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.

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....

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.

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.

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

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...

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.

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.

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…

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

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.

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.

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

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.

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...

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

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

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

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 ...

A Differentiation Framework for Maritime Clusters: Comparisons across Europe

Directory of Open Access Journals (Sweden)

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.

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.

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.

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

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.

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.

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.

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]...

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.

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

Myofascial trigger points in cluster headache patients: a case series

Directory of Open Access Journals (Sweden)

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.

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.

[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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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....

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.

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.

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...

Validating clustering of molecular dynamics simulations using polymer models

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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

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...

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.

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)

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.

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...

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.

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/.

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...

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.

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.

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.

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.

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.

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.

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...

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

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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.

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

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.

Extracting biologically significant patterns from short time series gene expression data

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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/.

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

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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.

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.

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.

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.

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

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.

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

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.

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

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

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.

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...

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

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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.

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

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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.

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.

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.

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)

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...

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.

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

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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.

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.

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...

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.

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.

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.

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.

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

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

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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.

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.

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.

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.

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.

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...

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...

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.

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

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

[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.

[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.

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.

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.

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...

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

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

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.

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.

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.

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.

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.

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

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.

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.

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…

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.

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.

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.

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...

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)

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...

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.

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 ...

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

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

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.

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

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.

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.)

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.

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.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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

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

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.

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)

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

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

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.

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

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.

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

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.

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.

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.

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)

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.

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

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.

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).

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...

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.

Riemannian multi-manifold modeling and clustering in brain networks

Science.gov (United States)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

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

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)

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,

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...

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)...

A missed Fe-S cluster handoff causes a metabolic shakeup.

Science.gov (United States)

Berteau, Olivier

2018-05-25

The general framework of pathways by which iron-sulfur (Fe-S) clusters are assembled in cells is well-known, but the cellular consequences of disruptions to that framework are not fully understood. Crooks et al. report a novel cellular system that creates an acute Fe-S cluster deficiency, using mutants of ISCU, the main scaffold protein for Fe-S cluster assembly. Surprisingly, the resultant metabolic reprogramming leads to the accumulation of lipid droplets, a situation encountered in many poorly understood pathological conditions, highlighting unanticipated links between Fe-S assembly machinery and human disease. © 2018 Berteau.

Electronic and magnetic properties of small rhodium clusters

Energy Technology Data Exchange (ETDEWEB)

Soon, Yee Yeen; Yoon, Tiem Leong [School of Physics, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia); Lim, Thong Leng [Faculty of Engineering and Technology, Multimedia University, Melaka Campus, 75450 Melaka (Malaysia)

2015-04-24

We report a theoretical study of the electronic and magnetic properties of rhodium-atomic clusters. The lowest energy structures at the semi-empirical level of rhodium clusters are first obtained from a novel global-minimum search algorithm, known as PTMBHGA, where Gupta potential is used to describe the atomic interaction among the rhodium atoms. The structures are then re-optimized at the density functional theory (DFT) level with exchange-correlation energy approximated by Perdew-Burke-Ernzerhof generalized gradient approximation. For the purpose of calculating the magnetic moment of a given cluster, we calculate the optimized structure as a function of the spin multiplicity within the DFT framework. The resultant magnetic moments with the lowest energies so obtained allow us to work out the magnetic moment as a function of cluster size. Rhodium atomic clusters are found to display a unique variation in the magnetic moment as the cluster size varies. However, Rh{sub 4} and Rh{sub 6} are found to be nonmagnetic. Electronic structures of the magnetic ground-state structures are also investigated within the DFT framework. The results are compared against those based on different theoretical approaches available in the literature.

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...

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 ...

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.

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.

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)

Research on retailer data clustering algorithm based on Spark

Science.gov (United States)

Huang, Qiuman; Zhou, Feng

2017-03-01

Big data analysis is a hot topic in the IT field now. Spark is a high-reliability and high-performance distributed parallel computing framework for big data sets. K-means algorithm is one of the classical partition methods in clustering algorithm. In this paper, we study the k-means clustering algorithm on Spark. Firstly, the principle of the algorithm is analyzed, and then the clustering analysis is carried out on the supermarket customers through the experiment to find out the different shopping patterns. At the same time, this paper proposes the parallelization of k-means algorithm and the distributed computing framework of Spark, and gives the concrete design scheme and implementation scheme. This paper uses the two-year sales data of a supermarket to validate the proposed clustering algorithm and achieve the goal of subdividing customers, and then analyze the clustering results to help enterprises to take different marketing strategies for different customer groups to improve sales performance.

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

An Online Multisensor Data Fusion Framework for Radar Emitter Classification

Directory of Open Access Journals (Sweden)

Dongqing Zhou

2016-01-01

Full Text Available Radar emitter classification is a special application of data clustering for classifying unknown radar emitters in airborne electronic support system. In this paper, a novel online multisensor data fusion framework is proposed for radar emitter classification under the background of network centric warfare. The framework is composed of local processing and multisensor fusion processing, from which the rough and precise classification results are obtained, respectively. What is more, the proposed algorithm does not need prior knowledge and training process; it can dynamically update the number of the clusters and the cluster centers when new pulses arrive. At last, the experimental results show that the proposed framework is an efficacious way to solve radar emitter classification problem in networked warfare.

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).

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).

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...

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...

Long-term memory and volatility clustering in high-frequency price changes

Science.gov (United States)

oh, Gabjin; Kim, Seunghwan; Eom, Cheoljun

2008-02-01

We studied the long-term memory in diverse stock market indices and foreign exchange rates using Detrended Fluctuation Analysis (DFA). For all high-frequency market data studied, no significant long-term memory property was detected in the return series, while a strong long-term memory property was found in the volatility time series. The possible causes of the long-term memory property were investigated using the return data filtered by the AR(1) model, reflecting the short-term memory property, the GARCH(1,1) model, reflecting the volatility clustering property, and the FIGARCH model, reflecting the long-term memory property of the volatility time series. The memory effect in the AR(1) filtered return and volatility time series remained unchanged, while the long-term memory property diminished significantly in the volatility series of the GARCH(1,1) filtered data. Notably, there is no long-term memory property, when we eliminate the long-term memory property of volatility by the FIGARCH model. For all data used, although the Hurst exponents of the volatility time series changed considerably over time, those of the time series with the volatility clustering effect removed diminish significantly. Our results imply that the long-term memory property of the volatility time series can be attributed to the volatility clustering observed in the financial time series.

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...

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

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...

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

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Data Analysis and Visualization (IDAV) and the Department of Computer Science, University of California, Davis, One Shields Avenue, Davis CA 95616, USA,; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,' ' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA; Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA; Life Sciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA,; Computer Science Division,University of California, Berkeley, CA, USA,; Computer Science Department, University of California, Irvine, CA, USA,; All authors are with the Berkeley Drosophila Transcription Network Project, Lawrence Berkeley National Laboratory,; Rubel, Oliver; Weber, Gunther H.; Huang, Min-Yu; Bethel, E. Wes; Biggin, Mark D.; Fowlkes, Charless C.; Hendriks, Cris L. Luengo; Keranen, Soile V. E.; Eisen, Michael B.; Knowles, David W.; Malik, Jitendra; Hagen, Hans; Hamann, Bernd

2008-05-12

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

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.

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

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.

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…

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.

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...

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...

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.

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.

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...

Using time series structural characteristics to analyze grain prices in food insecure countries

Science.gov (United States)

Davenport, Frank; Funk, Chris

2015-01-01

Two components of food security monitoring are accurate forecasts of local grain prices and the ability to identify unusual price behavior. We evaluated a method that can both facilitate forecasts of cross-country grain price data and identify dissimilarities in price behavior across multiple markets. This method, characteristic based clustering (CBC), identifies similarities in multiple time series based on structural characteristics in the data. Here, we conducted a simulation experiment to determine if CBC can be used to improve the accuracy of maize price forecasts. We then compared forecast accuracies among clustered and non-clustered price series over a rolling time horizon. We found that the accuracy of forecasts on clusters of time series were equal to or worse than forecasts based on individual time series. However, in the following experiment we found that CBC was still useful for price analysis. We used the clusters to explore the similarity of price behavior among Kenyan maize markets. We found that price behavior in the isolated markets of Mandera and Marsabit has become increasingly dissimilar from markets in other Kenyan cities, and that these dissimilarities could not be explained solely by geographic distance. The structural isolation of Mandera and Marsabit that we find in this paper is supported by field studies on food security and market integration in Kenya. Our results suggest that a market with a unique price series (as measured by structural characteristics that differ from neighboring markets) may lack market integration and food security.

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.

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.

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...

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.

Evaluation of clustering statistics with N-body simulations

International Nuclear Information System (INIS)

Quinn, T.R.

1986-01-01

Two series of N-body simulations are used to determine the effectiveness of various clustering statistics in revealing initial conditions from evolved models. All the simulations contained 16384 particles and were integrated with the PPPM code. One series is a family of models with power at only one wavelength. The family contains five models with the wavelength of the power separated by factors of √2. The second series is a family of all equal power combinations of two wavelengths taken from the first series. The clustering statistics examined are the two point correlation function, the multiplicity function, the nearest neighbor distribution, the void probability distribution, the distribution of counts in cells, and the peculiar velocity distribution. It is found that the covariance function, the nearest neighbor distribution, and the void probability distribution are relatively insensitive to the initial conditions. The distribution of counts in cells show a little more sensitivity, but the multiplicity function is the best of the statistics considered for revealing the initial conditions

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.

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

A non-voxel-based broad-beam (NVBB) framework for IMRT treatment planning.

Science.gov (United States)

Lu, Weiguo

2010-12-07

We present a novel framework that enables very large scale intensity-modulated radiation therapy (IMRT) planning in limited computation resources with improvements in cost, plan quality and planning throughput. Current IMRT optimization uses a voxel-based beamlet superposition (VBS) framework that requires pre-calculation and storage of a large amount of beamlet data, resulting in large temporal and spatial complexity. We developed a non-voxel-based broad-beam (NVBB) framework for IMRT capable of direct treatment parameter optimization (DTPO). In this framework, both objective function and derivative are evaluated based on the continuous viewpoint, abandoning 'voxel' and 'beamlet' representations. Thus pre-calculation and storage of beamlets are no longer needed. The NVBB framework has linear complexities (O(N(3))) in both space and time. The low memory, full computation and data parallelization nature of the framework render its efficient implementation on the graphic processing unit (GPU). We implemented the NVBB framework and incorporated it with the TomoTherapy treatment planning system (TPS). The new TPS runs on a single workstation with one GPU card (NVBB-GPU). Extensive verification/validation tests were performed in house and via third parties. Benchmarks on dose accuracy, plan quality and throughput were compared with the commercial TomoTherapy TPS that is based on the VBS framework and uses a computer cluster with 14 nodes (VBS-cluster). For all tests, the dose accuracy of these two TPSs is comparable (within 1%). Plan qualities were comparable with no clinically significant difference for most cases except that superior target uniformity was seen in the NVBB-GPU for some cases. However, the planning time using the NVBB-GPU was reduced many folds over the VBS-cluster. In conclusion, we developed a novel NVBB framework for IMRT optimization. The continuous viewpoint and DTPO nature of the algorithm eliminate the need for beamlets and lead to better plan

A non-voxel-based broad-beam (NVBB) framework for IMRT treatment planning

Energy Technology Data Exchange (ETDEWEB)

Lu Weiguo, E-mail: wlu@tomotherapy.co [TomoTherapy Inc., 1240 Deming Way, Madison, WI 53717 (United States)

2010-12-07

We present a novel framework that enables very large scale intensity-modulated radiation therapy (IMRT) planning in limited computation resources with improvements in cost, plan quality and planning throughput. Current IMRT optimization uses a voxel-based beamlet superposition (VBS) framework that requires pre-calculation and storage of a large amount of beamlet data, resulting in large temporal and spatial complexity. We developed a non-voxel-based broad-beam (NVBB) framework for IMRT capable of direct treatment parameter optimization (DTPO). In this framework, both objective function and derivative are evaluated based on the continuous viewpoint, abandoning 'voxel' and 'beamlet' representations. Thus pre-calculation and storage of beamlets are no longer needed. The NVBB framework has linear complexities (O(N{sup 3})) in both space and time. The low memory, full computation and data parallelization nature of the framework render its efficient implementation on the graphic processing unit (GPU). We implemented the NVBB framework and incorporated it with the TomoTherapy treatment planning system (TPS). The new TPS runs on a single workstation with one GPU card (NVBB-GPU). Extensive verification/validation tests were performed in house and via third parties. Benchmarks on dose accuracy, plan quality and throughput were compared with the commercial TomoTherapy TPS that is based on the VBS framework and uses a computer cluster with 14 nodes (VBS-cluster). For all tests, the dose accuracy of these two TPSs is comparable (within 1%). Plan qualities were comparable with no clinically significant difference for most cases except that superior target uniformity was seen in the NVBB-GPU for some cases. However, the planning time using the NVBB-GPU was reduced many folds over the VBS-cluster. In conclusion, we developed a novel NVBB framework for IMRT optimization. The continuous viewpoint and DTPO nature of the algorithm eliminate the need for beamlets

Lopsidedness of Self-consistent Galaxies Caused by the External Field Effect of Clusters

Energy Technology Data Exchange (ETDEWEB)

Wu, Xufen [CAS Key Laboratory for Research in Galaxies and Cosmology, Department of Astronomy, University of Science and Technology of China, Hefei, 230026 (China); Wang, Yougang [Key Laboratory of Computational Astrophysics, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100012 (China); Feix, Martin [CNRS, UMR 7095 and UPMC, Institut d’Astrophysique de Paris, 98 bis Boulevard Arago, F-75014 Paris (France); Zhao, HongSheng, E-mail: xufenwu@ustc.edu.cn [School of Physics and Astronomy, University of St Andrews, North Haugh, Fife, KY16 9SS (United Kingdom)

2017-08-01

Adopting Schwarzschild’s orbit-superposition technique, we construct a series of self-consistent galaxy models, embedded in the external field of galaxy clusters in the framework of Milgrom’s MOdified Newtonian Dynamics (MOND). These models represent relatively massive ellipticals with a Hernquist radial profile at various distances from the cluster center. Using N -body simulations, we perform a first analysis of these models and their evolution. We find that self-gravitating axisymmetric density models, even under a weak external field, lose their symmetry by instability and generally evolve to triaxial configurations. A kinematic analysis suggests that the instability originates from both box and nonclassified orbits with low angular momentum. We also consider a self-consistent isolated system that is then placed in a strong external field and allowed to evolve freely. This model, just like the corresponding equilibrium model in the same external field, eventually settles to a triaxial equilibrium as well, but has a higher velocity radial anisotropy and is rounder. The presence of an external field in the MOND universe generically predicts some lopsidedness of galaxy shapes.

Lopsidedness of Self-consistent Galaxies Caused by the External Field Effect of Clusters

Science.gov (United States)

Wu, Xufen; Wang, Yougang; Feix, Martin; Zhao, HongSheng

2017-08-01

Adopting Schwarzschild’s orbit-superposition technique, we construct a series of self-consistent galaxy models, embedded in the external field of galaxy clusters in the framework of Milgrom’s MOdified Newtonian Dynamics (MOND). These models represent relatively massive ellipticals with a Hernquist radial profile at various distances from the cluster center. Using N-body simulations, we perform a first analysis of these models and their evolution. We find that self-gravitating axisymmetric density models, even under a weak external field, lose their symmetry by instability and generally evolve to triaxial configurations. A kinematic analysis suggests that the instability originates from both box and nonclassified orbits with low angular momentum. We also consider a self-consistent isolated system that is then placed in a strong external field and allowed to evolve freely. This model, just like the corresponding equilibrium model in the same external field, eventually settles to a triaxial equilibrium as well, but has a higher velocity radial anisotropy and is rounder. The presence of an external field in the MOND universe generically predicts some lopsidedness of galaxy shapes.

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.

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…

Link-Prediction Enhanced Consensus Clustering for Complex Networks (Open Access)

Science.gov (United States)

2016-05-20

RESEARCH ARTICLE Link-Prediction Enhanced Consensus Clustering for Complex Networks Matthew Burgess1*, Eytan Adar1,2, Michael Cafarella1 1Computer...consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that...types of complex networks exhibit community structure: groups of highly connected nodes. Communities or clusters often reflect nodes that share similar

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…

α clustering with a hollow structure: Geometrical structure of α clusters from platonic solids to fullerene shape

Science.gov (United States)

Tohsaki, Akihiro; Itagaki, Naoyuki

2018-01-01

We study α -cluster structure based on the geometric configurations with a microscopic framework, which takes full account of the Pauli principle, and which also employs an effective internucleon force including finite-range three-body terms suitable for microscopic α -cluster models. Here, special attention is focused upon the α clustering with a hollow structure; all the α clusters are put on the surface of a sphere. All the platonic solids (five regular polyhedra) and the fullerene-shaped polyhedron coming from icosahedral structure are considered. Furthermore, two configurations with dual polyhedra, hexahedron-octahedron and dodecahedron-icosahedron, are also scrutinized. When approaching each other from large distances with these symmetries, α clusters create certain local energy pockets. As a consequence, we insist on the possible existence of α clustering with a geometric shape and hollow structure, which is favored from Coulomb energy point of view. Especially, two configurations, that is, dual polyhedra of dodecahedron-icosahedron and fullerene, have a prominent hollow structure compared with the other six configurations.

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.

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.

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.

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)

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.

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.

Multi-view clustering via multi-manifold regularized non-negative matrix factorization.

Science.gov (United States)

Zong, Linlin; Zhang, Xianchao; Zhao, Long; Yu, Hong; Zhao, Qianli

2017-04-01

Non-negative matrix factorization based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, non-negative matrix factorization fails to preserve the locally geometrical structure of the data space. In this paper, we propose a multi-manifold regularized non-negative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering. MMNMF incorporates consensus manifold and consensus coefficient matrix with multi-manifold regularization to preserve the locally geometrical structure of the multi-view data space. We use two methods to construct the consensus manifold and two methods to find the consensus coefficient matrix, which leads to four instances of the framework. Experimental results show that the proposed algorithms outperform existing non-negative matrix factorization based algorithms for multi-view clustering. Copyright © 2017 Elsevier Ltd. All rights reserved.

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)

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.

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

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.

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...

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.

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.

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.

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.)

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.)

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)

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

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.

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.

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.

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.

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...

Implementation of Grid-computing Framework for Simulation in Multi-scale Structural Analysis

Directory of Open Access Journals (Sweden)

Data Iranata

2010-05-01

Full Text Available A new grid-computing framework for simulation in multi-scale structural analysis is presented. Two levels of parallel processing will be involved in this framework: multiple local distributed computing environments connected by local network to form a grid-based cluster-to-cluster distributed computing environment. To successfully perform the simulation, a large-scale structural system task is decomposed into the simulations of a simplified global model and several detailed component models using various scales. These correlated multi-scale structural system tasks are distributed among clusters and connected together in a multi-level hierarchy and then coordinated over the internet. The software framework for supporting the multi-scale structural simulation approach is also presented. The program architecture design allows the integration of several multi-scale models as clients and servers under a single platform. To check its feasibility, a prototype software system has been designed and implemented to perform the proposed concept. The simulation results show that the software framework can increase the speedup performance of the structural analysis. Based on this result, the proposed grid-computing framework is suitable to perform the simulation of the multi-scale structural analysis.

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

Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data

Energy Technology Data Exchange (ETDEWEB)

Jin, Ling; Lee, Doris; Sim, Alex; Borgeson, Sam; Wu, Kesheng; Spurlock, C. Anna; Todd, Annika

2017-03-21

Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.

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

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 ...

Atomic and electronic structure of clusters from car-Parrinello method

International Nuclear Information System (INIS)

Kumar, V.

1994-06-01

With the development of ab-initio molecular dynamics method, it has now become possible to study the static and dynamical properties of clusters containing up to a few tens of atoms. Here I present a review of the method within the framework of the density functional theory and pseudopotential approach to represent the electron-ion interaction and discuss some of its applications to clusters. Particular attention is focussed on the structure and bonding properties of clusters as a function of their size. Applications to clusters of alkali metals and Al, non-metal - metal transition in divalent metal clusters, molecular clusters of carbon and Sb are discussed in detail. Some results are also presented on mixed clusters. (author). 121 refs, 24 ifigs

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.

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.

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...

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.

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...

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.

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.

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.

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.

Return periods of losses associated with European windstorm series in a changing climate

International Nuclear Information System (INIS)

Karremann, Melanie K; Pinto, Joaquim G; Reyers, Mark; Klawa, Matthias

2014-01-01

Possible future changes of clustering and return periods (RPs) of European storm series with high potential losses are quantified. Historical storm series are identified using 40 winters of reanalysis. Time series of top events (1, 2 or 5 year return levels (RLs)) are used to assess RPs of storm series both empirically and theoretically. Additionally, 800 winters of general circulation model simulations for present (1960–2000) and future (2060–2100) climate conditions are investigated. Clustering is identified for most countries, and estimated RPs are similar for reanalysis and present day simulations. Future changes of RPs are estimated for fixed RLs and fixed loss index thresholds. For the former, shorter RPs are found for Western Europe, but changes are small and spatially heterogeneous. For the latter, which combines the effects of clustering and event ranking shifts, shorter RPs are found everywhere except for Mediterranean countries. These changes are generally not statistically significant between recent and future climate. However, the RPs for the fixed loss index approach are mostly beyond the range of pre-industrial natural climate variability. This is not true for fixed RLs. The quantification of losses associated with storm series permits a more adequate windstorm risk assessment in a changing climate. (letter)

Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms

OpenAIRE

Chen, Pin-Yu; Hero, Alfred O.

2017-01-01

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. Non-standard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a multilayer spectral graph clustering (SGC) framework that performs convex layer aggregation. Under a multilayer signal plus noise model, we provide a phase transition analys...

Dynamic parallel ROOT facility clusters on the Alice Environment

International Nuclear Information System (INIS)

Luzzi, C; Betev, L; Carminati, F; Grigoras, C; Saiz, P; Manafov, A

2012-01-01

The ALICE collaboration has developed a production environment (AliEn) that implements the full set of the Grid tools enabling the full offline computational work-flow of the experiment, simulation, reconstruction and data analysis, in a distributed and heterogeneous computing environment. In addition to the analysis on the Grid, ALICE uses a set of local interactive analysis facilities installed with the Parallel ROOT Facility (PROOF). PROOF enables physicists to analyze medium-sized (order of 200-300 TB) data sets on a short time scale. The default installation of PROOF is on a static dedicated cluster, typically 200-300 cores. This well-proven approach, has its limitations, more specifically for analysis of larger datasets or when the installation of a dedicated cluster is not possible. Using a new framework called PoD (Proof on Demand), PROOF can be used directly on Grid-enabled clusters, by dynamically assigning interactive nodes on user request. The integration of Proof on Demand in the AliEn framework provides private dynamic PROOF clusters as a Grid service. This functionality is transparent to the user who will submit interactive jobs to the AliEn system.

A framework for delineating the regional boundaries of PM2.5 pollution: A case study of China.

Science.gov (United States)

Liu, Jianzheng; Li, Weifeng; Wu, Jiansheng

2018-04-01

Fine particulate matter (PM 2.5 ) pollution has been a major issue in many countries. Considerable studies have demonstrated that PM 2.5 pollution is a regional issue, but little research has been done to investigate the regional extent of PM 2.5 pollution or to define areas in which PM 2.5 pollutants interact. To allow for a better understanding of the regional nature and spatial patterns of PM 2.5 pollution, This study proposes a novel framework for delineating regional boundaries of PM 2.5 pollution. The framework consists of four steps, including cross-correlation analysis, time-series clustering, generation of Voronoi polygons, and polygon smoothing using polynomial approximation with exponential kernel method. Using the framework, the regional PM 2.5 boundaries for China are produced and the boundaries define areas where the monthly PM 2.5 time series of any two cities show, on average, more than 50% similarity with each other. These areas demonstrate straightforwardly that PM 2.5 pollution is not limited to a single city or a single province. We also found that the PM 2.5 areas in China tend to be larger in cold months, but more fragmented in warm months, suggesting that, in cold months, the interactions between PM 2.5 concentrations in adjacent cities are stronger than in warmer months. The proposed framework provides a tool to delineate PM 2.5 boundaries and identify areas where PM 2.5 pollutants interact. It can help define air pollution management zones and assess impacts related to PM 2.5 pollution. It can also be used in analyses of other air pollutants. Copyright © 2017 Elsevier Ltd. All rights reserved.

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...

Momentum-space cluster dual-fermion method

Science.gov (United States)

Iskakov, Sergei; Terletska, Hanna; Gull, Emanuel

2018-03-01

Recent years have seen the development of two types of nonlocal extensions to the single-site dynamical mean field theory. On one hand, cluster approximations, such as the dynamical cluster approximation, recover short-range momentum-dependent correlations nonperturbatively. On the other hand, diagrammatic extensions, such as the dual-fermion theory, recover long-ranged corrections perturbatively. The correct treatment of both strong short-ranged and weak long-ranged correlations within the same framework is therefore expected to lead to a quick convergence of results, and offers the potential of obtaining smooth self-energies in nonperturbative regimes of phase space. In this paper, we present an exact cluster dual-fermion method based on an expansion around the dynamical cluster approximation. Unlike previous formulations, our method does not employ a coarse-graining approximation to the interaction, which we show to be the leading source of error at high temperature, and converges to the exact result independently of the size of the underlying cluster. We illustrate the power of the method with results for the second-order cluster dual-fermion approximation to the single-particle self-energies and double occupancies.

A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

International Nuclear Information System (INIS)

Jin, Cheng Hao; Pok, Gouchol; Lee, Yongmi; Park, Hyun-Woo; Kim, Kwang Deuk; Yun, Unil; Ryu, Keun Ho

2015-01-01

Highlights: • A novel pattern sequence-based direct time series forecasting method was proposed. • Due to the use of SOM’s topology preserving property, only SOM can be applied. • SCPSNSP only deals with the cluster patterns not each specific time series value. • SCPSNSP performs better than recently developed forecasting algorithms. - Abstract: In this paper, we propose a new day-ahead direct time series forecasting method for competitive electricity markets based on clustering and next symbol prediction. In the clustering step, pattern sequence and their topology relations are obtained from self organizing map time series clustering. In the next symbol prediction step, with each cluster label in the pattern sequence represented as a pair of its topologically identical coordinates, artificial neural network is used to predict the topological coordinates of next day by training the relationship between previous daily pattern sequence and its next day pattern. According to the obtained topology relations, the nearest nonzero hits pattern is assigned to next day so that the whole time series values can be directly forecasted from the assigned cluster pattern. The proposed method was evaluated on Spanish, Australian and New York electricity markets and compared with PSF and some of the most recently published forecasting methods. Experimental results show that the proposed method outperforms the best forecasting methods at least 3.64%

VARIABLE STARS IN LARGE MAGELLANIC CLOUD GLOBULAR CLUSTERS. II. NGC 1786

International Nuclear Information System (INIS)

Kuehn, Charles A.; Smith, Horace A.; De Lee, Nathan; Catelan, Márcio; Pritzl, Barton J.; Borissova, Jura

2012-01-01

This is the second in a series of papers studying the variable stars in Large Magellanic Cloud globular clusters. The primary goal of this series is to study how RR Lyrae stars in Oosterhoff-intermediate systems compare to their counterparts in Oosterhoff I/II systems. In this paper, we present the results of our new time-series B–V photometric study of the globular cluster NGC 1786. A total of 65 variable stars were identified in our field of view. These variables include 53 RR Lyraes (27 RRab, 18 RRc, and 8 RRd), 3 classical Cepheids, 1 Type II Cepheid, 1 Anomalous Cepheid, 2 eclipsing binaries, 3 Delta Scuti/SX Phoenicis variables, and 2 variables of undetermined type. Photometric parameters for these variables are presented. We present physical properties for some of the RR Lyrae stars, derived from Fourier analysis of their light curves. We discuss several different indicators of Oosterhoff type which indicate that the Oosterhoff classification of NGC 1786 is not as clear cut as what is seen in most globular clusters.

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

Interatomic forces and bonding mechanisms in MgO clusters

International Nuclear Information System (INIS)

Wright, N.F.; Painter, G.S.

1990-01-01

We report results from a first-principles local spin density quantum mechanical study of the energetics and elastic properties of a series of magnesium-oxygen clusters of various morphologies. The role of quantum effects, e.g. covalency, in the bonding character of diatomic MgO is determined by comparison of classical and quantum restoring force curves. The dependence of binding properties on geometry and metal to oxygen ratio is determined by comparison of binding energy curves for a series of clusters. Results show that while gross features of the binding curves may be represented by simple interatomic potentials, details require the many body corrections of a full quantum treatment. 6 refs., 5 figs

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.

Price Formation Based on Particle-Cluster Aggregation

Science.gov (United States)

Wang, Shijun; Zhang, Changshui

In the present work, we propose a microscopic model of financial markets based on particle-cluster aggregation on a two-dimensional small-world information network in order to simulate the dynamics of the stock markets. "Stylized facts" of the financial market time series, such as fat-tail distribution of returns, volatility clustering and multifractality, are observed in the model. The results of the model agree with empirical data taken from historical records of the daily closures of the NYSE composite index.

Detecting method for crude oil price fluctuation mechanism under different periodic time series

International Nuclear Information System (INIS)

Gao, Xiangyun; Fang, Wei; An, Feng; Wang, Yue

2017-01-01

Highlights: • We proposed the concept of autoregressive modes to indicate the fluctuation patterns. • We constructed transmission networks for studying the fluctuation mechanism. • There are different fluctuation mechanism under different periodic time series. • Only a few types of autoregressive modes control the fluctuations in crude oil price. • There are cluster effects during the fluctuation mechanism of autoregressive modes. - Abstract: Current existing literatures can characterize the long-term fluctuation of crude oil price time series, however, it is difficult to detect the fluctuation mechanism specifically under short term. Because each fluctuation pattern for one short period contained in a long-term crude oil price time series have dynamic characteristics of diversity; in other words, there exhibit various fluctuation patterns in different short periods and transmit to each other, which reflects the reputedly complicate and chaotic oil market. Thus, we proposed an incorporated method to detect the fluctuation mechanism, which is the evolution of the different fluctuation patterns over time from the complex network perspective. We divided crude oil price time series into segments using sliding time windows, and defined autoregressive modes based on regression models to indicate the fluctuation patterns of each segment. Hence, the transmissions between different types of autoregressive modes over time form a transmission network that contains rich dynamic information. We then capture transmission characteristics of autoregressive modes under different periodic time series through the structure features of the transmission networks. The results indicate that there are various autoregressive modes with significantly different statistical characteristics under different periodic time series. However, only a few types of autoregressive modes and transmission patterns play a major role in the fluctuation mechanism of the crude oil price, and these

Clustering of European winter storms: A multi-model perspective

Science.gov (United States)

Renggli, Dominik; Buettner, Annemarie; Scherb, Anke; Straub, Daniel; Zimmerli, Peter

2016-04-01

The storm series over Europe in 1990 (Daria, Vivian, Wiebke, Herta) and 1999 (Anatol, Lothar, Martin) are very well known. Such clusters of severe events strongly affect the seasonally accumulated damage statistics. The (re)insurance industry has quantified clustering by using distribution assumptions deduced from the historical storm activity of the last 30 to 40 years. The use of storm series simulated by climate models has only started recently. Climate model runs can potentially represent 100s to 1000s of years, allowing a more detailed quantification of clustering than the history of the last few decades. However, it is unknown how sensitive the representation of clustering is to systematic biases. Using a multi-model ensemble allows quantifying that uncertainty. This work uses CMIP5 decadal ensemble hindcasts to study clustering of European winter storms from a multi-model perspective. An objective identification algorithm extracts winter storms (September to April) in the gridded 6-hourly wind data. Since the skill of European storm predictions is very limited on the decadal scale, the different hindcast runs are interpreted as independent realizations. As a consequence, the available hindcast ensemble represents several 1000 simulated storm seasons. The seasonal clustering of winter storms is quantified using the dispersion coefficient. The benchmark for the decadal prediction models is the 20th Century Reanalysis. The decadal prediction models are able to reproduce typical features of the clustering characteristics observed in the reanalysis data. Clustering occurs in all analyzed models over the North Atlantic and European region, in particular over Great Britain and Scandinavia as well as over Iberia (i.e. the exit regions of the North Atlantic storm track). Clustering is generally weaker in the models compared to reanalysis, although the differences between different models are substantial. In contrast to existing studies, clustering is driven by weak

Study of pressure losses in the EL 4 cluster

International Nuclear Information System (INIS)

Berriaud, Ch.

1964-01-01

The evolution of research on the EL-4 cluster is examined here from the pressure losses point of view. These may be split up into separate pressure losses along the rode and in pressure losses corresponding to various particularities of the cluster. Tests have been carried out on series of three or four clusters placed in a channel. Water was first used, and then carbon dioxide at 60 bars. In all cases the following two parameters were varied: the Reynolds' number, and the rotation of a cluster around its axis with respect to the surrounding clusters. The influence of the gap between to successive clusters has also been studied. The first tests were carried out on clusters without jackets, the subsequent ones on clusters fitted with jackets. It was thus possible to study various types of element assemblies. The results are given in the form of curves representing: the evolution of the independent pressure loss coefficients as a function of the Reynolds number. (author) [fr

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.

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.

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)

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)

Excited states of virtual clusters in a nucleus and the processes of quasi-elastic cluster knock-out at high energies

International Nuclear Information System (INIS)

Golovanova, N.F.; Il'in, I.M.; Neudatchin, V.G.; Smirnov, Yu.F.; Tchuvil'sky, Yu.M.

1976-01-01

The quasi-elastic knock-out of nucleon clusters from nuclei by an incident high-energy hadron is considered within the framework of the Glauber-Sitenko multiple scattering theory. It is shown that the significant contribution to the cross section for the process comes not only from the hadron elastic scattering by a nonexcited virtual cluster but also from collisions with an excited virtual cluster, accompanied by de-excitation of this cluster. This necessitates modification of the usual theory of quasi-elastic cluster knock-out. First, the angular correlations of the knocked-out cluster and scattered hadron are no longer determined by the momentum distribution of the cluster in the nucleus. They are determined by another form factor F(q) which can be called the modified momentum distribution. Secondly, the meaning and values of the effective numbers of clusters Nsup(eff) have been changed. Thirdly, the characteristics of the processes depend not only on the modulus of momentum q, which the cluster had in the nucleus, but also on its direction relative to an incident beam. A method has been developed for the calculation of the fractional parentage coefficients, which are necessary for the calculation of the cluster knock-out from the p-shell nuclei. (Auth.)

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.

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).

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

Bitwise dimensional co-clustering for analytical workloads

NARCIS (Netherlands)

Baumann, Stephan; Boncz, Peter; Sattler, Kai Uwe

2016-01-01

Analytical workloads in data warehouses often include heavy joins where queries involve multiple fact tables in addition to the typical star-patterns, dimensional grouping and selections. In this paper we propose a new processing and storage framework called bitwise dimensional co-clustering (BDCC)

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.

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).

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

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

Singlet-paired coupled cluster theory for open shells

Science.gov (United States)

Gomez, John A.; Henderson, Thomas M.; Scuseria, Gustavo E.

2016-06-01

Restricted single-reference coupled cluster theory truncated to single and double excitations accurately describes weakly correlated systems, but often breaks down in the presence of static or strong correlation. Good coupled cluster energies in the presence of degeneracies can be obtained by using a symmetry-broken reference, such as unrestricted Hartree-Fock, but at the cost of good quantum numbers. A large body of work has shown that modifying the coupled cluster ansatz allows for the treatment of strong correlation within a single-reference, symmetry-adapted framework. The recently introduced singlet-paired coupled cluster doubles (CCD0) method is one such model, which recovers correct behavior for strong correlation without requiring symmetry breaking in the reference. Here, we extend singlet-paired coupled cluster for application to open shells via restricted open-shell singlet-paired coupled cluster singles and doubles (ROCCSD0). The ROCCSD0 approach retains the benefits of standard coupled cluster theory and recovers correct behavior for strongly correlated, open-shell systems using a spin-preserving ROHF reference.

Singlet-paired coupled cluster theory for open shells

International Nuclear Information System (INIS)

Gomez, John A.; Henderson, Thomas M.; Scuseria, Gustavo E.

2016-01-01

Restricted single-reference coupled cluster theory truncated to single and double excitations accurately describes weakly correlated systems, but often breaks down in the presence of static or strong correlation. Good coupled cluster energies in the presence of degeneracies can be obtained by using a symmetry-broken reference, such as unrestricted Hartree-Fock, but at the cost of good quantum numbers. A large body of work has shown that modifying the coupled cluster ansatz allows for the treatment of strong correlation within a single-reference, symmetry-adapted framework. The recently introduced singlet-paired coupled cluster doubles (CCD0) method is one such model, which recovers correct behavior for strong correlation without requiring symmetry breaking in the reference. Here, we extend singlet-paired coupled cluster for application to open shells via restricted open-shell singlet-paired coupled cluster singles and doubles (ROCCSD0). The ROCCSD0 approach retains the benefits of standard coupled cluster theory and recovers correct behavior for strongly correlated, open-shell systems using a spin-preserving ROHF reference.

Reactivity of Cubane-Type [(OC)(3)MFe(3)S(4)(SR)(3)](3-) Clusters (M = Mo, W): Interconversion with Cuboidal [Fe(3)S(4)](0) Clusters and Electron Transfer.

Science.gov (United States)

Raebiger, James W.; Crawford, Charles A.; Zhou, Jian; Holm, R. H.

1997-03-12

The title clusters, several examples of which have been reported earlier, have been prepared by two different methods and subjected to structural and reactivity studies. The compounds (Et(4)N)(3)[(OC)(3)MFe(3)S(4)(Smes)(3)].MeCN (M = Mo/W) are isomorphous and crystallize in monoclinic space group P2(1)/n with a = 13.412(1)/13.297(1) Å, b = 19.0380(1)/18.9376(3) Å, c = 26.4210(1)/26.2949(1) Å, beta = 97.87(1)/97.549(1) degrees, and Z = 4. The clusters contain long M-S (2.62/2.59 Å) and M-Fe (3.22/3.19 Å) bonds, consistent with the reported structure of [(OC)(3)MoFe(3)S(4)(SEt)(3)](3-) (3). Reaction of [(OC)(3)MoFe(3)S(4)(LS(3))](3-) (7) with CO in the presence of NaPF(6) affords cuboidal [Fe(3)S(4)(LS(3))](3-) (9), also prepared in this laboratory by another route as a synthetic analogue of protein-bound [Fe(3)S(4)](0) clusters. The clusters [Fe(3)S(4)(SR)(3)](3-) (R = mes, Et), of limited stability, were generated by the same reaction. Treatment of 9 with [M(CO)(3)(MeCN)(3)] affords 7 and its M = W analogue. The clusters [(OC)(3)MFe(3)S(4)(SR)(3)](3-) form a four-member electron transfer series in which the 3- cluster can be once reduced (4-) and twice oxidized (2-, 1-) to afford clusters of the indicated charges. The correct assignment of redox couple to potential in the redox series of six clusters is presented, correcting an earlier misassignment of the redox series of 3. Carbonyl stretching frequencies are shown to be sensitive to cluster oxidation state, showing that the M sites and Fe(3)S(4) fragments are electronically coupled despite the long bond distances. (LS(3) = 1,3,5-tris((4,6-dimethyl-3-mercaptophenyl)thio)-2,4,6-tris(p-tolylthio)benzenate(3-); mes = mesityl.)

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.

Kepler Science Operations Center Pipeline Framework

Science.gov (United States)

Klaus, Todd C.; McCauliff, Sean; Cote, Miles T.; Girouard, Forrest R.; Wohler, Bill; Allen, Christopher; Middour, Christopher; Caldwell, Douglas A.; Jenkins, Jon M.

2010-01-01

The Kepler mission is designed to continuously monitor up to 170,000 stars at a 30 minute cadence for 3.5 years searching for Earth-size planets. The data are processed at the Science Operations Center (SOC) at NASA Ames Research Center. Because of the large volume of data and the memory and CPU-intensive nature of the analysis, significant computing hardware is required. We have developed generic pipeline framework software that is used to distribute and synchronize the processing across a cluster of CPUs and to manage the resulting products. The framework is written in Java and is therefore platform-independent, and scales from a single, standalone workstation (for development and research on small data sets) to a full cluster of homogeneous or heterogeneous hardware with minimal configuration changes. A plug-in architecture provides customized control of the unit of work without the need to modify the framework itself. Distributed transaction services provide for atomic storage of pipeline products for a unit of work across a relational database and the custom Kepler DB. Generic parameter management and data accountability services are provided to record the parameter values, software versions, and other meta-data used for each pipeline execution. A graphical console allows for the configuration, execution, and monitoring of pipelines. An alert and metrics subsystem is used to monitor the health and performance of the pipeline. The framework was developed for the Kepler project based on Kepler requirements, but the framework itself is generic and could be used for a variety of applications where these features are needed.

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.

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)

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.

EMACSS: Evolve Me A Cluster of StarS

Science.gov (United States)

Alexander, Poul E. R.; Gieles, Mark

2012-03-01

The star cluster evolution code Evolve Me A Cluster of StarS (EMACSS) is a simple yet physically motivated computational model that describes the evolution of some fundamental properties of star clusters in static tidal fields. The prescription is based upon the flow of energy within the cluster, which is a constant fraction of the total energy per half-mass relaxation time. According to Henon's predictions, this flow is independent of the precise mechanisms for energy production within the core, and therefore does not require a complete description of the many-body interactions therein. Dynamical theory and analytic descriptions of escape mechanisms is used to construct a series of coupled differential equations expressing the time evolution of cluster mass and radius for a cluster of equal-mass stars. These equations are numerically solved using a fourth-order Runge-Kutta integration kernel; the results were benchmarked against a data base of direct N-body simulations. EMACSS is publicly available and reproduces the N-body results to within 10 per cent accuracy for the entire post-collapse evolution of star clusters.

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

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...

First-principles studies on graphene-supported transition metal clusters

International Nuclear Information System (INIS)

Sahoo, Sanjubala; Khanna, Shiv N.; Gruner, Markus E.; Entel, Peter

2014-01-01

Theoretical studies on the structure, stability, and magnetic properties of icosahedral TM 13 (TM = Fe, Co, Ni) clusters, deposited on pristine (defect free) and defective graphene sheet as well as graphene flakes, have been carried out within a gradient corrected density functional framework. The defects considered in our study include a carbon vacancy for the graphene sheet and a five-membered and a seven-membered ring structures for graphene flakes (finite graphene chunks). It is observed that the presence of defect in the substrate has a profound influence on the electronic structure and magnetic properties of graphene-transition metal complexes, thereby increasing the binding strength of the TM cluster on to the graphene substrate. Among TM 13 clusters, Co 13 is absorbed relatively more strongly on pristine and defective graphene as compared to Fe 13 and Ni 13 clusters. The adsorbed clusters show reduced magnetic moment compared to the free clusters

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.

Gold cluster carbonyls: saturated adsorption of CO on gold cluster cations, vibrational spectroscopy, and implications for their structures.

Science.gov (United States)

Fielicke, André; von Helden, Gert; Meijer, Gerard; Pedersen, David B; Simard, Benoit; Rayner, David M

2005-06-15

We report on the interaction of carbon monoxide with cationic gold clusters in the gas phase. Successive adsorption of CO molecules on the Au(n)(+) clusters proceeds until a cluster size specific saturation coverage is reached. Structural information for the bare gold clusters is obtained by comparing the saturation stoichiometry with the number of available equivalent sites presented by candidate structures of Au(n)(+). Our findings are in agreement with the planar structures of the Au(n)(+) cluster cations with n < or = 7 that are suggested by ion mobility experiments [Gilb, S.; Weis, P.; Furche, F.; Ahlrichs, R.; Kappes, M. M. J. Chem. Phys. 2001, 116, 4094]. By inference we also establish the structure of the saturated Au(n)(CO)(m)(+) complexes. In certain cases we find evidence suggesting that successive adsorption of CO can distort the metal cluster framework. In addition, the vibrational spectra of the Au(n)(CO)(m)(+) complexes in both the CO stretching region and in the region of the Au-C stretch and the Au-C-O bend are measured using infrared photodepletion spectroscopy. The spectra further aid in the structure determination of Au(n)(+), provide information on the structure of the Au(n)(+)-CO complexes, and can be compared with spectra of CO adsorbates on deposited clusters or surfaces.

Cluster structure of 20Ne and 40Ca

International Nuclear Information System (INIS)

Taniguchi, Yasutaka

2004-01-01

A d-constraint for calculating the wave functions of various kinds of configurations of cluster structure and optimizing the inside wave functions of the cluster was developed. The wave functions of various kinds of cluster structures were calculated by constraining and energy variation of the antisymmetrized molecular dynamics wave functions. The cluster structure of nucleus was reproduced by linear combination of the above wave functions by the generator coordinate method. By superposition of both wave functions calculated using d-constraint and β-constraint, K π =O 3 + rotation band of 20 Ne was reproduced. The excitation energies of 20 Ne were calculated. The result of calculation energies of α- 36 Ar structure of 40 Ca are higher values than expected them. Framework, AMD wave function, constraint, calculation results and discussions are stated. (S.Y.)

Extending cluster lot quality assurance sampling designs for surveillance programs.

Science.gov (United States)

Hund, Lauren; Pagano, Marcello

2014-07-20

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate. Copyright © 2014 John Wiley & Sons, Ltd.

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.

Novel Clustering Method Based on K-Medoids and Mobility Metric

Directory of Open Access Journals (Sweden)

Y. Hamzaoui

2018-06-01

Full Text Available The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head.

Microscopic description of the nuclear-cluster theory

International Nuclear Information System (INIS)

Tang, Y.C.

1980-01-01

The purpose of this series of lectures is to explain the foundation of, the techniques used in, and the results obtained by microscopic cluster theory (MCT). In particular, the important role played by the Pauli principle in determining nuclear characteristics will be extensively discussed

Metal-adeninate vertices for the construction of an exceptionally porous metal-organic framework.

Science.gov (United States)

An, Jihyun; Farha, Omar K; Hupp, Joseph T; Pohl, Ehmke; Yeh, Joanne I; Rosi, Nathaniel L

2012-01-03

Metal-organic frameworks comprising metal-carboxylate cluster vertices and long, branched organic linkers are the most porous materials known, and therefore have attracted tremendous attention for many applications, including gas storage, separations, catalysis and drug delivery. To increase metal-organic framework porosity, the size and complexity of linkers has increased. Here we present a promising alternative strategy for constructing mesoporous metal-organic frameworks that addresses the size of the vertex rather than the length of the organic linker. This approach uses large metal-biomolecule clusters, in particular zinc-adeninate building units, as vertices to construct bio-MOF-100, an exclusively mesoporous metal-organic framework. Bio-MOF-100 exhibits a high surface area (4,300 m(2) g(-1)), one of the lowest crystal densities (0.302 g cm(-3)) and the largest metal-organic framework pore volume reported to date (4.3 cm(3) g(-1)).

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…

Oriented cluster perforating technology and its application in horizontal wells

Directory of Open Access Journals (Sweden)

Huabin Chen

2016-11-01

Full Text Available An oriented cluster perforating technology, which integrates both advantages of cluster and oriented perforating, will help solve a series of technical complexities in horizontal well drilling. For realizing its better application in oil and gas development, a series of technologies were developed including perforator self-weight eccentricity, matching of the electronic selective module codes with the surface program control, axial centralized contact signal transmission, and post-perforation intercluster sealing insulation. In this way, the following functions could be realized, such as cable-transmission horizontal well perforator self-weight orientation, dynamic signal transmission, reliable addressing & selective perforation and post-perforation intercluster sealing. The combined perforation and bridge plug or the multi-cluster perforation can be fulfilled in one trip of perforation string. As a result, the horizontal-well oriented cluster perforating technology based on cable conveying was developed. This technology was successfully applied in unconventional gas reservoir exploitation, such as shale gas and coalbed methane, with accurate orientation, reliable selective perforation and satisfactory inter-cluster sealing. The horizontal-well oriented cluster perforating technology benefits the orientation of horizontal well drilling with a definite target and direction, which provides a powerful support for the subsequent reservoir stimulation. It also promotes the fracturing fluid to sweep the principal pay zones to the maximum extent. Moreover, it is conductive to the formation of complex fracture networks in the reservoirs, making quality and efficient development of unconventional gas reservoirs possible.

Fragmentation pathways of tungsten hexacarbonyl clusters upon electron ionization

Energy Technology Data Exchange (ETDEWEB)

Neustetter, M.; Jabbour Al Maalouf, E.; Denifl, S., E-mail: Stephan.Denifl@uibk.ac.at, E-mail: plimaovieira@fct.unl.pt [Institut für Ionenphysik und Angewandte Physik, Universität Innsbruck, Technikerstrasse 25, A-6020 Innsbruck (Austria); Limão-Vieira, P., E-mail: Stephan.Denifl@uibk.ac.at, E-mail: plimaovieira@fct.unl.pt [Institut für Ionenphysik und Angewandte Physik, Universität Innsbruck, Technikerstrasse 25, A-6020 Innsbruck (Austria); Laboratório de Colisões Atómicas e Moleculares, CEFITEC, Departamento de Física, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica (Portugal)

2016-08-07

Electron ionization of neat tungsten hexacarbonyl (W(CO){sub 6}) clusters has been investigated in a crossed electron-molecular beam experiment coupled with a mass spectrometer system. The molecule is used for nanofabrication processes through electron beam induced deposition and ion beam induced deposition techniques. Positive ion mass spectra of W(CO){sub 6} clusters formed by electron ionization at 70 eV contain the ion series of the type W(CO){sub n}{sup +} (0 ≤ n ≤ 6) and W{sub 2}(CO){sub n}{sup +} (0 ≤ n ≤ 12). In addition, a series of peaks are observed and have been assigned to WC(CO){sub n}{sup +} (0 ≤ n ≤ 3) and W{sub 2}C(CO){sub n}{sup +} (0 ≤ n ≤ 10). A distinct change of relative fragment ion intensity can be observed for clusters compared to the single molecule. The characteristic fragmentation pattern obtained in the mass spectra can be explained by a sequential decay of the ionized organometallic, which is also supported by the study of the clusters when embedded in helium nanodroplets. In addition, appearance energies for the dissociative ionization channels for singly charged ions have been estimated from experimental ion efficiency curves.

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.

A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment

Directory of Open Access Journals (Sweden)

Simone Pascucci

2018-04-01

Full Text Available The recognition of spatial patterns within agricultural fields, presenting similar yield potential areas, stable through time, is very important for optimizing agricultural practices. This study proposes the evaluation of different clustering methodologies applied to multispectral satellite time series for retrieving temporally stable (constant patterns in agricultural fields, related to within-field yield spatial distribution. The ability of different clustering procedures for the recognition and mapping of constant patterns in fields of cereal crops was assessed. Crop vigor patterns, considered to be related to soils characteristics, and possibly indicative of yield potential, were derived by applying the different clustering algorithms to time series of Landsat images acquired on 94 agricultural fields near Rome (Italy. Two different approaches were applied and validated using Landsat 7 and 8 archived imagery. The first approach automatically extracts and calculates for each field of interest (FOI the Normalized Difference Vegetation Index (NDVI, then exploits the standard K-means clustering algorithm to derive constant patterns at the field level. The second approach applies novel clustering procedures directly to spectral reflectance time series, in particular: (1 standard K-means; (2 functional K-means; (3 multivariate functional principal components clustering analysis; (4 hierarchical clustering. The different approaches were validated through cluster accuracy estimates on a reference set of FOIs for which yield maps were available for some years. Results show that multivariate functional principal components clustering, with an a priori determination of the optimal number of classes for each FOI, provides a better accuracy than those of standard clustering algorithms. The proposed novel functional clustering methodologies are effective and efficient for constant pattern retrieval and can be used for a sustainable management of

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...

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.

Investigating the usefulness of a cluster-based trend analysis to detect visual field progression in patients with open-angle glaucoma.

Science.gov (United States)

Aoki, Shuichiro; Murata, Hiroshi; Fujino, Yuri; Matsuura, Masato; Miki, Atsuya; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Hirasawa, Kazunori; Shoji, Nobuyuki; Asaoka, Ryo

2017-12-01

To investigate the usefulness of the Octopus (Haag-Streit) EyeSuite's cluster trend analysis in glaucoma. Ten visual fields (VFs) with the Humphrey Field Analyzer (Carl Zeiss Meditec), spanning 7.7 years on average were obtained from 728 eyes of 475 primary open angle glaucoma patients. Mean total deviation (mTD) trend analysis and EyeSuite's cluster trend analysis were performed on various series of VFs (from 1st to 10th: VF1-10 to 6th to 10th: VF6-10). The results of the cluster-based trend analysis, based on different lengths of VF series, were compared against mTD trend analysis. Cluster-based trend analysis and mTD trend analysis results were significantly associated in all clusters and with all lengths of VF series. Between 21.2% and 45.9% (depending on VF series length and location) of clusters were deemed to progress when the mTD trend analysis suggested no progression. On the other hand, 4.8% of eyes were observed to progress using the mTD trend analysis when cluster trend analysis suggested no progression in any two (or more) clusters. Whole field trend analysis can miss local VF progression. Cluster trend analysis appears as robust as mTD trend analysis and useful to assess both sectorial and whole field progression. Cluster-based trend analyses, in particular the definition of two or more progressing cluster, may help clinicians to detect glaucomatous progression in a timelier manner than using a whole field trend analysis, without significantly compromising specificity. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

Science.gov (United States)

Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

2018-04-15

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

Series of coordination polymers based on 4-(5-sulfo-quinolin-8-yloxy) phthalate and bipyridinyl coligands: Structure diversity and properties

Energy Technology Data Exchange (ETDEWEB)

Feng, Xun [College of Chemistry and Chemical Engineering, Luoyang Normal University, Luoyang 471022 (China); Liu, Jing [College of Chemistry and Molecular Engineering, Zhengzhou University, Zhengzhou 450001 (China); College of Chemistry and Chemical Engineering, Luoyang Normal University, Luoyang 471022 (China); Li, Jin; Ma, Lu-Fang [College of Chemistry and Chemical Engineering, Luoyang Normal University, Luoyang 471022 (China); Wang, Li-Ya, E-mail: wlya@lynu.edu.cn [College of Chemistry and Chemical Engineering, Luoyang Normal University, Luoyang 471022 (China); College of Chemistry and Pharmacy Engineering, Nanyang Normal University, Nanyang 473601 (China); Ng, Seik-Weng [Department of Chemistry, University of Malaya, Kuala Lumpur 50603 (Malaysia); Chemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 80203 (Saudi Arabia); Qin, Guo-Zhan [College of Chemistry and Chemical Engineering, Luoyang Normal University, Luoyang 471022 (China)

2015-10-15

Reactions between later metal salts and conjugational N-hetrocyclic sulfonate/ carboxylic acid under the presence of bipyridyl auxiliary ligands afforded a series of manganese, nickel, zinc, silver, cadmium coordination polymers bearing with phenyl pendant arm attached to quinoline skeletons, and they have been characterized by elements analysis, thermogravimetry, infrared spectroscopy and single-crystal X-ray diffraction studying. The series of polymers show interesting structural diversity in coordination environment, dimensions and topologies. They are all built from 2-D networks constructed from metal cluster through sulfonate or carboxylate groups, as the secondary building unit (SBU). The thermalgravimetric analyses show that they display framework stabilities in solid state. Variable-temperature magnetic susceptibility studies reveal the existence of antiferromagnetic interactions between adjacent Mn (II) ions in 1, and ferromagnetic interactions between Ni(II) ions for 2, respectively. The photo-luminescence properties of 3-5 have also been investigated systemically. - Highlights: • A series of coordination polymers based on later transition metal ions have been obtained. • They contain conjugational N-hetrocyclic sulfonate-carboxylic acid and bipyridyl auxiliary ligands. • They have been characterized systemically. • They exhibit structure diversity and interesting properties.

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.

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.

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.

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

A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

Directory of Open Access Journals (Sweden)

Weiwei Lin

2016-01-01

Full Text Available 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 machine power efficiency-aware greedy scheduling algorithm (VPEGS. As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.

IMPOSING A LAGRANGIAN PARTICLE FRAMEWORK ON AN EULERIAN HYDRODYNAMICS INFRASTRUCTURE IN FLASH

International Nuclear Information System (INIS)

Dubey, A.; Daley, C.; Weide, K.; Graziani, C.; ZuHone, J.; Ricker, P. M.

2012-01-01

In many astrophysical simulations, both Eulerian and Lagrangian quantities are of interest. For example, in a galaxy cluster merger simulation, the intracluster gas can have Eulerian discretization, while dark matter can be modeled using particles. FLASH, a component-based scientific simulation code, superimposes a Lagrangian framework atop an adaptive mesh refinement Eulerian framework to enable such simulations. The discretization of the field variables is Eulerian, while the Lagrangian entities occur in many different forms including tracer particles, massive particles, charged particles in particle-in-cell mode, and Lagrangian markers to model fluid-structure interactions. These widely varying roles for Lagrangian entities are possible because of the highly modular, flexible, and extensible architecture of the Lagrangian framework. In this paper, we describe the Lagrangian framework in FLASH in the context of two very different applications, Type Ia supernovae and galaxy cluster mergers, which use the Lagrangian entities in fundamentally different ways.

Imposing a Lagrangian Particle Framework on an Eulerian Hydrodynamics Infrastructure in Flash

Science.gov (United States)

Dubey, A.; Daley, C.; ZuHone, J.; Ricker, P. M.; Weide, K.; Graziani, C.

2012-01-01

In many astrophysical simulations, both Eulerian and Lagrangian quantities are of interest. For example, in a galaxy cluster merger simulation, the intracluster gas can have Eulerian discretization, while dark matter can be modeled using particles. FLASH, a component-based scientific simulation code, superimposes a Lagrangian framework atop an adaptive mesh refinement Eulerian framework to enable such simulations. The discretization of the field variables is Eulerian, while the Lagrangian entities occur in many different forms including tracer particles, massive particles, charged particles in particle-in-cell mode, and Lagrangian markers to model fluid structure interactions. These widely varying roles for Lagrangian entities are possible because of the highly modular, flexible, and extensible architecture of the Lagrangian framework. In this paper, we describe the Lagrangian framework in FLASH in the context of two very different applications, Type Ia supernovae and galaxy cluster mergers, which use the Lagrangian entities in fundamentally different ways.

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

A Titanium–Organic Framework as an Exemplar of Combining the Chemistry of Metal– and Covalent–Organic Frameworks

Energy Technology Data Exchange (ETDEWEB)

Nguyen, Ha L.; Gándara, Felipe; Furukawa, Hiroyasu; Doan, Tan L. H.; Cordova, Kyle E.; Yaghi, Omar M.

2016-04-06

A crystalline material with a two-dimensional structure, termed metal–organic framework-901 (MOF-901), was prepared using a strategy that combines the chemistry of MOFs and covalent–organic frameworks (COFs). This strategy involves in situ generation of an amine-functionalized titanium oxo cluster, Ti6O6(OCH3)6(AB)6 (AB = 4-aminobenzoate), which was linked with benzene-1,4-dialdehyde using imine condensation reactions, typical of COFs. The crystal structure of MOF-901 is composed of hexagonal porous layers that are likely stacked in staggered conformation (hxl topology). This MOF represents the first example of combining metal cluster chemistry with dynamic organic covalent bond formation to give a new crystalline, extended framework of titanium metal, which is rarely used in MOFs. The incorporation of Ti(IV) units made MOF-901 useful in the photocatalyzed polymerization of methyl methacrylate (MMA). The resulting polyMMA product was obtained with a high-number-average molar mass (26 850 g mol–1) and low polydispersity index (1.6), which in many respects are better than those achieved by the commercially available photocatalyst (P-25 TiO2). Additionally, the catalyst can be isolated, reused, and recycled with no loss in performance.

Information extraction from dynamic PS-InSAR time series using machine learning

Science.gov (United States)

van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

2017-12-01

Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account

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

Science.gov (United States)

Ye, Qing; Pan, Hao; Liu, Changhua

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) is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE). The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

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

Directory of Open Access Journals (Sweden)

Qing Ye

2015-01-01

Full Text Available 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 is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE. The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.

Binding motif of terminal alkynes on gold clusters.

Science.gov (United States)

Maity, Prasenjit; Takano, Shinjiro; Yamazoe, Seiji; Wakabayashi, Tomonari; Tsukuda, Tatsuya

2013-06-26

Gold clusters protected by terminal alkynes (1-octyne (OC-H), phenylacetylene (PA-H) and 9-ethynyl-phenanthrene (EPT-H)) were prepared by the ligand exchange of small (diameter alkynes on Au clusters was investigated using various spectroscopic methods. FTIR and Raman spectroscopy revealed that terminal hydrogen is lost during the ligand exchange and that the C≡C bond of the alkynyl group is weakened upon attachment to the Au clusters. Acidification of the water phase after the ligand exchange indicated that the ligation of alkynyl groups to the Au clusters proceeds via deprotonation of the alkynes. A series of precisely defined Au clusters, Au34(PA)16, Au54(PA)26, Au30(EPT)13, Au35(EPT)18, and Au(41-43)(EPT)(21-23), were synthesized and characterized in detail to obtain further insight into the interfacial structures. Careful mass analysis confirmed the ligation of the alkynes in the dehydrogenated form. An upright configuration of the alkynes on Au clusters was suggested from the Au to alkyne ratios and photoluminescence from the excimer of the EPT ligands. EXAFS analysis implied that the alkynyl carbon is bound to bridged or hollow sites on the cluster surface.

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.

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.

Environmental effects on stellar populations of star clusters and dwarf galaxies

Science.gov (United States)

Pasetto, Stefano; Cropper, Mark; Fujita, Yutaka; Chiosi, Cesare; Grebel, Eva K.

2017-03-01

We investigate the competitive role of the different dissipative phenomena acting on the onset of star formation of gravitationally bound systems in an external environment. Ram pressure, Kelvin-Helmholtz and Rayleigh-Taylor instabilities, and tidal forces are accounted for separately in an analytical framework and compared in their role in influencing the star forming regions. We present an analytical criterion to elucidate the dependence of star formation in a spherical stellar system on its surrounding environment. We consider the different signatures of these phenomena in synthetically realized colour-magnitude diagrams (CMDs) of the orbiting system thus investigating the detectability limits of these different effects for future observational projects and their relevance. The developed theoretical framework has direct applications to the cases of massive star clusters, dwarf galaxies in galaxy clusters and dwarf galaxies orbiting our Milky Way system, as well as any primordial gas-rich cluster of stars orbiting within its host galaxy.

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.

Theory of homogeneous condensation from small nuclei. I. Modified Mayer theory of physical clusters

International Nuclear Information System (INIS)

Lockett, A.M. III

1980-01-01

A theory of physical clusters is developed within the framework of the Theory of Imperfect Gases. Physical monomers and clusters are redefined diagrammatically thereby removing the unphysical nature of the usual Mayer clusters while retaining essentially all of the desirable features of the Mayer theory. The resulting formulation is simple, unambiguous, and well suited for incorporation into a kinetic theory of condensation which is computationally tractable

A holistic image segmentation framework for cloud detection and extraction

Science.gov (United States)

Shen, Dan; Xu, Haotian; Blasch, Erik; Horvath, Gregory; Pham, Khanh; Zheng, Yufeng; Ling, Haibin; Chen, Genshe

2013-05-01

Atmospheric clouds are commonly encountered phenomena affecting visual tracking from air-borne or space-borne sensors. Generally clouds are difficult to detect and extract because they are complex in shape and interact with sunlight in a complex fashion. In this paper, we propose a clustering game theoretic image segmentation based approach to identify, extract, and patch clouds. In our framework, the first step is to decompose a given image containing clouds. The problem of image segmentation is considered as a "clustering game". Within this context, the notion of a cluster is equivalent to a classical equilibrium concept from game theory, as the game equilibrium reflects both the internal and external (e.g., two-player) cluster conditions. To obtain the evolutionary stable strategies, we explore three evolutionary dynamics: fictitious play, replicator dynamics, and infection and immunization dynamics (InImDyn). Secondly, we use the boundary and shape features to refine the cloud segments. This step can lower the false alarm rate. In the third step, we remove the detected clouds and patch the empty spots by performing background recovery. We demonstrate our cloud detection framework on a video clip provides supportive results.

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

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

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

Infrared spectroscopic studies on the cluster size dependence of charge carrier structure in nitrous oxide cluster anions

International Nuclear Information System (INIS)

Thompson, Michael C.; Weber, J. Mathias

2016-01-01

We report infrared photodissociation spectra of nitrous oxide cluster anions of the form (N 2 O) n O − (n = 1–12) and (N 2 O) n − (n = 7–15) in the region 800–1600 cm −1 . The charge carriers in these ions are NNO 2 − and O − for (N 2 O) n O − clusters with a solvation induced core ion switch, and N 2 O − for (N 2 O) n − clusters. The N–N and N–O stretching vibrations of N 2 O − (solvated by N 2 O) are reported for the first time, and they are found at (1595 ± 3) cm −1 and (894 ± 5) cm −1 , respectively. We interpret our infrared spectra by comparison with the existing photoelectron spectroscopy data and with computational data in the framework of density functional theory.

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.

Flexible single-layer ionic organic-inorganic frameworks towards precise nano-size separation

Science.gov (United States)

Yue, Liang; Wang, Shan; Zhou, Ding; Zhang, Hao; Li, Bao; Wu, Lixin

2016-02-01

Consecutive two-dimensional frameworks comprised of molecular or cluster building blocks in large area represent ideal candidates for membranes sieving molecules and nano-objects, but challenges still remain in methodology and practical preparation. Here we exploit a new strategy to build soft single-layer ionic organic-inorganic frameworks via electrostatic interaction without preferential binding direction in water. Upon consideration of steric effect and additional interaction, polyanionic clusters as connection nodes and cationic pseudorotaxanes acting as bridging monomers connect with each other to form a single-layer ionic self-assembled framework with 1.4 nm layer thickness. Such soft supramolecular polymer frameworks possess uniform and adjustable ortho-tetragonal nanoporous structure in pore size of 3.4-4.1 nm and exhibit greatly convenient solution processability. The stable membranes maintaining uniform porous structure demonstrate precisely size-selective separation of semiconductor quantum dots within 0.1 nm of accuracy and may hold promise for practical applications in selective transport, molecular separation and dialysis systems.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

Directory of Open Access Journals (Sweden)

Vasileios Karyotis

2018-04-01

Full Text Available In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

Molecular cluster theory of chemical bonding in actinide oxide

International Nuclear Information System (INIS)

Ellis, D.E.; Gubanov, V.A.; Rosen, A.

1978-01-01

The electronic structure of actinide monoxides AcO and dioxides AcO 2 , where Ac = Th, U, Np, Pu, Am, Cm and Bk has been studied by molecular cluster methods based on the first-principles one-electron local density theory. Molecular orbitals for nearest neighbor clusters AcO 10- 6 and AcO 12- 8 representative of monoxide and dioxide lattices were obtained using non-relativistic spin-restricted and spin-polarized Hartree-Fock-Slater models for the entire series. Fully relativistic Dirac-Slater calculations were performed for ThO, UO and NpO in order to explore magnitude of spin-orbit splittings and level shifts in valence structure. Self-consistent iterations were carried out for NpO, in which the NpO 6 cluster was embedded in the molecular field of the solid. Finally, a ''moment polarized'' model which combines both spin-polarization and relativistic effects in a consistent fashion was applied to the NpO system. Covalent mixing of oxygen 2p and Ac 5f orbitals was found to increase rapidly across the actinide series; metal s,p,d covalency was found to be nearly constant. Mulliken atomic population analysis of cluster eigenvectors shows that free-ion crystal field models are unreliable, except for the light actinides. X-ray photoelectron line shapes have been calculated and are found to compare rather well with experimental data on the dioxides

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

Science.gov (United States)

Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Variable Stars in Large Magellanic Cloud Globular Clusters. III. Reticulum

Science.gov (United States)

Kuehn, Charles A.; Dame, Kyra; Smith, Horace A.; Catelan, Márcio; Jeon, Young-Beom; Nemec, James M.; Walker, Alistair R.; Kunder, Andrea; Pritzl, Barton J.; De Lee, Nathan; Borissova, Jura

2013-06-01

This is the third in a series of papers studying the variable stars in old globular clusters in the Large Magellanic Cloud. The primary goal of this series is to look at how the characteristics and behavior of RR Lyrae stars in Oosterhoff-intermediate systems compare to those of their counterparts in Oosterhoff-I/II systems. In this paper we present the results of our new time-series BVI photometric study of the globular cluster Reticulum. We found a total of 32 variables stars (22 RRab, 4 RRc, and 6 RRd stars) in our field of view. We present photometric parameters and light curves for these stars. We also present physical properties, derived from Fourier analysis of light curves, for some of the RR Lyrae stars. We discuss the Oosterhoff classification of Reticulum and use our results to re-derive the distance modulus and age of the cluster. Based on observations taken with the SMARTS 1.3 m telescope operated by the SMARTS Consortium and observations taken at the Southern Astrophysical Research (SOAR) telescope, which is a joint project of the Ministério da Ciência, Tecnologia, e Inovação (MCTI) da República Federativa do Brasil, the U.S. National Optical Astronomy Observatory (NOAO), the University of North Carolina at Chapel Hill (UNC), and Michigan State University (MSU).

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods

International Nuclear Information System (INIS)

Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H.

2010-01-01

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

Hedgehog bases for A{sub n} cluster polylogarithms and an application to six-point amplitudes

Energy Technology Data Exchange (ETDEWEB)

Parker, Daniel E.; Scherlis, Adam; Spradlin, Marcus; Volovich, Anastasia [Department of Physics, Brown University, Providence RI 02912 (United States)

2015-11-20

Multi-loop scattering amplitudes in N=4 Yang-Mills theory possess cluster algebra structure. In order to develop a computational framework which exploits this connection, we show how to construct bases of Goncharov polylogarithm functions, at any weight, whose symbol alphabet consists of cluster coordinates on the A{sub n} cluster algebra. Using such a basis we present a new expression for the 2-loop 6-particle NMHV amplitude which makes some of its cluster structure manifest.

Er web- og tv-serien SKAM en serie om skam?

DEFF Research Database (Denmark)

Christensen, Christa Lykke

2017-01-01

In this article, I discuss whether the Norwegian teen web series drama SKAM (NRK, 2015-2017) is really about shame and to what extent the fictional characters of the series feel ashamed. The theoretical framework is based on a social psychological conceptualization of shame, supplemented by a micro...

Multiscale synchrony behaviors of paired financial time series by 3D multi-continuum percolation

Science.gov (United States)

Wang, M.; Wang, J.; Wang, B. T.

2018-02-01

Multiscale synchrony behaviors and nonlinear dynamics of paired financial time series are investigated, in an attempt to study the cross correlation relationships between two stock markets. A random stock price model is developed by a new system called three-dimensional (3D) multi-continuum percolation system, which is utilized to imitate the formation mechanism of price dynamics and explain the nonlinear behaviors found in financial time series. We assume that the price fluctuations are caused by the spread of investment information. The cluster of 3D multi-continuum percolation represents the cluster of investors who share the same investment attitude. In this paper, we focus on the paired return series, the paired volatility series, and the paired intrinsic mode functions which are decomposed by empirical mode decomposition. A new cross recurrence quantification analysis is put forward, combining with multiscale cross-sample entropy, to investigate the multiscale synchrony of these paired series from the proposed model. The corresponding research is also carried out for two China stock markets as comparison.

From virtual clustering analysis to self-consistent clustering analysis: a mathematical study

Science.gov (United States)

Tang, Shaoqiang; Zhang, Lei; Liu, Wing Kam

2018-03-01

In this paper, we propose a new homogenization algorithm, virtual clustering analysis (VCA), as well as provide a mathematical framework for the recently proposed self-consistent clustering analysis (SCA) (Liu et al. in Comput Methods Appl Mech Eng 306:319-341, 2016). In the mathematical theory, we clarify the key assumptions and ideas of VCA and SCA, and derive the continuous and discrete Lippmann-Schwinger equations. Based on a key postulation of "once response similarly, always response similarly", clustering is performed in an offline stage by machine learning techniques (k-means and SOM), and facilitates substantial reduction of computational complexity in an online predictive stage. The clear mathematical setup allows for the first time a convergence study of clustering refinement in one space dimension. Convergence is proved rigorously, and found to be of second order from numerical investigations. Furthermore, we propose to suitably enlarge the domain in VCA, such that the boundary terms may be neglected in the Lippmann-Schwinger equation, by virtue of the Saint-Venant's principle. In contrast, they were not obtained in the original SCA paper, and we discover these terms may well be responsible for the numerical dependency on the choice of reference material property. Since VCA enhances the accuracy by overcoming the modeling error, and reduce the numerical cost by avoiding an outer loop iteration for attaining the material property consistency in SCA, its efficiency is expected even higher than the recently proposed SCA algorithm.

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...

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

Innovation Cluster and Economic Development in Bucharest Ilfov Region

Directory of Open Access Journals (Sweden)

Ana Cristina Adumitroaei

2013-08-01

Full Text Available Simultaneous globalisation tendencies have created policy challenges for national and local governments. One response to these challenges has been a dramatic proliferation of development policies based on clusters of firms and industries. In EU Strategy 2020 – COM 546/6.10.2010 Initiative “An Union of Innovation”, COM 614/27.10.2010 Initiative “Industrial Policy in the Globalization Era” innovative clusters were considered the “engine” of economic development. They represent a framework for business development, collaboration between companies, universities, research institutions, suppliers, customers and competitors located in the same geographical area. Clusters of small and medium sized firms in developing economies are coming under increased pressure from competition as products mature, technology becomes widely available, and companies seek lower cost locations for production. In this paper, we consider that the cluster is an engine for economic development in our region and we need to have a regional strategy for clusters in Bucharest Ilfov Regional Development Plan for 2014-2020.

Hadoop Cluster Deployment: A Methodological Approach

Directory of Open Access Journals (Sweden)

Ronaldo Celso Messias Correia

2018-05-01

Full Text Available For a long time, data has been treated as a general problem because it just represents fractions of an event without any relevant purpose. However, the last decade has been just about information and how to get it. Seeking meaning in data and trying to solve scalability problems, many frameworks have been developed to improve data storage and its analysis. As a framework, Hadoop was presented as a powerful tool to deal with large amounts of data. However, it still causes doubts about how to deal with its deployment and if there is any reliable method to compare the performance of distinct Hadoop clusters. This paper presents a methodology based on benchmark analysis to guide the Hadoop cluster deployment. The experiments employed The Apache Hadoop and the Hadoop distributions of Cloudera, Hortonworks, and MapR, analyzing the architectures on local and on clouding—using centralized and geographically distributed servers. The results show the methodology can be dynamically applied on a reliable comparison among different architectures. Additionally, the study suggests that the knowledge acquired can be used to improve the data analysis process by understanding the Hadoop architecture.

Clustering Using Boosted Constrained k-Means Algorithm

Directory of Open Access Journals (Sweden)

Masayuki Okabe

2018-03-01

Full Text Available This article proposes a constrained clustering algorithm with competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learning-based methods. Since it simply adds a function into the data assignment process of the k-means algorithm to check for constraint violations, it often exploits only a small number of constraints. Metric learning-based methods, which exploit constraints to create a new metric for data similarity, have shown promising results although the methods proposed so far are often slow depending on the amount of data or number of feature dimensions. We present a method that exploits the advantages of the constrained k-means and metric learning approaches. It incorporates a mechanism for accepting constraint priorities and a metric learning framework based on the boosting principle into a constrained k-means algorithm. In the framework, a metric is learned in the form of a kernel matrix that integrates weak cluster hypotheses produced by the constrained k-means algorithm, which works as a weak learner under the boosting principle. Experimental results for 12 data sets from 3 data sources demonstrated that our method has performance competitive to those of state-of-the-art constrained clustering methods for most data sets and that it takes much less computation time. Experimental evaluation demonstrated the effectiveness of controlling the constraint priorities by using the boosting principle and that our constrained k-means algorithm functions correctly as a weak learner of boosting.

PSMA-Targeted Polygadolinium Clusters: A Novel Agent for Imaging Prostate Cancer

National Research Council Canada - National Science Library

Messerle, Louis

2007-01-01

Controlled hydrolysis of lanthanide element or yttrium salts in the presence of aminoacids yields a series of polynuclear clusters with two, four, twelve, fourteen, and fifteen lanthanide or yttrium...

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.

a novel two – factor high order fuzzy time series with applications to ...

African Journals Online (AJOL)

HOD

objectively with multiple – factor fuzzy time series, recurrent number of fuzzy relationships, and assigning weights to elements of fuzzy forecasting rules. In this paper, a novel two – factor high – order fuzzy time series forecasting method based on fuzzy C-means clustering and particle swarm optimization is proposed to ...

Clustering-based analysis for residential district heating data

DEFF Research Database (Denmark)

Gianniou, Panagiota; Liu, Xiufeng; Heller, Alfred

2018-01-01

The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze r....... These findings will be valuable for district heating utilities and energy planners to optimize their operations, design demand-side management strategies, and develop targeting energy-efficiency programs or policies.......The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze...... residential heating consumption data and evaluate information included in national building databases. The proposed method uses the K-means algorithm to segment consumption groups based on consumption intensity and representative patterns and ranks the groups according to daily consumption. This paper also...

Insight into dynamic genome imaging: Canonical framework identification and high-throughput analysis.

Science.gov (United States)

Ronquist, Scott; Meixner, Walter; Rajapakse, Indika; Snyder, John

2017-07-01

The human genome is dynamic in structure, complicating researcher's attempts at fully understanding it. Time series "Fluorescent in situ Hybridization" (FISH) imaging has increased our ability to observe genome structure, but due to cell type and experimental variability this data is often noisy and difficult to analyze. Furthermore, computational analysis techniques are needed for homolog discrimination and canonical framework detection, in the case of time-series images. In this paper we introduce novel ideas for nucleus imaging analysis, present findings extracted using dynamic genome imaging, and propose an objective algorithm for high-throughput, time-series FISH imaging. While a canonical framework could not be detected beyond statistical significance in the analyzed dataset, a mathematical framework for detection has been outlined with extension to 3D image analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

INTERNATIONAL BEHAVIOUR AND PERFORMANCE BASED ROMANIAN ENTREPRENEURIAL AND TRADITIONAL FIRM CLUSTERS

Directory of Open Access Journals (Sweden)

FEDER Emoke - Szidonia

2015-07-01

Full Text Available The micro, small and medium-sized firms (SMEs present a key interest at European level due to their potential positive influence on regional, national and firm level competitiveness. At a certain moment in time, internationalisation became an expected and even unavoidable strategy in firms’ future development, growth and evolution. From theoretical perspective, an integrative complementarily approach is adopted concerning the dominant paradigm of stage models from incremental internationalisation theory and the emergent paradigm of international entrepreneurship theory. Several researcher calls for empirical testing of different theoretical frameworks and international firms. Therefore, the first aim of the quantitative study is to empirically prove, the existence of various internationalisation behaviour configuration based clusters, like sporadic and traditional international firms, born-again global and born global firms, within the framework of Romanian SMEs. Secondly, within the research framework the study propose to assess different distinguishing internationalisation behavioural characteristics and patterns for the delimited clusters, in terms of foreign market scope, internationalisation pace and rhythm, initial and current entry modes, international product portfolio and commitment. Thirdly, internationalisation cluster membership and patterns differential influence and contribution is analysed on firm level international business performance, as internationalisation degree, financial and marketing measures. The framework was tested on a transversal sample consisting of 140 Romanian internationalised SMEs. Findings are especially useful for entrepreneurs and SME managers presenting various decisional possibilities and options on internationalisation behaviours and performance. These emphasize the importance of internationalisation scope, pace, object and opportunity seeking, along with positive influence on performance, indifferent

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.

Comparative analysis on the selection of number of clusters in community detection

Science.gov (United States)

Kawamoto, Tatsuro; Kabashima, Yoshiyuki

2018-02-01

We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

Energy transfer rates in inhomogeneous van der Waals clusters

International Nuclear Information System (INIS)

Desfrancois, C.; Schermann, J.P.

1991-01-01

The internal energy exchange inside an inhomogeneous van der Waals cluster are investigated by means of molecular dynamic calculations. The very long time scales for relaxation of the high frequency degrees of freedom are examined within the framework of Nekhoroshev's theorem. (orig.)

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.

On the preequilibrium emission of clusters

Energy Technology Data Exchange (ETDEWEB)

Lunev, V.P.; Masterov, V.S.; Pronyaev, A.V. [Institute of Physics and Power Engineering, Obninsk (Russian Federation)] [and others

1995-10-01

An approach for the description of the preequilibrium emission of light composite particles in the framework of the exciton model is proposed. The description is analogous to the Iwamoto-Harada (I-H) model in which formation factors (FF) of clusters are obtained and the possibility of pick up process is taken into account. In the model proposed phase-space volume corresponding some arbitrary type of cluster with fixed excitation energies of nucleons picked up below and above Fermi Surface (FS) is calculated. This allows the authors to obtain the correct distribution of excitation energy between particle and hole degrees of freedom in the final state density of system: compound nucleus - cluster. The simple factorized form of the final state density of system can be obtained by introducing the average values of excitation energy of cluster constituent particles. The result of I-H treatment is valid only if one neglects the hole energy of picked up m particles, and thus it results in the overestimation of final states density and correspondingly overestimates cross-sections in comparison with the approach proposed. The numerical calculations of modified formation factors (MFF) of alpha particle and tritium are performed.

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks.

Science.gov (United States)

Wu, Jibing; Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.

Non-parametric co-clustering of large scale sparse bipartite networks on the GPU

DEFF Research Database (Denmark)

Hansen, Toke Jansen; Mørup, Morten; Hansen, Lars Kai

2011-01-01

of row and column clusters from a hypothesis space of an infinite number of clusters. To reach large scale applications of co-clustering we exploit that parameter inference for co-clustering is well suited for parallel computing. We develop a generic GPU framework for efficient inference on large scale...... sparse bipartite networks and achieve a speedup of two orders of magnitude compared to estimation based on conventional CPUs. In terms of scalability we find for networks with more than 100 million links that reliable inference can be achieved in less than an hour on a single GPU. To efficiently manage...

Big Data Analytics for Demand Response: Clustering Over Space and Time

Energy Technology Data Exchange (ETDEWEB)

Chelmis, Charalampos [Univ. of Southern California, Los Angeles, CA (United States); Kolte, Jahanvi [Nirma Univ., Gujarat (India); Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States)

2015-10-29

The pervasive deployment of advanced sensing infrastructure in Cyber-Physical systems, such as the Smart Grid, has resulted in an unprecedented data explosion. Such data exhibit both large volumes and high velocity characteristics, two of the three pillars of Big Data, and have a time-series notion as datasets in this context typically consist of successive measurements made over a time interval. Time-series data can be valuable for data mining and analytics tasks such as identifying the “right” customers among a diverse population, to target for Demand Response programs. However, time series are challenging to mine due to their high dimensionality. In this paper, we motivate this problem using a real application from the smart grid domain. We explore novel representations of time-series data for BigData analytics, and propose a clustering technique for determining natural segmentation of customers and identification of temporal consumption patterns. Our method is generizable to large-scale, real-world scenarios, without making any assumptions about the data. We evaluate our technique using real datasets from smart meters, totaling ~ 18,200,000 data points, and show the efficacy of our technique in efficiency detecting the number of optimal number of clusters.

Recent development in clusters of rare earths and actinides. Chemistry and materials

Energy Technology Data Exchange (ETDEWEB)

Zheng, Zhiping (ed.) [Arizona Univ., Tucson, AZ (United States). Dept. of Chemistry and Biochemistry

2017-02-01

This book contains the following eight contributions: 1. Lanthanide Hydroxide Cluster Complexes via Ligand-Controlled Hydrolysis of the Lanthanide Ions (Zhonghao Zhang, Yanan Zhang, and Zhiping Zheng); 2. Synthesis and Structures of Lanthanide-Transition Metal Clusters (Xiu-Ying Zheng, Xiang-Jian Kong, and La-Sheng Long); 3. Hydrothermal Synthesis of Lanthanide and Lanthanide-Transition-Metal Cluster Organic Frameworks via Synergistic Coordination Strategy (Jian-Wen Cheng and Guo-Yu Yang); 4. Oxo Clusters of 5f Elements (Sarah Hickam and Peter C.); 5. Construction and Luminescence Properties of 4f and d-4f Clusters with Salen-Type Schiff Base Ligands (Xiaoping Yang, Shiqing Wang, Chengri Wang, Shaoming Huang, and Richard A.); 6. 4f-Clusters for Cryogenic Magnetic Cooling (Yan-Cong Chen, Jun-Liang Liu, and Ming-Liang Tong); 7. Lanthanide Clusters Toward Single-Molecule Magnets (Tian Han, You-Song Ding, and Yan-Zhen Zheng); 8. Molecular Rare Earth Hydride Clusters (Takanori Shima and Zhaomin Hou).

Recent development in clusters of rare earths and actinides. Chemistry and materials

International Nuclear Information System (INIS)

Zheng, Zhiping

2017-01-01

This book contains the following eight contributions: 1. Lanthanide Hydroxide Cluster Complexes via Ligand-Controlled Hydrolysis of the Lanthanide Ions (Zhonghao Zhang, Yanan Zhang, and Zhiping Zheng); 2. Synthesis and Structures of Lanthanide-Transition Metal Clusters (Xiu-Ying Zheng, Xiang-Jian Kong, and La-Sheng Long); 3. Hydrothermal Synthesis of Lanthanide and Lanthanide-Transition-Metal Cluster Organic Frameworks via Synergistic Coordination Strategy (Jian-Wen Cheng and Guo-Yu Yang); 4. Oxo Clusters of 5f Elements (Sarah Hickam and Peter C.); 5. Construction and Luminescence Properties of 4f and d-4f Clusters with Salen-Type Schiff Base Ligands (Xiaoping Yang, Shiqing Wang, Chengri Wang, Shaoming Huang, and Richard A.); 6. 4f-Clusters for Cryogenic Magnetic Cooling (Yan-Cong Chen, Jun-Liang Liu, and Ming-Liang Tong); 7. Lanthanide Clusters Toward Single-Molecule Magnets (Tian Han, You-Song Ding, and Yan-Zhen Zheng); 8. Molecular Rare Earth Hydride Clusters (Takanori Shima and Zhaomin Hou).

Decentralized cooperative spectrum sensing for ad-hoc disaster relief network clusters

DEFF Research Database (Denmark)

Pratas, Nuno; Marchetti, Nicola; Prasad, Neeli R.

2010-01-01

cooperative schemes becomes essential. A cluster based decentralized orchestration cooperative sensing scheme is proposed, where each node in the cluster decides which spectrum it should monitor, according to the past sensing decisions of all the cluster nodes. The proposed scheme performance is evaluated...... through a framework, which allows gauging the accuracy of multi narrow-band spectrum sensing cooperative schemes as well as to gauge the error in the estimation of each of the channels un-occupancy. Through that evaluation it is shown that the proposed decentralized scheme performance reaches...... the performance of the correspondent centralized scheme while outperforming the Round Robin scheme....

Collateral Damage: the Implications of Utrecht Star Cluster Astrophysics for Galaxy Evolution

Science.gov (United States)

Kruijssen, J. M. D.

2013-01-01

Until the early 2000s, the research portfolio of the Astronomical Institute in Utrecht (SIU) did not include galaxy evolution. Somewhat serendipitously, this changed with the advent of the star cluster group. In only a few years, a simple framework was developed to describe and quantify the properties of dynamically evolving star cluster populations. Since then, the ‘Utrecht cluster disruption model’ has shown that the galactic environment plays an important role in setting the evolution of stellar clusters. From this simple result, it follows that cluster populations bear some imprint of the characteristics and histories of their host galaxies, and that star clusters can be used to trace galaxy evolution—an aim for which the Utrecht star cluster models were never designed, but which they are well-capable of fulfilling. I review some of the work in this direction, with a strong emphasis on the contributions from the SIU.

MANAGEMENT APPROACH BETWEEN BUSINESS CLUSTER SUCCESS AND SOFT LEADER CHARACTERISTICS

Directory of Open Access Journals (Sweden)

Robert Lippert

2014-05-01

Full Text Available One of the potential aspects of economic growth lies in focusing on furtherance the development of business clusters. By linking the complementary competencies of profit oriented enterprises, NGO-s, universities, research institutes and local authorities, the innovation potential and the productivity are significantly increased. The present study investigates a specific and challenging managerial activity, the role of the cluster manager. The aim of the research is to reveal the intrinsic motivation of cluster operations and to demonstrate the importance of the manager in the efficient and sustainable operation. An empirical research has been conducted involving cluster managers and member organisations through an extensive questionnaire survey in Hungary. First, determinant factors of cluster success have been identified. By using these factors, as the operational activity of the cluster, as well as the satisfaction of the members in the field of innovation and productivity, a new continuous three-dimensional maturity model has been introduced to evaluate the cluster success. Mapping the soft factors, organisational culture and leadership roles have been assessed by applying Competing Values Framework method. The results of the research depict the correlation found between soft leader characteristics and cluster success.

Developing an external domino accident prevention framework : Hazwim

NARCIS (Netherlands)

Reniers, Genserik L L; Dullaert, W.; Ale, B. J.M.; Soudan, K.

Empirical research on major accident safety in the second largest chemical cluster worldwide, the Antwerp port area, supports the design of a meta-technical framework for optimizing external domino prevention. First, the majority of Seveso top tier companies have expressed a willingness to cooperate

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)…

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)

EVOLUTION AND DISTRIBUTION OF MAGNETIC FIELDS FROM ACTIVE GALACTIC NUCLEI IN GALAXY CLUSTERS. II. THE EFFECTS OF CLUSTER SIZE AND DYNAMICAL STATE

International Nuclear Information System (INIS)

Xu Hao; Li Hui; Collins, David C.; Li, Shengtai; Norman, Michael L.

2011-01-01

Theory and simulations suggest that magnetic fields from radio jets and lobes powered by their central super massive black holes can be an important source of magnetic fields in the galaxy clusters. This is Paper II in a series of studies where we present self-consistent high-resolution adaptive mesh refinement cosmological magnetohydrodynamic simulations that simultaneously follow the formation of a galaxy cluster and evolution of magnetic fields ejected by an active galactic nucleus. We studied 12 different galaxy clusters with virial masses ranging from 1 x 10 14 to 2 x 10 15 M sun . In this work, we examine the effects of the mass and merger history on the final magnetic properties. We find that the evolution of magnetic fields is qualitatively similar to those of previous studies. In most clusters, the injected magnetic fields can be transported throughout the cluster and be further amplified by the intracluster medium (ICM) turbulence during the cluster formation process with hierarchical mergers, while the amplification history and the magnetic field distribution depend on the cluster formation and magnetism history. This can be very different for different clusters. The total magnetic energies in these clusters are between 4 x 10 57 and 10 61 erg, which is mainly decided by the cluster mass, scaling approximately with the square of the total mass. Dynamically older relaxed clusters usually have more magnetic fields in their ICM. The dynamically very young clusters may be magnetized weakly since there is not enough time for magnetic fields to be amplified.

Stroke localization and classification using microwave tomography with k-means clustering and support vector machine.

Science.gov (United States)

Guo, Lei; Abbosh, Amin

2018-05-01

For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of symptoms. In this paper, a microwave-based stroke localization and classification framework is proposed. It is based on microwave tomography, k-means clustering, and a support vector machine (SVM) method. The dielectric profile of the brain is first calculated using the Born iterative method, whereas the amplitude of the dielectric profile is then taken as the input to k-means clustering. The cluster is selected as the feature vector for constructing and testing the SVM. A database of MRI-derived realistic head phantoms at different signal-to-noise ratios is used in the classification procedure. The performance of the proposed framework is evaluated using the receiver operating characteristic (ROC) curve. The results based on a two-dimensional framework show that 88% classification accuracy, with a sensitivity of 91% and a specificity of 87%, can be achieved. Bioelectromagnetics. 39:312-324, 2018. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

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.

Clustering of Mycobacterium tuberculosis Cases in Acapulco: Spoligotyping and Risk Factors

Directory of Open Access Journals (Sweden)

Elizabeth Nava-Aguilera

2011-01-01

Full Text Available Recurrence and reinfection of tuberculosis have quite different implications for prevention. We identified 267 spoligotypes of Mycobacterium tuberculosis from consecutive tuberculosis patients in Acapulco, Mexico, to assess the level of clustering and risk factors for clustered strains. Point cluster analysis examined spatial clustering. Risk analysis relied on the Mantel Haenszel procedure to examine bivariate associations, then to develop risk profiles of combinations of risk factors. Supplementary analysis of the spoligotyping data used SpolTools. Spoligotyping identified 85 types, 50 of them previously unreported. The five most common spoligotypes accounted for 55% of tuberculosis cases. One cluster of 70 patients (26% of the series produced a single spoligotype from the Manila Family (Clade EAI2. The high proportion (78% of patients infected with cluster strains is compatible with recent transmission of TB in Acapulco. Geomatic analysis showed no spatial clustering; clustering was associated with a risk profile of uneducated cases who lived in single-room dwellings. The Manila emerging strain accounted for one in every four cases, confirming that one strain can predominate in a hyperendemic area.

Calculational framework for safety analyses of non-reactor nuclear facilities

International Nuclear Information System (INIS)

Coleman, J.R.

1994-01-01

A calculational framework for the consequences analysis of non-reactor nuclear facilities is presented. The analysis framework starts with accident scenarios which are developed through a traditional hazard analysis and continues with a probabilistic framework for the consequences analysis. The framework encourages the use of response continua derived from engineering judgment and traditional deterministic engineering analyses. The general approach consists of dividing the overall problem into a series of interrelated analysis cells and then devising Markov chain like probability transition matrices for each of the cells. An advantage of this division of the problem is that intermediate output (as probability state vectors) are generated at each calculational interface. The series of analyses when combined yield risk analysis output. The analysis approach is illustrated through application to two non-reactor nuclear analyses: the Ulysses Space Mission, and a hydrogen burn in the Hanford waste storage tanks

Propuesta teorica de factores que impulsan la colaboracion interempresarial en la etapa de la conformacion de los Clusters

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Rolando Porchini

2010-01-01

Full Text Available Present research intends to clarify relationship between intercompanies collaboration and cluster successful conformation. This project shows theoretical concepts about clusters, origins and how clusters evolution parallels the globalization process. The investigation also clarifies differences about concepts of intercompanies cooperation and collaboration used so far without distinction. Actual scientific literature is analyzed about early phase of cluster conformation. intercompanies collaboration (C.I. is considered key to cluster successful conformation, and highlights which key factors are most relevant in cluster conformation, its consolidation and its competitiveness. This is why this research is important regarding what theoretical framework lies behind the 7 factors recognized as the intercompanies collaboration (C.I. construct. Such factors are: i Interchange of strategic information (I.E., ii formalized and consensual rules (R.C.; iii preexistence of particular strategies (P.E.; iv Process of firms selection (P.S.; v Government roll as facilitator (R.G.; vi Expected leadership in first cluster president (L.P. and vii Expected leadership in first cluster manager (L.G.. This theoretical framework is the first part of an investigation presented here in qualitative terms and the quantitative results will be presented shortly. Finally, some recommendations are presented useful to new clusters being founded in the state as well in Mexico.

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.

Single cyanide-bridged Mo(W)/S/Cu cluster-based coordination polymers: Reactant- and stoichiometry-dependent syntheses, effective photocatalytic properties

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jinfang, E-mail: zjf260@jiangnan.edu.cn [China-Australia Joint Research Center for Functional Molecular Materials, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122 (China); Wang, Chao [China-Australia Joint Research Center for Functional Molecular Materials, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122 (China); Wang, Yinlin; Chen, Weitao [China-Australia Joint Research Center for Functional Molecular Materials, Scientific Research Academy, Jiangsu University, Zhenjiang 212013 (China); Cifuentes, Marie P.; Humphrey, Mark G. [Research School of Chemistry, Australian National University, Canberra ACT 0200 (Australia); Zhang, Chi, E-mail: chizhang@jiangnan.edu.cn [China-Australia Joint Research Center for Functional Molecular Materials, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122 (China)

2015-11-15

The systematic study on the reaction variables affecting single cyanide-bridged Mo(W)/S/Cu cluster-based coordination polymers (CPs) is firstly demonstrated. Five anionic single cyanide-bridged Mo(W)/S/Cu cluster-based CPs {[Pr_4N][WS_4Cu_3(CN)_2]}{sub n} (1), {[Pr_4N][WS_4Cu_4(CN)_3]}{sub n} (2), {[Pr_4N][WOS_3Cu_3(CN)_2]}{sub n} (3), {[Bu_4N][WOS_3Cu_3(CN)_2]}{sub n} (4) and {[Bu_4N][MoOS_3Cu_3(CN)_2]}{sub n} (5) were prepared by varying the molar ratios of the starting materials, and the specific cations, cluster building blocks and central metal atoms in the cluster building blocks. 1 possesses an anionic 3D diamondoid framework constructed from 4-connected T-shaped clusters [WS{sub 4}Cu{sub 3}]{sup +} and single CN{sup −} bridges. 2 is fabricated from 6-connected planar ‘open’ clusters [WS{sub 4}Cu{sub 4}]{sup 2+} and single CN{sup −} bridges, forming an anionic 3D architecture with an “ACS” topology. 3 and 4 exhibit novel anionic 2-D double-layer networks, both constructed from nest-shaped clusters [WOS{sub 3}Cu{sub 3}]{sup +} linked by single CN{sup −} bridges, but containing the different cations [Pr{sub 4}N]{sup +} and [Bu{sub 4}N]{sup +}, respectively. 5 is constructed from nest-shaped clusters [MoOS{sub 3}Cu{sub 3}]{sup +} and single CN{sup −} bridges, with an anionic 3D diamondoid framework. The anionic frameworks of 1-5, all sustained by single CN{sup −} bridges, are non-interpenetrating and exhibit huge potential void volumes. Employing differing molar ratios of the reactants and varying the cluster building blocks resulted in differing single cyanide-bridged Mo(W)/S/Cu cluster-based CPs, while replacing the cation ([Pr{sub 4}N]{sup +} vs. [Bu{sub 4}N]{sup +}) was found to have negligible impact on the nature of the architecture. Unexpectedly, replacement of the central metal atom (W vs. Mo) in the cluster building blocks had a pronounced effect on the framework. Furthermore, the photocatalytic activities of heterothiometallic

Single cyanide-bridged Mo(W)/S/Cu cluster-based coordination polymers: Reactant- and stoichiometry-dependent syntheses, effective photocatalytic properties

International Nuclear Information System (INIS)

Zhang, Jinfang; Wang, Chao; Wang, Yinlin; Chen, Weitao; Cifuentes, Marie P.; Humphrey, Mark G.; Zhang, Chi

2015-01-01

The systematic study on the reaction variables affecting single cyanide-bridged Mo(W)/S/Cu cluster-based coordination polymers (CPs) is firstly demonstrated. Five anionic single cyanide-bridged Mo(W)/S/Cu cluster-based CPs {[Pr_4N][WS_4Cu_3(CN)_2]}_n (1), {[Pr_4N][WS_4Cu_4(CN)_3]}_n (2), {[Pr_4N][WOS_3Cu_3(CN)_2]}_n (3), {[Bu_4N][WOS_3Cu_3(CN)_2]}_n (4) and {[Bu_4N][MoOS_3Cu_3(CN)_2]}_n (5) were prepared by varying the molar ratios of the starting materials, and the specific cations, cluster building blocks and central metal atoms in the cluster building blocks. 1 possesses an anionic 3D diamondoid framework constructed from 4-connected T-shaped clusters [WS_4Cu_3]"+ and single CN"− bridges. 2 is fabricated from 6-connected planar ‘open’ clusters [WS_4Cu_4]"2"+ and single CN"− bridges, forming an anionic 3D architecture with an “ACS” topology. 3 and 4 exhibit novel anionic 2-D double-layer networks, both constructed from nest-shaped clusters [WOS_3Cu_3]"+ linked by single CN"− bridges, but containing the different cations [Pr_4N]"+ and [Bu_4N]"+, respectively. 5 is constructed from nest-shaped clusters [MoOS_3Cu_3]"+ and single CN"− bridges, with an anionic 3D diamondoid framework. The anionic frameworks of 1-5, all sustained by single CN"− bridges, are non-interpenetrating and exhibit huge potential void volumes. Employing differing molar ratios of the reactants and varying the cluster building blocks resulted in differing single cyanide-bridged Mo(W)/S/Cu cluster-based CPs, while replacing the cation ([Pr_4N]"+ vs. [Bu_4N]"+) was found to have negligible impact on the nature of the architecture. Unexpectedly, replacement of the central metal atom (W vs. Mo) in the cluster building blocks had a pronounced effect on the framework. Furthermore, the photocatalytic activities of heterothiometallic cluster-based CPs were firstly explored by monitoring the photodegradation of methylene blue (MB) under visible light irradiation, which reveals that 2

CROSS-CORRELATION WEAK LENSING OF SDSS GALAXY CLUSTERS. I. MEASUREMENTS

International Nuclear Information System (INIS)

Sheldon, Erin S.; Johnston, David E.; Scranton, Ryan; Koester, Benjamin P.; Oyaizu, Hiroaki; Cunha, Carlos; Lima, Marcos; Frieman, Joshua A.; McKay, Timothy A.; Lin Huan; Annis, James; Wechsler, Risa H.; Mandelbaum, Rachel; Bahcall, Neta A.; Fukugita, Masataka

2009-01-01

This is the first in a series of papers on the weak lensing effect caused by clusters of galaxies in Sloan Digital Sky Survey. The photometrically selected cluster sample, known as MaxBCG, includes ∼130,000 objects between redshift 0.1 and 0.3, ranging in size from small groups to massive clusters. We split the clusters into bins of richness and luminosity and stack the surface density contrast to produce mean radial profiles. The mean profiles are detected over a range of scales, from the inner halo (25 kpc h -1 ) well into the surrounding large-scale structure (30 Mpc h -1 ), with a significance of 15 to 20 in each bin. The signal over this large range of scales is best interpreted in terms of the cluster-mass cross-correlation function. We pay careful attention to sources of systematic error, correcting for them where possible. The resulting signals are calibrated to the ∼10% level, with the dominant remaining uncertainty being the redshift distribution of the background sources. We find that the profiles scale strongly with richness and luminosity. We find that the signal within a given richness bin depends upon luminosity, suggesting that luminosity is more closely correlated with mass than galaxy counts. We split the samples by redshift but detect no significant evolution. The profiles are not well described by power laws. In a subsequent series of papers, we invert the profiles to three-dimensional mass profiles, show that they are well fit by a halo model description, measure mass-to-light ratios, and provide a cosmological interpretation.

Application of cloud computing in power routing for clusters of microgrids using oblivious network routing algorithm

DEFF Research Database (Denmark)

Amini, M. Hadi; Broojeni, Kianoosh G.; Dragicevic, Tomislav

2017-01-01

of microgrid while preventing congestion as well as minimizing the power loss. Then, we present a two-layer simulation platform which considers both communication layer and physical layer of the microgrids' cluster. In order to improve the security of communication network, we perform the computations...... regarding the oblivious power routing via a cloud-based network. The proposed framework can be used for further studies that deal with the real-time simulation of the clusters of microgrids. In order to validate the effectiveness of the proposed framework, we implement our proposed oblivious routing scheme...

A data-driven approach to estimating the number of clusters in hierarchical clustering [version 1; referees: 2 approved, 1 approved with reservations

Directory of Open Access Journals (Sweden)

Antoine E. Zambelli

2016-12-01

Full Text Available DNA microarray and gene expression problems often require a researcher to perform clustering on their data in a bid to better understand its structure. In cases where the number of clusters is not known, one can resort to hierarchical clustering methods. However, there currently exist very few automated algorithms for determining the true number of clusters in the data. We propose two new methods (mode and maximum difference for estimating the number of clusters in a hierarchical clustering framework to create a fully automated process with no human intervention. These methods are compared to the established elbow and gap statistic algorithms using simulated datasets and the Biobase Gene ExpressionSet. We also explore a data mixing procedure inspired by cross validation techniques. We find that the overall performance of the maximum difference method is comparable or greater to that of the gap statistic in multi-cluster scenarios, and achieves that performance at a fraction of the computational cost. This method also responds well to our mixing procedure, which opens the door to future research. We conclude that both the mode and maximum difference methods warrant further study related to their mixing and cross-validation potential. We particularly recommend the use of the maximum difference method in multi-cluster scenarios given its accuracy and execution times, and present it as an alternative to existing algorithms.

Isoreticular metal-organic frameworks, process for forming the same, and systematic design of pore size and functionality therein, with application for gas storage

Science.gov (United States)

Yaghi, Omar M.; Eddaoudi, Mohamed; Li, Hailian; Kim, Jaheon; Rosi, Nathaniel

2007-03-27

The ability to design and construct solid-state materials with pre-determined structures is a grand challenge in chemistry. An inventive strategy based on reticulating metal ions and organic carboxylate links into extended networks has been advanced to a point that has allowed the design of porous structures in which pore size and functionality can be varied systematically. MOF-5, a prototype of a new class of porous materials and one that is constructed from octahedral Zn--O--C clusters and benzene links, was used to demonstrate that its 3-D porous system can be functionalized with the organic groups, --Br, --NH2, --OC3H7, --OC5H11, --H4C2, and --H4C4, and its pore size expanded with the long molecular struts biphenyl, tetrahydropyrene, pyrene, and terphenyl. The ability to direct the formation of the octahedral clusters in the presence of a desired carboxylate link is an essential feature of this strategy, which resulted in the design of an isoreticular (having the same framework topology) series of sixteen well-defined materials whose crystals have open space representing up to 91.1% of the crystal volume, and homogeneous periodic pores that can be incrementally varied from 3.8 to 28.8 angstroms. Unlike the unpredictable nature of zeolite and other molecular sieve syntheses, the deliberate control exercised at the molecular level in the design of these crystals is expected to have tremendous implications on materials properties and future technologies. Indeed, data indicate that members of this series represent the first monocrystalline mesoporous organic/inorganic frameworks, and exhibit the highest capacity for methane storage (155 cm3/cm3 at 36 atm) and the lowest densities (0.41 to 0.21 g/cm3) attained to date for any crystalline material at room temperature.

Typical Periods for Two-Stage Synthesis by Time-Series Aggregation with Bounded Error in Objective Function

Energy Technology Data Exchange (ETDEWEB)

Bahl, Björn; Söhler, Theo; Hennen, Maike; Bardow, André, E-mail: andre.bardow@ltt.rwth-aachen.de [Institute of Technical Thermodynamics, RWTH Aachen University, Aachen (Germany)

2018-01-08

Two-stage synthesis problems simultaneously consider here-and-now decisions (e.g., optimal investment) and wait-and-see decisions (e.g., optimal operation). The optimal synthesis of energy systems reveals such a two-stage character. The synthesis of energy systems involves multiple large time series such as energy demands and energy prices. Since problem size increases with the size of the time series, synthesis of energy systems leads to complex optimization problems. To reduce the problem size without loosing solution quality, we propose a method for time-series aggregation to identify typical periods. Typical periods retain the chronology of time steps, which enables modeling of energy systems, e.g., with storage units or start-up cost. The aim of the proposed method is to obtain few typical periods with few time steps per period, while accurately representing the objective function of the full time series, e.g., cost. Thus, we determine the error of time-series aggregation as the cost difference between operating the optimal design for the aggregated time series and for the full time series. Thereby, we rigorously bound the maximum performance loss of the optimal energy system design. In an initial step, the proposed method identifies the best length of typical periods by autocorrelation analysis. Subsequently, an adaptive procedure determines aggregated typical periods employing the clustering algorithm k-medoids, which groups similar periods into clusters and selects one representative period per cluster. Moreover, the number of time steps per period is aggregated by a novel clustering algorithm maintaining chronology of the time steps in the periods. The method is iteratively repeated until the error falls below a threshold value. A case study based on a real-world synthesis problem of an energy system shows that time-series aggregation from 8,760 time steps to 2 typical periods with each 2 time steps results in an error smaller than the optimality gap of

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.

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.

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.

Effects of stomata clustering on leaf gas exchange.

Science.gov (United States)

Lehmann, Peter; Or, Dani

2015-09-01

A general theoretical framework for quantifying the stomatal clustering effects on leaf gaseous diffusive conductance was developed and tested. The theory accounts for stomatal spacing and interactions among 'gaseous concentration shells'. The theory was tested using the unique measurements of Dow et al. (2014) that have shown lower leaf diffusive conductance for a genotype of Arabidopsis thaliana with clustered stomata relative to uniformly distributed stomata of similar size and density. The model accounts for gaseous diffusion: through stomatal pores; via concentration shells forming at pore apertures that vary with stomata spacing and are thus altered by clustering; and across the adjacent air boundary layer. Analytical approximations were derived and validated using a numerical model for 3D diffusion equation. Stomata clustering increases the interactions among concentration shells resulting in larger diffusive resistance that may reduce fluxes by 5-15%. A similar reduction in conductance was found for clusters formed by networks of veins. The study resolves ambiguities found in the literature concerning stomata end-corrections and stomatal shape, and provides a new stomata density threshold for diffusive interactions of overlapping vapor shells. The predicted reduction in gaseous exchange due to clustering, suggests that guard cell function is impaired, limiting stomatal aperture opening. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Towards a New Generation of Time-Series Visualization Tools in the ESA Heliophysics Science Archives

Science.gov (United States)

Perez, H.; Martinez, B.; Cook, J. P.; Herment, D.; Fernandez, M.; De Teodoro, P.; Arnaud, M.; Middleton, H. R.; Osuna, P.; Arviset, C.

2017-12-01

During the last decades a varied set of Heliophysics missions have allowed the scientific community to gain a better knowledge on the solar atmosphere and activity. The remote sensing images of missions such as SOHO have paved the ground for Helio-based spatial data visualization software such as JHelioViewer/Helioviewer. On the other hand, the huge amount of in-situ measurements provided by other missions such as Cluster provide a wide base for plot visualization software whose reach is still far from being fully exploited. The Heliophysics Science Archives within the ESAC Science Data Center (ESDC) already provide a first generation of tools for time-series visualization focusing on each mission's needs: visualization of quicklook plots, cross-calibration time series, pre-generated/on-demand multi-plot stacks (Cluster), basic plot zoom in/out options (Ulysses) and easy navigation through the plots in time (Ulysses, Cluster, ISS-Solaces). However, as the needs evolve and the scientists involved in new missions require to plot multi-variable data, heat maps stacks interactive synchronization and axis variable selection among other improvements. The new Heliophysics archives (such as Solar Orbiter) and the evolution of existing ones (Cluster) intend to address these new challenges. This paper provides an overview of the different approaches for visualizing time-series followed within the ESA Heliophysics Archives and their foreseen evolution.

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

Sinter-Resistant Platinum Catalyst Supported by Metal-Organic Framework.

Science.gov (United States)

Kim, In Soo; Li, Zhanyong; Zheng, Jian; Platero-Prats, Ana E; Mavrandonakis, Andreas; Pellizzeri, Steven; Ferrandon, Magali; Vjunov, Aleksei; Gallington, Leighanne C; Webber, Thomas E; Vermeulen, Nicolaas A; Penn, R Lee; Getman, Rachel B; Cramer, Christopher J; Chapman, Karena W; Camaioni, Donald M; Fulton, John L; Lercher, Johannes A; Farha, Omar K; Hupp, Joseph T; Martinson, Alex B F

2018-01-22

Single atoms and few-atom clusters of platinum are uniformly installed on the zirconia nodes of a metal-organic framework (MOF) NU-1000 via targeted vapor-phase synthesis. The catalytic Pt clusters, site-isolated by organic linkers, are shown to exhibit high catalytic activity for ethylene hydrogenation while exhibiting resistance to sintering up to 200 °C. In situ IR spectroscopy reveals the presence of both single atoms and few-atom clusters that depend upon synthesis conditions. Operando X-ray absorption spectroscopy and X-ray pair distribution analyses reveal unique changes in chemical bonding environment and cluster size stability while on stream. Density functional theory calculations elucidate a favorable reaction pathway for ethylene hydrogenation with the novel catalyst. These results provide evidence that atomic layer deposition (ALD) in MOFs is a versatile approach to the rational synthesis of size-selected clusters, including noble metals, on a high surface area support. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Institutes of innovative development: Their role in regional clusters

Directory of Open Access Journals (Sweden)

Bykova Anna

2011-01-01

Full Text Available The role of technological innovation in enhancing competitive advantage at the level of individual companies and industries, regions, and even countries, has increased interest in the innovation component of the cluster, and has led to revision of the concept of the treatment of cluster effects and of approaches to their study. As a result of theoretical research and analysis of practical situations, in the late 1990s W. Feldman and J. Audretsch developed a theory of economic development through the establishment of innovation clusters. In this paper we aim to identify the quantitative link between the participation in innovation clusters and universities, research centres, and other institutes of innovative development; we will also try to find the key factors affecting them. We used econometric procedures for 413 companies (based on the data of accounting and statistical reports of the Perm region (Russia. The regression outcomes allow defining the ‘stimulating’ factors affecting participation in cluster relationships. The quantitative analysis was supplemented by in-depth interviews on different types of relationship forms among companies and institutes promoting innovation within the framework of a cluster concept.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

Science.gov (United States)

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.