statistical method. Within the framework of the research, the concept of regional integration policy is specified, and its integration core – cluster – is allocated. The authors work out an algorithm of regional clustering, which will ensure the growth of economy and tax income. Measures have been proposed to optimize the organizational mechanism of interaction between the participants of the territorial cluster and the authorities that allow to ensure the effective functioning of clusters, including taxation clusters. Based on the results of studying the existing methods for assessing the effectiveness of cluster policy, the authors propose their own approach to evaluating the consequences of implementing the regional integration policy, according to which the list of quantitative and qualitative indicators is defined. The present article systematizes the experience and results of the cluster policy of certain European countries, that made it possible to determine the prospects and synergetic effect from the development of clusters as an integration foundation of regional policy in the Russian Federation. The authors carry out the analysis of activity of cluster formations using the example of the Rostov region – a leader in the formation of conditions for the cluster policy development in the Southern Federal District. 11 clusters and cluster initiatives are developing in this region. As a result, the authors propose measures for support of the already existing clusters and creation of the new ones.

  13. Neurostimulation in cluster headache

    DEFF Research Database (Denmark)

    Pedersen, Jeppe L; Barloese, Mads; Jensen, Rigmor H

    2013-01-01

    PURPOSE OF REVIEW: Neurostimulation has emerged as a viable treatment for intractable chronic cluster headache. Several therapeutic strategies are being investigated including stimulation of the hypothalamus, occipital nerves and sphenopalatine ganglion. The aim of this review is to provide...... effective strategy must be preferred as first-line therapy for intractable chronic cluster headache....

  14. Cauchy cluster process

    DEFF Research Database (Denmark)

    Ghorbani, Mohammad

    2013-01-01

    In this paper we introduce an instance of the well-know Neyman–Scott cluster process model with clusters having a long tail behaviour. In our model the offspring points are distributed around the parent points according to a circular Cauchy distribution. Using a modified Cramér-von Misses test...

  15. When Clusters become Networks

    NARCIS (Netherlands)

    S.M.W. Phlippen (Sandra); G.A. van der Knaap (Bert)

    2007-01-01

    textabstractPolicy makers spend large amounts of public resources on the foundation of science parks and other forms of geographically clustered business activities, in order to stimulate regional innovation. Underlying the relation between clusters and innovation is the assumption that co-located

  16. Coma cluster of galaxies

    Science.gov (United States)

    1999-01-01

    Atlas Image mosaic, covering 34' x 34' on the sky, of the Coma cluster, aka Abell 1656. This is a particularly rich cluster of individual galaxies (over 1000 members), most prominently the two giant ellipticals, NGC 4874 (right) and NGC 4889 (left). The remaining members are mostly smaller ellipticals, but spiral galaxies are also evident in the 2MASS image. The cluster is seen toward the constellation Coma Berenices, but is actually at a distance of about 100 Mpc (330 million light years, or a redshift of 0.023) from us. At this distance, the cluster is in what is known as the 'Hubble flow,' or the overall expansion of the Universe. As such, astronomers can measure the Hubble Constant, or the universal expansion rate, based on the distance to this cluster. Large, rich clusters, such as Coma, allow astronomers to measure the 'missing mass,' i.e., the matter in the cluster that we cannot see, since it gravitationally influences the motions of the member galaxies within the cluster. The near-infrared maps the overall luminous mass content of the member galaxies, since the light at these wavelengths is dominated by the more numerous older stellar populations. Galaxies, as seen by 2MASS, look fairly smooth and homogeneous, as can be seen from the Hubble 'tuning fork' diagram of near-infrared galaxy morphology. Image mosaic by S. Van Dyk (IPAC).

  17. Cluster growth kinetics

    International Nuclear Information System (INIS)

    Dubovik, V.M.; Gal'perin, A.G.; Rikhvitskij, V.S.; Lushnikov, A.A.

    2000-01-01

    Processes of some traffic blocking coming into existence are considered as probabilistic ones. We study analytic solutions for models for the dynamics of both cluster growth and cluster growth with fragmentation in the systems of finite number of objects. Assuming rates constancy of both coalescence and fragmentation, the models under consideration are linear on the probability functions

  18. Alpha clustering in nuclei

    International Nuclear Information System (INIS)

    Hodgson, P.E.

    1990-01-01

    The effects of nucleon clustering in nuclei are described, with reference to both nuclear structure and nuclear reactions, and the advantages of using the cluster formalism to describe a range of phenomena are discussed. It is shown that bound and scattering alpha-particle states can be described in a unified way using an energy-dependent alpha-nucleus potential. (author)

  19. Can Single-Reference Coupled Cluster Theory Describe Static Correlation?

    Science.gov (United States)

    Bulik, Ireneusz W; Henderson, Thomas M; Scuseria, Gustavo E

    2015-07-14

    While restricted single-reference coupled cluster theory truncated to singles and doubles (CCSD) provides very accurate results for weakly correlated systems, it usually fails in the presence of static or strong correlation. This failure is generally attributed to the qualitative breakdown of the reference, and can accordingly be corrected by using a multideterminant reference, including higher-body cluster operators in the ansatz, or allowing symmetry breaking in the reference. None of these solutions are ideal; multireference coupled cluster is not black box, including higher-body cluster operators is computationally demanding, and allowing symmetry breaking leads to the loss of good quantum numbers. It has long been recognized that quasidegeneracies can instead be treated by modifying the coupled cluster ansatz. The recently introduced pair coupled cluster doubles (pCCD) approach is one such example which avoids catastrophic failures and accurately models strong correlations in a symmetry-adapted framework. Here, we generalize pCCD to a singlet-paired coupled cluster model (CCD0) intermediate between coupled cluster doubles and pCCD, yielding a method that possesses the invariances of the former and much of the stability of the latter. Moreover, CCD0 retains the full structure of coupled cluster theory, including a fermionic wave function, antisymmetric cluster amplitudes, and well-defined response equations and density matrices.

  20. Multiscaling and clustering of volatility

    Science.gov (United States)

    Pasquini, Michele; Serva, Maurizio

    1999-07-01

    The dynamics of prices in stock markets has been studied intensively both experimentally (data analysis) and theoretically (models). Nevertheless, while the distribution of returns of the most important indices is known to be a truncated Lévy, the behaviour of volatility correlations is still poorly understood. What is well known is that absolute returns have memory on a long time range, this phenomenon is known in financial literature as clustering of volatility. In this paper we show that volatility correlations are power laws with a non-unique scaling exponent. This kind of multiscale phenomenology is known to be relevant in fully developed turbulence and in disordered systems and it is pointed out here for the first time for a financial series. In our study we consider the New York Stock Exchange (NYSE) daily index, from January 1966 to June 1998, for a total of 8180 working days.

  1. Negotiating Cluster Boundaries

    DEFF Research Database (Denmark)

    Giacomin, Valeria

    2017-01-01

    Palm oil was introduced to Malay(si)a as an alternative to natural rubber, inheriting its cluster organizational structure. In the late 1960s, Malaysia became the world’s largest palm oil exporter. Based on archival material from British colonial institutions and agency houses, this paper focuses...... on the governance dynamics that drove institutional change within this cluster during decolonization. The analysis presents three main findings: (i) cluster boundaries are defined by continuous tug-of-war style negotiations between public and private actors; (ii) this interaction produces institutional change...... within the cluster, in the form of cumulative ‘institutional rounds’ – the correction or disruption of existing institutions or the creation of new ones; and (iii) this process leads to a broader inclusion of local actors in the original cluster configuration. The paper challenges the prevalent argument...

  2. 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.

  3. Fourier Series Optimization Opportunity

    Science.gov (United States)

    Winkel, Brian

    2008-01-01

    This note discusses the introduction of Fourier series as an immediate application of optimization of a function of more than one variable. Specifically, it is shown how the study of Fourier series can be motivated to enrich a multivariable calculus class. This is done through discovery learning and use of technology wherein students build the…

  4. Visualizing the Geometric Series.

    Science.gov (United States)

    Bennett, Albert B., Jr.

    1989-01-01

    Mathematical proofs often leave students unconvinced or without understanding of what has been proved, because they provide no visual-geometric representation. Presented are geometric models for the finite geometric series when r is a whole number, and the infinite geometric series when r is the reciprocal of a whole number. (MNS)

  5. Upper Gastrointestinal (GI) Series

    Science.gov (United States)

    ... standard barium upper GI series, which uses only barium a double-contrast upper GI series, which uses both air and ... evenly coat your upper GI tract with the barium. If you are having a double-contrast study, you will swallow gas-forming crystals that ...

  6. SERI Wind Energy Program

    Energy Technology Data Exchange (ETDEWEB)

    Noun, R. J.

    1983-06-01

    The SERI Wind Energy Program manages the areas or innovative research, wind systems analysis, and environmental compatibility for the U.S. Department of Energy. Since 1978, SERI wind program staff have conducted in-house aerodynamic and engineering analyses of novel concepts for wind energy conversion and have managed over 20 subcontracts to determine technical feasibility; the most promising of these concepts is the passive blade cyclic pitch control project. In the area of systems analysis, the SERI program has analyzed the impact of intermittent generation on the reliability of electric utility systems using standard utility planning models. SERI has also conducted methodology assessments. Environmental issues related to television interference and acoustic noise from large wind turbines have been addressed. SERI has identified the causes, effects, and potential control of acoustic noise emissions from large wind turbines.

  7. Herd Clustering: A synergistic data clustering approach using collective intelligence

    KAUST Repository

    Wong, Kachun; Peng, Chengbin; Li, Yue; Chan, Takming

    2014-01-01

    , this principle is used to develop a new clustering algorithm. Inspired by herd behavior, the clustering method is a synergistic approach using collective intelligence called Herd Clustering (HC). The novel part is laid in its first stage where data instances

  8. Self-similar gravitational clustering

    International Nuclear Information System (INIS)

    Efstathiou, G.; Fall, S.M.; Hogan, C.

    1979-01-01

    The evolution of gravitational clustering is considered and several new scaling relations are derived for the multiplicity function. These include generalizations of the Press-Schechter theory to different densities and cosmological parameters. The theory is then tested against multiplicity function and correlation function estimates for a series of 1000-body experiments. The results are consistent with the theory and show some dependence on initial conditions and cosmological density parameter. The statistical significance of the results, however, is fairly low because of several small number effects in the experiments. There is no evidence for a non-linear bootstrap effect or a dependence of the multiplicity function on the internal dynamics of condensed groups. Empirical estimates of the multiplicity function by Gott and Turner have a feature near the characteristic luminosity predicted by the theory. The scaling relations allow the inference from estimates of the galaxy luminosity function that galaxies must have suffered considerable dissipation if they originally formed from a self-similar hierarchy. A method is also developed for relating the multiplicity function to similar measures of clustering, such as those of Bhavsar, for the distribution of galaxies on the sky. These are shown to depend on the luminosity function in a complicated way. (author)

  9. Gaussian mixture clustering and imputation of microarray data.

    Science.gov (United States)

    Ouyang, Ming; Welsh, William J; Georgopoulos, Panos

    2004-04-12

    In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.

  10. On the cluster propagator in quantum field theory

    International Nuclear Information System (INIS)

    Mogilevskij, O.A.

    1983-01-01

    The problem is discussed whether it is possible to describe the multiple production processes within the framework of nonlocal quantum field theory. The interaction between the cluster field and the field of scalar particles is introduced. By means of summing up a definite class of Feynman diagrams the cluster propagator with the decreasing imaginary part containing the information about the hadron mass spectrum is obtained

  11. Multiple Time Series Ising Model for Financial Market Simulations

    International Nuclear Information System (INIS)

    Takaishi, Tetsuya

    2015-01-01

    In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

  12. Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture

    Science.gov (United States)

    Sanfilippo, Antonio [Richland, WA; Calapristi, Augustin J [West Richland, WA; Crow, Vernon L [Richland, WA; Hetzler, Elizabeth G [Kennewick, WA; Turner, Alan E [Kennewick, WA

    2009-12-22

    Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

  13. Cluster-cluster correlations and constraints on the correlation hierarchy

    Science.gov (United States)

    Hamilton, A. J. S.; Gott, J. R., III

    1988-01-01

    The hypothesis that galaxies cluster around clusters at least as strongly as they cluster around galaxies imposes constraints on the hierarchy of correlation amplitudes in hierachical clustering models. The distributions which saturate these constraints are the Rayleigh-Levy random walk fractals proposed by Mandelbrot; for these fractal distributions cluster-cluster correlations are all identically equal to galaxy-galaxy correlations. If correlation amplitudes exceed the constraints, as is observed, then cluster-cluster correlations must exceed galaxy-galaxy correlations, as is observed.

  14. Formation of stable products from cluster-cluster collisions

    International Nuclear Information System (INIS)

    Alamanova, Denitsa; Grigoryan, Valeri G; Springborg, Michael

    2007-01-01

    The formation of stable products from copper cluster-cluster collisions is investigated by using classical molecular-dynamics simulations in combination with an embedded-atom potential. The dependence of the product clusters on impact energy, relative orientation of the clusters, and size of the clusters is studied. The structures and total energies of the product clusters are analysed and compared with those of the colliding clusters before impact. These results, together with the internal temperature, are used in obtaining an increased understanding of cluster fusion processes

  15. SASP - Symposium on atomic, cluster and surface physics `94

    Energy Technology Data Exchange (ETDEWEB)

    Maerk, T D; Schrittwieser, R; Smith, D

    1994-12-31

    This international symposium (Founding Chairman: W. Lindinger, Innsbruck) is one in a continuing biennial series of conferences which seeks to promote the growth of scientific knowledge and its effective exchange among scientists in the field of atomic, molecular, cluster and surface physics and related areas. The symposium deals in particular with interactions between ions, electrons, photons, atoms, molecules, and clusters and their interactions with surfaces. (author).

  16. Divergent Perturbation Series

    International Nuclear Information System (INIS)

    Suslov, I.M.

    2005-01-01

    Various perturbation series are factorially divergent. The behavior of their high-order terms can be determined by Lipatov's method, which involves the use of instanton configurations of appropriate functional integrals. When the Lipatov asymptotic form is known and several lowest order terms of the perturbation series are found by direct calculation of diagrams, one can gain insight into the behavior of the remaining terms of the series, which can be resummed to solve various strong-coupling problems in a certain approximation. This approach is demonstrated by determining the Gell-Mann-Low functions in φ 4 theory, QED, and QCD with arbitrary coupling constants. An overview of the mathematical theory of divergent series is presented, and interpretation of perturbation series is discussed. Explicit derivations of the Lipatov asymptotic form are presented for some basic problems in theoretical physics. A solution is proposed to the problem of renormalon contributions, which hampered progress in this field in the late 1970s. Practical perturbation-series summation schemes are described both for a coupling constant of order unity and in the strong-coupling limit. An interpretation of the Borel integral is given for 'non-Borel-summable' series. Higher order corrections to the Lipatov asymptotic form are discussed

  17. Tune Your Brown Clustering, Please

    DEFF Research Database (Denmark)

    Derczynski, Leon; Chester, Sean; Bøgh, Kenneth Sejdenfaden

    2015-01-01

    Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly...

  18. Cluster Management Institutionalization

    DEFF Research Database (Denmark)

    Normann, Leo; Agger Nielsen, Jeppe

    2015-01-01

    of how it was legitimized as a “ready-to-use” management model. Further, our account reveals how cluster management translated into considerably different local variants as it travelled into specific organizations. However, these processes have not occurred sequentially with cluster management first...... legitimized at the field level, then spread, and finally translated into action in the adopting organizations. Instead, we observed entangled field and organizational-level processes. Accordingly, we argue that cluster management institutionalization is most readily understood by simultaneously investigating...

  19. The concept of cluster

    DEFF Research Database (Denmark)

    Laursen, Lea Louise Holst; Møller, Jørgen

    2013-01-01

    villages in order to secure their future. This paper will address the concept of cluster-villages as a possible approach to strengthen the conditions of contemporary Danish villages. Cluster-villages is a concept that gather a number of villages in a network-structure where the villages both work together...... to forskellige positioner ser vi en ny mulighed for landsbyudvikling, som vi kalder Clustervillages. In order to investigate the potentials and possibilities of the cluster-village concept the paper will seek to unfold the concept strategically; looking into the benefits of such concept. Further, the paper seeks...

  20. Raspberry Pi super cluster

    CERN Document Server

    Dennis, Andrew K

    2013-01-01

    This book follows a step-by-step, tutorial-based approach which will teach you how to develop your own super cluster using Raspberry Pi computers quickly and efficiently.Raspberry Pi Super Cluster is an introductory guide for those interested in experimenting with parallel computing at home. Aimed at Raspberry Pi enthusiasts, this book is a primer for getting your first cluster up and running.Basic knowledge of C or Java would be helpful but no prior knowledge of parallel computing is necessary.

  1. Introduction to cluster dynamics

    CERN Document Server

    Reinhard, Paul-Gerhard

    2008-01-01

    Clusters as mesoscopic particles represent an intermediate state of matter between single atoms and solid material. The tendency to miniaturise technical objects requires knowledge about systems which contain a ""small"" number of atoms or molecules only. This is all the more true for dynamical aspects, particularly in relation to the qick development of laser technology and femtosecond spectroscopy. Here, for the first time is a highly qualitative introduction to cluster physics. With its emphasis on cluster dynamics, this will be vital to everyone involved in this interdisciplinary subje

  2. Contextualizing the Cluster

    DEFF Research Database (Denmark)

    Giacomin, Valeria

    This dissertation examines the case of the palm oil cluster in Malaysia and Indonesia, today one of the largest agricultural clusters in the world. My analysis focuses on the evolution of the cluster from the 1880s to the 1970s in order to understand how it helped these two countries to integrate...... into the global economy in both colonial and post-colonial times. The study is based on empirical material drawn from five UK archives and background research using secondary sources, interviews, and archive visits to Malaysia and Singapore. The dissertation comprises three articles, each discussing a major under...

  3. Atomic cluster collisions

    Science.gov (United States)

    Korol, Andrey V.; Solov'yov, Andrey

    2013-01-01

    Atomic cluster collisions are a field of rapidly emerging research interest by both experimentalists and theorists. The international symposium on atomic cluster collisions (ISSAC) is the premier forum to present cutting-edge research in this field. It was established in 2003 and the most recent conference was held in Berlin, Germany in July of 2011. This Topical Issue presents original research results from some of the participants, who attended this conference. This issues specifically focuses on two research areas, namely Clusters and Fullerenes in External Fields and Nanoscale Insights in Radiation Biodamage.

  4. Analysis of the project synthesis goal cluster orientation and inquiry emphasis of elementary science textbooks

    Science.gov (United States)

    Staver, John R.; Bay, Mary

    The purpose of this descriptive study was to examine selected units of commonly used elementary science texts, using the Project Synthesis goal clusters as a framework for part of the examination. An inquiry classification scheme was used for the remaining segment. Four questions were answered: (1) To what extent do elementary science textbooks focus on each Project Synthesis goal cluster? (2) In which part of the text is such information found? (3) To what extent are the activities and experiments merely verifications of information already introduced in the text? (4) If inquiry is present in an activity, then what is the level of such inquiry?Eleven science textbook series, which comprise approximately 90 percent of the national market, were selected for analysis. Two units, one primary (K-3) and one intermediate (4-6), were selected for analysis by first identifying units common to most series, then randomly selecting one primary and one intermediate unit for analysis.Each randomly selected unit was carefully read, using the sentence as the unit of analysis. Each declarative and interrogative sentence in the body of the text was classified as: (1) academic; (2) personal; (3) career; or (4) societal in its focus. Each illustration, except those used in evaluation items, was similarly classified. Each activity/experiment and each miscellaneous sentence in end-of-chapter segments labelled review, summary, evaluation, etc., were similarly classified. Finally, each activity/experiment, as a whole, was categorized according to a four-category inquiry scheme (confirmation, structured inquiry, guided inquiry, open inquiry).In general, results of the analysis are: (1) most text prose focuses on academic science; (2) most remaining text prose focuses on the personal goal cluster; (3) the career and societal goal clusters receive only minor attention; (4) text illustrations exhibit a pattern similar to text prose; (5) text activities/experiments are academic in orientation

  5. Cluster analysis of word frequency dynamics

    Science.gov (United States)

    Maslennikova, Yu S.; Bochkarev, V. V.; Belashova, I. A.

    2015-01-01

    This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations.

  6. Cluster analysis of word frequency dynamics

    International Nuclear Information System (INIS)

    Maslennikova, Yu S; Bochkarev, V V; Belashova, I A

    2015-01-01

    This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations

  7. Massive open star clusters using the VVV survey. II. Discovery of six clusters with Wolf-Rayet stars

    Science.gov (United States)

    Chené, A.-N.; Borissova, J.; Bonatto, C.; Majaess, D. J.; Baume, G.; Clarke, J. R. A.; Kurtev, R.; Schnurr, O.; Bouret, J.-C.; Catelan, M.; Emerson, J. P.; Feinstein, C.; Geisler, D.; de Grijs, R.; Hervé, A.; Ivanov, V. D.; Kumar, M. S. N.; Lucas, P.; Mahy, L.; Martins, F.; Mauro, F.; Minniti, D.; Moni Bidin, C.

    2013-01-01

    Context. The ESO Public Survey "VISTA Variables in the Vía Láctea" (VVV) provides deep multi-epoch infrared observations for an unprecedented 562 sq. degrees of the Galactic bulge, and adjacent regions of the disk. Nearly 150 new open clusters and cluster candidates have been discovered in this survey. Aims: This is the second in a series of papers about young, massive open clusters observed using the VVV survey. We present the first study of six recently discovered clusters. These clusters contain at least one newly discovered Wolf-Rayet (WR) star. Methods: Following the methodology presented in the first paper of the series, wide-field, deep JHKs VVV observations, combined with new infrared spectroscopy, are employed to constrain fundamental parameters for a subset of clusters. Results: We find that the six studied stellar groups are real young (2-7 Myr) and massive (between 0.8 and 2.2 × 103 M⊙) clusters. They are highly obscured (AV ~ 5-24 mag) and compact (1-2 pc). In addition to WR stars, two of the six clusters also contain at least one red supergiant star, and one of these two clusters also contains a blue supergiant. We claim the discovery of 8 new WR stars, and 3 stars showing WR-like emission lines which could be classified WR or OIf. Preliminary analysis provides initial masses of ~30-50 M⊙ for the WR stars. Finally, we discuss the spiral structure of the Galaxy using the six new clusters as tracers, together with the previously studied VVV clusters. Based on observations with ISAAC, VLT, ESO (programme 087.D-0341A), New Technology Telescope at ESO's La Silla Observatory (programme 087.D-0490A) and with the Clay telescope at the Las Campanas Observatory (programme CN2011A-086). Also based on data from the VVV survey (programme 172.B-2002).

  8. Comparative analysis of clustering methods for gene expression time course data

    Directory of Open Access Journals (Sweden)

    Ivan G. Costa

    2004-01-01

    Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

  9. Fluorocarbon adsorption in hierarchical porous frameworks

    NARCIS (Netherlands)

    Motkuri, R.K.; Annapureddy, H.V.R.; Vijaykumar, M.; Schaef, H.T.; Martin, P.F.; McGrail, B.P.; Dang, L.X.; Krishna, R.; Thallapally, P.K.

    2014-01-01

    Metal-organic frameworks comprise an important class of solid-state materials and have potential for many emerging applications such as energy storage, separation, catalysis and bio-medical. Here we report the adsorption behaviour of a series of fluorocarbon derivatives on a set of microporous and

  10. Magnetic behaviour in metal-organic frameworks

    Indian Academy of Sciences (India)

    The article describes the synthesis, structure and magnetic investigations of a series of metal-organic framework compounds formed with Mn+2 and Ni+2 ions. The structures, determined using the single crystal X-ray diffraction, indicated that the structures possess two- and three-dimensional structures with magnetically ...

  11. Unsupervised active learning based on hierarchical graph-theoretic clustering.

    Science.gov (United States)

    Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

    2009-10-01

    Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

  12. Metal cluster compounds - chemistry and importance; clusters containing isolated main group element atoms, large metal cluster compounds, cluster fluxionality

    International Nuclear Information System (INIS)

    Walther, B.

    1988-01-01

    This part of the review on metal cluster compounds deals with clusters containing isolated main group element atoms, with high nuclearity clusters and metal cluster fluxionality. It will be obvious that main group element atoms strongly influence the geometry, stability and reactivity of the clusters. High nuclearity clusters are of interest in there own due to the diversity of the structures adopted, but their intermediate position between molecules and the metallic state makes them a fascinating research object too. These both sites of the metal cluster chemistry as well as the frequently observed ligand and core fluxionality are related to the cluster metal and surface analogy. (author)

  13. Analysing Stable Time Series

    National Research Council Canada - National Science Library

    Adler, Robert

    1997-01-01

    We describe how to take a stable, ARMA, time series through the various stages of model identification, parameter estimation, and diagnostic checking, and accompany the discussion with a goodly number...

  14. Multivariate Time Series Search

    Data.gov (United States)

    National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

  15. Historical Climatology Series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Historical Climatology Series (HCS) is a set of climate-related publications published by NOAA's National Climatic Data Center beginning in 1978. HCS is...

  16. Disentangling Porterian Clusters

    DEFF Research Database (Denmark)

    Jagtfelt, Tue

    , contested theory become so widely disseminated and applied as a normative and prescriptive strategy for economic development? The dissertation traces the introduction of the cluster notion into the EU’s Lisbon Strategy and demonstrates how its inclusion originates from Porter’s colleagues: Professor Örjan...... to his membership on the Commission on Industrial Competitiveness, and that the cluster notion found in his influential book, Nations, represents a significant shift in his conception of cluster compared with his early conceptions. This shift, it is argued, is a deliberate attempt by Porter to create...... a paradigmatic textbook that follows Kuhn’s blueprint for scientific revolutions by instilling Nations with circular references and thus creating a local linguistic holism conceptualized through an encompassing notion of cluster. The dissertation concludes that the two research questions are philosophically...

  17. Remarks on stellar clusters

    International Nuclear Information System (INIS)

    Teller, E.

    1985-01-01

    In the following, a few simple remarks on the evolution and properties of stellar clusters will be collected. In particular, globular clusters will be considered. Though details of such clusters are often not known, a few questions can be clarified with the help of primitive arguments. These are:- why are spherical clusters spherical, why do they have high densities, why do they consist of approximately a million stars, how may a black hole of great mass form within them, may they be the origin of gamma-ray bursts, may their invisible remnants account for the missing mass of our galaxy. The available data do not warrant a detailed evaluation. However, it is remarkable that exceedingly simple models can shed some light on the questions enumerated above. (author)

  18. From collisions to clusters

    DEFF Research Database (Denmark)

    Loukonen, Ville; Bork, Nicolai; Vehkamaki, Hanna

    2014-01-01

    -principles molecular dynamics collision simulations of (sulphuric acid)1(water)0, 1 + (dimethylamine) → (sulphuric acid)1(dimethylamine)1(water)0, 1 cluster formation processes. The simulations indicate that the sticking factor in the collisions is unity: the interaction between the molecules is strong enough...... control. As a consequence, the clusters show very dynamic ion pair structure, which differs from both the static structure optimisation calculations and the equilibrium first-principles molecular dynamics simulations. In some of the simulation runs, water mediates the proton transfer by acting as a proton...... to overcome the possible initial non-optimal collision orientations. No post-collisional cluster break up is observed. The reasons for the efficient clustering are (i) the proton transfer reaction which takes place in each of the collision simulations and (ii) the subsequent competition over the proton...

  19. Clustering of Emerging Flux

    Science.gov (United States)

    Ruzmaikin, A.

    1997-01-01

    Observations show that newly emerging flux tends to appear on the Solar surface at sites where there is flux already. This results in clustering of solar activity. Standard dynamo theories do not predict this effect.

  20. How Clusters Work

    Science.gov (United States)

    Technology innovation clusters are geographic concentrations of interconnected companies, universities, and other organizations with a focus on environmental technology. They play a key role in addressing the nation’s pressing environmental problems.

  1. Evolution of clustered storage

    CERN Multimedia

    CERN. Geneva; Van de Vyvre, Pierre

    2007-01-01

    The session actually featured two presentations: * Evolution of clustered storage by Lance Hukill, Quantum Corporation * ALICE DAQ - Usage of a Cluster-File System: Quantum StorNext by Pierre Vande Vyvre, CERN-PH the second one prepared at short notice by Pierre (thanks!) to present how the Quantum technologies are being used in the ALICE experiment. The abstract to Mr Hukill's follows. Clustered Storage is a technology that is driven by business and mission applications. The evolution of Clustered Storage solutions starts first at the alignment between End-users needs and Industry trends: * Push-and-Pull between managing for today versus planning for tomorrow * Breaking down the real business problems to the core applications * Commoditization of clients, servers, and target devices * Interchangeability, Interoperability, Remote Access, Centralized control * Oh, and yes, there is a budget and the "real world" to deal with This presentation will talk through these needs and trends, and then ask the question, ...

  2. Galaxy clusters and cosmology

    CERN Document Server

    White, S

    1994-01-01

    Galaxy clusters are the largest coherent objects in Universe. It has been known since 1933 that their dynamical properties require either a modification of the theory of gravity, or the presence of a dominant component of unseen material of unknown nature. Clusters still provide the best laboratories for studying the amount and distribution of this dark matter relative to the material which can be observed directly -- the galaxies themselves and the hot,X-ray-emitting gas which lies between them.Imaging and spectroscopy of clusters by satellite-borne X -ray telescopes has greatly improved our knowledge of the structure and composition of this intergalactic medium. The results permit a number of new approaches to some fundamental cosmological questions,but current indications from the data are contradictory. The observed irregularity of real clusters seems to imply recent formation epochs which would require a universe with approximately the critical density. On the other hand, the large baryon fraction observ...

  3. Applications of Clustering

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. Applications of Clustering. Biology – medical imaging, bioinformatics, ecology, phylogenies problems etc. Market research. Data Mining. Social Networks. Any problem measuring similarity/correlation. (dimensions represent different parameters)

  4. Clustering Game Behavior Data

    DEFF Research Database (Denmark)

    Bauckhage, C.; Drachen, Anders; Sifa, Rafet

    2015-01-01

    of the causes, the proliferation of behavioral data poses the problem of how to derive insights therefrom. Behavioral data sets can be large, time-dependent and high-dimensional. Clustering offers a way to explore such data and to discover patterns that can reduce the overall complexity of the data. Clustering...... and other techniques for player profiling and play style analysis have, therefore, become popular in the nascent field of game analytics. However, the proper use of clustering techniques requires expertise and an understanding of games is essential to evaluate results. With this paper, we address game data...... scientists and present a review and tutorial focusing on the application of clustering techniques to mine behavioral game data. Several algorithms are reviewed and examples of their application shown. Key topics such as feature normalization are discussed and open problems in the context of game analytics...

  5. Clustering on Membranes

    DEFF Research Database (Denmark)

    Johannes, Ludger; Pezeshkian, Weria; Ipsen, John H

    2018-01-01

    Clustering of extracellular ligands and proteins on the plasma membrane is required to perform specific cellular functions, such as signaling and endocytosis. Attractive forces that originate in perturbations of the membrane's physical properties contribute to this clustering, in addition to direct...... protein-protein interactions. However, these membrane-mediated forces have not all been equally considered, despite their importance. In this review, we describe how line tension, lipid depletion, and membrane curvature contribute to membrane-mediated clustering. Additional attractive forces that arise...... from protein-induced perturbation of a membrane's fluctuations are also described. This review aims to provide a survey of the current understanding of membrane-mediated clustering and how this supports precise biological functions....

  6. Air void clustering.

    Science.gov (United States)

    2015-06-01

    Air void clustering around coarse aggregate in concrete has been identified as a potential source of : low strengths in concrete mixes by several Departments of Transportation around the country. Research was : carried out to (1) develop a quantitati...

  7. What semi-inclusive data say about clusters

    International Nuclear Information System (INIS)

    Arneodo, A.; Plaut, G.

    1976-01-01

    A global analysis of inclusive high-energy multi-particle production data in the cluster model framework is extended to semi-inclusive data. The cluster model embodies leading particle effects and kinematical constraints, which are shown to be of great importance. It appears that models with light clusters decaying on average into approximately equal to 2 charged particles, with a rapidity width delta approximately equal to 0.6 - 0.7 and a distribution much narrower than a Poisson-type, allow one to fit in a nice way both inclusive and semi-inclusive data. It is pointed out that the most constraining semi-inclusive data are those regarding longitudinal correlations, which definitely exclude heavy cluster models, whereas the data on zone characteristics only bear out that a non-negligible percentage of clusters have to carry an electric charge. (Auth.)

  8. Search for Formation Criteria for Globular Cluster Systems

    Science.gov (United States)

    Nuritdinov, S. N.; Mirtadjieva, K. T.; Tadjibaev, I. U.

    2005-01-01

    Star cluster formation is a major mode of star formation in the extreme conditions of interacting galaxies and violent starbursts. By studying ages and metallicities of young metal-enhanced star clusters in mergers / merger remnants we can learn about the violent star formation history of these galaxies and eventually about galaxy formation and evolution. We will present a new set of evolutionary synthesis models of our GALEV code specially developed to account for the gaseous emission of presently forming star clusters and an advanced tool to compare large model grids with multi-color broad-band observations becoming presently available in large amounts. Such observations are an ecomonic way to determine the parameters of young star clusters as will be shown in the presentation. First results of newly-born clusters in mergers and starburst galaxies are presented and compared to the well-studied old globulars and interpreted in the framework of galaxy formation / evolution.

  9. Long time series

    DEFF Research Database (Denmark)

    Hisdal, H.; Holmqvist, E.; Hyvärinen, V.

    Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the......Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the...

  10. Speaker segmentation and clustering

    OpenAIRE

    Kotti, M; Moschou, V; Kotropoulos, C

    2008-01-01

    07.08.13 KB. Ok to add the accepted version to Spiral, Elsevier says ok whlile mandate not enforced. This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker...

  11. Fermion cluster algorithms

    International Nuclear Information System (INIS)

    Chandrasekharan, Shailesh

    2000-01-01

    Cluster algorithms have been recently used to eliminate sign problems that plague Monte-Carlo methods in a variety of systems. In particular such algorithms can also be used to solve sign problems associated with the permutation of fermion world lines. This solution leads to the possibility of designing fermion cluster algorithms in certain cases. Using the example of free non-relativistic fermions we discuss the ideas underlying the algorithm

  12. BUILDING e-CLUSTERS

    OpenAIRE

    Milan Davidovic

    2013-01-01

    E-clusters are strategic alliance in TIMES technology sector (Telecommunication, Information technology, Multimedia, Entertainment, Security) where products and processes are digitalized. They enable horizontal and vertical integration of small and medium companies and establish new added value e-chains. E-clusters also build supply chains based on cooperation relationship, innovation, organizational knowledge and compliance of intellectual properties. As an innovative approach for economic p...

  13. Clusters and exotic processes

    International Nuclear Information System (INIS)

    Schiffer, J.P.

    1975-01-01

    An attempt is made to present some data which may be construed as indicating that perhaps clusters play a role in high energy and exotic pion or kaon interactions with complex (A much greater than 16) nuclei. Also an attempt is made to summarize some very recent experimental work on pion interactions with nuclei which may or may not in the end support a picture in which clusters play an important role. (U.S.)

  14. Clustering stock market companies via chaotic map synchronization

    Science.gov (United States)

    Basalto, N.; Bellotti, R.; De Carlo, F.; Facchi, P.; Pascazio, S.

    2005-01-01

    A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series to the coupling strengths between maps. The simulation of a chaotic map dynamics gives rise to a natural partition of the data, as companies belonging to the same industrial branch are often grouped together. The identification of clusters of companies of a given stock market index can be exploited in the portfolio optimization strategies.

  15. Resonant heating of a cluster plasma by intense laser light

    International Nuclear Information System (INIS)

    Antonsen, Thomas M. Jr.; Taguchi, Toshihiro; Gupta, Ayush; Palastro, John; Milchberg, Howard M.

    2005-01-01

    Gases of atomic clusters are interaction media for laser pulse propagation with properties useful for applications such as extreme ultraviolet (EUV) and x-ray microscopy, harmonic generation, EUV lithography, and laser plasma acceleration. To understand cluster heating and expansion, a series of two- and three-dimensional electrostatic particle in cell simulations of the explosion of argon clusters of diameter in the range 20 nm-53 nm have been preformed. The studies show that heating is dominated by a nonlinear, resonant absorption process that gives rise to a size-dependent intensity threshold for strong absorption and that controls the dielectric properties of the cluster. Electrons are first accelerated out from the cluster and then driven back into it by the combined effects of the laser field and the electrostatic field produced by the laser-driven charge separation. Above the intensity threshold for strong heating there is a dramatic increase in the production of energetic particles and harmonic radiation. The dielectric properties of a gas of clusters are determined by the ensemble average cluster polarizability. Individual electrons contribute to the polarizability differently depending on whether they are in the core of the cluster or in the outer edge. Consequently, there can be large fluctuations in polarizability during the heating of a cluster

  16. Clustering recommenders in collaborative filtering using explicit trust information

    KAUST Repository

    Pitsilis, Georgios

    2011-01-01

    In this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like k-Means and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust could be exploited with AP for forming clusters of high quality. We conducted a series of evaluation tests using data from a real Recommender system Epinions.com from which we derived conclusions about the usefulness of trust information in forming clusters of Recommenders. Moreover, from our results we conclude that the potential advantages in using clustering can be enlarged by making use of the information that Social Networks can provide. © 2011 International Federation for Information Processing.

  17. Robust continuous clustering.

    Science.gov (United States)

    Shah, Sohil Atul; Koltun, Vladlen

    2017-09-12

    Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank.

  18. Inferring a Drive-Response Network from Time Series of Topological Measures in Complex Networks with Transfer Entropy

    Directory of Open Access Journals (Sweden)

    Xinbo Ai

    2014-11-01

    Full Text Available Topological measures are crucial to describe, classify and understand complex networks. Lots of measures are proposed to characterize specific features of specific networks, but the relationships among these measures remain unclear. Taking into account that pulling networks from different domains together for statistical analysis might provide incorrect conclusions, we conduct our investigation with data observed from the same network in the form of simultaneously measured time series. We synthesize a transfer entropy-based framework to quantify the relationships among topological measures, and then to provide a holistic scenario of these measures by inferring a drive-response network. Techniques from Symbolic Transfer Entropy, Effective Transfer Entropy, and Partial Transfer Entropy are synthesized to deal with challenges such as time series being non-stationary, finite sample effects and indirect effects. We resort to kernel density estimation to assess significance of the results based on surrogate data. The framework is applied to study 20 measures across 2779 records in the Technology Exchange Network, and the results are consistent with some existing knowledge. With the drive-response network, we evaluate the influence of each measure by calculating its strength, and cluster them into three classes, i.e., driving measures, responding measures and standalone measures, according to the network communities.

  19. Building a Continental Scale Land Cover Monitoring Framework for Australia

    Science.gov (United States)

    Thankappan, Medhavy; Lymburner, Leo; Tan, Peter; McIntyre, Alexis; Curnow, Steven; Lewis, Adam

    2012-04-01

    Land cover information is critical for national reporting and decision making in Australia. A review of information requirements for reporting on national environmental indicators identified the need for consistent land cover information to be compared against a baseline. A Dynamic Land Cover Dataset (DLCD) for Australia has been developed by Geoscience Australia and the Australian Bureau of Agriculture and Resource Economics and Sciences (ABARES) recently, to provide a comprehensive and consistent land cover information baseline to enable monitoring and reporting for sustainable farming practices, water resource management, soil erosion, and forests at national and regional scales. The DLCD was produced from the analysis of Enhanced Vegetation Index (EVI) data at 250-metre resolution derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period from 2000 to 2008. The EVI time series data for each pixel was modelled as 12 coefficients based on the statistical, phenological and seasonal characteristics. The time series were then clustered in coefficients spaces and labelled using ancillary information on vegetation and land use at the catchment scale. The accuracy of the DLCD was assessed using field survey data over 25,000 locations provided by vegetation and land management agencies in State and Territory jurisdictions, and by ABARES. The DLCD is seen as the first in a series of steps to build a framework for national land cover monitoring in Australia. A robust methodology to provide annual updates to the DLCD is currently being developed at Geoscience Australia. There is also a growing demand from the user community for land cover information at better spatial resolution than currently available through the DLCD. Global land cover mapping initiatives that rely on Earth observation data offer many opportunities for national and international programs to work in concert and deliver better outcomes by streamlining efforts on development and

  20. Determination of atomic cluster structure with cluster fusion algorithm

    DEFF Research Database (Denmark)

    Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.

    2005-01-01

    We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters.......We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters....