JMS: An Open Source Workflow Management System and Web-Based Cluster Front-End for High Performance Computing.

Science.gov (United States)

Brown, David K; Penkler, David L; Musyoka, Thommas M; Bishop, Özlem Tastan

2015-01-01

Complex computational pipelines are becoming a staple of modern scientific research. Often these pipelines are resource intensive and require days of computing time. In such cases, it makes sense to run them over high performance computing (HPC) clusters where they can take advantage of the aggregated resources of many powerful computers. In addition to this, researchers often want to integrate their workflows into their own web servers. In these cases, software is needed to manage the submission of jobs from the web interface to the cluster and then return the results once the job has finished executing. We have developed the Job Management System (JMS), a workflow management system and web interface for high performance computing (HPC). JMS provides users with a user-friendly web interface for creating complex workflows with multiple stages. It integrates this workflow functionality with the resource manager, a tool that is used to control and manage batch jobs on HPC clusters. As such, JMS combines workflow management functionality with cluster administration functionality. In addition, JMS provides developer tools including a code editor and the ability to version tools and scripts. JMS can be used by researchers from any field to build and run complex computational pipelines and provides functionality to include these pipelines in external interfaces. JMS is currently being used to house a number of bioinformatics pipelines at the Research Unit in Bioinformatics (RUBi) at Rhodes University. JMS is an open-source project and is freely available at https://github.com/RUBi-ZA/JMS.

JMS: An Open Source Workflow Management System and Web-Based Cluster Front-End for High Performance Computing.

Directory of Open Access Journals (Sweden)

David K Brown

Full Text Available Complex computational pipelines are becoming a staple of modern scientific research. Often these pipelines are resource intensive and require days of computing time. In such cases, it makes sense to run them over high performance computing (HPC clusters where they can take advantage of the aggregated resources of many powerful computers. In addition to this, researchers often want to integrate their workflows into their own web servers. In these cases, software is needed to manage the submission of jobs from the web interface to the cluster and then return the results once the job has finished executing. We have developed the Job Management System (JMS, a workflow management system and web interface for high performance computing (HPC. JMS provides users with a user-friendly web interface for creating complex workflows with multiple stages. It integrates this workflow functionality with the resource manager, a tool that is used to control and manage batch jobs on HPC clusters. As such, JMS combines workflow management functionality with cluster administration functionality. In addition, JMS provides developer tools including a code editor and the ability to version tools and scripts. JMS can be used by researchers from any field to build and run complex computational pipelines and provides functionality to include these pipelines in external interfaces. JMS is currently being used to house a number of bioinformatics pipelines at the Research Unit in Bioinformatics (RUBi at Rhodes University. JMS is an open-source project and is freely available at https://github.com/RUBi-ZA/JMS.

JMS: An Open Source Workflow Management System and Web-Based Cluster Front-End for High Performance Computing

Science.gov (United States)

Brown, David K.; Penkler, David L.; Musyoka, Thommas M.; Bishop, Özlem Tastan

2015-01-01

Complex computational pipelines are becoming a staple of modern scientific research. Often these pipelines are resource intensive and require days of computing time. In such cases, it makes sense to run them over high performance computing (HPC) clusters where they can take advantage of the aggregated resources of many powerful computers. In addition to this, researchers often want to integrate their workflows into their own web servers. In these cases, software is needed to manage the submission of jobs from the web interface to the cluster and then return the results once the job has finished executing. We have developed the Job Management System (JMS), a workflow management system and web interface for high performance computing (HPC). JMS provides users with a user-friendly web interface for creating complex workflows with multiple stages. It integrates this workflow functionality with the resource manager, a tool that is used to control and manage batch jobs on HPC clusters. As such, JMS combines workflow management functionality with cluster administration functionality. In addition, JMS provides developer tools including a code editor and the ability to version tools and scripts. JMS can be used by researchers from any field to build and run complex computational pipelines and provides functionality to include these pipelines in external interfaces. JMS is currently being used to house a number of bioinformatics pipelines at the Research Unit in Bioinformatics (RUBi) at Rhodes University. JMS is an open-source project and is freely available at https://github.com/RUBi-ZA/JMS. PMID:26280450

High-performance dynamic quantum clustering on graphics processors

Energy Technology Data Exchange (ETDEWEB)

Wittek, Peter, E-mail: peterwittek@acm.org [Swedish School of Library and Information Science, University of Boras, Boras (Sweden)

2013-01-15

Clustering methods in machine learning may benefit from borrowing metaphors from physics. Dynamic quantum clustering associates a Gaussian wave packet with the multidimensional data points and regards them as eigenfunctions of the Schroedinger equation. The clustering structure emerges by letting the system evolve and the visual nature of the algorithm has been shown to be useful in a range of applications. Furthermore, the method only uses matrix operations, which readily lend themselves to parallelization. In this paper, we develop an implementation on graphics hardware and investigate how this approach can accelerate the computations. We achieve a speedup of up to two magnitudes over a multicore CPU implementation, which proves that quantum-like methods and acceleration by graphics processing units have a great relevance to machine learning.

Performance Evaluation of Spectral Clustering Algorithm using Various Clustering Validity Indices

OpenAIRE

M. T. Somashekara; D. Manjunatha

2014-01-01

In spite of the popularity of spectral clustering algorithm, the evaluation procedures are still in developmental stage. In this article, we have taken benchmarking IRIS dataset for performing comparative study of twelve indices for evaluating spectral clustering algorithm. The results of the spectral clustering technique were also compared with k-mean algorithm. The validity of the indices was also verified with accuracy and (Normalized Mutual Information) NMI score. Spectral clustering algo...

First high energy hydrogen cluster beams

International Nuclear Information System (INIS)

Gaillard, M.J.; Genre, R.; Hadinger, G.; Martin, J.

1993-03-01

The hydrogen cluster accelerator of the Institut de Physique Nucleaire de Lyon (IPN Lyon) has been upgraded by adding a Variable Energy Post-accelerator of RFQ type (VERFQ). This operation has been performed in the frame of a collaboration between KfK Karlsruhe, IAP Frankfurt and IPN Lyon. The facility has been designed to deliver beams of mass selected Hn + clusters, n chosen between 3 and 49, in the energy range 65-100 keV/u. For the first time, hydrogen clusters have been accelerated at energies as high as 2 MeV. This facility opens new fields for experiments which will greatly benefit from a velocity range never available until now for such exotic projectiles. (author) 13 refs.; 1 fig

High-performance dynamic quantum clustering on graphics processors

International Nuclear Information System (INIS)

Wittek, Peter

2013-01-01

Clustering methods in machine learning may benefit from borrowing metaphors from physics. Dynamic quantum clustering associates a Gaussian wave packet with the multidimensional data points and regards them as eigenfunctions of the Schrödinger equation. The clustering structure emerges by letting the system evolve and the visual nature of the algorithm has been shown to be useful in a range of applications. Furthermore, the method only uses matrix operations, which readily lend themselves to parallelization. In this paper, we develop an implementation on graphics hardware and investigate how this approach can accelerate the computations. We achieve a speedup of up to two magnitudes over a multicore CPU implementation, which proves that quantum-like methods and acceleration by graphics processing units have a great relevance to machine learning.

CTEx Beowulf cluster for MCNP performance

International Nuclear Information System (INIS)

Gonzaga, Roberto N.; Amorim, Aneuri S. de; Balthar, Mario Cesar V.

2011-01-01

This work is an introduction to the CTEx Nuclear Defense Department's Beowulf Cluster. Building a Beowulf Cluster is a complex learning process that greatly depends upon your hardware and software requirements. The feasibility and efficiency of performing MCNP5 calculations with a small, heterogeneous computing cluster built in Red Hat's Fedora Linux operating system personal computers (PC) are explored. The performance increases that may be expected with such clusters are estimated for cases that typify general radiation transport calculations. Our results show that the speed increase from additional slave PCs is nearly linear up to 10 processors. The pre compiled parallel binary version of MCNP uses the Message-Passing Interface (MPI) protocol. The use of this pre compiled parallel version of MCNP5 with the MPI protocol on a small, heterogeneous computing cluster built from Red Hat's Fedora Linux operating system PCs is the subject of this work. (author)

Comparing the performance of biomedical clustering methods

DEFF Research Database (Denmark)

Wiwie, Christian; Baumbach, Jan; Röttger, Richard

2015-01-01

expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future......Identifying groups of similar objects is a popular first step in biomedical data analysis, but it is error-prone and impossible to perform manually. Many computational methods have been developed to tackle this problem. Here we assessed 13 well-known methods using 24 data sets ranging from gene....... This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide...

Cluster-based DBMS Management Tool with High-Availability

Directory of Open Access Journals (Sweden)

Jae-Woo Chang

2005-02-01

Full Text Available A management tool which is needed for monitoring and managing cluster-based DBMSs has been little studied. So, we design and implement a cluster-based DBMS management tool with high-availability that monitors the status of nodes in a cluster system as well as the status of DBMS instances in a node. The tool enables users to recognize a single virtual system image and provides them with the status of all the nodes and resources in the system by using a graphic user interface (GUI. By using a load balancer, our management tool can increase the performance of a cluster-based DBMS as well as can overcome the limitation of the existing parallel DBMSs.

Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis.

Science.gov (United States)

Zhang, Chao; Zhang, Pengcheng; Zhang, Weizhan

2017-09-27

A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF) manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

Performance assessment of the SIMFAP parallel cluster at IFIN-HH Bucharest

International Nuclear Information System (INIS)

Adam, Gh.; Adam, S.; Ayriyan, A.; Dushanov, E.; Hayryan, E.; Korenkov, V.; Lutsenko, A.; Mitsyn, V.; Sapozhnikova, T.; Sapozhnikov, A; Streltsova, O.; Buzatu, F.; Dulea, M.; Vasile, I.; Sima, A.; Visan, C.; Busa, J.; Pokorny, I.

2008-01-01

Performance assessment and case study outputs of the parallel SIMFAP cluster at IFIN-HH Bucharest point to its effective and reliable operation. A comparison with results on the supercomputing system in LIT-JINR Dubna adds insight on resource allocation for problem solving by parallel computing. The solution of models asking for very large numbers of knots in the discretization mesh needs the migration to high performance computing based on parallel cluster architectures. The acquisition of ready-to-use parallel computing facilities being beyond limited budgetary resources, the solution at IFIN-HH was to buy the hardware and the inter-processor network, and to implement by own efforts the open software concerning both the operating system and the parallel computing standard. The present paper provides a report demonstrating the successful solution of these tasks. The implementation of the well-known HPL (High Performance LINPACK) Benchmark points to the effective and reliable operation of the cluster. The comparison of HPL outputs obtained on parallel clusters of different magnitudes shows that there is an optimum range of the order N of the linear algebraic system over which a given parallel cluster provides optimum parallel solutions. For the SIMFAP cluster, this range can be inferred to correspond to about 1 to 2 x 10 4 linear algebraic equations. For an algorithm of polynomial complexity N α the task sharing among p processors within a parallel solution mainly follows an (N/p)α behaviour under peak performance achievement. Thus, while the problem complexity remains the same, a substantial decrease of the coefficient of the leading order of the polynomial complexity is achieved. (authors)

High performance data transfer

Science.gov (United States)

Cottrell, R.; Fang, C.; Hanushevsky, A.; Kreuger, W.; Yang, W.

2017-10-01

The exponentially increasing need for high speed data transfer is driven by big data, and cloud computing together with the needs of data intensive science, High Performance Computing (HPC), defense, the oil and gas industry etc. We report on the Zettar ZX software. This has been developed since 2013 to meet these growing needs by providing high performance data transfer and encryption in a scalable, balanced, easy to deploy and use way while minimizing power and space utilization. In collaboration with several commercial vendors, Proofs of Concept (PoC) consisting of clusters have been put together using off-the- shelf components to test the ZX scalability and ability to balance services using multiple cores, and links. The PoCs are based on SSD flash storage that is managed by a parallel file system. Each cluster occupies 4 rack units. Using the PoCs, between clusters we have achieved almost 200Gbps memory to memory over two 100Gbps links, and 70Gbps parallel file to parallel file with encryption over a 5000 mile 100Gbps link.

Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis

Directory of Open Access Journals (Sweden)

Chao Zhang

2017-09-01

Full Text Available A wireless-powered sensor network (WPSN consisting of one hybrid access point (HAP, a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

Latent Cluster Analysis of Instructional Practices Reported by High- and Low-performing Mathematics Teachers in Four Countries

OpenAIRE

Cheng, Qiang; Hsu, Hsien-Yuan

2017-01-01

Using Trends in International Mathematics and Science Study (TIMSS) 2011 eighth-grade international dataset, this study explored the profiles of instructional practices reported by high- and low-performing mathematics teachers across the US, Finland, Korea, and Russia. Concepts of conceptual teaching and procedural teaching were used to frame the design of the current study. Latent cluster analysis was applied in the investigation of the profiles of mathematics teachers’ instructional practic...

LATENT CLUSTER ANALYSIS OF INSTRUCTIONAL PRACTICES REPORTED BY HIGH- AND LOW-PERFORMING MATHEMATICS TEACHERS IN FOUR COUNTRIES

Directory of Open Access Journals (Sweden)

Qiang Cheng

2017-06-01

Full Text Available Using Trends in International Mathematics and Science Study (TIMSS 2011 eighth-grade international dataset, this study explored the profiles of instructional practices reported by high- and low-performing mathematics teachers across the US, Finland, Korea, and Russia. Concepts of conceptual teaching and procedural teaching were used to frame the design of the current study. Latent cluster analysis was applied in the investigation of the profiles of mathematics teachers’ instructional practices across the four education systems. It was found that all mathematics teachers in the high- and low-performing groups used procedurally as well as conceptually oriented practices in their teaching. However, one group of high-performing mathematics teachers from the U.S. sample and all the high-performing teachers from Finland, Korea, and Russia showed more frequent use of conceptually oriented practices than their corresponding low-performing teachers. Another group of U.S. high-performing mathematics teachers showed a distinctive procedurally oriented pattern, which presented a rather different picture. Such results provide useful suggestions for practitioners and policy makers in their effort to improve mathematics teaching and learning in the US and in other countries as well.DOI: http://dx.doi.org/10.22342/jme.8.2.4066.115-132

Performance of the cluster-jet target for PANDA

Energy Technology Data Exchange (ETDEWEB)

Hergemoeller, Ann-Katrin; Bonaventura, Daniel; Grieser, Silke; Hetz, Benjamin; Koehler, Esperanza; Khoukaz, Alfons [Institut fuer Kernphysik, Westfaelische Wilhelms-Universitaet Muenster, 48149 Muenster (Germany)

2016-07-01

The success of storage ring experiments strongly depends on the choice of the target. For this purpose, a very appropriate internal target for such an experiment is a cluster-jet target, which will be the first operated target at the PANDA experiment at FAIR. In this kind of target the cluster beam itself is formed due to the expansion of pre-cooled gases within a Laval nozzle and is prepared afterwards via two orifices, the skimmer and the collimator. The target prototype, operating successfully for years at the University of Muenster, provides routinely target thicknesses of more than 2 x 10{sup 15} (atoms)/(cm{sup 2}) in a distance of 2.1 m behind the nozzle. Based on the results of the performance of the cluster target prototype the final cluster-jet target source was designed and set into operation in Muenster as well. Besides the monitoring of the cluster beam itself and the thickness with two different monitoring systems at this target, investigations on the cluster mass via Mie scattering will be performed. In this presentation an overview of the cluster target design, its performance and the Mie scattering method are presented and discussed.

High-dimensional cluster analysis with the Masked EM Algorithm

Science.gov (United States)

Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.

2014-01-01

Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data. PMID:25149694

Performance Evaluation of Incremental K-means Clustering Algorithm

OpenAIRE

Chakraborty, Sanjay; Nagwani, N. K.

2014-01-01

The incremental K-means clustering algorithm has already been proposed and analysed in paper [Chakraborty and Nagwani, 2011]. It is a very innovative approach which is applicable in periodically incremental environment and dealing with a bulk of updates. In this paper the performance evaluation is done for this incremental K-means clustering algorithm using air pollution database. This paper also describes the comparison on the performance evaluations between existing K-means clustering and i...

Confined SnO2 quantum-dot clusters in graphene sheets as high-performance anodes for lithium-ion batteries.

Science.gov (United States)

Zhu, Chengling; Zhu, Shenmin; Zhang, Kai; Hui, Zeyu; Pan, Hui; Chen, Zhixin; Li, Yao; Zhang, Di; Wang, Da-Wei

2016-05-16

Construction of metal oxide nanoparticles as anodes is of special interest for next-generation lithium-ion batteries. The main challenge lies in their rapid capacity fading caused by the structural degradation and instability of solid-electrolyte interphase (SEI) layer during charge/discharge process. Herein, we address these problems by constructing a novel-structured SnO2-based anode. The novel structure consists of mesoporous clusters of SnO2 quantum dots (SnO2 QDs), which are wrapped with reduced graphene oxide (RGO) sheets. The mesopores inside the clusters provide enough room for the expansion and contraction of SnO2 QDs during charge/discharge process while the integral structure of the clusters can be maintained. The wrapping RGO sheets act as electrolyte barrier and conductive reinforcement. When used as an anode, the resultant composite (MQDC-SnO2/RGO) shows an extremely high reversible capacity of 924 mAh g(-1) after 200 cycles at 100 mA g(-1), superior capacity retention (96%), and outstanding rate performance (505 mAh g(-1) after 1000 cycles at 1000 mA g(-1)). Importantly, the materials can be easily scaled up under mild conditions. Our findings pave a new way for the development of metal oxide towards enhanced lithium storage performance.

GALAXY CLUSTERS AT HIGH REDSHIFT AND EVOLUTION OF BRIGHTEST CLUSTER GALAXIES

International Nuclear Information System (INIS)

Wen, Z. L.; Han, J. L.

2011-01-01

Identification of high-redshift clusters is important for studies of cosmology and cluster evolution. Using photometric redshifts of galaxies, we identify 631 clusters from the Canada-France-Hawaii Telescope (CFHT) wide field, 202 clusters from the CFHT deep field, 187 clusters from the Cosmic Evolution Survey (COSMOS) field, and 737 clusters from the Spitzer Wide-area InfraRed Extragalactic Survey (SWIRE) field. The redshifts of these clusters are in the range 0.1 ∼ + - m 3.6 μ m colors of the BCGs are consistent with a stellar population synthesis model in which the BCGs are formed at redshift z f ≥ 2 and evolved passively. The g' - z' and B - m 3.6μm colors of the BCGs at redshifts z > 0.8 are systematically bluer than the passive evolution model for galaxies formed at z f ∼ 2, indicating star formation in high-redshift BCGs.

Cost/Performance Ratio Achieved by Using a Commodity-Based Cluster

Science.gov (United States)

Lopez, Isaac

2001-01-01

Researchers at the NASA Glenn Research Center acquired a commodity cluster based on Intel Corporation processors to compare its performance with a traditional UNIX cluster in the execution of aeropropulsion applications. Since the cost differential of the clusters was significant, a cost/performance ratio was calculated. After executing a propulsion application on both clusters, the researchers demonstrated a 9.4 cost/performance ratio in favor of the Intel-based cluster. These researchers utilize the Aeroshark cluster as one of the primary testbeds for developing NPSS parallel application codes and system software. The Aero-shark cluster provides 64 Intel Pentium II 400-MHz processors, housed in 32 nodes. Recently, APNASA - a code developed by a Government/industry team for the design and analysis of turbomachinery systems was used for a simulation on Glenn's Aeroshark cluster.

Designing a High Performance Parallel Personal Cluster

OpenAIRE

Kapanova, K. G.; Sellier, J. M.

2016-01-01

Today, many scientific and engineering areas require high performance computing to perform computationally intensive experiments. For example, many advances in transport phenomena, thermodynamics, material properties, computational chemistry and physics are possible only because of the availability of such large scale computing infrastructures. Yet many challenges are still open. The cost of energy consumption, cooling, competition for resources have been some of the reasons why the scientifi...

Dark matter phenomenology of high-speed galaxy cluster collisions

International Nuclear Information System (INIS)

Mishchenko, Yuriy; Ji, Chueng-Ryong

2017-01-01

We perform a general computational analysis of possible post-collision mass distributions in high-speed galaxy cluster collisions in the presence of self-interacting dark matter. Using this analysis, we show that astrophysically weakly self-interacting dark matter can impart subtle yet measurable features in the mass distributions of colliding galaxy clusters even without significant disruptions to the dark matter halos of the colliding galaxy clusters themselves. Most profound such evidence is found to reside in the tails of dark matter halos' distributions, in the space between the colliding galaxy clusters. Such features appear in our simulations as shells of scattered dark matter expanding in alignment with the outgoing original galaxy clusters, contributing significant densities to projected mass distributions at large distances from collision centers and large scattering angles of up to 90 "c"i"r"c"l"e. Our simulations indicate that as much as 20% of the total collision's mass may be deposited into such structures without noticeable disruptions to the main galaxy clusters. Such structures at large scattering angles are forbidden in purely gravitational high-speed galaxy cluster collisions. Convincing identification of such structures in real colliding galaxy clusters would be a clear indication of the self-interacting nature of dark matter. Our findings may offer an explanation for the ring-like dark matter feature recently identified in the long-range reconstructions of the mass distribution of the colliding galaxy cluster CL0024+017. (orig.)

Dark matter phenomenology of high-speed galaxy cluster collisions

Energy Technology Data Exchange (ETDEWEB)

Mishchenko, Yuriy [Izmir University of Economics, Faculty of Engineering, Izmir (Turkey); Ji, Chueng-Ryong [North Carolina State University, Department of Physics, Raleigh, NC (United States)

2017-08-15

We perform a general computational analysis of possible post-collision mass distributions in high-speed galaxy cluster collisions in the presence of self-interacting dark matter. Using this analysis, we show that astrophysically weakly self-interacting dark matter can impart subtle yet measurable features in the mass distributions of colliding galaxy clusters even without significant disruptions to the dark matter halos of the colliding galaxy clusters themselves. Most profound such evidence is found to reside in the tails of dark matter halos' distributions, in the space between the colliding galaxy clusters. Such features appear in our simulations as shells of scattered dark matter expanding in alignment with the outgoing original galaxy clusters, contributing significant densities to projected mass distributions at large distances from collision centers and large scattering angles of up to 90 {sup circle}. Our simulations indicate that as much as 20% of the total collision's mass may be deposited into such structures without noticeable disruptions to the main galaxy clusters. Such structures at large scattering angles are forbidden in purely gravitational high-speed galaxy cluster collisions. Convincing identification of such structures in real colliding galaxy clusters would be a clear indication of the self-interacting nature of dark matter. Our findings may offer an explanation for the ring-like dark matter feature recently identified in the long-range reconstructions of the mass distribution of the colliding galaxy cluster CL0024+017. (orig.)

Family and academic performance: identifying high school student profiles

Directory of Open Access Journals (Sweden)

Alicia Aleli Chaparro Caso López

2016-01-01

Full Text Available The objective of this study was to identify profiles of high school students, based on variables related to academic performance, socioeconomic status, cultural capital and family organization. A total of 21,724 high school students, from the five municipalities of the state of Baja California, took part. A K-means cluster analysis was performed to identify the profiles. The analyses identified two clearly-defined clusters: Cluster 1 grouped together students with high academic performance and who achieved higher scores for socioeconomic status, cultural capital and family involvement, whereas Cluster 2 brought together students with low academic achievement, and who also obtained lower scores for socioeconomic status and cultural capital, and had less family involvement. It is concluded that the family variables analyzed form student profiles that can be related to academic achievement.

Performance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA Tesla GPU Cluster

Energy Technology Data Exchange (ETDEWEB)

Allada, Veerendra, Benjegerdes, Troy; Bode, Brett

2009-08-31

Commodity clusters augmented with application accelerators are evolving as competitive high performance computing systems. The Graphical Processing Unit (GPU) with a very high arithmetic density and performance per price ratio is a good platform for the scientific application acceleration. In addition to the interconnect bottlenecks among the cluster compute nodes, the cost of memory copies between the host and the GPU device have to be carefully amortized to improve the overall efficiency of the application. Scientific applications also rely on efficient implementation of the BAsic Linear Algebra Subroutines (BLAS), among which the General Matrix Multiply (GEMM) is considered as the workhorse subroutine. In this paper, they study the performance of the memory copies and GEMM subroutines that are critical to port the computational chemistry algorithms to the GPU clusters. To that end, a benchmark based on the NetPIPE framework is developed to evaluate the latency and bandwidth of the memory copies between the host and the GPU device. The performance of the single and double precision GEMM subroutines from the NVIDIA CUBLAS 2.0 library are studied. The results have been compared with that of the BLAS routines from the Intel Math Kernel Library (MKL) to understand the computational trade-offs. The test bed is a Intel Xeon cluster equipped with NVIDIA Tesla GPUs.

Performance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA Tesla GPU Cluster

International Nuclear Information System (INIS)

Allada, Veerendra; Benjegerdes, Troy; Bode, Brett

2009-01-01

Commodity clusters augmented with application accelerators are evolving as competitive high performance computing systems. The Graphical Processing Unit (GPU) with a very high arithmetic density and performance per price ratio is a good platform for the scientific application acceleration. In addition to the interconnect bottlenecks among the cluster compute nodes, the cost of memory copies between the host and the GPU device have to be carefully amortized to improve the overall efficiency of the application. Scientific applications also rely on efficient implementation of the BAsic Linear Algebra Subroutines (BLAS), among which the General Matrix Multiply (GEMM) is considered as the workhorse subroutine. In this paper, they study the performance of the memory copies and GEMM subroutines that are critical to port the computational chemistry algorithms to the GPU clusters. To that end, a benchmark based on the NetPIPE framework is developed to evaluate the latency and bandwidth of the memory copies between the host and the GPU device. The performance of the single and double precision GEMM subroutines from the NVIDIA CUBLAS 2.0 library are studied. The results have been compared with that of the BLAS routines from the Intel Math Kernel Library (MKL) to understand the computational trade-offs. The test bed is a Intel Xeon cluster equipped with NVIDIA Tesla GPUs.

Confined SnO2 quantum-dot clusters in graphene sheets as high-performance anodes for lithium-ion batteries

Science.gov (United States)

Zhu, Chengling; Zhu, Shenmin; Zhang, Kai; Hui, Zeyu; Pan, Hui; Chen, Zhixin; Li, Yao; Zhang, Di; Wang, Da-Wei

2016-01-01

Construction of metal oxide nanoparticles as anodes is of special interest for next-generation lithium-ion batteries. The main challenge lies in their rapid capacity fading caused by the structural degradation and instability of solid-electrolyte interphase (SEI) layer during charge/discharge process. Herein, we address these problems by constructing a novel-structured SnO2-based anode. The novel structure consists of mesoporous clusters of SnO2 quantum dots (SnO2 QDs), which are wrapped with reduced graphene oxide (RGO) sheets. The mesopores inside the clusters provide enough room for the expansion and contraction of SnO2 QDs during charge/discharge process while the integral structure of the clusters can be maintained. The wrapping RGO sheets act as electrolyte barrier and conductive reinforcement. When used as an anode, the resultant composite (MQDC-SnO2/RGO) shows an extremely high reversible capacity of 924 mAh g−1 after 200 cycles at 100 mA g−1, superior capacity retention (96%), and outstanding rate performance (505 mAh g−1 after 1000 cycles at 1000 mA g−1). Importantly, the materials can be easily scaled up under mild conditions. Our findings pave a new way for the development of metal oxide towards enhanced lithium storage performance. PMID:27181691

Analysis of SCTP and TCP based communication in high-speed clusters

International Nuclear Information System (INIS)

Kozlovszky, M.; Berceli, T.; Kutor, L.

2006-01-01

Performance and financial constraints are pushing modern DAQs (Data Acquisition Systems) to use distributed cluster environments instead of monolith one-box systems. Inside clusters application communication layers should support outstanding high performance requirements. We are currently investigating different network protocols that could meet the requirements of high speed/low latency peer-to-peer communication within DAQ clusters. We have carried out various performance measurements with TCP and SCTP over Fast and Gigabit Ethernet. We are focusing on Ethernet Technologies, because this transport medium is broad deployed, cost efficient and it has much better cost/throughput ratio than other available communication alternatives (e.g.: Myrinet, Infiniband). During this study, a protocol performance measurement application with different peer transport components has been developed. In the first part of the paper, we give a short comparison of the two protocols (SCTP and TCP), and an introduction of the transport layer structure developed. Later on we discuss the performance results of single/multi-stream peer-to-peer communication, give overview about application code transition possibilities from application developer point of view between the two protocols, and draw conclusions about usability

Centroid based clustering of high throughput sequencing reads based on n-mer counts.

Science.gov (United States)

Solovyov, Alexander; Lipkin, W Ian

2013-09-08

Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free comparison (in particular, comparison using sequence composition) to the challenge of sequence clustering. We study several centroid based algorithms for clustering sequences based on word counts. Study of their performance shows that using k-means algorithm with or without the data whitening is efficient from the computational point of view. A higher clustering accuracy can be achieved using the soft expectation maximization method, whereby each sequence is attributed to each cluster with a specific probability. We implement an open source tool for alignment-free clustering. It is publicly available from github: https://github.com/luscinius/afcluster. We show the utility of alignment-free sequence clustering for high throughput sequencing analysis despite its limitations. In particular, it allows one to perform assembly with reduced resources and a minimal loss of quality. The major factor affecting performance of alignment-free read clustering is the length of the read.

Operational mesoscale atmospheric dispersion prediction using high performance parallel computing cluster for emergency response

International Nuclear Information System (INIS)

Srinivas, C.V.; Venkatesan, R.; Muralidharan, N.V.; Das, Someshwar; Dass, Hari; Eswara Kumar, P.

2005-08-01

An operational atmospheric dispersion prediction system is implemented on a cluster super computer for 'Online Emergency Response' for Kalpakkam nuclear site. The numerical system constitutes a parallel version of a nested grid meso-scale meteorological model MM5 coupled to a random walk particle dispersion model FLEXPART. The system provides 48 hour forecast of the local weather and radioactive plume dispersion due to hypothetical air borne releases in a range of 100 km around the site. The parallel code was implemented on different cluster configurations like distributed and shared memory systems. Results of MM5 run time performance for 1-day prediction are reported on all the machines available for testing. A reduction of 5 times in runtime is achieved using 9 dual Xeon nodes (18 physical/36 logical processors) compared to a single node sequential run. Based on the above run time results a cluster computer facility with 9-node Dual Xeon is commissioned at IGCAR for model operation. The run time of a triple nested domain MM5 is about 4 h for 24 h forecast. The system has been operated continuously for a few months and results were ported on the IMSc home page. Initial and periodic boundary condition data for MM5 are provided by NCMRWF, New Delhi. An alternative source is found to be NCEP, USA. These two sources provide the input data to the operational models at different spatial and temporal resolutions and using different assimilation methods. A comparative study on the results of forecast is presented using these two data sources for present operational use. Slight improvement is noticed in rainfall, winds, geopotential heights and the vertical atmospheric structure while using NCEP data probably because of its high spatial and temporal resolution. (author)

Enhanced high-order harmonic generation from Argon-clusters

NARCIS (Netherlands)

Tao, Yin; Hagmeijer, Rob; Bastiaens, Hubertus M.J.; Goh, S.J.; van der Slot, P.J.M.; Biedron, S.; Milton, S.; Boller, Klaus J.

2017-01-01

High-order harmonic generation (HHG) in clusters is of high promise because clusters appear to offer an increased optical nonlinearity. We experimentally investigate HHG from Argon clusters in a supersonic gas jet that can generate monomer-cluster mixtures with varying atomic number density and

High-performance scientific computing in the cloud

Science.gov (United States)

Jorissen, Kevin; Vila, Fernando; Rehr, John

2011-03-01

Cloud computing has the potential to open up high-performance computational science to a much broader class of researchers, owing to its ability to provide on-demand, virtualized computational resources. However, before such approaches can become commonplace, user-friendly tools must be developed that hide the unfamiliar cloud environment and streamline the management of cloud resources for many scientific applications. We have recently shown that high-performance cloud computing is feasible for parallelized x-ray spectroscopy calculations. We now present benchmark results for a wider selection of scientific applications focusing on electronic structure and spectroscopic simulation software in condensed matter physics. These applications are driven by an improved portable interface that can manage virtual clusters and run various applications in the cloud. We also describe a next generation of cluster tools, aimed at improved performance and a more robust cluster deployment. Supported by NSF grant OCI-1048052.

On the performance limiting behavior of defect clusters in commercial silicon solar cells

Energy Technology Data Exchange (ETDEWEB)

Sopori, B.L.; Chen, W.; Jones, K. [National Renewable Energy Lab., Golden, CO (United States); Gee, J. [Sandia National Labs., Albuquerque, NM (United States)

1998-09-01

The authors report the observation of defect clusters in high-quality, commercial silicon solar cell substrates. The nature of the defect clusters, their mechanism of formation, and precipitation of metallic impurities at the defect clusters are discussed. This defect configuration influences the device performance in a unique way--by primarily degrading the voltage-related parameters. Network modeling is used to show that, in an N/P junction device, these regions act as shunts that dissipate power generated within the cell.

Cluster size matters: Size-driven performance of subnanometer clusters in catalysis, electrocatalysis and Li-air batteries

Science.gov (United States)

Vajda, Stefan

2015-03-01

This paper discusses the strongly size-dependent performance of subnanometer cluster based catalysts in 1) heterogeneous catalysis, 2) electrocatalysis and 3) Li-air batteries. The experimental studies are based on I. fabrication of ultrasmall clusters with atomic precision control of particle size and their deposition on oxide and carbon based supports; II. test of performance, III. in situand ex situ X-ray characterization of cluster size, shape and oxidation state; and IV.electron microscopies. Heterogeneous catalysis. The pronounced effect of cluster size and support on the performance of the catalyst (catalyst activity and the yield of Cn products) will be illustrated on the example of nickel and cobalt clusters in Fischer-Tropsch reaction. Electrocatalysis. The study of the oxygen evolution reaction (OER) on size-selected palladium clusters supported on ultrananocrystalline diamond show pronounced size effects. While Pd4 clusters show no reaction, Pd6 and Pd17 clusters are among the most active catalysts known (in in terms of turnover rate per Pd atom). The system (soft-landed Pd4, Pd6, or Pd17 clusters on an UNCD Si coated electrode) shows stable electrochemical potentials over several cycles, and the characterization of the electrodes show no evidence for evolution or dissolution of either the support Theoretical calculations suggest that this striking difference may be a demonstration that bridging Pd-Pd sites, which are only present in three-dimensional clusters, are active for the oxygen evolution reaction in Pd6O6. Li-air batteries. The studies show that sub-nm silver clusters have dramatic size-dependent effect on the lowering of the overpotential, charge capacity, morphology of the discharge products, as well as on the morphology of the nm size building blocks of the discharge products. The results suggest that by precise control of the active surface sites on the cathode, the performance of Li-air cells can be significantly improved

High Intensity Femtosecond XUV Pulse Interactions with Atomic Clusters: Final Report

Energy Technology Data Exchange (ETDEWEB)

Ditmire, Todd [Univ. of Texas, Austin, TX (United States). Center for High Energy Density Science

2016-10-12

We propose to expand our recent studies on the interactions of intense extreme ultraviolet (XUV) femtosecond pulses with atomic and molecular clusters. The work described follows directly from work performed under BES support for the past grant period. During this period we upgraded the THOR laser at UT Austin by replacing the regenerative amplifier with optical parametric amplification (OPA) using BBO crystals. This increased the contrast of the laser, the total laser energy to ~1.2 J , and decreased the pulse width to below 30 fs. We built a new all reflective XUV harmonic beam line into expanded lab space. This enabled an increase influence by a factor of 25 and an increase in the intensity by a factor of 50. The goal of the program proposed in this renewal is to extend this class of experiments to available higher XUV intensity and a greater range of wavelengths. In particular we plan to perform experiments to confirm our hypothesis about the origin of the high charge states in these exploding clusters, an effect which we ascribe to plasma continuum lowering (ionization potential depression) in a cluster nano-­plasma. To do this we will perform experiments in which XUV pulses of carefully chosen wavelength irradiate clusters composed of only low-Z atoms and clusters with a mixture of this low-­Z atom with higher Z atoms. The latter clusters will exhibit higher electron densities and will serve to lower the ionization potential further than in the clusters composed only of low Z atoms. This should have a significant effect on the charge states produced in the exploding cluster. We will also explore the transition of explosions in these XUV irradiated clusters from hydrodynamic expansion to Coulomb explosion. The work proposed here will explore clusters of a wider range of constituents, including clusters from solids. Experiments on clusters from solids will be enabled by development we performed during the past grant period in which we constructed and

Performance clustering and incentives in the UK pension fund industry

OpenAIRE

David Blake; Bruce N. Lehmann; Allan Timmermann

2002-01-01

Despite pension fund managers being largely unconstrained in their investment decisions, this paper reports evidence of clustering in the performance of a large cross-section of UK pension fund managers around the median fund manager. This finding is explained in terms of the predominance of a single investment style (balanced management), the fee structures and incentives operating in the UK pension fund industry to maximise relative rather than absolute performance, the high concentration i...

Towards Urban High-technolgy Clusters: An International Comparison

Directory of Open Access Journals (Sweden)

Júlia Bosch

2012-01-01

Full Text Available This paper presents the results of a comparative study of 23 urban or regional high-technology clusters (media, ICT, energy, biotechnology all over the world, analyzing how they were created, how they are managed and how they operate, and the strategies followed to improve and excel in their fields of action. Special attention is given to issues related to descriptive aspects, R&D, performance of the clusters, location factors and incentives to attract companies. The empirical analysis applied to the identified clusters was done through a questionnaire sent to the representatives of the cluster’s management. When analyzing the data, the study has combined quantitative and qualitative methods, depending on the information to be processed. The data collection was done through a selection of indicators chosen in order to cover the different elements that cluster literature coincide in consider essential to develop a competitive economic cluster in urban regions. The main obstacle we find with the information available to carry out this study has been its heterogeneity and different quality of the data. 22@Barcelona appears to be in a good position to compete with other excelling clusters, but it still needs to improve in areas such as financial supply for R&D and start-ups and coordination between the different actors involved in urban economic development. Our research also contributes to the discussion on the role of public institutions in the cluster development policies. In the clusters studied here, especially in Barcelona, we have seen that a capable and resourceful public administration can determine the success of the cluster initiative.

Cluster-assembled overlayers and high-temperature superconductors

International Nuclear Information System (INIS)

Ohno, T.R.; Yang, Y.; Kroll, G.H.; Krause, K.; Schmidt, L.D.; Weaver, J.H.; Kimachi, Y.; Hidaka, Y.; Pan, S.H.; de Lozanne, A.L.

1991-01-01

X-ray photoemission results for interfaces prepared by cluster assembly with nanometer-size clusters deposited on high-T c superconductors (HTS's) show a reduction in reactivity because atom interactions with the surface are replaced by cluster interactions. Results for conventional atom deposition show the formation of overlayer oxides that are related to oxygen depletion and disruption of the near-surface region of the HTS's. For cluster assembly of Cr and Cu, there is a very thin reacted region on single-crystal Bi 2 Sr 2 CaCu 2 O 8 . Reduced reactivity is observed for Cr cluster deposition on single-crystal YBa 2 Cu 3 O 7 -based interfaces. There is no evidence of chemical modification of the surface for Ge and Au cluster assembly on Bi 2 Sr 2 CaCu 2 O 8 (100). The overlayer grown by Au cluster assembly on Bi 2 Sr 2 CaCu 2 O 8 covers the surface at low temperature but roughening occurs upon warming to 300 K. Scanning-tunneling-microscopy results for the Au(cluster)/Bi 2 Sr 2 CaCu 2 O 8 system warmed to 300 K shows individual clusters that have coalesced into large clusters. These results offer insight into the role of surface energies and cluster interactions in determining the overlayer morphology. Transmission-electron-microscopy results for Cu cluster assembly on silica show isolated irregularly shaped clusters that do not interact at low coverage. Sintering and labyrinth formation is observed at intermediate coverage and, ultimately, a continuous film is achieved at high coverage. Silica surface wetting by Cu clusters demonstrates that dispersive force are important for these small clusters

Improved Ant Colony Clustering Algorithm and Its Performance Study

Science.gov (United States)

Gao, Wei

2016-01-01

Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering. PMID:26839533

Implementation and performance of the ATLAS pixel clustering neural networks

CERN Document Server

Gagnon, Louis-Guillaume; The ATLAS collaboration

2018-01-01

The high particle densities produced by the Large Hadron Collider (LHC) mean that in the ATLAS pixel detector the clusters of deposited charge start to merge. A neural network-based approach is used to estimate the number of particles contributing to each cluster, and to accurately estimate the hit positions even in the presence of multiple particles. This talk thoroughly describes the algorithm and its implementation as well as present a set of benchmark performance measurements. The problem is most acute in the core of high-momentum jets where the average separation between particles becomes comparable to the detector granularity. This is further complicated by the high number of interactions per bunch crossing. Both these issues will become worse as the Run 3 and HL-LHC programme require analysis of higher and higher pT jets, while the interaction multiplicity rises. Future prospects in the context of LHC Run 3 and the upcoming ATLAS inner detector upgrade are also discussed.

Graphic-Card Cluster for Astrophysics (GraCCA) -- Performance Tests

OpenAIRE

Schive, Hsi-Yu; Chien, Chia-Hung; Wong, Shing-Kwong; Tsai, Yu-Chih; Chiueh, Tzihong

2007-01-01

In this paper, we describe the architecture and performance of the GraCCA system, a Graphic-Card Cluster for Astrophysics simulations. It consists of 16 nodes, with each node equipped with 2 modern graphic cards, the NVIDIA GeForce 8800 GTX. This computing cluster provides a theoretical performance of 16.2 TFLOPS. To demonstrate its performance in astrophysics computation, we have implemented a parallel direct N-body simulation program with shared time-step algorithm in this system. Our syste...

Family-based clusters of cognitive test performance in familial schizophrenia

Directory of Open Access Journals (Sweden)

Partonen Timo

2004-07-01

Full Text Available Abstract Background Cognitive traits derived from neuropsychological test data are considered to be potential endophenotypes of schizophrenia. Previously, these traits have been found to form a valid basis for clustering samples of schizophrenia patients into homogeneous subgroups. We set out to identify such clusters, but apart from previous studies, we included both schizophrenia patients and family members into the cluster analysis. The aim of the study was to detect family clusters with similar cognitive test performance. Methods Test scores from 54 randomly selected families comprising at least two siblings with schizophrenia spectrum disorders, and at least two unaffected family members were included in a complete-linkage cluster analysis with interactive data visualization. Results A well-performing, an impaired, and an intermediate family cluster emerged from the analysis. While the neuropsychological test scores differed significantly between the clusters, only minor differences were observed in the clinical variables. Conclusions The visually aided clustering algorithm was successful in identifying family clusters comprising both schizophrenia patients and their relatives. The present classification method may serve as a basis for selecting phenotypically more homogeneous groups of families in subsequent genetic analyses.

A Fast General-Purpose Clustering Algorithm Based on FPGAs for High-Throughput Data Processing

CERN Document Server

Annovi, A; The ATLAS collaboration; Castegnaro, A; Gatta, M

2012-01-01

We present a fast general-purpose algorithm for high-throughput clustering of data ”with a two dimensional organization”. The algorithm is designed to be implemented with FPGAs or custom electronics. The key feature is a processing time that scales linearly with the amount of data to be processed. This means that clustering can be performed in pipeline with the readout, without suffering from combinatorial delays due to looping multiple times through all the data. This feature makes this algorithm especially well suited for problems where the data has high density, e.g. in the case of tracking devices working under high-luminosity condition such as those of LHC or Super-LHC. The algorithm is organized in two steps: the first step (core) clusters the data; the second step analyzes each cluster of data to extract the desired information. The current algorithm is developed as a clustering device for modern high-energy physics pixel detectors. However, the algorithm has much broader field of applications. In ...

Cluster analysis of received constellations for optical performance monitoring

NARCIS (Netherlands)

van Weerdenburg, J.J.A.; van Uden, R.; Sillekens, E.; de Waardt, H.; Koonen, A.M.J.; Okonkwo, C.

2016-01-01

Performance monitoring based on centroid clustering to investigate constellation generation offsets. The tool allows flexibility in constellation generation tolerances by forwarding centroids to the demapper. The relation of fibre nonlinearities and singular value decomposition of intra-cluster

Clustering high dimensional data using RIA

Energy Technology Data Exchange (ETDEWEB)

Aziz, Nazrina [School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah (Malaysia)

2015-05-15

Clustering may simply represent a convenient method for organizing a large data set so that it can easily be understood and information can efficiently be retrieved. However, identifying cluster in high dimensionality data sets is a difficult task because of the curse of dimensionality. Another challenge in clustering is some traditional functions cannot capture the pattern dissimilarity among objects. In this article, we used an alternative dissimilarity measurement called Robust Influence Angle (RIA) in the partitioning method. RIA is developed using eigenstructure of the covariance matrix and robust principal component score. We notice that, it can obtain cluster easily and hence avoid the curse of dimensionality. It is also manage to cluster large data sets with mixed numeric and categorical value.

Electron acceleration via high contrast laser interacting with submicron clusters

International Nuclear Information System (INIS)

Zhang Lu; Chen Liming; Wang Weiming; Yan Wenchao; Yuan Dawei; Mao Jingyi; Wang Zhaohua; Liu Cheng; Shen Zhongwei; Li Yutong; Dong Quanli; Lu Xin; Ma Jinglong; Wei Zhiyi; Faenov, Anatoly; Pikuz, Tatiana; Li Dazhang; Sheng Zhengming; Zhang Jie

2012-01-01

We experimentally investigated electron acceleration from submicron size argon clusters-gas target irradiated by a 100 fs, 10 TW laser pulses having a high-contrast. Electron beams are observed in the longitudinal and transverse directions to the laser propagation. The measured energy of the longitudinal electron reaches 600 MeV and the charge of the electron beam in the transverse direction is more than 3 nC. A two-dimensional particle-in-cell simulation of the interaction has been performed and it shows an enhancement of electron charge by using the cluster-gas target.

High-performance computing — an overview

Science.gov (United States)

Marksteiner, Peter

1996-08-01

An overview of high-performance computing (HPC) is given. Different types of computer architectures used in HPC are discussed: vector supercomputers, high-performance RISC processors, various parallel computers like symmetric multiprocessors, workstation clusters, massively parallel processors. Software tools and programming techniques used in HPC are reviewed: vectorizing compilers, optimization and vector tuning, optimization for RISC processors; parallel programming techniques like shared-memory parallelism, message passing and data parallelism; and numerical libraries.

Heterogeneous Gpu&Cpu Cluster For High Performance Computing In Cryptography

Directory of Open Access Journals (Sweden)

Michał Marks

2012-01-01

Full Text Available This paper addresses issues associated with distributed computing systems andthe application of mixed GPU&CPU technology to data encryption and decryptionalgorithms. We describe a heterogenous cluster HGCC formed by twotypes of nodes: Intel processor with NVIDIA graphics processing unit and AMDprocessor with AMD graphics processing unit (formerly ATI, and a novel softwareframework that hides the heterogeneity of our cluster and provides toolsfor solving complex scientific and engineering problems. Finally, we present theresults of numerical experiments. The considered case study is concerned withparallel implementations of selected cryptanalysis algorithms. The main goal ofthe paper is to show the wide applicability of the GPU&CPU technology tolarge scale computation and data processing.

Comparison of wind mill cluster performance: A multicriteria approach

Energy Technology Data Exchange (ETDEWEB)

Rajakumar, D.G.; Nagesha, N. [Visvesvaraya Technological Univ., Karnataka (India)

2012-07-01

Energy is a crucial input for the economic and social development of any nation. Both renewable and non-renewable energy contribute in meeting the total requirement of the economy. As an affordable and clean energy source, wind energy is amongst the world's fastest growing renewable energy forms. Though there are several wind-mill clusters producing energy in different geographical locations, evaluating their performance is a complex task and not much of literature is available in this area. In this backdrop, an attempt is made in the current paper to estimate the performance of a wind-mill cluster through an index called Cluster Performance Index (CPI) adopting a multi-criteria approach. The proposed CPI comprises four criteria viz., Technical Performance Indicators (TePI), Economic Performance Indicators (EcPI), Environmental Performance Indicators (EnPI), and Sociological Performance Indicators (SoPI). Under each performance criterion a total of ten parameters are considered with five subjective and five objective oriented responses. The methodology is implemented by collecting empirical data from three wind-mill clusters located at Chitradurga, Davangere, and Gadag in the southern Indian State of Karnataka. Totally fifteen different stake holders are consulted through a set of structured researcher administered questionnaire to collect the relevant data in each wind farm. Stake holders involved engineers working in wind farms, wind farm developers, Government officials from energy department and a few selected residential people near the wind farms. The results of the study revealed that Chitradurga wind farm performed much better with a CPI of 45.267 as compared to Gadag (CPI of 28.362) and Davangere (CPI of 19.040) wind farms. (Author)

Communication Software Performance for Linux Clusters with Mesh Connections

Energy Technology Data Exchange (ETDEWEB)

Jie Chen; William Watson

2003-09-01

Recent progress in copper based commodity Gigabit Ethernet interconnects enables constructing clusters to achieve extremely high I/O bandwidth at low cost with mesh connections. However, the TCP/IP protocol stack cannot match the improved performance of Gigabit Ethernet networks especially in the case of multiple interconnects on a single host. In this paper, we evaluate and compare the performance characteristics of TCP/IP and M-VIA software that is an implementation of VIA.In particular, we focus on the performance of the software systems for a mesh communication architecture and demonstrate the feasibility of using multiple Gigabit Ethernet cards on one host to achieve aggregated bandwidth and latency that are not only better than what TCP provides but also compare favorably to some of the special purpose high-speed networks. In addition, implementation of a new M-VIA driver for one type of Gigabit Ethernet card will be discussed.

Comparative Performance Of Using PCA With K-Means And Fuzzy C Means Clustering For Customer Segmentation

Directory of Open Access Journals (Sweden)

Fahmida Afrin

2015-08-01

Full Text Available Abstract Data mining is the process of analyzing data and discovering useful information. Sometimes it is called knowledge Discovery. Clustering refers to groups whereas data are grouped in such a way that the data in one cluster are similar data in different clusters are dissimilar. Many data mining technologies are developed for customer segmentation. PCA is working as a preprocessor of Fuzzy C means and K- means for reducing the high dimensional and noisy data. There are many clustering method apply on customer segmentation. In this paper the performance of Fuzzy C means and K-means after implementing Principal Component Analysis is analyzed. We analyze the performance on a standard dataset for these algorithms. The results indicate that PCA based fuzzy clustering produces better results than PCA based K-means and is a more stable method for customer segmentation.

Relevant Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2009-01-01

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

Performance comparison analysis library communication cluster system using merge sort

Science.gov (United States)

Wulandari, D. A. R.; Ramadhan, M. E.

2018-04-01

Begins by using a single processor, to increase the speed of computing time, the use of multi-processor was introduced. The second paradigm is known as parallel computing, example cluster. The cluster must have the communication potocol for processing, one of it is message passing Interface (MPI). MPI have many library, both of them OPENMPI and MPICH2. Performance of the cluster machine depend on suitable between performance characters of library communication and characters of the problem so this study aims to analyze the comparative performances libraries in handling parallel computing process. The case study in this research are MPICH2 and OpenMPI. This case research execute sorting’s problem to know the performance of cluster system. The sorting problem use mergesort method. The research method is by implementing OpenMPI and MPICH2 on a Linux-based cluster by using five computer virtual then analyze the performance of the system by different scenario tests and three parameters for to know the performance of MPICH2 and OpenMPI. These performances are execution time, speedup and efficiency. The results of this study showed that the addition of each data size makes OpenMPI and MPICH2 have an average speed-up and efficiency tend to increase but at a large data size decreases. increased data size doesn’t necessarily increased speed up and efficiency but only execution time example in 100000 data size. OpenMPI has a execution time greater than MPICH2 example in 1000 data size average execution time with MPICH2 is 0,009721 and OpenMPI is 0,003895 OpenMPI can customize communication needs.

Performance of a Real-time Multipurpose 2-Dimensional Clustering Algorithm Developed for the ATLAS Experiment

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00372074; The ATLAS collaboration; Sotiropoulou, Calliope Louisa; Annovi, Alberto; Kordas, Kostantinos

2016-01-01

In this paper the performance of the 2D pixel clustering algorithm developed for the Input Mezzanine card of the ATLAS Fast TracKer system is presented. Fast TracKer is an approved ATLAS upgrade that has the goal to provide a complete list of tracks to the ATLAS High Level Trigger for each level-1 accepted event, at up to 100 kHz event rate with a very small latency, in the order of 100µs. The Input Mezzanine card is the input stage of the Fast TracKer system. Its role is to receive data from the silicon detector and perform real time clustering, thus to reduce the amount of data propagated to the subsequent processing levels with minimal information loss. We focus on the most challenging component on the Input Mezzanine card, the 2D clustering algorithm executed on the pixel data. We compare two different implementations of the algorithm. The first is one called the ideal one which searches clusters of pixels in the whole silicon module at once and calculates the cluster centroids exploiting the whole avail...

Performance of a Real-time Multipurpose 2-Dimensional Clustering Algorithm Developed for the ATLAS Experiment

CERN Document Server

Gkaitatzis, Stamatios; The ATLAS collaboration

2016-01-01

In this paper the performance of the 2D pixel clustering algorithm developed for the Input Mezzanine card of the ATLAS Fast TracKer system is presented. Fast TracKer is an approved ATLAS upgrade that has the goal to provide a complete list of tracks to the ATLAS High Level Trigger for each level-1 accepted event, at up to 100 kHz event rate with a very small latency, in the order of 100 µs. The Input Mezzanine card is the input stage of the Fast TracKer system. Its role is to receive data from the silicon detector and perform real time clustering, thus to reduce the amount of data propagated to the subsequent processing levels with minimal information loss. We focus on the most challenging component on the Input Mezzanine card, the 2D clustering algorithm executed on the pixel data. We compare two different implementations of the algorithm. The first is one called the ideal one which searches clusters of pixels in the whole silicon module at once and calculates the cluster centroids exploiting the whole avai...

Distributed MDSplus database performance with Linux clusters

International Nuclear Information System (INIS)

Minor, D.H.; Burruss, J.R.

2006-01-01

The staff at the DIII-D National Fusion Facility, operated for the USDOE by General Atomics, are investigating the use of grid computing and Linux technology to improve performance in our core data management services. We are in the process of converting much of our functionality to cluster-based and grid-enabled software. One of the most important pieces is a new distributed version of the MDSplus scientific data management system that is presently used to support fusion research in over 30 countries worldwide. To improve data handling performance, the staff is investigating the use of Linux clusters for both data clients and servers. The new distributed capability will result in better load balancing between these clients and servers, and more efficient use of network resources resulting in improved support of the data analysis needs of the scientific staff

High performance computing in Windows Azure cloud

OpenAIRE

Ambruš, Dejan

2013-01-01

High performance, security, availability, scalability, flexibility and lower costs of maintenance have essentially contributed to the growing popularity of cloud computing in all spheres of life, especially in business. In fact cloud computing offers even more than this. With usage of virtual computing clusters a runtime environment for high performance computing can be efficiently implemented also in a cloud. There are many advantages but also some disadvantages of cloud computing, some ...

A highly efficient multi-core algorithm for clustering extremely large datasets

Directory of Open Access Journals (Sweden)

Kraus Johann M

2010-04-01

Full Text Available Abstract Background In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. Results We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization. Conclusions Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.

Benzoate-Induced High-Nuclearity Silver Thiolate Clusters.

Science.gov (United States)

Su, Yan-Min; Liu, Wei; Wang, Zhi; Wang, Shu-Ao; Li, Yan-An; Yu, Fei; Zhao, Quan-Qin; Wang, Xing-Po; Tung, Chen-Ho; Sun, Di

2018-04-03

Compared with the well-known anion-templated effects in shaping silver thiolate clusters, the influence from the organic ligands in the outer shell is still poorly understood. Herein, three new benzoate-functionalized high-nuclearity silver(I) thiolate clusters are isolated and characterized for the first time in the presence of diverse anion templates such as S 2- , α-[Mo 5 O 18 ] 6- , and MoO 4 2- . Single-crystal X-ray analysis reveals that the nuclearities of the three silver clusters (SD/Ag28, SD/Ag29, SD/Ag30) vary from 32 to 38 to 78 with co-capped tBuS - and benzoate ligands on the surface. SD/Ag28 is a turtle-like cluster comprising a Ag 29 shell caging a Ag 3 S 3 trigon in the center, whereas SD/Ag29 is a prolate Ag 38 sphere templated by the α-[Mo 5 O 18 ] 6- anion. Upon changing from benzoate to methoxyl-substituted benzoate, SD/Ag30 is isolated as a very complicated core-shell spherical cluster composed of a Ag 57 shell and a vase-like Ag 21 S 13 core. Four MoO 4 2- anions are arranged in a supertetrahedron and located in the interstice between the core and shell. Introduction of the bulky benzoate changes elaborately the nuclearity and arrangements of silver polygons on the shell of silver clusters, which is exemplified by comparing SD/Ag28 and a known similar silver thiolate cluster. The three new clusters emit luminescence in the near-infrared (NIR) region and show different thermochromic luminescence properties. This work presents a flexible approach to synthetic studies of high-nuclearity silver clusters decorated by different benzoates, and structural modulations are also achieved. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

AN EFFECTIVE MULTI-CLUSTERING ANONYMIZATION APPROACH USING DISCRETE COMPONENT TASK FOR NON-BINARY HIGH DIMENSIONAL DATA SPACES

Directory of Open Access Journals (Sweden)

L.V. Arun Shalin

2016-01-01

Full Text Available Clustering is a process of grouping elements together, designed in such a way that the elements assigned to similar data points in a cluster are more comparable to each other than the remaining data points in a cluster. During clustering certain difficulties related when dealing with high dimensional data are ubiquitous and abundant. Works concentrated using anonymization method for high dimensional data spaces failed to address the problem related to dimensionality reduction during the inclusion of non-binary databases. In this work we study methods for dimensionality reduction for non-binary database. By analyzing the behavior of dimensionality reduction for non-binary database, results in performance improvement with the help of tag based feature. An effective multi-clustering anonymization approach called Discrete Component Task Specific Multi-Clustering (DCTSM is presented for dimensionality reduction on non-binary database. To start with we present the analysis of attribute in the non-binary database and cluster projection identifies the sparseness degree of dimensions. Additionally with the quantum distribution on multi-cluster dimension, the solution for relevancy of attribute and redundancy on non-binary data spaces is provided resulting in performance improvement on the basis of tag based feature. Multi-clustering tag based feature reduction extracts individual features and are correspondingly replaced by the equivalent feature clusters (i.e. tag clusters. During training, the DCTSM approach uses multi-clusters instead of individual tag features and then during decoding individual features is replaced by corresponding multi-clusters. To measure the effectiveness of the method, experiments are conducted on existing anonymization method for high dimensional data spaces and compared with the DCTSM approach using Statlog German Credit Data Set. Improved tag feature extraction and minimum error rate compared to conventional anonymization

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience

Science.gov (United States)

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R.; Bock, Davi D.; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C.; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R. Clay; Smith, Stephen J.; Szalay, Alexander S.; Vogelstein, Joshua T.; Vogelstein, R. Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes— neural connectivity maps of the brain—using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems—reads to parallel disk arrays and writes to solid-state storage—to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization. PMID:24401992

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience.

Science.gov (United States)

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R; Bock, Davi D; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R Clay; Smith, Stephen J; Szalay, Alexander S; Vogelstein, Joshua T; Vogelstein, R Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes - neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

Screen media usage, sleep time and academic performance in adolescents: clustering a self-organizing maps analysis.

Science.gov (United States)

Peiró-Velert, Carmen; Valencia-Peris, Alexandra; González, Luis M; García-Massó, Xavier; Serra-Añó, Pilar; Devís-Devís, José

2014-01-01

Screen media usage, sleep time and socio-demographic features are related to adolescents' academic performance, but interrelations are little explored. This paper describes these interrelations and behavioral profiles clustered in low and high academic performance. A nationally representative sample of 3,095 Spanish adolescents, aged 12 to 18, was surveyed on 15 variables linked to the purpose of the study. A Self-Organizing Maps analysis established non-linear interrelationships among these variables and identified behavior patterns in subsequent cluster analyses. Topological interrelationships established from the 15 emerging maps indicated that boys used more passive videogames and computers for playing than girls, who tended to use mobile phones to communicate with others. Adolescents with the highest academic performance were the youngest. They slept more and spent less time using sedentary screen media when compared to those with the lowest performance, and they also showed topological relationships with higher socioeconomic status adolescents. Cluster 1 grouped boys who spent more than 5.5 hours daily using sedentary screen media. Their academic performance was low and they slept an average of 8 hours daily. Cluster 2 gathered girls with an excellent academic performance, who slept nearly 9 hours per day, and devoted less time daily to sedentary screen media. Academic performance was directly related to sleep time and socioeconomic status, but inversely related to overall sedentary screen media usage. Profiles from the two clusters were strongly differentiated by gender, age, sedentary screen media usage, sleep time and academic achievement. Girls with the highest academic results had a medium socioeconomic status in Cluster 2. Findings may contribute to establishing recommendations about the timing and duration of screen media usage in adolescents and appropriate sleep time needed to successfully meet the demands of school academics and to improve

Screen media usage, sleep time and academic performance in adolescents: clustering a self-organizing maps analysis.

Directory of Open Access Journals (Sweden)

Carmen Peiró-Velert

Full Text Available Screen media usage, sleep time and socio-demographic features are related to adolescents' academic performance, but interrelations are little explored. This paper describes these interrelations and behavioral profiles clustered in low and high academic performance. A nationally representative sample of 3,095 Spanish adolescents, aged 12 to 18, was surveyed on 15 variables linked to the purpose of the study. A Self-Organizing Maps analysis established non-linear interrelationships among these variables and identified behavior patterns in subsequent cluster analyses. Topological interrelationships established from the 15 emerging maps indicated that boys used more passive videogames and computers for playing than girls, who tended to use mobile phones to communicate with others. Adolescents with the highest academic performance were the youngest. They slept more and spent less time using sedentary screen media when compared to those with the lowest performance, and they also showed topological relationships with higher socioeconomic status adolescents. Cluster 1 grouped boys who spent more than 5.5 hours daily using sedentary screen media. Their academic performance was low and they slept an average of 8 hours daily. Cluster 2 gathered girls with an excellent academic performance, who slept nearly 9 hours per day, and devoted less time daily to sedentary screen media. Academic performance was directly related to sleep time and socioeconomic status, but inversely related to overall sedentary screen media usage. Profiles from the two clusters were strongly differentiated by gender, age, sedentary screen media usage, sleep time and academic achievement. Girls with the highest academic results had a medium socioeconomic status in Cluster 2. Findings may contribute to establishing recommendations about the timing and duration of screen media usage in adolescents and appropriate sleep time needed to successfully meet the demands of school academics and

Effects of Cluster Location on Human Performance on the Traveling Salesperson Problem

Science.gov (United States)

MacGregor, James N.

2013-01-01

Most models of human performance on the traveling salesperson problem involve clustering of nodes, but few empirical studies have examined effects of clustering in the stimulus array. A recent exception varied degree of clustering and concluded that the more clustered a stimulus array, the easier a TSP is to solve (Dry, Preiss, & Wagemans,…

A High-Order CFS Algorithm for Clustering Big Data

Directory of Open Access Journals (Sweden)

Fanyu Bu

2016-01-01

Full Text Available With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i an adaptive dropout deep learning model to learn features from each type of data, (ii a feature tensor model to capture the correlations of heterogeneous data, and (iii a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

Highly reddened Ara cluster revisited

International Nuclear Information System (INIS)

Koornneef, J.

1976-01-01

New infrared observations in a highly reddened (Asub(V)>12sup(m)) cluster in Ara are presented and discussed. Some of the stars, previously classified as M-type supergiants, exhibit a strong infrared excess to be attributed to thermospheres

Clustering Professional Basketball Players by Performance

OpenAIRE

Patel, Riki

2017-01-01

Basketball players are traditionally grouped into five distinct positions, but these designationsare quickly becoming outdated. We attempt to reclassify players into new groupsbased on personal performance in the 2016-2017 NBA regular season. Two dimensionalityreduction techniques, t-Distributed Stochastic Neighbor Embedding (t-SNE) and principalcomponent analysis (PCA), were employed to reduce 18 classic metrics down to two dimensionsfor visualization. k-means clustering discovered four grou...

Performance of networks of artificial neurons: The role of clustering

International Nuclear Information System (INIS)

Kim, Beom Jun

2004-01-01

The performance of the Hopfield neural network model is numerically studied on various complex networks, such as the Watts-Strogatz network, the Barabasi-Albert network, and the neuronal network of Caenorhabditis elegans. Through the use of a systematic way of controlling the clustering coefficient, with the degree of each neuron kept unchanged, we find that the networks with the lower clustering exhibit much better performance. The results are discussed in the practical viewpoint of application, and the biological implications are also suggested

Clusters of PCS for high-speed computation for modelling of the climate

International Nuclear Information System (INIS)

Pabon C, Jose Daniel; Eslava R, Jesus Antonio; Montoya G, Gerardo de Jesus

2001-01-01

In order to create high speed computing capability, the Program of Post grade in Meteorology of the Department of Geosciences, National University of Colombia installed a cluster of 8 PCs for parallel processing. This high-speed processing machine was tested with the Climate Community Model (CCM3). In this paper, the results related to the performance of this machine are presented

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment.

Directory of Open Access Journals (Sweden)

Aline Guttmann

Full Text Available In cluster detection of disease, the use of local cluster detection tests (CDTs is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps.

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

Science.gov (United States)

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

Clustering Dycom

KAUST Repository

Minku, Leandro L.

2017-10-06

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

Scalability of DL_POLY on High Performance Computing Platform

Directory of Open Access Journals (Sweden)

Mabule Samuel Mabakane

2017-12-01

Full Text Available This paper presents a case study on the scalability of several versions of the molecular dynamics code (DL_POLY performed on South Africa‘s Centre for High Performance Computing e1350 IBM Linux cluster, Sun system and Lengau supercomputers. Within this study different problem sizes were designed and the same chosen systems were employed in order to test the performance of DL_POLY using weak and strong scalability. It was found that the speed-up results for the small systems were better than large systems on both Ethernet and Infiniband network. However, simulations of large systems in DL_POLY performed well using Infiniband network on Lengau cluster as compared to e1350 and Sun supercomputer.

A CLUSTER IN THE MAKING: ALMA REVEALS THE INITIAL CONDITIONS FOR HIGH-MASS CLUSTER FORMATION

International Nuclear Information System (INIS)

Rathborne, J. M.; Contreras, Y.; Longmore, S. N.; Bastian, N.; Jackson, J. M.; Alves, J. F.; Bally, J.; Foster, J. B.; Garay, G.; Kruijssen, J. M. D.; Testi, L.; Walsh, A. J.

2015-01-01

G0.253+0.016 is a molecular clump that appears to be on the verge of forming a high-mass cluster: its extremely low dust temperature, high mass, and high density, combined with its lack of prevalent star formation, make it an excellent candidate for an Arches-like cluster in a very early stage of formation. Here we present new Atacama Large Millimeter/Sub-millimeter Array observations of its small-scale (∼0.07 pc) 3 mm dust continuum and molecular line emission from 17 different species that probe a range of distinct physical and chemical conditions. The data reveal a complex network of emission features with a complicated velocity structure: there is emission on all spatial scales, the morphology of which ranges from small, compact regions to extended, filamentary structures that are seen in both emission and absorption. The dust column density is well traced by molecules with higher excitation energies and critical densities, consistent with a clump that has a denser interior. A statistical analysis supports the idea that turbulence shapes the observed gas structure within G0.253+0.016. We find a clear break in the turbulent power spectrum derived from the optically thin dust continuum emission at a spatial scale of ∼0.1 pc, which may correspond to the spatial scale at which gravity has overcome the thermal pressure. We suggest that G0.253+0.016 is on the verge of forming a cluster from hierarchical, filamentary structures that arise from a highly turbulent medium. Although the stellar distribution within high-mass Arches-like clusters is compact, centrally condensed, and smooth, the observed gas distribution within G0.253+0.016 is extended, with no high-mass central concentration, and has a complex, hierarchical structure. If this clump gives rise to a high-mass cluster and its stars are formed from this initially hierarchical gas structure, then the resulting cluster must evolve into a centrally condensed structure via a dynamical process

Charge-sign-clustering observed in high-multiplicity, high-energy heavy-ion collisions

International Nuclear Information System (INIS)

Takahashi, Y.; Gregory, J.C.; Hayashi, T.

1989-01-01

Charge-sign distribution in 200 GeV/amu heavy-ion collisions is studied with the Magnetic-Interferometric-Emulsion-Chamber (MAGIC) for central collision events in 16 O + Pb and 32 S + Pb interactions. Charge-sign clustering is observed in most of the fully-analyzed events. A statistical 'run-test' is performed for each measured event, which shows significant deviation from a Gaussian distribution (0,1) expected for random-charge distribution. Candidates of charge clusters have 5 - 10 multiplicity of like-sign particles, and are often accompanied by opposite-sign clusters. Observed clustering of identical charges is more significant in the fragmentation region than in the central region. Two-particle Bose-Einstein interference and other effects are discussed for the run-test examination. (author)

Performance Evaluation of Hadoop-based Large-scale Network Traffic Analysis Cluster

Directory of Open Access Journals (Sweden)

Tao Ran

2016-01-01

Full Text Available As Hadoop has gained popularity in big data era, it is widely used in various fields. The self-design and self-developed large-scale network traffic analysis cluster works well based on Hadoop, with off-line applications running on it to analyze the massive network traffic data. On purpose of scientifically and reasonably evaluating the performance of analysis cluster, we propose a performance evaluation system. Firstly, we set the execution times of three benchmark applications as the benchmark of the performance, and pick 40 metrics of customized statistical resource data. Then we identify the relationship between the resource data and the execution times by a statistic modeling analysis approach, which is composed of principal component analysis and multiple linear regression. After training models by historical data, we can predict the execution times by current resource data. Finally, we evaluate the performance of analysis cluster by the validated predicting of execution times. Experimental results show that the predicted execution times by trained models are within acceptable error range, and the evaluation results of performance are accurate and reliable.

A new physical performance classification system for elite handball players: cluster analysis

Science.gov (United States)

Chirosa, Ignacio J.; Robinson, Joseph E.; van der Tillaar, Roland; Chirosa, Luis J.; Martín, Isidoro Martínez

2016-01-01

Abstract The aim of the present study was to identify different cluster groups of handball players according to their physical performance level assessed in a series of physical assessments, which could then be used to design a training program based on individual strengths and weaknesses, and to determine which of these variables best identified elite performance in a group of under-19 [U19] national level handball players. Players of the U19 National Handball team (n=16) performed a set of tests to determine: 10 m (ST10) and 20 m (ST20) sprint time, ball release velocity (BRv), countermovement jump (CMJ) height and squat jump (SJ) height. All players also performed an incremental-load bench press test to determine the 1 repetition maximum (1RMest), the load corresponding to maximum mean power (LoadMP), the mean propulsive phase power at LoadMP (PMPPMP) and the peak power at LoadMP (PPEAKMP). Cluster analyses of the test results generated four groupings of players. The variables best able to discriminate physical performance were BRv, ST20, 1RMest, PPEAKMP and PMPPMP. These variables could help coaches identify talent or monitor the physical performance of athletes in their team. Each cluster of players has a particular weakness related to physical performance and therefore, the cluster results can be applied to a specific training programmed based on individual needs. PMID:28149376

A new physical performance classification system for elite handball players: cluster analysis

Directory of Open Access Journals (Sweden)

Bautista Iker J.

2016-06-01

Full Text Available The aim of the present study was to identify different cluster groups of handball players according to their physical performance level assessed in a series of physical assessments, which could then be used to design a training program based on individual strengths and weaknesses, and to determine which of these variables best identified elite performance in a group of under-19 [U19] national level handball players. Players of the U19 National Handball team (n=16 performed a set of tests to determine: 10 m (ST10 and 20 m (ST20 sprint time, ball release velocity (BRv, countermovement jump (CMJ height and squat jump (SJ height. All players also performed an incremental-load bench press test to determine the 1 repetition maximum (1RMest, the load corresponding to maximum mean power (LoadMP, the mean propulsive phase power at LoadMP (PMPPMP and the peak power at LoadMP (PPEAKMP. Cluster analyses of the test results generated four groupings of players. The variables best able to discriminate physical performance were BRv, ST20, 1RMest, PPEAKMP and PMPPMP. These variables could help coaches identify talent or monitor the physical performance of athletes in their team. Each cluster of players has a particular weakness related to physical performance and therefore, the cluster results can be applied to a specific training programmed based on individual needs.

Robust continuous clustering.

Science.gov (United States)

Shah, Sohil Atul; Koltun, Vladlen

2017-09-12

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank.

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

International Nuclear Information System (INIS)

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

2017-01-01

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

Parameters that affect parallel processing for computational electromagnetic simulation codes on high performance computing clusters

Science.gov (United States)

Moon, Hongsik

changing computer hardware platforms in order to provide fast, accurate and efficient solutions to large, complex electromagnetic problems. The research in this dissertation proves that the performance of parallel code is intimately related to the configuration of the computer hardware and can be maximized for different hardware platforms. To benchmark and optimize the performance of parallel CEM software, a variety of large, complex projects are created and executed on a variety of computer platforms. The computer platforms used in this research are detailed in this dissertation. The projects run as benchmarks are also described in detail and results are presented. The parameters that affect parallel CEM software on High Performance Computing Clusters (HPCC) are investigated. This research demonstrates methods to maximize the performance of parallel CEM software code.

Membership determination of open clusters based on a spectral clustering method

Science.gov (United States)

Gao, Xin-Hua

2018-06-01

We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

Supercomputer and cluster performance modeling and analysis efforts:2004-2006.

Energy Technology Data Exchange (ETDEWEB)

Sturtevant, Judith E.; Ganti, Anand; Meyer, Harold (Hal) Edward; Stevenson, Joel O.; Benner, Robert E., Jr. (.,; .); Goudy, Susan Phelps; Doerfler, Douglas W.; Domino, Stefan Paul; Taylor, Mark A.; Malins, Robert Joseph; Scott, Ryan T.; Barnette, Daniel Wayne; Rajan, Mahesh; Ang, James Alfred; Black, Amalia Rebecca; Laub, Thomas William; Vaughan, Courtenay Thomas; Franke, Brian Claude

2007-02-01

This report describes efforts by the Performance Modeling and Analysis Team to investigate performance characteristics of Sandia's engineering and scientific applications on the ASC capability and advanced architecture supercomputers, and Sandia's capacity Linux clusters. Efforts to model various aspects of these computers are also discussed. The goals of these efforts are to quantify and compare Sandia's supercomputer and cluster performance characteristics; to reveal strengths and weaknesses in such systems; and to predict performance characteristics of, and provide guidelines for, future acquisitions and follow-on systems. Described herein are the results obtained from running benchmarks and applications to extract performance characteristics and comparisons, as well as modeling efforts, obtained during the time period 2004-2006. The format of the report, with hypertext links to numerous additional documents, purposefully minimizes the document size needed to disseminate the extensive results from our research.

Performance criteria for graph clustering and Markov cluster experiments

NARCIS (Netherlands)

S. van Dongen

2000-01-01

textabstractIn~[1] a cluster algorithm for graphs was introduced called the Markov cluster algorithm or MCL~algorithm. The algorithm is based on simulation of (stochastic) flow in graphs by means of alternation of two operators, expansion and inflation. The results in~[2] establish an intrinsic

Implications of multiple high-redshift galaxy clusters

International Nuclear Information System (INIS)

Hoyle, Ben; Jimenez, Raul; Verde, Licia

2011-01-01

To date, 14 high-redshift (z>1.0) galaxy clusters with mass measurements have been observed, spectroscopically confirmed, and are reported in the literature. These objects should be exceedingly rare in the standard Λ cold dark matter (ΛCDM) model. We conservatively approximate the selection functions of these clusters' parent surveys and quantify the tension between the abundances of massive clusters as predicted by the standard ΛCDM model and the observed ones. We alleviate the tension, considering non-Gaussian primordial perturbations of the local type, characterized by the parameter f NL , and derive constraints on f NL arising from the mere existence of these clusters. At the 95% confidence level, f NL >467, with cosmological parameters fixed to their most likely WMAP5 values, or f NL > or approx. 123 (at 95% confidence) if we marginalize over prior WMAP5 parameters. In combination with f NL constraints from cosmic microwave background and halo bias, this determination implies a scale dependence of f NL at ≅3σ. Given the assumptions made in the analysis, we expect any future improvements to the modeling of the non-Gaussian mass function, survey volumes, or selection functions to increase the significance of f NL >0 found here. In order to reconcile these massive, high-z clusters with f NL =0, their masses would need to be systematically lowered by 1.5σ, or the σ 8 parameter should be ∼3σ higher than cosmic microwave background (and large-scale structure) constraints. The existence of these objects is a puzzle: it either represents a challenge to the ΛCDM paradigm or it is an indication that the mass estimates of clusters are dramatically more uncertain than we think.

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...

A Facile Synthesis of Graphene-WO3 Nanowire Clusters with High Photocatalytic Activity for O2 Evolution

Directory of Open Access Journals (Sweden)

M.-J. Zhou

2014-01-01

Full Text Available In the present work, graphene-WO3 nanowire clusters were synthesized via a facile hydrothermal method. The obtained graphene-WO3 nanowire clusters were characterized by X-ray diffraction (XRD, transmission electron microscopy (TEM, X-ray photoelectron spectroscopy (XPS, Fourier transform infrared spectroscopy (FT-IR, Raman spectroscopy, and ultraviolet-visible diffuse reflectance spectroscopy (DRS techniques. The photocatalytic oxygen (O2 evolution properties of the as-synthesized samples were investigated by measuring the amount of evolved O2 from water splitting. The graphene-WO3 nanowire clusters exhibited enhanced performance compared to pure WO3 nanowire clusters for O2 evolution. The amount of evolved O2 from water splitting after 8 h for the graphene-WO3 nanowire clusters is ca. 0.345 mmol/L, which is more than 1.9 times as much as that of the pure WO3 nanowire clusters (ca. 0.175 mmol/L. The high photocatalytic activity of the graphene-WO3 nanowire clusters was attributed to a high charge transfer rate in the presence of graphene.

A high performance scientific cloud computing environment for materials simulations

OpenAIRE

Jorissen, Kevin; Vila, Fernando D.; Rehr, John J.

2011-01-01

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including...

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

Energy Technology Data Exchange (ETDEWEB)

Aad, G. [CPPM, Aix-Marseille Univ. et CNRS/IN2P3, Marseille (France); Abbott, B. [Oklahoma Univ., Norman, OK (United States). Homer L. Dodge Dept. of Physics and Astronomy; Abdallah, J. [Academia Sinica, Taipei (China). Inst. of Physics; Collaboration: ATLAS Collaboration; and others

2017-07-15

The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS. (orig.)

Clustering for high-dimension, low-sample size data using distance vectors

OpenAIRE

Terada, Yoshikazu

2013-01-01

In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure. We point out the important fact that it is not the closeness, but the "values" of distance that contain information of the cluster structure in high-dimensional space. Based on this fact, we propose an efficient and simple clustering approach, called distance vector clustering, for HDLSS data. Under the assumptions given in the work of Hall et al. (2005), w...

Scalability of a Low-Cost Multi-Teraflop Linux Cluster for High-End Classical Atomistic and Quantum Mechanical Simulations

Science.gov (United States)

Kikuchi, Hideaki; Kalia, Rajiv K.; Nakano, Aiichiro; Vashishta, Priya; Shimojo, Fuyuki; Saini, Subhash

2003-01-01

Scalability of a low-cost, Intel Xeon-based, multi-Teraflop Linux cluster is tested for two high-end scientific applications: Classical atomistic simulation based on the molecular dynamics method and quantum mechanical calculation based on the density functional theory. These scalable parallel applications use space-time multiresolution algorithms and feature computational-space decomposition, wavelet-based adaptive load balancing, and spacefilling-curve-based data compression for scalable I/O. Comparative performance tests are performed on a 1,024-processor Linux cluster and a conventional higher-end parallel supercomputer, 1,184-processor IBM SP4. The results show that the performance of the Linux cluster is comparable to that of the SP4. We also study various effects, such as the sharing of memory and L2 cache among processors, on the performance.

[Electronic and structural properties of individual nanometer-size supported metallic clusters]. Final performance report

Energy Technology Data Exchange (ETDEWEB)

Reifenberger, R.

1993-09-01

This report summarizes the work performed under contract DOE-FCO2-84ER45162. During the past ten years, our study of electron emission from laser-illuminated field emission tips has taken on a broader scope by addressing problems of direct interest to those concerned with the unique physical and chemical properties of nanometer-size clusters. The work performed has demonstrated that much needed data can be obtained on individual nanometer-size clusters supported on a wide-variety of different substrates. The work was performed in collaboration with R.P. Andres in the School of Chemical Engineering at Purdue University. The Multiple Expansion Cluster Source developed by Andres and his students was essential for producing the nanometer-size clusters studied. The following report features a discussion of these results. This report provides a motivation for studying the properties of nanometer-size clusters and summarizes the results obtained.

Studies on high performance Timeslice building on the CBM FLES

Energy Technology Data Exchange (ETDEWEB)

Hartmann, Helvi [Frankfurt Institute for Advanced Studies, Goethe University, Frankfurt (Germany); Collaboration: CBM-Collaboration

2015-07-01

In contrast to already existing high energy physics experiments the Compressed Baryonic Matter (CBM) experiment collects all data untriggered. The First-level Event Selector (FLES), which denotes a high performance computer cluster, processes the very high incoming data rate of 1 TByte/s and performs a full online event reconstruction. For this task it needs to access the raw detector data in time intervals referred to as Timeslices. In order to construct the Timeslices, the FLES Timeslice building has to combine data from all input links and distribute them via a high-performance network to the compute nodes. For fast data transfer the Infiniband network has proven to be appropriate. One option to address the network is using Infiniband (RDMA) Verbs directly and potentially making best use of Infiniband. However, it is a very low-level implementation relying on the hardware and neglecting other possible network technologies in the future. Another approach is to apply a high-level API like MPI which is independent of the underlying hardware and suitable for less error prone software development. I present the given possibilities and show the results of benchmarks ran on high-performance computing clusters. The solutions are evaluated regarding the Timeslice building in CBM.

Micromagnetics on high-performance workstation and mobile computational platforms

Science.gov (United States)

Fu, S.; Chang, R.; Couture, S.; Menarini, M.; Escobar, M. A.; Kuteifan, M.; Lubarda, M.; Gabay, D.; Lomakin, V.

2015-05-01

The feasibility of using high-performance desktop and embedded mobile computational platforms is presented, including multi-core Intel central processing unit, Nvidia desktop graphics processing units, and Nvidia Jetson TK1 Platform. FastMag finite element method-based micromagnetic simulator is used as a testbed, showing high efficiency on all the platforms. Optimization aspects of improving the performance of the mobile systems are discussed. The high performance, low cost, low power consumption, and rapid performance increase of the embedded mobile systems make them a promising candidate for micromagnetic simulations. Such architectures can be used as standalone systems or can be built as low-power computing clusters.

Inter-firm relations in SME clusters and the link to marketing performance

OpenAIRE

Lamprinopoulou, C.; Tregear, A.

2011-01-01

Purpose – Networks are increasingly recognised as being important to successful marketing amongst small and medium-sized enterprises (SMEs). Thepurpose of this study is to investigate the structure and content of network relations amongst SME clusters, and explore the link to marketing performance.Design/methodology/approach – Following a review of the literature on SME networks and marketing performance, case study analysis isperformed on four SME clusters in the Greek agrifood sector.Findin...

Effects of cluster vs. traditional plyometric training sets on maximal-intensity exercise performance.

Science.gov (United States)

Asadi, Abbas; Ramírez-Campillo, Rodrigo

2016-01-01

The aim of this study was to compare the effects of 6-week cluster versus traditional plyometric training sets on jumping ability, sprint and agility performance. Thirteen college students were assigned to a cluster sets group (N=6) or traditional sets group (N=7). Both training groups completed the same training program. The traditional group completed five sets of 20 repetitions with 2min of rest between sets each session, while the cluster group completed five sets of 20 [2×10] repetitions with 30/90-s rest each session. Subjects were evaluated for countermovement jump (CMJ), standing long jump (SLJ), t test, 20-m and 40-m sprint test performance before and after the intervention. Both groups had similar improvements (Psets methods resulted in greater adaptations in sprint performance, while the cluster sets method resulted in greater jump and agility adaptations. Copyright © 2016 The Lithuanian University of Health Sciences. Production and hosting by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

Performance Analysis of a Cluster-Based MAC Protocol for Wireless Ad Hoc Networks

Directory of Open Access Journals (Sweden)

Jesús Alonso-Zárate

2010-01-01

Full Text Available An analytical model to evaluate the non-saturated performance of the Distributed Queuing Medium Access Control Protocol for Ad Hoc Networks (DQMANs in single-hop networks is presented in this paper. DQMAN is comprised of a spontaneous, temporary, and dynamic clustering mechanism integrated with a near-optimum distributed queuing Medium Access Control (MAC protocol. Clustering is executed in a distributed manner using a mechanism inspired by the Distributed Coordination Function (DCF of the IEEE 802.11. Once a station seizes the channel, it becomes the temporary clusterhead of a spontaneous cluster and it coordinates the peer-to-peer communications between the clustermembers. Within each cluster, a near-optimum distributed queuing MAC protocol is executed. The theoretical performance analysis of DQMAN in single-hop networks under non-saturation conditions is presented in this paper. The approach integrates the analysis of the clustering mechanism into the MAC layer model. Up to the knowledge of the authors, this approach is novel in the literature. In addition, the performance of an ad hoc network using DQMAN is compared to that obtained when using the DCF of the IEEE 802.11, as a benchmark reference.

A high performance scientific cloud computing environment for materials simulations

Science.gov (United States)

Jorissen, K.; Vila, F. D.; Rehr, J. J.

2012-09-01

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.

In-clustering effects in InAlN and InGaN revealed by high pressure studies

DEFF Research Database (Denmark)

Gorczyca, I.; Suski, T.; Kaminska, A.

2010-01-01

results are compared with the results of photoluminescence measurements performed at high hydrostatic pressures on InAlN and InGaN quasi-bulk epilayers. We discuss the modification of the uppermost valence band due to formation of In clusters which, together with the related lattice relaxations, may......Electronic band structure calculations of InAlN and InGaN under pressure are presented for two different arrangements of the In atoms, uniform and clustered. The band gap pressure coefficients exhibit strong bowing, and the effect is especially large when indium atoms are clustered. The theoretical...

Preparation of aligned W{sub 18}O{sub 49} nanowire clusters with high photocatalytic activity

Energy Technology Data Exchange (ETDEWEB)

Zhang, Ning [State Key Laboratory of Inorganic Synthesis & Preparative Chemistry, Jilin University, Changchun 130012 (China); Zhao, Yafei, E-mail: zhaoyafei007@126.com [State Key Laboratory of Inorganic Synthesis & Preparative Chemistry, Jilin University, Changchun 130012 (China); School of Chemical Engineering and Energy, Zhengzhou University, Zhengzhou, Henan 450001 (China); Lu, Yanjie [School of Chemical Engineering and Energy, Zhengzhou University, Zhengzhou, Henan 450001 (China); Zhu, Guangshan, E-mail: zhugs@jlu.edu.cn [State Key Laboratory of Inorganic Synthesis & Preparative Chemistry, Jilin University, Changchun 130012 (China)

2017-04-15

Highlights: • Aligned W{sub 18}O{sub 49} nanowire clusters were prepared by a facile hydrothermal method. • W{sub 18}O{sub 49} has unique structure, high degree of crystallinity and large surface area. • W{sub 18}O{sub 49} nanowire clusters exhibited high photocatalytic degradation activity. - Abstract: The aligned W{sub 18}O{sub 49} nanowire clusters were synthesized via a facile and economic ethanol-assisted hydrothermal method using peroxopolytungstic acid as precursor. Results show that the as-prepared W{sub 18}O{sub 49} exhibits a high yield and ultrathin structure with preferential growth direction along [0 1 0]. The amount of peroxopolytungstic acid and reaction time play significant role on the morphology of W{sub 18}O{sub 49} nanowires. The nanowires have unique structure, high degree of crystallinity, large specific surface area, and large number of defects such as oxygen vacancies, which are responsible for their high photocatalytic performance for degradation of methylene blue. The photocatalytic conversion of methylene blue can reach above 98% after degradation. W{sub 18}O{sub 49} also exhibits good photodegradation stability after five cycles of reuse. The results demonstrate that the as-prepared W{sub 18}O{sub 49} nanowire clusters are expected to be a promising material for applications in the field of environment.

FLOCK cluster analysis of mast cell event clustering by high-sensitivity flow cytometry predicts systemic mastocytosis.

Science.gov (United States)

Dorfman, David M; LaPlante, Charlotte D; Pozdnyakova, Olga; Li, Betty

2015-11-01

In our high-sensitivity flow cytometric approach for systemic mastocytosis (SM), we identified mast cell event clustering as a new diagnostic criterion for the disease. To objectively characterize mast cell gated event distributions, we performed cluster analysis using FLOCK, a computational approach to identify cell subsets in multidimensional flow cytometry data in an unbiased, automated fashion. FLOCK identified discrete mast cell populations in most cases of SM (56/75 [75%]) but only a minority of non-SM cases (17/124 [14%]). FLOCK-identified mast cell populations accounted for 2.46% of total cells on average in SM cases and 0.09% of total cells on average in non-SM cases (P < .0001) and were predictive of SM, with a sensitivity of 75%, a specificity of 86%, a positive predictive value of 76%, and a negative predictive value of 85%. FLOCK analysis provides useful diagnostic information for evaluating patients with suspected SM, and may be useful for the analysis of other hematopoietic neoplasms. Copyright© by the American Society for Clinical Pathology.

Gas expulsion in highly substructured embedded star clusters

Science.gov (United States)

Farias, J. P.; Fellhauer, M.; Smith, R.; Domínguez, R.; Dabringhausen, J.

2018-06-01

We investigate the response of initially substructured, young, embedded star clusters to instantaneous gas expulsion of their natal gas. We introduce primordial substructure to the stars and the gas by simplistically modelling the star formation process so as to obtain a variety of substructure distributed within our modelled star-forming regions. We show that, by measuring the virial ratio of the stars alone (disregarding the gas completely), we can estimate how much mass a star cluster will retain after gas expulsion to within 10 per cent accuracy, no matter how complex the background structure of the gas is, and we present a simple analytical recipe describing this behaviour. We show that the evolution of the star cluster while still embedded in the natal gas, and the behaviour of the gas before being expelled, is crucial process that affect the time-scale on which the cluster can evolve into a virialized spherical system. Embedded star clusters that have high levels of substructure are subvirial for longer times, enabling them to survive gas expulsion better than a virialized and spherical system. By using a more realistic treatment for the background gas than our previous studies, we find it very difficult to destroy the young clusters with instantaneous gas expulsion. We conclude that gas removal may not be the main culprit for the dissolution of young star clusters.

Innovation performance and clusters: a dynamic capability perspective on regional technology clusters

OpenAIRE

Röttmer, Nicole

2009-01-01

This research provides a novel, empirically tested, actionable theory of cluster innovativeness. Cluster innovativeness has for long been subject of research and resulting policy efforts. The cluster's endowment with assets, such as specialized labor, firms, research institutes, existing regional networks and a specific culture are, among others, recognized as sources of innovativeness. While the asset structure of clusters as been subject to a variety of research efforts, the evidence on the...

THE XMM CLUSTER SURVEY: THE BUILD-UP OF STELLAR MASS IN BRIGHTEST CLUSTER GALAXIES AT HIGH REDSHIFT

International Nuclear Information System (INIS)

Stott, J. P.; Collins, C. A.; Hilton, M.; Capozzi, D.; Sahlen, M.; Lloyd-Davies, E.; Hosmer, M.; Liddle, A. R.; Mehrtens, N.; Romer, A. K.; Miller, C. J.; Stanford, S. A.; Viana, P. T. P.; Davidson, M.; Hoyle, B.; Kay, S. T.; Nichol, R. C.

2010-01-01

We present deep J- and K s -band photometry of 20 high redshift galaxy clusters between z = 0.8 and1.5, 19 of which are observed with the MOIRCS instrument on the Subaru telescope. By using near-infrared light as a proxy for stellar mass we find the surprising result that the average stellar mass of Brightest Cluster Galaxies (BCGs) has remained constant at ∼9 x 10 11 M sun since z ∼ 1.5. We investigate the effect on this result of differing star formation histories generated by three well-known and independent stellar population codes and find it to be robust for reasonable, physically motivated choices of age and metallicity. By performing Monte Carlo simulations we find that the result is unaffected by any correlation between BCG mass and cluster mass in either the observed or model clusters. The large stellar masses imply that the assemblage of these galaxies took place at the same time as the initial burst of star formation. This result leads us to conclude that dry merging has had little effect on the average stellar mass of BCGs over the last 9-10 Gyr in stark contrast to the predictions of semi-analytic models, based on the hierarchical merging of dark matter halos, which predict a more protracted mass build-up over a Hubble time. However, we discuss that there is potential for reconciliation between observation and theory if there is a significant growth of material in the intracluster light over the same period.

A critical cluster analysis of 44 indicators of author-level performance

DEFF Research Database (Denmark)

Wildgaard, Lorna Elizabeth

2016-01-01

-four indicators of individual researcher performance were computed using the data. The clustering solution was supported by continued reference to the researcher’s curriculum vitae, an effect analysis and a risk analysis. Disciplinary appropriate indicators were identified and used to divide the researchers......This paper explores a 7-stage cluster methodology as a process to identify appropriate indicators for evaluation of individual researchers at a disciplinary and seniority level. Publication and citation data for 741 researchers from 4 disciplines was collected in Web of Science. Forty...... of statistics in research evaluation. The strength of the 7-stage cluster methodology is that it makes clear that in the evaluation of individual researchers, statistics cannot stand alone. The methodology is reliant on contextual information to verify the bibliometric values and cluster solution...

Cluster computing for lattice QCD simulations

International Nuclear Information System (INIS)

Coddington, P.D.; Williams, A.G.

2000-01-01

Full text: Simulations of lattice quantum chromodynamics (QCD) require enormous amounts of compute power. In the past, this has usually involved sharing time on large, expensive machines at supercomputing centres. Over the past few years, clusters of networked computers have become very popular as a low-cost alternative to traditional supercomputers. The dramatic improvements in performance (and more importantly, the ratio of price/performance) of commodity PCs, workstations, and networks have made clusters of off-the-shelf computers an attractive option for low-cost, high-performance computing. A major advantage of clusters is that since they can have any number of processors, they can be purchased using any sized budget, allowing research groups to install a cluster for their own dedicated use, and to scale up to more processors if additional funds become available. Clusters are now being built for high-energy physics simulations. Wuppertal has recently installed ALiCE, a cluster of 128 Alpha workstations running Linux, with a peak performance of 158 G flops. The Jefferson Laboratory in the US has a 16 node Alpha cluster and plans to upgrade to a 256 processor machine. In Australia, several large clusters have recently been installed. Swinburne University of Technology has a cluster of 64 Compaq Alpha workstations used for astrophysics simulations. Early this year our DHPC group constructed a cluster of 116 dual Pentium PCs (i.e. 232 processors) connected by a Fast Ethernet network, which is used by chemists at Adelaide University and Flinders University to run computational chemistry codes. The Australian National University has recently installed a similar PC cluster with 192 processors. The Centre for the Subatomic Structure of Matter (CSSM) undertakes large-scale high-energy physics calculations, mainly lattice QCD simulations. The choice of the computer and network hardware for a cluster depends on the particular applications to be run on the machine. Our

Innovation performance and clusters : a dynamic capability perspective on regional technology clusters

NARCIS (Netherlands)

Röttmer, Nicole

2009-01-01

This research provides a novel, empirically tested, actionable theory of cluster innovativeness. Cluster innovativeness has for long been subject of research and resulting policy efforts. The cluster's endowment with assets, such as specialized labor, firms, research institutes, existing regional

Assessment of repeatability of composition of perfumed waters by high-performance liquid chromatography combined with numerical data analysis based on cluster analysis (HPLC UV/VIS - CA).

Science.gov (United States)

Ruzik, L; Obarski, N; Papierz, A; Mojski, M

2015-06-01

High-performance liquid chromatography (HPLC) with UV/VIS spectrophotometric detection combined with the chemometric method of cluster analysis (CA) was used for the assessment of repeatability of composition of nine types of perfumed waters. In addition, the chromatographic method of separating components of the perfume waters under analysis was subjected to an optimization procedure. The chromatograms thus obtained were used as sources of data for the chemometric method of cluster analysis (CA). The result was a classification of a set comprising 39 perfumed water samples with a similar composition at a specified level of probability (level of agglomeration). A comparison of the classification with the manufacturer's declarations reveals a good degree of consistency and demonstrates similarity between samples in different classes. A combination of the chromatographic method with cluster analysis (HPLC UV/VIS - CA) makes it possible to quickly assess the repeatability of composition of perfumed waters at selected levels of probability. © 2014 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

High intensive short laser pulse interaction with submicron clusters media

International Nuclear Information System (INIS)

Faenov, A. Ya

2008-01-01

The interaction of short intense laser pulses with structured targets, such as clusters, exhibits unique features, stemming from the enhanced absorption of the incident laser light compared to solid targets. Due to the increased absorption, these targets are heated significantly, leading to enhanced emission of x rays in the keV range and generation of electrons and multiple charged ions with kinetic energies from tens of keV to tens of MeV. Possible applications of these targets can be an electron/ion source for a table top accelerator, a neutron source for a material damage study, or an x ray source for microscopy or lithography. The overview of recent results, obtained by the high intensive short laser pulse interaction with different submicron clusters media will be presented. High resolution K and L shell spectra of plasma generated by superintense laser irradiation of micron sized Ar, Kr and Xe clusters have been measured with intensity 10"17"-10"19"W/cm"2"and a pulse duration of 30-1000fs. It is found that hot electrons produced by high contrast laser pulses allow the isochoric heating of clusters and shift the ion balance toward the higher charge states, which enhances both the X ray line yield and the ion kinetic energy. Irradiation of clusters, produced from such gas mixture, by a fs Ti:Sa laser pulses allows to enhance the soft X ray radiation of Heβ(665.7eV)and Lyα(653.7eV)of Oxygen in 2-8 times compare with the case of using as targets pure CO"2"or N"2"O clusters and reach values 2.8x10"10"(∼3μJ)and 2.7x10"10"(∼2.9μJ)ph/(sr·pulse), respectively. Nanostructure conventional soft X ray images of 100nm thick Mo and Zr foils in a wide field of view (cm"2"scale)with high spatial resolution (700nm)are obtained using the LiF crystals as soft X ray imaging detectors. When the target used for the ion acceleration studies consists of solid density clusters embedded into the background gas, its irradiation by high intensity laser light makes the target

Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

Directory of Open Access Journals (Sweden)

Tushar H Jaware

2013-10-01

Full Text Available Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented.

Assessment of In-Cloud Enterprise Resource Planning System Performed in a Virtual Cluster

Directory of Open Access Journals (Sweden)

Bao Rong Chang

2015-01-01

Full Text Available This paper introduces a high-performed high-availability in-cloud enterprise resources planning (in-cloud ERP which has deployed in the virtual machine cluster. The proposed approach can resolve the crucial problems of ERP failure due to unexpected downtime and failover between physical hosts in enterprises, causing operation termination and hence data loss. Besides, the proposed one together with the access control authentication and network security is capable of preventing intrusion hacked and/or malicious attack via internet. Regarding system assessment, cost-performance (C-P ratio, a remarkable cost effectiveness evaluation, has been applied to several remarkable ERP systems. As a result, C-P ratio evaluated from the experiments shows that the proposed approach outperforms two well-known benchmark ERP systems, namely, in-house ECC 6.0 and in-cloud ByDesign.

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

International Nuclear Information System (INIS)

Komatitsch, Dimitri; Erlebacher, Gordon; Goeddeke, Dominik; Michea, David

2010-01-01

We implement a high-order finite-element application, which performs the numerical simulation of seismic wave propagation resulting for instance from earthquakes at the scale of a continent or from active seismic acquisition experiments in the oil industry, on a large cluster of NVIDIA Tesla graphics cards using the CUDA programming environment and non-blocking message passing based on MPI. Contrary to many finite-element implementations, ours is implemented successfully in single precision, maximizing the performance of current generation GPUs. We discuss the implementation and optimization of the code and compare it to an existing very optimized implementation in C language and MPI on a classical cluster of CPU nodes. We use mesh coloring to efficiently handle summation operations over degrees of freedom on an unstructured mesh, and non-blocking MPI messages in order to overlap the communications across the network and the data transfer to and from the device via PCIe with calculations on the GPU. We perform a number of numerical tests to validate the single-precision CUDA and MPI implementation and assess its accuracy. We then analyze performance measurements and depending on how the problem is mapped to the reference CPU cluster, we obtain a speedup of 20x or 12x.

Laval nozzles for cluster-jet targets

Energy Technology Data Exchange (ETDEWEB)

Grieser, Silke; Bonaventura, Daniel; Hergemoeller, Ann-Katrin; Hetz, Benjamin; Koehler, Esperanza; Lessmann, Lukas; Khoukaz, Alfons [Institut fuer Kernphysik, Westfaelische Wilhelms-Universitaet Muenster, 48149 Muenster (Germany)

2016-07-01

Cluster-jet targets are highly suited for storage ring experiments due to the fact that they provide high and constant beam densities. Therefore, a cluster-jet target is planned to be the first internal target for the PANDA experiment at FAIR. A cluster source generates a continuous flow of cryogenic solid clusters by the expansion of pre-cooled gases within fine Laval nozzles. For the production of clusters the geometry of the nozzle is crucial. The production of such nozzles with their complex inner geometry represents a major technical challenge. The possibility to produce new fine Laval nozzles ensures the operation of cluster-jet targets, e.g. for the PANDA experiment, and opens the way for future investigations on the cluster production process to match the required targets performance. Optimizations on the recently developed production process and the fabrication of new glass nozzles were done. Initial measurements of these nozzles at the PANDA cluster-jet target prototype and the investigation of the cluster beam origin within the nozzle will be presented and discussed. For the future more Laval nozzles with different geometries will be produced and additional measurements with these new nozzles at the PANDA cluster-jet target prototype towards higher performance will be realized.

Performance Modeling of Hybrid MPI/OpenMP Scientific Applications on Large-scale Multicore Cluster Systems

KAUST Repository

Wu, Xingfu; Taylor, Valerie

2011-01-01

In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore clusters: IBM POWER4, POWER5+ and Blue Gene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore clusters because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyro kinetic Toroidal Code in magnetic fusion to validate our performance model of the hybrid application on these multicore clusters. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore clusters. © 2011 IEEE.

Performance Modeling of Hybrid MPI/OpenMP Scientific Applications on Large-scale Multicore Cluster Systems

KAUST Repository

Wu, Xingfu

2011-08-01

In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore clusters: IBM POWER4, POWER5+ and Blue Gene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore clusters because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyro kinetic Toroidal Code in magnetic fusion to validate our performance model of the hybrid application on these multicore clusters. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore clusters. © 2011 IEEE.

Development of a small-scale computer cluster

Science.gov (United States)

Wilhelm, Jay; Smith, Justin T.; Smith, James E.

2008-04-01

An increase in demand for computing power in academia has necessitated the need for high performance machines. Computing power of a single processor has been steadily increasing, but lags behind the demand for fast simulations. Since a single processor has hard limits to its performance, a cluster of computers can have the ability to multiply the performance of a single computer with the proper software. Cluster computing has therefore become a much sought after technology. Typical desktop computers could be used for cluster computing, but are not intended for constant full speed operation and take up more space than rack mount servers. Specialty computers that are designed to be used in clusters meet high availability and space requirements, but can be costly. A market segment exists where custom built desktop computers can be arranged in a rack mount situation, gaining the space saving of traditional rack mount computers while remaining cost effective. To explore these possibilities, an experiment was performed to develop a computing cluster using desktop components for the purpose of decreasing computation time of advanced simulations. This study indicates that small-scale cluster can be built from off-the-shelf components which multiplies the performance of a single desktop machine, while minimizing occupied space and still remaining cost effective.

Symptom clusters in patients with high-grade glioma.

Science.gov (United States)

Fox, Sherry W; Lyon, Debra; Farace, Elana

2007-01-01

To describe the co-occurring symptoms (depression, fatigue, pain, sleep disturbance, and cognitive impairment), quality of life (QoL), and functional status in patients with high-grade glioma. Correlational, descriptive study of 73 participants with high-grade glioma in the U.S. Nine brief measures were obtained with a mailed survey. Participants were recruited from the online message board of The Healing Exchange BRAIN TRUST, a nonprofit organization dedicated to improving quality of life for people with brain tumors. Two symptom cluster models were examined. Four co-occurring symptoms were significantly correlated with each other and explained 29% of the variance in QoL: depression, fatigue, sleep disturbance, and cognitive impairment. Depression, fatigue, sleep disturbance, cognitive impairment, and pain were significantly correlated with each other and explained 62% of the variance in functional status. The interrelationships of the symptoms examined in this study and their relationships with QoL and functional status meet the criteria for defining a symptom cluster. The differences in the models of QoL and functional status indicates that symptom clusters may have unique characteristics in patients with gliomas.

Implementation of a cluster Beowulf

International Nuclear Information System (INIS)

Victorino Guzman, Jorge Enrique

2001-01-01

One of the simulation systems that put a great stress on computational resources and performance are the climatic models, with a high cost of implementation, making difficult its acquisition. An alternative that offers good performance at a reasonable cost is the construction of Cluster Beowulf that allows to emulate the behaviour of a computer with several processors. In the present article we discuss the requirements of hardware for the construction of the Cluster Beowulf, the software resources for the implementation of the model CCM3.6 and the performance of the Cluster Beowulf, of the Group of Investigation in Meteorology at the National University of Colombia, with different number of processors

Understanding the stable boron clusters: A bond model and first-principles calculations based on high-throughput screening

International Nuclear Information System (INIS)

Xu, Shao-Gang; Liao, Ji-Hai; Zhao, Yu-Jun; Yang, Xiao-Bao

2015-01-01

The unique electronic property induced diversified structure of boron (B) cluster has attracted much interest from experimentalists and theorists. B 30–40 were reported to be planar fragments of triangular lattice with proper concentrations of vacancies recently. Here, we have performed high-throughput screening for possible B clusters through the first-principles calculations, including various shapes and distributions of vacancies. As a result, we have determined the structures of B n clusters with n = 30–51 and found a stable planar cluster of B 49 with a double-hexagon vacancy. Considering the 8-electron rule and the electron delocalization, a concise model for the distribution of the 2c–2e and 3c–2e bonds has been proposed to explain the stability of B planar clusters, as well as the reported B cages

A critical cluster analysis of 44 indicators of author-level performance

DEFF Research Database (Denmark)

Wildgaard, Lorna Elizabeth

2015-01-01

. Publication and citation data for 741 researchers across Astronomy, Environmental Science, Philosophy and Public Health was collected in Web of Science (WoS). Forty-four indicators of individual performance were computed using the data. A two-step cluster analysis using IBM SPSS version 22 was performed...

A General Purpose High Performance Linux Installation Infrastructure

International Nuclear Information System (INIS)

Wachsmann, Alf

2002-01-01

With more and more and larger and larger Linux clusters, the question arises how to install them. This paper addresses this question by proposing a solution using only standard software components. This installation infrastructure scales well for a large number of nodes. It is also usable for installing desktop machines or diskless Linux clients, thus, is not designed for cluster installations in particular but is, nevertheless, highly performant. The infrastructure proposed uses PXE as the network boot component on the nodes. It uses DHCP and TFTP servers to get IP addresses and a bootloader to all nodes. It then uses kickstart to install Red Hat Linux over NFS. We have implemented this installation infrastructure at SLAC with our given server hardware and installed a 256 node cluster in 30 minutes. This paper presents the measurements from this installation and discusses the bottlenecks in our installation

Analysis and modeling of social influence in high performance computing workloads

KAUST Repository

Zheng, Shuai; Shae, Zon Yin; Zhang, Xiangliang; Jamjoom, Hani T.; Fong, Liana

2011-01-01

Social influence among users (e.g., collaboration on a project) creates bursty behavior in the underlying high performance computing (HPC) workloads. Using representative HPC and cluster workload logs, this paper identifies, analyzes, and quantifies

Performance of clustering techniques for solving multi depot vehicle routing problem

Directory of Open Access Journals (Sweden)

Eliana M. Toro-Ocampo

2016-01-01

Full Text Available The vehicle routing problem considering multiple depots is classified as NP-hard. MDVRP determines simultaneously the routes of a set of vehicles and aims to meet a set of clients with a known demand. The objective function of the problem is to minimize the total distance traveled by the routes given that all customers must be served considering capacity constraints in depots and vehicles. This paper presents a hybrid methodology that combines agglomerative clustering techniques to generate initial solutions with an iterated local search algorithm (ILS to solve the problem. Although previous studies clustering methods have been proposed like strategies to generate initial solutions, in this work the search is intensified on the information generated after applying the clustering technique. Besides an extensive analysis on the performance of techniques, and their effect in the final solution is performed. The operation of the proposed methodology is feasible and effective to solve the problem regarding the quality of the answers and computational times obtained on request evaluated literature

Splitting Strip Detector Clusters in Dense Environments

CERN Document Server

Nachman, Benjamin Philip; The ATLAS collaboration

2018-01-01

Tracking in high density environments, particularly in high energy jets, plays an important role in many physics analyses at the LHC. In such environments, there is significant degradation of track reconstruction performance. Between runs 1 and 2, ATLAS implemented an algorithm that splits pixel clusters originating from multiple charged particles, using charge information, resulting in the recovery of much of the lost efficiency. However, no attempt was made in prior work to split merged clusters in the Semi Conductor Tracker (SCT), which does not measure charge information. In spite of the lack of charge information in SCT, a cluster-splitting algorithm has been developed in this work. It is based primarily on the difference between the observed cluster width and the expected cluster width, which is derived from track incidence angle. The performance of this algorithm is found to be competitive with the existing pixel cluster splitting based on track information.

Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

Directory of Open Access Journals (Sweden)

Ayad Hendalianpour

2016-11-01

Full Text Available Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

Performance quantification of clustering algorithms for false positive removal in fMRI by ROC curves

Directory of Open Access Journals (Sweden)

André Salles Cunha Peres

Full Text Available Abstract Introduction Functional magnetic resonance imaging (fMRI is a non-invasive technique that allows the detection of specific cerebral functions in humans based on hemodynamic changes. The contrast changes are about 5%, making visual inspection impossible. Thus, statistic strategies are applied to infer which brain region is engaged in a task. However, the traditional methods like general linear model and cross-correlation utilize voxel-wise calculation, introducing a lot of false-positive data. So, in this work we tested post-processing cluster algorithms to diminish the false-positives. Methods In this study, three clustering algorithms (the hierarchical cluster, k-means and self-organizing maps were tested and compared for false-positive removal in the post-processing of cross-correlation analyses. Results Our results showed that the hierarchical cluster presented the best performance to remove the false positives in fMRI, being 2.3 times more accurate than k-means, and 1.9 times more accurate than self-organizing maps. Conclusion The hierarchical cluster presented the best performance in false-positive removal because it uses the inconsistency coefficient threshold, while k-means and self-organizing maps utilize a priori cluster number (centroids and neurons number; thus, the hierarchical cluster avoids clustering scattered voxels, as the inconsistency coefficient threshold allows only the voxels to be clustered that are at a minimum distance to some cluster.

Observations of High Dispersion Clusters of Galaxies: Constraints on Cold Dark Matter

Science.gov (United States)

Oegerle, William R.; Hill, John M.; Fitchett, Michael J.

1995-07-01

We have studied the dynamics of several Abell clusters of galaxies, which were previously reported to have large velocity dispersions, and hence very large masses. In particular, we have investigated the assertion of Frenk et al. (1990) that clusters with intrinsic velocity dispersions ~> 1200 km s^-1^ are extremely rare in the universe, and that large observed dispersions are due to projection effects. We report redshifts for 303 galaxies in the fields of A1775, A2029, A2142, and A2319, obtained with the Nessie multifiber spectrograph at the Mayall 4 m telescope. A1775 appears to be two poor, interacting clusters, separated in velocity space by ~3075 km s^-1^ (in the cluster rest frame). A2029 has a velocity dispersion of 1436 km s^-1^, based on 85 cluster member redshifts. There is evidence that a group or poor cluster of galaxies of slightly different redshift is projected onto (or is merging with) the core of A2029. However, the combined kinematic and x-ray data for A2029 argue for an intrinsically large dispersion for this cluster. Based on redshifts for 103 members of A2142, we find a dispersion of 1280 km s^-1^, and evidence for subclustering. With 130 redshifts in the A2319 field, we have isolated a subcluster ~10' NW of the cD galaxy. After its removal, A2319 has a velocity dispersion of 1324 km s^-1^. The data obtained here have been combined with recent optical and X-ray data for other supposedly high-mass clusters to study the cluster velocity dispersion distribution in a sample of Abell clusters. We find that clusters with true velocity dispersions ~> 1200 km s^-1^ are not extremely rare, but account for ~5% of all Abell clusters with R >= 0. If these clusters are in virial equilibrium, then our results are inconsistent with a high-bias (b~>22), high-density CDM model.

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

Ionized-cluster source based on high-pressure corona discharge

International Nuclear Information System (INIS)

Lokuliyanage, K.; Huber, D.; Zappa, F.; Scheier, P.

2006-01-01

Full text: It has been demonstrated that energetic beams of large clusters, with thousands of atoms, can be a powerful tool for surface modification. Normally ionized cluster beams are obtained by electron impact on neutral beams produced in a supersonic expansion. At the University of Innsbruck we are pursuing the realization of a high current cluster ion source based on the corona discharge.The idea in the present case is that the ionization should occur prior to the supersonic expansion, thus supersede the need of subsequent electron impact. In this contribution we present the project of our source in its initial stage. The intensity distribution of cluster sizes as a function of the source parameters, such as input pressure, temperature and gap voltage, are investigated with the aid of a custom-built time of flight mass spectrometer. (author)

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

Science.gov (United States)

Fang, Yuling; Chen, Qingkui; Xiong, Neal N; Zhao, Deyu; Wang, Jingjuan

2017-08-04

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

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

Science.gov (United States)

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

2011-11-01

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

Two-stage clustering (TSC: a pipeline for selecting operational taxonomic units for the high-throughput sequencing of PCR amplicons.

Directory of Open Access Journals (Sweden)

Xiao-Tao Jiang

Full Text Available Clustering 16S/18S rRNA amplicon sequences into operational taxonomic units (OTUs is a critical step for the bioinformatic analysis of microbial diversity. Here, we report a pipeline for selecting OTUs with a relatively low computational demand and a high degree of accuracy. This pipeline is referred to as two-stage clustering (TSC because it divides tags into two groups according to their abundance and clusters them sequentially. The more abundant group is clustered using a hierarchical algorithm similar to that in ESPRIT, which has a high degree of accuracy but is computationally costly for large datasets. The rarer group, which includes the majority of tags, is then heuristically clustered to improve efficiency. To further improve the computational efficiency and accuracy, two preclustering steps are implemented. To maintain clustering accuracy, all tags are grouped into an OTU depending on their pairwise Needleman-Wunsch distance. This method not only improved the computational efficiency but also mitigated the spurious OTU estimation from 'noise' sequences. In addition, OTUs clustered using TSC showed comparable or improved performance in beta-diversity comparisons compared to existing OTU selection methods. This study suggests that the distribution of sequencing datasets is a useful property for improving the computational efficiency and increasing the clustering accuracy of the high-throughput sequencing of PCR amplicons. The software and user guide are freely available at http://hwzhoulab.smu.edu.cn/paperdata/.

Random matrix improved subspace clustering

KAUST Repository

Couillet, Romain

2017-03-06

This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based on vector channel observations in the context of massive MIMO wireless communication networks is provided.

Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

Directory of Open Access Journals (Sweden)

Thenmozhi Srinivasan

2015-01-01

Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

Text Clustering Algorithm Based on Random Cluster Core

Directory of Open Access Journals (Sweden)

Huang Long-Jun

2016-01-01

Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.

Simultaneous determination of 19 flavonoids in commercial trollflowers by using high-performance liquid chromatography and classification of samples by hierarchical clustering analysis.

Science.gov (United States)

Song, Zhiling; Hashi, Yuki; Sun, Hongyang; Liang, Yi; Lan, Yuexiang; Wang, Hong; Chen, Shizhong

2013-12-01

The flowers of Trollius species, named Jin Lianhua in Chinese, are widely used traditional Chinese herbs with vital biological activity that has been used for several decades in China to treat upper respiratory infections, pharyngitis, tonsillitis, and bronchitis. We developed a rapid and reliable method for simultaneous quantitative analysis of 19 flavonoids in trollflowers by using high-performance liquid chromatography (HPLC). Chromatography was performed on Inertsil ODS-3 C18 column, with gradient elution methanol-acetonitrile-water with 0.02% (v/v) formic acid. Content determination was used to evaluate the quality of commercial trollflowers from different regions in China, while three Trollius species (Trollius chinensis Bunge, Trollius ledebouri Reichb, Trollius buddae Schipcz) were explicitly distinguished by using hierarchical clustering analysis. The linearity, precision, accuracy, limit of detection, and limit of quantification were validated for the quantification method, which proved sensitive, accurate and reproducible indicating that the proposed approach was applicable for the routine analysis and quality control of trollflowers. © 2013.

High-mass stars in Milky Way clusters

Science.gov (United States)

Negueruela, Ignacio

2017-11-01

Young open clusters are our laboratories for studying high-mass star formation and evolution. Unfortunately, the information that they provide is difficult to interpret, and sometimes contradictory. In this contribution, I present a few examples of the uncertainties that we face when confronting observations with theoretical models and our own assumptions.

Effect of pretreatment temperature on catalytic performance of the catalysts derived from cobalt carbonyl cluster in Fischer-Tropsch Synthesis

Directory of Open Access Journals (Sweden)

Byambasuren O

2017-02-01

Full Text Available The monometallic cobalt-based catalysts were prepared by pretreating the catalysts derived from carbonyl cluster precursor (CO6Co2CC(COOH2 supported on γ-Al2O3 with hydrogen at 180, 220, and 260°C respectively. The temperature effect of the pretreatments on the structure evolution of cluster precursors and the catalytic performance of the Fischer-Tropsch (F-T synthesis was investigated. The pretreated catalyst at 220°C with unique phase structure exhibited best catalytic activity and selectivity among three pretreated catalysts. Moreover, the catalysts exhibited high dispersion due to the formation of hydrogen bonds between the cluster precursor and γ-Al2O3 support.

Farmer Performance under Competitive Pressure in Agro-cluster Regions

NARCIS (Netherlands)

Wardhana, D.; Ihle, R.; Heijman, W.J.M.

2017-01-01

Agro-clusters would allow farmers to acquire positive and negative externalities. On one hand, smallholder farmers in spatial proximity are likely to benefit from this concentration; on the other hand, they incur high competitive pressure from other neighboring farmers. We examine the link between

Stochastic clustering of material surface under high-heat plasma load

Science.gov (United States)

Budaev, Viacheslav P.

2017-11-01

The results of a study of a surface formed by high-temperature plasma loads on various materials such as tungsten, carbon and stainless steel are presented. High-temperature plasma irradiation leads to an inhomogeneous stochastic clustering of the surface with self-similar granularity - fractality on the scale from nanoscale to macroscales. Cauliflower-like structure of tungsten and carbon materials are formed under high heat plasma load in fusion devices. The statistical characteristics of hierarchical granularity and scale invariance are estimated. They differ qualitatively from the roughness of the ordinary Brownian surface, which is possibly due to the universal mechanisms of stochastic clustering of material surface under the influence of high-temperature plasma.

Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali.

Science.gov (United States)

Minetti, Andrea; Riera-Montes, Margarita; Nackers, Fabienne; Roederer, Thomas; Koudika, Marie Hortense; Sekkenes, Johanne; Taconet, Aurore; Fermon, Florence; Touré, Albouhary; Grais, Rebecca F; Checchi, Francesco

2012-10-12

Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required. We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.

Evaluating the Efficacy of the Cloud for Cluster Computation

Science.gov (United States)

Knight, David; Shams, Khawaja; Chang, George; Soderstrom, Tom

2012-01-01

Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 ?s inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.

A GMBCG GALAXY CLUSTER CATALOG OF 55,424 RICH CLUSTERS FROM SDSS DR7

International Nuclear Information System (INIS)

Hao Jiangang; Annis, James; Johnston, David E.; McKay, Timothy A.; Evrard, August; Siegel, Seth R.; Gerdes, David; Koester, Benjamin P.; Rykoff, Eli S.; Rozo, Eduardo; Wechsler, Risa H.; Busha, Michael; Becker, Matthew; Sheldon, Erin

2010-01-01

We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red-sequence plus brightest cluster galaxy (BCG) feature, which is unique for galaxy clusters and does not exist among field galaxies. Red-sequence clustering in color space is detected using an Error Corrected Gaussian Mixture Model. We run GMBCG on 8240 deg 2 of photometric data from SDSS DR7 to assemble the largest ever optical galaxy cluster catalog, consisting of over 55,000 rich clusters across the redshift range from 0.1 < z < 0.55. We present Monte Carlo tests of completeness and purity and perform cross-matching with X-ray clusters and with the maxBCG sample at low redshift. These tests indicate high completeness and purity across the full redshift range for clusters with 15 or more members.

A GMBCG galaxy cluster catalog of 55,880 rich clusters from SDSS DR7

Energy Technology Data Exchange (ETDEWEB)

Hao, Jiangang; McKay, Timothy A.; Koester, Benjamin P.; Rykoff, Eli S.; Rozo, Eduardo; Annis, James; Wechsler, Risa H.; Evrard, August; Siegel, Seth R.; Becker, Matthew; Busha, Michael; /Fermilab /Michigan U. /Chicago U., Astron. Astrophys. Ctr. /UC, Santa Barbara /KICP, Chicago /KIPAC, Menlo Park /SLAC /Caltech /Brookhaven

2010-08-01

We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy clusters and does not exist among field galaxies. Red sequence clustering in color space is detected using an Error Corrected Gaussian Mixture Model. We run GMBCG on 8240 square degrees of photometric data from SDSS DR7 to assemble the largest ever optical galaxy cluster catalog, consisting of over 55,000 rich clusters across the redshift range from 0.1 < z < 0.55. We present Monte Carlo tests of completeness and purity and perform cross-matching with X-ray clusters and with the maxBCG sample at low redshift. These tests indicate high completeness and purity across the full redshift range for clusters with 15 or more members.

Metal cluster compounds - chemistry and importance; clusters containing isolated main group element atoms, large metal cluster compounds, cluster fluxionality

International Nuclear Information System (INIS)

Walther, B.

1988-01-01

This part of the review on metal cluster compounds deals with clusters containing isolated main group element atoms, with high nuclearity clusters and metal cluster fluxionality. It will be obvious that main group element atoms strongly influence the geometry, stability and reactivity of the clusters. High nuclearity clusters are of interest in there own due to the diversity of the structures adopted, but their intermediate position between molecules and the metallic state makes them a fascinating research object too. These both sites of the metal cluster chemistry as well as the frequently observed ligand and core fluxionality are related to the cluster metal and surface analogy. (author)

Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

Science.gov (United States)

Yelland, Lisa N; Salter, Amy B; Ryan, Philip

2011-10-15

Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

Effects of cluster vs. traditional plyometric training sets on maximal-intensity exercise performance

Directory of Open Access Journals (Sweden)

Abbas Asadi

2016-01-01

Conclusions: Although both plyometric training methods improved lower body maximal-intensity exercise performance, the traditional sets methods resulted in greater adaptations in sprint performance, while the cluster sets method resulted in greater jump and agility adaptations.

Acquisition of a High Performance Computer Cluster for Materials Research and Education

Science.gov (United States)

2015-04-17

NUMBER 5b. GRANT NUMBER 5a. CONTRACT NUMBER Form Approved OMB NO. 0704-0188 3. DATES COVERED (From - To) - UU UU UU UU 17-04-2015 1-Feb-2014 31-Jan...significantly reduce the time and labour required for materials development. The proposed cluster will also play an important role for education and...the paradigm of materials design based on time-consuming trial-and-error experiments and significantly reduce the time and labour required for

Mesophase Formation Stabilizes High-purity Magic-sized Clusters

KAUST Repository

Nevers, Douglas R.; Williamson, Curtis B.; Savitzky, Benjamin H; Hadar, Ido; Banin, Uri; Kourkoutis, Lena F.; Hanrath, Tobias; Robinson, Richard D.

2018-01-01

Magic-sized clusters (MSCs) are renowned for their identical size and closed-shell stability that inhibit conventional nanoparticle (NP) growth processes. Though MSCs have been of increasing interest, understanding the reaction pathways toward their nucleation and stabilization is an outstanding issue. In this work, we demonstrate that high concentration synthesis (1000 mM) promotes a well-defined reaction pathway to form high-purity MSCs (>99.9%). The MSCs are resistant to typical growth and dissolution processes. Based on insights from in-situ X-ray scattering analysis, we attribute this stability to the accompanying production of a large, hexagonal organic-inorganic mesophase (>100 nm grain size) that arrests growth of the MSCs and prevents NP growth. At intermediate concentrations (500 mM), the MSC mesophase forms, but is unstable, resulting in NP growth at the expense of the assemblies. These results provide an alternate explanation for the high stability of MSCs. Whereas the conventional mantra has been that the stability of MSCs derives from the precise arrangement of the inorganic structures (i.e., closed-shell atomic packing), we demonstrate that anisotropic clusters can also be stabilized by self-forming fibrous mesophase assemblies. At lower concentration (<200 mM or >16 acid-to-metal), MSCs are further destabilized and NPs formation dominates that of MSCs. Overall, the high concentration approach intensifies and showcases inherent concentration-dependent surfactant phase behavior that is not accessible in conventional (i.e., dilute) conditions. This work provides not only a robust method to synthesize, stabilize, and study identical MSC products, but also uncovers an underappreciated stabilizing interaction between surfactants and clusters.

Mesophase Formation Stabilizes High-purity Magic-sized Clusters

KAUST Repository

Nevers, Douglas R.

2018-01-27

Magic-sized clusters (MSCs) are renowned for their identical size and closed-shell stability that inhibit conventional nanoparticle (NP) growth processes. Though MSCs have been of increasing interest, understanding the reaction pathways toward their nucleation and stabilization is an outstanding issue. In this work, we demonstrate that high concentration synthesis (1000 mM) promotes a well-defined reaction pathway to form high-purity MSCs (>99.9%). The MSCs are resistant to typical growth and dissolution processes. Based on insights from in-situ X-ray scattering analysis, we attribute this stability to the accompanying production of a large, hexagonal organic-inorganic mesophase (>100 nm grain size) that arrests growth of the MSCs and prevents NP growth. At intermediate concentrations (500 mM), the MSC mesophase forms, but is unstable, resulting in NP growth at the expense of the assemblies. These results provide an alternate explanation for the high stability of MSCs. Whereas the conventional mantra has been that the stability of MSCs derives from the precise arrangement of the inorganic structures (i.e., closed-shell atomic packing), we demonstrate that anisotropic clusters can also be stabilized by self-forming fibrous mesophase assemblies. At lower concentration (<200 mM or >16 acid-to-metal), MSCs are further destabilized and NPs formation dominates that of MSCs. Overall, the high concentration approach intensifies and showcases inherent concentration-dependent surfactant phase behavior that is not accessible in conventional (i.e., dilute) conditions. This work provides not only a robust method to synthesize, stabilize, and study identical MSC products, but also uncovers an underappreciated stabilizing interaction between surfactants and clusters.

Using intelligent clustering techniques to classify the energy performance of school buildings

Energy Technology Data Exchange (ETDEWEB)

Santamouris, M.; Sfakianaki, K.; Papaglastra, M.; Pavlou, C.; Doukas, P.; Geros, V.; Assimakopoulos, M.N.; Zerefos, S. [University of Athens, Department of Physics, Division of Applied Physics, Laboratory of Meteorology, Athens (Greece); Mihalakakou, G.; Gaitani, N. [University of Ioannina, Department of Environmental and Natural Resources Management, Agrinio (Greece); Patargias, P. [University of Peloponnesus, Faculty of Human Sciences and Cultural Studies, Department of History, Kalamata (Greece); Primikiri, E. [University of Patras, Department of Architecture, Patras (Greece); Mitoula, R. [Charokopion University of Athens, Athens (Greece)

2007-07-01

The present paper deals with the energy performance, energy classification and rating and the global environmental quality of school buildings. A new energy classification technique based on intelligent clustering methodologies is proposed. Energy rating of school buildings provides specific information on their energy consumption and efficiency relative to the other buildings of similar nature and permits a better planning of interventions to improve its energy performance. The overall work reported in the present paper, is carried out in three phases. During the first phase energy consumption data have been collected through energy surveys performed in 320 schools in Greece. In the second phase an innovative energy rating scheme based on fuzzy clustering techniques has been developed, while in the third phase, 10 schools have been selected and detailed measurements of their energy efficiency and performance as well as of the global environmental quality have been performed using a specific experimental protocol. The proposed energy rating method has been applied while the main environmental and energy problems have been identified. The potential for energy and environmental improvements has been assessed. (author)

CONSTRAINING CLUSTER PHYSICS WITH THE SHAPE OF X-RAY CLUSTERS: COMPARISON OF LOCAL X-RAY CLUSTERS VERSUS ΛCDM CLUSTERS

International Nuclear Information System (INIS)

Lau, Erwin T.; Nagai, Daisuke; Kravtsov, Andrey V.; Vikhlinin, Alexey; Zentner, Andrew R.

2012-01-01

Recent simulations of cluster formation have demonstrated that condensation of baryons into central galaxies during cluster formation can drive the shape of the gas distribution in galaxy clusters significantly rounder out to their virial radius. These simulations generally predict stellar fractions within cluster virial radii that are ∼2-3 times larger than the stellar masses deduced from observations. In this paper, we compare ellipticity profiles of simulated clusters performed with varying input physics (radiative cooling, star formation, and supernova feedback) to the cluster ellipticity profiles derived from Chandra and ROSAT observations, in an effort to constrain the fraction of gas that cools and condenses into the central galaxies within clusters. We find that local relaxed clusters have an average ellipticity of ε = 0.18 ± 0.05 in the radial range of 0.04 ≤ r/r 500 ≤ 1. At larger radii r > 0.1r 500 , the observed ellipticity profiles agree well with the predictions of non-radiative simulations. In contrast, the ellipticity profiles of simulated clusters that include dissipative gas physics deviate significantly from the observed ellipticity profiles at all radii. The dissipative simulations overpredict (underpredict) ellipticity in the inner (outer) regions of galaxy clusters. By comparing simulations with and without dissipative gas physics, we show that gas cooling causes the gas distribution to be more oblate in the central regions, but makes the outer gas distribution more spherical. We find that late-time gas cooling and star formation are responsible for the significantly oblate gas distributions in cluster cores, but the gas shapes outside of cluster cores are set primarily by baryon dissipation at high redshift (z ≥ 2). Our results indicate that the shapes of X-ray emitting gas in galaxy clusters, especially at large radii, can be used to place constraints on cluster gas physics, making it potential probes of the history of baryonic

Performance of small cluster surveys and the clustered LQAS design to estimate local-level vaccination coverage in Mali

Directory of Open Access Journals (Sweden)

Minetti Andrea

2012-10-01

Full Text Available Abstract Background Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS approach has been proposed as an alternative, as smaller sample sizes are required. Methods We explored (i the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A. Results VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i health areas not requiring supplemental activities; ii health areas requiring additional vaccination; iii health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3, standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans. Conclusions Small sample cluster surveys (10 × 15 are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.

Characterization of magnetic Ni clusters on graphene scaffold after high vacuum annealing

Energy Technology Data Exchange (ETDEWEB)

Zhang, Zhenjun, E-mail: zzhang1@albany.edu; Matsubayashi, Akitomo, E-mail: norwegianwood.1984@gmail.com; Grisafe, Benjamin, E-mail: bgrisafe@albany.edu; Lee, Ji Ung, E-mail: jlee1@albany.edu; Lloyd, James R., E-mail: JLloyd@sunycnse.com

2016-02-15

Magnetic Ni nanoclusters were synthesized by electron beam deposition utilizing CVD graphene as a scaffold. The subsequent clusters were subjected to high vacuum (5−8 x10{sup −7} torr) annealing between 300 and 600 °C. The chemical stability, optical and morphological changes were characterized by X-ray photoemission microscopy, Raman spectroscopy, atomic force microscopy and magnetic measurement. Under ambient exposure, nickel nanoparticles were observed to be oxidized quickly, forming antiferromagnetic nickel oxide. Here, we report that the majority of the oxidized nickel is in non-stoichiometric form and can be reduced under high vacuum at temperature as low as 300 °C. Importantly, the resulting annealed clusters were relatively stable and no further oxidation was detectable after three weeks of air exposure at room temperature. - Highlights: • Random oriented nickel clusters were assembled on monolayer graphene scaffold. • Nickel oxide shell was effectively reduced at moderate temperature. • Coercivity of nickel clusters are greatly improved after high vacuum annealing.

Conveyor Performance based on Motor DC 12 Volt Eg-530ad-2f using K-Means Clustering

Science.gov (United States)

Arifin, Zaenal; Artini, Sri DP; Much Ibnu Subroto, Imam

2017-04-01

To produce goods in industry, a controlled tool to improve production is required. Separation process has become a part of production process. Separation process is carried out based on certain criteria to get optimum result. By knowing the characteristics performance of a controlled tools in separation process the optimum results is also possible to be obtained. Clustering analysis is popular method for clustering data into smaller segments. Clustering analysis is useful to divide a group of object into a k-group in which the member value of the group is homogeny or similar. Similarity in the group is set based on certain criteria. The work in this paper based on K-Means method to conduct clustering of loading in the performance of a conveyor driven by a dc motor 12 volt eg-530-2f. This technique gives a complete clustering data for a prototype of conveyor driven by dc motor to separate goods in term of height. The parameters involved are voltage, current, time of travelling. These parameters give two clusters namely optimal cluster with center of cluster 10.50 volt, 0.3 Ampere, 10.58 second, and unoptimal cluster with center of cluster 10.88 volt, 0.28 Ampere and 40.43 second.

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.)

Depressive Symptom Clusters and Neuropsychological Performance in Mild Alzheimer's and Cognitively Normal Elderly

Directory of Open Access Journals (Sweden)

James R. Hall

2011-01-01

Full Text Available Objectives. Determine the relationship between depressive symptom clusters and neuropsychological test performance in an elderly cohort of cognitively normal controls and mild Alzheimer's disease (AD. Design. Cross-sectional analysis. Setting. Four health science centers in Texas. Participants. 628 elderly individuals (272 diagnosed with mild AD and 356 controls from ongoing longitudinal study of Alzheimer's disease. Measurements. Standard battery of neuropsychological tests and the 30-item Geriatric Depression Scale with regressions model generated on GDS-30 subscale scores (dysphoria, apathy, meaninglessness and cognitive impairment as predictors and neuropsychological tests as outcome variables. Follow-up analyses by gender were conducted. Results. For AD, all symptom clusters were related to specific neurocognitive domains; among controls apathy and cognitive impairment were significantly related to neuropsychological functioning. The relationship between performance and symptom clusters was significantly different for males and females in each group. Conclusion. Findings suggest the need to examine disease status and gender when considering the impact of depressive symptoms on cognition.

High Performance Data Transfer for Distributed Data Intensive Sciences

Energy Technology Data Exchange (ETDEWEB)

Fang, Chin [Zettar Inc., Mountain View, CA (United States); Cottrell, R ' Les' A. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Hanushevsky, Andrew B. [SLAC National Accelerator Lab., Menlo Park, CA (United States); Kroeger, Wilko [SLAC National Accelerator Lab., Menlo Park, CA (United States); Yang, Wei [SLAC National Accelerator Lab., Menlo Park, CA (United States)

2017-03-06

We report on the development of ZX software providing high performance data transfer and encryption. The design scales in: computation power, network interfaces, and IOPS while carefully balancing the available resources. Two U.S. patent-pending algorithms help tackle data sets containing lots of small files and very large files, and provide insensitivity to network latency. It has a cluster-oriented architecture, using peer-to-peer technologies to ease deployment, operation, usage, and resource discovery. Its unique optimizations enable effective use of flash memory. Using a pair of existing data transfer nodes at SLAC and NERSC, we compared its performance to that of bbcp and GridFTP and determined that they were comparable. With a proof of concept created using two four-node clusters with multiple distributed multi-core CPUs, network interfaces and flash memory, we achieved 155Gbps memory-to-memory over a 2x100Gbps link aggregated channel and 70Gbps file-to-file with encryption over a 5000 mile 100Gbps link.

Training and validation of the ATLAS pixel clustering neural networks

CERN Document Server

The ATLAS collaboration

2018-01-01

The high centre-of-mass energy of the LHC gives rise to dense environments, such as the core of high-pT jets, in which the charge clusters left by ionising particles in the silicon sensors of the pixel detector can merge, compromising the tracking and vertexing efficiency. To recover optimal performance, a neural network-based approach is used to separate clusters originating from single and multiple particles and to estimate all hit positions within clusters. This note presents the training strategy employed and a set of benchmark performance measurements on a Monte Carlo sample of high-pT dijet events.

Performance improvement of haptic collision detection using subdivision surface and sphere clustering.

Directory of Open Access Journals (Sweden)

A Ram Choi

Full Text Available Haptics applications such as surgery simulations require collision detections that are more precise than others. An efficient collision detection method based on the clustering of bounding spheres was proposed in our prior study. This paper analyzes and compares the applied effects of the five most common subdivision surface methods on some 3D models for haptic collision detection. The five methods are Butterfly, Catmull-Clark, Mid-point, Loop, and LS3 (Least Squares Subdivision Surface. After performing a number of experiments, we have concluded that LS3 method is the most appropriate for haptic simulations. The more we applied surface subdivision, the more the collision detection results became precise. However, it is observed that the performance becomes better until a certain threshold and degrades afterward. In order to reduce the performance degradation, we adopted our prior work, which was the fast and precise collision detection method based on adaptive clustering. As a result, we obtained a notable improvement of the speed of collision detection.

Tracking the NGS revolution: managing life science research on shared high-performance computing clusters.

Science.gov (United States)

Dahlö, Martin; Scofield, Douglas G; Schaal, Wesley; Spjuth, Ola

2018-05-01

Next-generation sequencing (NGS) has transformed the life sciences, and many research groups are newly dependent upon computer clusters to store and analyze large datasets. This creates challenges for e-infrastructures accustomed to hosting computationally mature research in other sciences. Using data gathered from our own clusters at UPPMAX computing center at Uppsala University, Sweden, where core hour usage of ∼800 NGS and ∼200 non-NGS projects is now similar, we compare and contrast the growth, administrative burden, and cluster usage of NGS projects with projects from other sciences. The number of NGS projects has grown rapidly since 2010, with growth driven by entry of new research groups. Storage used by NGS projects has grown more rapidly since 2013 and is now limited by disk capacity. NGS users submit nearly twice as many support tickets per user, and 11 more tools are installed each month for NGS projects than for non-NGS projects. We developed usage and efficiency metrics and show that computing jobs for NGS projects use more RAM than non-NGS projects, are more variable in core usage, and rarely span multiple nodes. NGS jobs use booked resources less efficiently for a variety of reasons. Active monitoring can improve this somewhat. Hosting NGS projects imposes a large administrative burden at UPPMAX due to large numbers of inexperienced users and diverse and rapidly evolving research areas. We provide a set of recommendations for e-infrastructures that host NGS research projects. We provide anonymized versions of our storage, job, and efficiency databases.

Tracking the NGS revolution: managing life science research on shared high-performance computing clusters

Science.gov (United States)

2018-01-01

Abstract Background Next-generation sequencing (NGS) has transformed the life sciences, and many research groups are newly dependent upon computer clusters to store and analyze large datasets. This creates challenges for e-infrastructures accustomed to hosting computationally mature research in other sciences. Using data gathered from our own clusters at UPPMAX computing center at Uppsala University, Sweden, where core hour usage of ∼800 NGS and ∼200 non-NGS projects is now similar, we compare and contrast the growth, administrative burden, and cluster usage of NGS projects with projects from other sciences. Results The number of NGS projects has grown rapidly since 2010, with growth driven by entry of new research groups. Storage used by NGS projects has grown more rapidly since 2013 and is now limited by disk capacity. NGS users submit nearly twice as many support tickets per user, and 11 more tools are installed each month for NGS projects than for non-NGS projects. We developed usage and efficiency metrics and show that computing jobs for NGS projects use more RAM than non-NGS projects, are more variable in core usage, and rarely span multiple nodes. NGS jobs use booked resources less efficiently for a variety of reasons. Active monitoring can improve this somewhat. Conclusions Hosting NGS projects imposes a large administrative burden at UPPMAX due to large numbers of inexperienced users and diverse and rapidly evolving research areas. We provide a set of recommendations for e-infrastructures that host NGS research projects. We provide anonymized versions of our storage, job, and efficiency databases. PMID:29659792

Performance Based Clustering for Benchmarking of Container Ports: an Application of Dea and Cluster Analysis Technique

Directory of Open Access Journals (Sweden)

Jie Wu

2010-12-01

Full Text Available The operational performance of container ports has received more and more attentions in both academic and practitioner circles, the performance evaluation and process improvement of container ports have also been the focus of several studies. In this paper, Data Envelopment Analysis (DEA, an effective tool for relative efficiency assessment, is utilized for measuring the performances and benchmarking of the 77 world container ports in 2007. The used approaches in the current study consider four inputs (Capacity of Cargo Handling Machines, Number of Berths, Terminal Area and Storage Capacity and a single output (Container Throughput. The results for the efficiency scores are analyzed, and a unique ordering of the ports based on average cross efficiency is provided, also cluster analysis technique is used to select the more appropriate targets for poorly performing ports to use as benchmarks.

Marketing Strategies Influences On SMEs Cluster Performance

Directory of Open Access Journals (Sweden)

Satria Tirtayasa

2017-06-01

Full Text Available The contribution of larges businesses to make the economy growth is very important likewise the role of Small Business Enterprise SMEs where this industry has been believed so strong from the effect of crisis economic in Indonesia. In generaly SMEs owner are business manwoman that have minimum assets Rp. 500 million where their gathers together to make the cooperative assosiation in one area SMEs Cluster . So in this cooperative assosiation can be expected the SMEs businessman help each others with making colaboration internal or external networking to enhance their business performance. The internal networking simmilar with marketing strategies for instance strategi Promotion Distribution Production and Raw material supplay where the objectives are to enchance the SMEs performance. This study intends to examine the relationship between marketing strategies Promotion Distribution Production Raw material supplay on SMSEs performance. The marketing strategies consist of our dimentions as follows promotion distribution production and raw material supply. Meanwile SMEs business performance indicators are market share and profit margin. The sample of research is 70 SMEs businessman where spread of Medan Tembung District East Medan District and Medan Perjuangan District. This research had used questionnaires method to collect the data from SMEs businessman. The analysis method had used multiple regression and the result of the research reveals that promotion had positive and significant relationship with SMEs performance.Distribution had positive and significant relationship with SMEs performance Production has positive and significant relationship with SMEs performance.Raw material supplay had positiveve and significant relationship with SMEs performance. Furthermore Marketing Strategies Promotion Distribution Production Raw material supplay simultaneous have significant relationship on SMEs Performances.

Performance Analysis of Quality-of-Service Controls in a Cell-Cluster-Based Wireless ATM Network

Energy Technology Data Exchange (ETDEWEB)

Cho, Young Jong [Ajou University, Suwon (Korea, Republic of)

1997-04-01

In this paper, an efficient cell-cluster-based call control scheme with guaranteed quality-of-service(QoS) provision ing is presented for next generation wireless ATM networks and its performance is mathematically analyzed using the open queuing network. With the cell-cluster-based call control, at the time a mobile connection is admitted to the network, a virtual cell is constructed by choosing a group of neighboring base stations to which the call may probabilistic ally hand over and by assigning to the call a collection of virtual paths between the base stations. Within a micro cell/pico cell environment, it is seen that the cell-cluster-based call control can support effectively a very high rate of handovers, provides very high system capacity, and guarantees a high degree of frequency reuse over the same geographical region without requiring the intervention of the network call control processor each time a handover occurs. But since mobiles, once admitted, are free to roam within the virtual cell, congestion condition occurs in which the number of calls to be handled by one base station exceeds the cell sites` capacity of radio channel and consequently a predefined QoS provision cannot be guaranteed. So, there must be a call admission control function to limit the number of calls existing in a cell-cluster such that required QoS objectives are met. As call acceptance criteria for constant-bit-rate or realtime variable-bit-rate ATM connections, we define four mobile QoS metrics: new-call blocking probability, wireless channel utilization efficiency, congestion probability and normalized average congestion duration. In addition, for QoS provision ing to available-bit-rate, unspecified-bit-rate or non-realtime variable-bit-rate connections, we further define another QoS metric, the minimum threshold breaking probability. By using the open network queuing model, we derive closed form expressions for the five QoS metrics defined above and show that they can be

Energy Efficient Clustering Protocol to Enhance Performance of Heterogeneous Wireless Sensor Network: EECPEP-HWSN

Directory of Open Access Journals (Sweden)

Santosh V. Purkar

2018-01-01

Full Text Available Heterogeneous wireless sensor network (HWSN fulfills the requirements of researchers in the design of real life application to resolve the issues of unattended problem. But, the main constraint faced by researchers is the energy source available with sensor nodes. To prolong the life of sensor nodes and thus HWSN, it is necessary to design energy efficient operational schemes. One of the most suitable approaches to enhance energy efficiency is the clustering scheme, which enhances the performance parameters of WSN. A novel solution proposed in this article is to design an energy efficient clustering protocol for HWSN, to enhance performance parameters by EECPEP-HWSN. The proposed protocol is designed with three level nodes namely normal, advanced, and super, respectively. In the clustering process, for selection of cluster head we consider different parameters available with sensor nodes at run time that is, initial energy, hop count, and residual energy. This protocol enhances the energy efficiency of HWSN and hence improves energy remaining in the network, stability, lifetime, and hence throughput. It has been found that the proposed protocol outperforms than existing well-known LEACH, DEEC, and SEP with about 188, 150, and 141 percent respectively.

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Energy Technology Data Exchange (ETDEWEB)

Yoo, Wucherl [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Koo, Michelle [Univ. of California, Berkeley, CA (United States); Cao, Yu [California Inst. of Technology (CalTech), Pasadena, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Nugent, Peter [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Wu, Kesheng [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2016-09-17

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.

Performance study of a cluster calculation; parallelization and application under geant4

International Nuclear Information System (INIS)

Trabelsi, Abir

2007-01-01

This work concretizes the final studies project for engineering computer sciences, it is archived within the national center of nuclear sciences and technology. The project consists in studying the performance of a set of machines in order to determine the best architecture to assemble them in a cluster. As well as the parallelism and the parallel implementation of GEANT4, as a tool of simulation. The realisation of this project consists on : 1) programming with C++ and executing the two benchmarks P MV and PMM on each station; 2) Interpreting this result in order to show the best architecture of the cluster; 3) parallelism with TOP-C the two benchmarks; 4) Executing the two Top-C versions on the cluster; 5) Generalizing this results; 6)parallelism et executing the parallel version of GEANT4. (Author). 14 refs

Fragmentation of neutral carbon clusters formed by high velocity atomic collision

International Nuclear Information System (INIS)

Martinet, G.

2004-05-01

The aim of this work is to understand the fragmentation of small neutral carbon clusters formed by high velocity atomic collision on atomic gas. In this experiment, the main way of deexcitation of neutral clusters formed by electron capture with ionic species is the fragmentation. To measure the channels of fragmentation, a new detection tool based on shape analysis of current pulse delivered by semiconductor detectors has been developed. For the first time, all branching ratios of neutral carbon clusters are measured in an unambiguous way for clusters size up to 10 atoms. The measurements have been compared to a statistical model in microcanonical ensemble (Microcanonical Metropolis Monte Carlo). In this model, various structural properties of carbon clusters are required. These data have been calculated with Density Functional Theory (DFT-B3LYP) to find the geometries of the clusters and then with Coupled Clusters (CCSD(T)) formalism to obtain dissociation energies and other quantities needed to compute fragmentation calculations. The experimental branching ratios have been compared to the fragmentation model which has allowed to find an energy distribution deposited in the collision. Finally, specific cluster effect has been found namely a large population of excited states. This behaviour is completely different of the atomic carbon case for which the electron capture in the ground states predominates. (author)

Electron scattering in large water clusters from photoelectron imaging with high harmonic radiation.

Science.gov (United States)

Gartmann, Thomas E; Hartweg, Sebastian; Ban, Loren; Chasovskikh, Egor; Yoder, Bruce L; Signorell, Ruth

2018-06-06

Low-energy electron scattering in water clusters (H2O)n with average cluster sizes of n < 700 is investigated by angle-resolved photoelectron spectroscopy using high harmonic radiation at photon energies of 14.0, 20.3, and 26.5 eV for ionization from the three outermost valence orbitals. The measurements probe the evolution of the photoelectron anisotropy parameter β as a function of cluster size. A remarkably steep decrease of β with increasing cluster size is observed, which for the largest clusters reaches liquid bulk values. Detailed electron scattering calculations reveal that neither gas nor condensed phase scattering can explain the cluster data. Qualitative agreement between experiment and simulations is obtained with scattering calculations that treat cluster scattering as an intermediate case between gas and condensed phase scattering.

Performance enhancement of a web-based picture archiving and communication system using commercial off-the-shelf server clusters.

Science.gov (United States)

Liu, Yan-Lin; Shih, Cheng-Ting; Chang, Yuan-Jen; Chang, Shu-Jun; Wu, Jay

2014-01-01

The rapid development of picture archiving and communication systems (PACSs) thoroughly changes the way of medical informatics communication and management. However, as the scale of a hospital's operations increases, the large amount of digital images transferred in the network inevitably decreases system efficiency. In this study, a server cluster consisting of two server nodes was constructed. Network load balancing (NLB), distributed file system (DFS), and structured query language (SQL) duplication services were installed. A total of 1 to 16 workstations were used to transfer computed radiography (CR), computed tomography (CT), and magnetic resonance (MR) images simultaneously to simulate the clinical situation. The average transmission rate (ATR) was analyzed between the cluster and noncluster servers. In the download scenario, the ATRs of CR, CT, and MR images increased by 44.3%, 56.6%, and 100.9%, respectively, when using the server cluster, whereas the ATRs increased by 23.0%, 39.2%, and 24.9% in the upload scenario. In the mix scenario, the transmission performance increased by 45.2% when using eight computer units. The fault tolerance mechanisms of the server cluster maintained the system availability and image integrity. The server cluster can improve the transmission efficiency while maintaining high reliability and continuous availability in a healthcare environment.

Performance Enhancement of a Web-Based Picture Archiving and Communication System Using Commercial Off-the-Shelf Server Clusters

Directory of Open Access Journals (Sweden)

Yan-Lin Liu

2014-01-01

Full Text Available The rapid development of picture archiving and communication systems (PACSs thoroughly changes the way of medical informatics communication and management. However, as the scale of a hospital’s operations increases, the large amount of digital images transferred in the network inevitably decreases system efficiency. In this study, a server cluster consisting of two server nodes was constructed. Network load balancing (NLB, distributed file system (DFS, and structured query language (SQL duplication services were installed. A total of 1 to 16 workstations were used to transfer computed radiography (CR, computed tomography (CT, and magnetic resonance (MR images simultaneously to simulate the clinical situation. The average transmission rate (ATR was analyzed between the cluster and noncluster servers. In the download scenario, the ATRs of CR, CT, and MR images increased by 44.3%, 56.6%, and 100.9%, respectively, when using the server cluster, whereas the ATRs increased by 23.0%, 39.2%, and 24.9% in the upload scenario. In the mix scenario, the transmission performance increased by 45.2% when using eight computer units. The fault tolerance mechanisms of the server cluster maintained the system availability and image integrity. The server cluster can improve the transmission efficiency while maintaining high reliability and continuous availability in a healthcare environment.

High-Performance Parallel and Stream Processing of X-ray Microdiffraction Data on Multicores

International Nuclear Information System (INIS)

Bauer, Michael A; McIntyre, Stewart; Xie Yuzhen; Biem, Alain; Tamura, Nobumichi

2012-01-01

We present the design and implementation of a high-performance system for processing synchrotron X-ray microdiffraction (XRD) data in IBM InfoSphere Streams on multicore processors. We report on the parallel and stream processing techniques that we use to harvest the power of clusters of multicores to analyze hundreds of gigabytes of synchrotron XRD data in order to reveal the microtexture of polycrystalline materials. The timing to process one XRD image using one pipeline is about ten times faster than the best C program at present. With the support of InfoSphere Streams platform, our software is able to be scaled up to operate on clusters of multi-cores for processing multiple images concurrently. This system provides a high-performance processing kernel to achieve near real-time data analysis of image data from synchrotron experiments.

The first high resolution image of coronal gas in a starbursting cool core cluster

Science.gov (United States)

Johnson, Sean

2017-08-01

Galaxy clusters represent a unique laboratory for directly observing gas cooling and feedback due to their high masses and correspondingly high gas densities and temperatures. Cooling of X-ray gas observed in 1/3 of clusters, known as cool-core clusters, should fuel star formation at prodigious rates, but such high levels of star formation are rarely observed. Feedback from active galactic nuclei (AGN) is a leading explanation for the lack of star formation in most cool clusters, and AGN power is sufficient to offset gas cooling on average. Nevertheless, some cool core clusters exhibit massive starbursts indicating that our understanding of cooling and feedback is incomplete. Observations of 10^5 K coronal gas in cool core clusters through OVI emission offers a sensitive means of testing our understanding of cooling and feedback because OVI emission is a dominant coolant and sensitive tracer of shocked gas. Recently, Hayes et al. 2016 demonstrated that synthetic narrow-band imaging of OVI emission is possible through subtraction of long-pass filters with the ACS+SBC for targets at z=0.23-0.29. Here, we propose to use this exciting new technique to directly image coronal OVI emitting gas at high resolution in Abell 1835, a prototypical starbursting cool-core cluster at z=0.252. Abell 1835 hosts a strong cooling core, massive starburst, radio AGN, and at z=0.252, it offers a unique opportunity to directly image OVI at hi-res in the UV with ACS+SBC. With just 15 orbits of ACS+SBC imaging, the proposed observations will complete the existing rich multi-wavelength dataset available for Abell 1835 to provide new insights into cooling and feedback in clusters.

Metallicity in galactic clusters from high signal-to-noise spectroscopy

International Nuclear Information System (INIS)

Boesgaard, A.M.

1989-01-01

High-quality spectroscopic data on selected F dwarfs in six Galactic clusters are used to determine global (Fe/H) values for the clusters. For the two youngest clusters, Pleiades and Alpha Per, the (Fe/H) values are solar: 0.017 + or - 0.055. The Hyades and Praesepe are slightly metal-enhanced at (Fe/H) = + 0.125 + or - 0.032, even though they are an order of magnitude older than the Pleiades. Coma and the UMa Group at the age of the Hyades are slightly metal-deficient with (Fe/H) = - 0.082 + or - 0.039. The lack of an age-metallicity relationship indicates that the enrichment and mixing in the Galactic disk have not been uniform on time scales less than a billion years. 39 references

Ionization and fragmentation of water clusters by fast highly charged ions

International Nuclear Information System (INIS)

Adoui, L; Cassimi, A; Gervais, B; Grandin, J-P; Guillaume, L; Maisonny, R; Legendre, S; Tarisien, M; Lopez-Tarifa, P; Alcami, M; Martin, F; Politis, M-F; Penhoat, M-A Herve du; Vuilleumier, R; Gaigeot, M-P; Tavernelli, I

2009-01-01

We study the dissociative ionization of water clusters by impact of 12 MeV/u Ni 25+ ions. Cold target recoil ion momentum spectroscopy (COLTRIMS) is used to obtain information about stability, energetics and charge mobility of the ionized water clusters. An unusual stability of the H 9 O + 4 ion is observed, which could be the signature of the so-called Eigen structure in gas-phase water clusters. From the analysis of coincidences between charged fragments, we conclude that charge mobility is very high and is responsible for the formation of protonated water clusters, (H 2 O) n H + , that dominate the mass spectrum. These results are supported by Car-Parrinello molecular dynamics and time-dependent density functional theory simulations, which also reveal the mechanisms of such mobility.

M31 GLOBULAR CLUSTER ABUNDANCES FROM HIGH-RESOLUTION, INTEGRATED-LIGHT SPECTROSCOPY

International Nuclear Information System (INIS)

Colucci, Janet E.; Bernstein, Rebecca A.; Cameron, Scott; McWilliam, Andrew; Cohen, Judith G.

2009-01-01

We report the first detailed chemical abundances for five globular clusters (GCs) in M31 from high-resolution (R ∼ 25,000) spectroscopy of their integrated light (IL). These GCs are the first in a larger set of clusters observed as part of an ongoing project to study the formation history of M31 and its GC population. The data presented here were obtained with the HIRES echelle spectrograph on the Keck I telescope and are analyzed using a new IL spectra analysis method that we have developed. In these clusters, we measure abundances for Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Y, and Ba, ages ≥10 Gyr, and a range in [Fe/H] of -0.9 to -2.2. As is typical of Milky Way GCs, we find these M31 GCs to be enhanced in the α-elements Ca, Si, and Ti relative to Fe. We also find [Mg/Fe] to be low relative to other [α/Fe], and [Al/Fe] to be enhanced in the IL abundances. These results imply that abundances of Mg, Al (and likely O, Na) recovered from IL do display the inter- and intra-cluster abundance variations seen in individual Milky Way GC stars, and that special care should be taken in the future in interpreting low- or high-resolution IL abundances of GCs that are based on Mg-dominated absorption features. Fe-peak and the neutron-capture elements Ba and Y also follow Milky Way abundance trends. We also present high-precision velocity dispersion measurements for all five M31 GCs, as well as independent constraints on the reddening toward the clusters from our analysis.

ON THE ORIGIN OF HIGH-ALTITUDE OPEN CLUSTERS IN THE MILKY WAY

Energy Technology Data Exchange (ETDEWEB)

Martinez-Medina, L. A.; Pichardo, B.; Moreno, E.; Peimbert, A. [Instituto de Astronomía, Universidad Nacional Autónoma de México, A.P. 70-264, 04510, México, D.F., México (Mexico); Velazquez, H., E-mail: lamartinez@astro.unam.mx [Instituto de Astronomía, Universidad Nacional Autónoma de México, Apartado Postal 877, 22860 Ensenada, B.C., México (Mexico)

2016-01-20

We present a dynamical study of the effect of the bar and spiral arms on the simulated orbits of open clusters in the Galaxy. Specifically, this work is devoted to the puzzling presence of high-altitude open clusters in the Galaxy. For this purpose we employ a very detailed observationally motivated potential model for the Milky Way and a careful set of initial conditions representing the newly born open clusters in the thin disk. We find that the spiral arms are able to raise an important percentage of open clusters (about one-sixth of the total employed in our simulations, depending on the structural parameters of the arms) above the Galactic plane to heights beyond 200 pc, producing a bulge-shaped structure toward the center of the Galaxy. Contrary to what was expected, the spiral arms produce a much greater vertical effect on the clusters than the bar, both in quantity and height; this is due to the sharper concentration of the mass on the spiral arms, when compared to the bar. When a bar and spiral arms are included, spiral arms are still capable of raising an important percentage of the simulated open clusters through chaotic diffusion (as tested from classification analysis of the resultant high-z orbits), but the bar seems to restrain them, diminishing the elevation above the plane by a factor of about two.

Development of a high-performance image server using ATM technology

Science.gov (United States)

Do Van, Minh; Humphrey, Louis M.; Ravin, Carl E.

1996-05-01

The ability to display digital radiographs to a radiologist in a reasonable time has long been the goal of many PACS. Intelligent routing, or pre-fetching images, has become a solution whereby a system uses a set of rules to route the images to a pre-determined destination. Images would then be stored locally on a workstation for faster display times. Some PACS use a large, centralized storage approach and workstations retrieve images over high bandwidth connections. Another approach to image management is to provide a high performance, clustered storage system. This has the advantage of eliminating the complexity of pre-fetching and allows for rapid image display from anywhere within the hospital. We discuss the development of such a storage device, which provides extremely fast access to images across a local area network. Among the requirements for development of the image server were high performance, DICOM 3.0 compliance, and the use of industry standard components. The completed image server provides performance more than sufficient for use in clinical practice. Setting up modalities to send images to the image server is simple due to the adherence to the DICOM 3.0 specification. Using only off-the-shelf components allows us to keep the cost of the server relatively inexpensive and allows for easy upgrades as technology becomes more advanced. These factors make the image server ideal for use as a clustered storage system in a radiology department.

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.

Determination of atomic cluster structure with cluster fusion algorithm

DEFF Research Database (Denmark)

Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.

2005-01-01

We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters.......We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters....

Swarm v2: highly-scalable and high-resolution amplicon clustering.

Science.gov (United States)

Mahé, Frédéric; Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2015-01-01

Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

Swarm v2: highly-scalable and high-resolution amplicon clustering

Directory of Open Access Journals (Sweden)

Frédéric Mahé

2015-12-01

Full Text Available Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs, free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d, followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1 a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2 the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

Quest for Highly-connected MOF Platforms: Rare-Earth Polynuclear Clusters Versatility Meets Net Topology Needs.

KAUST Repository

Alezi, Dalal

2015-04-07

Gaining control over the assembly of highly porous rare-earth (RE) based metal-organic frameworks (MOFs) remains challenging. Here we report the latest discoveries on our continuous quest for highly-connected nets. The topological exploration based on the non-compatibility of 12-connected RE polynuclear carboxylate-based cluster, points of extension matching the 12 vertices of the cuboctahedron (cuo), with 3-connected organic ligands led to the discovery of two fascinating and highly-connected minimal edge-transitive nets, pek and aea. The reduced symmetry of the employed triangular tricarboxylate ligand, as compared to the prototype highly symmetrical 1,3,5-benzene(tris)benzoic acid guided the concurrent occurrence of nonanuclear [RE9(μ3-OH)12(μ3-O)2(O2C–)12] and hexanuclear [RE6(OH)8(O2C–)8] carboxylate-based clusters as 12-connected and 8-connected molecular building blocks in the structure of a 3-periodic pek-MOF based on a novel (3,8,12)-c trinodal net. The use of a tricarboxylate ligand with modified angles between carboxylate moieties led to the formation of a second MOF containing solely nonanuclear clusters and exhibiting once more a novel and a highly-connected (3,12,12)-c trinodal net with aea topology. Notably, it is the first time that RE-MOFs with double six-membered ring (d6R) secondary building units are isolated, representing therefore a critical step forward toward the design of novel and highly coordinated materials using the supermolecular building layer approach while considering the d6Rs as building pillars. Lastly, the potential of these new MOFs for gas separation/storage was investigated by performing gas adsorption studies of various probe gas molecules over a wide range of pressures. Noticeably, pek-MOF-1 showed excellent volumetric CO2 and CH4 uptakes at high pressures.

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

A hybridized K-means clustering approach for high dimensional ...

African Journals Online (AJOL)

International Journal of Engineering, Science and Technology ... Due to incredible growth of high dimensional dataset, conventional data base querying methods are inadequate to extract useful information, so researchers nowadays ... Recently cluster analysis is a popularly used data analysis method in number of areas.

Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

Directory of Open Access Journals (Sweden)

Yubo Jiao

2013-01-01

Full Text Available Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method.

A fuzzy clustering technique for calorimetric data reconstruction

International Nuclear Information System (INIS)

Sandhir, Radha Pyari; Muhuri, Sanjib; Nayak, Tapan K.

2010-01-01

In high energy physics experiments, electromagnetic calorimeters are used to measure shower particles produced in p-p or heavy-ion collisions. In order to extract information and reconstruct the characteristics of the various incoming particles, clustering is required to be performed on each of the calorimeter planes. Hard clustering techniques such as Local Maxima Search, Connected-cell Search and K-means Clustering simply assign a data point to a cluster. A data point either lies in a cluster or it does not, and so, overlapping clusters are hardly distinguishable. Fuzzy c-means clustering is a version of the k-means algorithm that incorporates fuzzy logic, so that each point has a weak or strong association to the cluster, determined by the inverse distance to the center of the cluster. The term fuzzy is used because an observation may in fact lie in more than one cluster simultaneously, though to different degrees called 'memberships', as is the case with many high energy physics applications. The centers obtained using the FCM algorithm are based on the geometric locations of the data points

Cluster Correlation in Mixed Models

Science.gov (United States)

Gardini, A.; Bonometto, S. A.; Murante, G.; Yepes, G.

2000-10-01

We evaluate the dependence of the cluster correlation length, rc, on the mean intercluster separation, Dc, for three models with critical matter density, vanishing vacuum energy (Λ=0), and COBE normalization: a tilted cold dark matter (tCDM) model (n=0.8) and two blue mixed models with two light massive neutrinos, yielding Ωh=0.26 and 0.14 (MDM1 and MDM2, respectively). All models approach the observational value of σ8 (and hence the observed cluster abundance) and are consistent with the observed abundance of damped Lyα systems. Mixed models have a motivation in recent results of neutrino physics; they also agree with the observed value of the ratio σ8/σ25, yielding the spectral slope parameter Γ, and nicely fit Las Campanas Redshift Survey (LCRS) reconstructed spectra. We use parallel AP3M simulations, performed in a wide box (of side 360 h-1 Mpc) and with high mass and distance resolution, enabling us to build artificial samples of clusters, whose total number and mass range allow us to cover the same Dc interval inspected through Automatic Plate Measuring Facility (APM) and Abell cluster clustering data. We find that the tCDM model performs substantially better than n=1 critical density CDM models. Our main finding, however, is that mixed models provide a surprisingly good fit to cluster clustering data.

Nuclear clustering - a cluster core model study

International Nuclear Information System (INIS)

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

2015-01-01

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

Relics in galaxy clusters at high radio frequencies

Science.gov (United States)

Kierdorf, M.; Beck, R.; Hoeft, M.; Klein, U.; van Weeren, R. J.; Forman, W. R.; Jones, C.

2017-04-01

Aims: We investigated the magnetic properties of radio relics located at the peripheries of galaxy clusters at high radio frequencies, where the emission is expected to be free of Faraday depolarization. The degree of polarization is a measure of the magnetic field compression and, hence, the Mach number. Polarization observations can also be used to confirm relic candidates. Methods: We observed three radio relics in galaxy clusters and one radio relic candidate at 4.85 and 8.35 GHz in total emission and linearly polarized emission with the Effelsberg 100-m telescope. In addition, we observed one radio relic candidate in X-rays with the Chandra telescope. We derived maps of polarization angle, polarization degree, and Faraday rotation measures. Results: The radio spectra of the integrated emission below 8.35 GHz can be well fitted by single power laws for all four relics. The flat spectra (spectral indices of 0.9 and 1.0) for the so-called Sausage relic in cluster CIZA J2242+53 and the so-called Toothbrush relic in cluster 1RXS 06+42 indicate that models describing the origin of relics have to include effects beyond the assumptions of diffuse shock acceleration. The spectra of the radio relics in ZwCl 0008+52 and in Abell 1612 are steep, as expected from weak shocks (Mach number ≈2.4). Polarization observations of radio relics offer a method of measuring the strength and geometry of the shock front. We find polarization degrees of more than 50% in the two prominent Mpc-sized radio relics, the Sausage and the Toothbrush, which are among the highest percentages of linear polarization detected in any extragalactic radio source to date. This is remarkable because the large beam size of the Effelsberg single-dish telescope corresponds to linear extensions of about 300 kpc at 8.35 GHz at the distances of the relics. The high degree of polarization indicates that the magnetic field vectors are almost perfectly aligned along the relic structure, as expected for shock

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.

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.

Post-Newtonian Dynamics in Dense Star Clusters: Highly Eccentric, Highly Spinning, and Repeated Binary Black Hole Mergers.

Science.gov (United States)

Rodriguez, Carl L; Amaro-Seoane, Pau; Chatterjee, Sourav; Rasio, Frederic A

2018-04-13

We present models of realistic globular clusters with post-Newtonian dynamics for black holes. By modeling the relativistic accelerations and gravitational-wave emission in isolated binaries and during three- and four-body encounters, we find that nearly half of all binary black hole mergers occur inside the cluster, with about 10% of those mergers entering the LIGO/Virgo band with eccentricities greater than 0.1. In-cluster mergers lead to the birth of a second generation of black holes with larger masses and high spins, which, depending on the black hole natal spins, can sometimes be retained in the cluster and merge again. As a result, globular clusters can produce merging binaries with detectable spins regardless of the birth spins of black holes formed from massive stars. These second-generation black holes would also populate any upper mass gap created by pair-instability supernovae.

Post-Newtonian Dynamics in Dense Star Clusters: Highly Eccentric, Highly Spinning, and Repeated Binary Black Hole Mergers

Science.gov (United States)

Rodriguez, Carl L.; Amaro-Seoane, Pau; Chatterjee, Sourav; Rasio, Frederic A.

2018-04-01

We present models of realistic globular clusters with post-Newtonian dynamics for black holes. By modeling the relativistic accelerations and gravitational-wave emission in isolated binaries and during three- and four-body encounters, we find that nearly half of all binary black hole mergers occur inside the cluster, with about 10% of those mergers entering the LIGO/Virgo band with eccentricities greater than 0.1. In-cluster mergers lead to the birth of a second generation of black holes with larger masses and high spins, which, depending on the black hole natal spins, can sometimes be retained in the cluster and merge again. As a result, globular clusters can produce merging binaries with detectable spins regardless of the birth spins of black holes formed from massive stars. These second-generation black holes would also populate any upper mass gap created by pair-instability supernovae.

Synthesis of Fe3O4 cluster microspheres/graphene aerogels composite as anode for high-performance lithium ion battery

Science.gov (United States)

Zhou, Shuai; Zhou, Yu; Jiang, Wei; Guo, Huajun; Wang, Zhixing; Li, Xinhai

2018-05-01

Iron oxides are considered as attractive electrode materials because of their capability of lithium storage, but their poor conductivity and large volume expansion lead to unsatisfactory cycling stability. We designed and synthesized a novel Fe3O4 cluster microspheres/Graphene aerogels composite (Fe3O4/GAs), where Fe3O4 nanoparticles were assembled into cluster microspheres and then embedded in 3D graphene aerogels framework. In the spheres, the sufficient free space between Fe3O4 nanoparticles could accommodate the volume change during cycling process. Graphene aerogel works as flexible and conductive matrix, which can not only significantly increase the mechanical stress, but also further improve the storage properties. The Fe3O4/GAs composite as an anode material exhibits high reversible capability and excellent cyclic capacity for lithium ion batteries (LIBs). A reversible capability of 650 mAh g-1 after 500 cycles at a current density of 1 A g-1 can be maintained. The superior storage capabilities of the composites make them potential anode materials for LIBs.

Enforcing Resource Sharing Agreements Among Distributed Server Clusters

National Research Council Canada - National Science Library

Zhao, Tao; Karamcheti, Vijay

2001-01-01

Future scalable, high throughput, and high performance applications are likely to execute on platforms constructed by clustering multiple autonomous distributed servers, with resource access governed...

Bimetallic Ag-Pt Sub-nanometer Supported Clusters as Highly Efficient and Robust Oxidation Catalysts

Energy Technology Data Exchange (ETDEWEB)

Negreiros, Fabio R. [CNR-ICCOM & IPCF, Consiglio Nazionale delle Ricerche, Pisa Italy; Halder, Avik [Materials Science Division, Argonne National Laboratory, Lemont IL USA; Yin, Chunrong [Materials Science Division, Argonne National Laboratory, Lemont IL USA; Singh, Akansha [Harish-Chandra Research Institute, HBNI, Chhatnag Road Jhunsi Allahabad 211019 India; Barcaro, Giovanni [CNR-ICCOM & IPCF, Consiglio Nazionale delle Ricerche, Pisa Italy; Sementa, Luca [CNR-ICCOM & IPCF, Consiglio Nazionale delle Ricerche, Pisa Italy; Tyo, Eric C. [Materials Science Division, Argonne National Laboratory, Lemont IL USA; Pellin, Michael J. [Materials Science Division, Argonne National Laboratory, Lemont IL USA; Bartling, Stephan [Institut für Physik, Universität Rostock, Rostock Germany; Meiwes-Broer, Karl-Heinz [Institut für Physik, Universität Rostock, Rostock Germany; Seifert, Sönke [X-ray Science Division, Argonne National Laboratory, Lemont IL USA; Sen, Prasenjit [Harish-Chandra Research Institute, HBNI, Chhatnag Road Jhunsi Allahabad 211019 India; Nigam, Sandeep [Chemistry Division, Bhabha Atomic Research Centre, Trombay Mumbai- 400 085 India; Majumder, Chiranjib [Chemistry Division, Bhabha Atomic Research Centre, Trombay Mumbai- 400 085 India; Fukui, Nobuyuki [East Tokyo Laboratory, Genesis Research Institute, Inc., Ichikawa Chiba 272-0001 Japan; Yasumatsu, Hisato [Cluster Research Laboratory, Toyota Technological Institute: in, East Tokyo Laboratory, Genesis Research Institute, Inc. Ichikawa, Chiba 272-0001 Japan; Vajda, Stefan [Materials Science Division, Argonne National Laboratory, Lemont IL USA; Nanoscience and Technology Division, Argonne National Laboratory, Lemont IL USA; Institute for Molecular Engineering, University of Chicago, Chicago IL USA; Fortunelli, Alessandro [CNR-ICCOM & IPCF, Consiglio Nazionale delle Ricerche, Pisa Italy; Materials and Process Simulation Center, California Institute of Technology, Pasadena CA USA

2017-12-29

A combined experimental and theoretical investigation of Ag-Pt sub-nanometer clusters as heterogeneous catalysts in the CO -> CO2 reaction (COox) is presented. Ag9Pt2 and Ag9Pt3 clusters are size-selected in the gas phase, deposited on an ultrathin amorphous alumina support, and tested as catalysts experimentally under realistic conditions and by first-principles simulations at realistic coverage. Insitu GISAXS/TPRx demonstrates that the clusters do not sinter or deactivate even after prolonged exposure to reactants at high temperature, and present comparable, extremely high COox catalytic efficiency. Such high activity and stability are ascribed to a synergic role of Ag and Pt in ultranano-aggregates, in which Pt anchors the clusters to the support and binds and activates two CO molecules, while Ag binds and activates O-2, and Ag/Pt surface proximity disfavors poisoning by CO or oxidized species.

The impact of export performance resources of companies belonging to clusters: a study in the French winery industry

Directory of Open Access Journals (Sweden)

Aurora Carneiro Zen

2014-12-01

Full Text Available The purpose of this paper was to analyze the impact of resources on export performance of clustered companies. We argue that the insertion in clusters provides access to resources that influence the internationalization process of firms. We conducted a survey in the French wine industry, the main consumer market in volume and second largest producer of wine in the world. The population of the study includes exporting French wineries, located in clusters. The sample consists of 130 French wine exporters, located in different wine clusters. In short, the results indicated that access to cluster’s resources has a positive impact on the process of internationalization and export performance of companies. One managerial implication of the research is the importance of commercial resources. The firms with higher export performance attributed greater importance to their commercial resources. Further studies may measure the utilization of resources in the internationalization strategy, and compare the importance and the use of resources in accordance with the level of export performance of companies.

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.

Science.gov (United States)

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

HIGH-RESOLUTION XMM-NEWTON SPECTROSCOPY OF THE COOLING FLOW CLUSTER A3112

Energy Technology Data Exchange (ETDEWEB)

Bulbul, G. Esra; Smith, Randall K.; Foster, Adam [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Cottam, Jean; Loewenstein, Michael; Mushotzky, Richard; Shafer, Richard, E-mail: ebulbul@cfa.harvard.edu [NASA Goddard Space Flight Center, Greenbelt, MD (United States)

2012-03-01

We examine high signal-to-noise XMM-Newton European Photon Imaging Camera (EPIC) and Reflection Grating Spectrometer (RGS) observations to determine the physical characteristics of the gas in the cool core and outskirts of the nearby rich cluster A3112. The XMM-Newton Extended Source Analysis Software data reduction and background modeling methods were used to analyze the XMM-Newton EPIC data. From the EPIC data, we find that the iron and silicon abundance gradients show significant increase toward the center of the cluster while the oxygen abundance profile is centrally peaked but has a shallower distribution than that of iron. The X-ray mass modeling is based on the temperature and deprojected density distributions of the intracluster medium determined from EPIC observations. The total mass of A3112 obeys the M-T scaling relations found using XMM-Newton and Chandra observations of massive clusters at r{sub 500}. The gas mass fraction f{sub gas} = 0.149{sup +0.036}{sub -0.032} at r{sub 500} is consistent with the seven-year Wilkinson Microwave Anisotropy Probe results. The comparisons of line fluxes and flux limits on the Fe XVII and Fe XVIII lines obtained from high-resolution RGS spectra indicate that there is no spectral evidence for cooler gas associated with the cluster with temperature below 1.0 keV in the central <38'' ({approx}52 kpc) region of A3112. High-resolution RGS spectra also yield an upper limit to the turbulent motions in the compact core of A3112 (206 km s{sup -1}). We find that the contribution of turbulence to total energy is less than 6%. This upper limit is consistent with the energy contribution measured in recent high-resolution simulations of relaxed galaxy clusters.

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions.

Science.gov (United States)

Bansal, Ravi; Peterson, Bradley S

2018-06-01

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal

Feasibility Study of Parallel Finite Element Analysis on Cluster-of-Clusters

Science.gov (United States)

Muraoka, Masae; Okuda, Hiroshi

With the rapid growth of WAN infrastructure and development of Grid middleware, it's become a realistic and attractive methodology to connect cluster machines on wide-area network for the execution of computation-demanding applications. Many existing parallel finite element (FE) applications have been, however, designed and developed with a single computing resource in mind, since such applications require frequent synchronization and communication among processes. There have been few FE applications that can exploit the distributed environment so far. In this study, we explore the feasibility of FE applications on the cluster-of-clusters. First, we classify FE applications into two types, tightly coupled applications (TCA) and loosely coupled applications (LCA) based on their communication pattern. A prototype of each application is implemented on the cluster-of-clusters. We perform numerical experiments executing TCA and LCA on both the cluster-of-clusters and a single cluster. Thorough these experiments, by comparing the performances and communication cost in each case, we evaluate the feasibility of FEA on the cluster-of-clusters.

A spatial scan statistic for nonisotropic two-level risk cluster.

Science.gov (United States)

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2012-01-30

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

Wind Energy Development in India and a Methodology for Evaluating Performance of Wind Farm Clusters

Directory of Open Access Journals (Sweden)

Sanjeev H. Kulkarni

2016-01-01

Full Text Available With maturity of advanced technologies and urgent requirement for maintaining a healthy environment with reasonable price, India is moving towards a trend of generating electricity from renewable resources. Wind energy production, with its relatively safer and positive environmental characteristics, has evolved from a marginal activity into a multibillion dollar industry today. Wind energy power plants, also known as wind farms, comprise multiple wind turbines. Though there are several wind-mill clusters producing energy in different geographical locations across the world, evaluating their performance is a complex task and is an important focus for stakeholders. In this work an attempt is made to estimate the performance of wind clusters employing a multicriteria approach. Multiple factors that affect wind farm operations are analyzed by taking experts opinions, and a performance ranking of the wind farms is generated. The weights of the selection criteria are determined by pairwise comparison matrices of the Analytic Hierarchy Process (AHP. The proposed methodology evaluates wind farm performance based on technical, economic, environmental, and sociological indicators. Both qualitative and quantitative parameters were considered. Empirical data were collected through questionnaire from the selected wind farms of Belagavi district in the Indian State of Karnataka. This proposed methodology is a useful tool for cluster analysis.

PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA

Directory of Open Access Journals (Sweden)

Y. Z. Gu

2017-09-01

Full Text Available Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China.

Evolution of Late-type Galaxies in a Cluster Environment: Effects of High-speed Multiple Encounters with Early-type Galaxies

Science.gov (United States)

Hwang, Jeong-Sun; Park, Changbom; Banerjee, Arunima; Hwang, Ho Seong

2018-04-01

Late-type galaxies falling into a cluster would evolve being influenced by the interactions with both the cluster and the nearby cluster member galaxies. Most numerical studies, however, tend to focus on the effects of the former with little work done on those of the latter. We thus perform a numerical study on the evolution of a late-type galaxy interacting with neighboring early-type galaxies at high speed using hydrodynamic simulations. Based on the information obtained from the Coma cluster, we set up the simulations for the case where a Milky Way–like late-type galaxy experiences six consecutive collisions with twice as massive early-type galaxies having hot gas in their halos at the closest approach distances of 15–65 h ‑1 kpc at the relative velocities of 1500–1600 km s‑1. Our simulations show that the evolution of the late-type galaxy can be significantly affected by the accumulated effects of the high-speed multiple collisions with the early-type galaxies, such as on cold gas content and star formation activity of the late-type galaxy, particularly through the hydrodynamic interactions between cold disk and hot gas halos. We find that the late-type galaxy can lose most of its cold gas after the six collisions and have more star formation activity during the collisions. By comparing our simulation results with those of galaxy–cluster interactions, we claim that the role of the galaxy–galaxy interactions on the evolution of late-type galaxies in clusters could be comparable with that of the galaxy–cluster interactions, depending on the dynamical history.

Partitional clustering algorithms

CERN Document Server

2015-01-01

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

Reproducibility of Cognitive Profiles in Psychosis Using Cluster Analysis.

Science.gov (United States)

Lewandowski, Kathryn E; Baker, Justin T; McCarthy, Julie M; Norris, Lesley A; Öngür, Dost

2018-04-01

Cognitive dysfunction is a core symptom dimension that cuts across the psychoses. Recent findings support classification of patients along the cognitive dimension using cluster analysis; however, data-derived groupings may be highly determined by sampling characteristics and the measures used to derive the clusters, and so their interpretability must be established. We examined cognitive clusters in a cross-diagnostic sample of patients with psychosis and associations with clinical and functional outcomes. We then compared our findings to a previous report of cognitive clusters in a separate sample using a different cognitive battery. Participants with affective or non-affective psychosis (n=120) and healthy controls (n=31) were administered the MATRICS Consensus Cognitive Battery, and clinical and community functioning assessments. Cluster analyses were performed on cognitive variables, and clusters were compared on demographic, cognitive, and clinical measures. Results were compared to findings from our previous report. A four-cluster solution provided a good fit to the data; profiles included a neuropsychologically normal cluster, a globally impaired cluster, and two clusters of mixed profiles. Cognitive burden was associated with symptom severity and poorer community functioning. The patterns of cognitive performance by cluster were highly consistent with our previous findings. We found evidence of four cognitive subgroups of patients with psychosis, with cognitive profiles that map closely to those produced in our previous work. Clusters were associated with clinical and community variables and a measure of premorbid functioning, suggesting that they reflect meaningful groupings: replicable, and related to clinical presentation and functional outcomes. (JINS, 2018, 24, 382-390).

Hall effect measurements of Frenkel defect clustering in aluminium during high-dose reactor irradiation at 4.6 K

International Nuclear Information System (INIS)

Boening, K.; Mauer, W.; Pfaendner, K.; Rosner, P.

1976-01-01

The low-field Hall coefficient R 0 of irradiated aluminium at 4.6 K is independent of the Frenkel defect (FD) concentration, however sensitively dependent of their configuration. Since measurement of R 0 is not too difficult, rather extensive investigations of FD clustering during irradiation can be performed, but only qualitative interpretations are possible. Several pure Al samples have been irradiated with reactor neutrons at 4.6 K up to very high doses phit resp. resistivity increments Δrho 0 (maximum 91% of extrapolated saturation value Δrho 0 sup(sat) approximately 980 nΩcm). The main results are 1.FD clustering within a single displacement cascade is not a very strong effect in Al, since the R 0 values are essentially the same after reactor and after electron irradiation. Rough cascade averages are: volume Vsub(c) approximately 2.1 x 10 5 at.vol. and FD concentration csub(c) approximately 1100 ppm. 2. There is practically no dose-dependent FD clustering up to Δrho 0 approximately 350 nΩcm, since R 0 remains essentially constant there. It follows that dose-dependent FD clustering can only occur for high-order overlap of cascade volumes. The differential dose curve dΔrho 0 /dphit is perfectly linear in Δrho 0 as long as R 0 = const. 3. For Δrho 0 > 350 nΩcm FD clustering becomes increasingly important and R 0 changes strongly. Surprisingly dR 0 /dphit approximately const whence there is a constant rate of cluster size increase in spite of the vanishing rate of FD production, evidence of the continuous regrouping of the lattice and its defects. (author)

Genetic search for an optimal power flow solution from a high density cluster

Energy Technology Data Exchange (ETDEWEB)

Amarnath, R.V. [Hi-Tech College of Engineering and Technology, Hyderabad (India); Ramana, N.V. [JNTU College of Engineering, Jagityala (India)

2008-07-01

This paper proposed a novel method to solve optimal power flow (OPF) problems. The method is based on a genetic algorithm (GA) search from a High Density Cluster (GAHDC). The algorithm of the proposed method includes 3 stages, notably (1) a suboptimal solution is obtained via a conventional analytical method, (2) a high density cluster, which consists of other suboptimal data points from the first stage, is formed using a density-based cluster algorithm, and (3) a genetic algorithm based search is carried out for the exact optimal solution from a low population sized, high density cluster. The final optimal solution thoroughly satisfies the well defined fitness function. A standard IEEE 30-bus test system was considered for the simulation study. Numerical results were presented and compared with the results of other approaches. It was concluded that although there is not much difference in numerical values, the proposed method has the advantage of minimal computational effort and reduced CPU time. As such, the method would be suitable for online applications such as the present Optimal Power Flow problem. 24 refs., 2 tabs., 4 figs.

A highly accurate positioning and orientation system based on the usage of four-cluster fibre optic gyros

International Nuclear Information System (INIS)

Zhang, Xiaoyue; Lin, Zhili; Zhang, Chunxi

2013-01-01

A highly accurate positioning and orientation technique based on four-cluster fibre optic gyros (FOGs) is presented. The four-cluster FOG inertial measurement unit (IMU) comprises three low-precision FOGs, one static high-precision FOG and three accelerometers. To realize high-precision positioning and orientation, the static alignment (north-seeking) before vehicle manoeuvre was divided into a low-precision self-alignment phase and a high-precision north-seeking (online calibration) phase. The high-precision FOG measurement information was introduced to obtain high-precision azimuth alignment (north-seeking) result and achieve online calibration of the low-precision three-cluster FOG. The results of semi-physical simulation were presented to validate the availability and utility of the highly accurate positioning and orientation technique based on the four-cluster FOGs. (paper)

TreeCluster: Massively scalable transmission clustering using phylogenetic trees

OpenAIRE

Moshiri, Alexander

2018-01-01

Background: The ability to infer transmission clusters from molecular data is critical to designing and evaluating viral control strategies. Viral sequencing datasets are growing rapidly, but standard methods of transmission cluster inference do not scale well beyond thousands of sequences. Results: I present TreeCluster, a cross-platform tool that performs transmission cluster inference on a given phylogenetic tree orders of magnitude faster than existing inference methods and supports multi...

Segmentation of High Angular Resolution Diffusion MRI using Sparse Riemannian Manifold Clustering

Science.gov (United States)

Wright, Margaret J.; Thompson, Paul M.; Vidal, René

2015-01-01

We address the problem of segmenting high angular resolution diffusion imaging (HARDI) data into multiple regions (or fiber tracts) with distinct diffusion properties. We use the orientation distribution function (ODF) to represent HARDI data and cast the problem as a clustering problem in the space of ODFs. Our approach integrates tools from sparse representation theory and Riemannian geometry into a graph theoretic segmentation framework. By exploiting the Riemannian properties of the space of ODFs, we learn a sparse representation for each ODF and infer the segmentation by applying spectral clustering to a similarity matrix built from these representations. In cases where regions with similar (resp. distinct) diffusion properties belong to different (resp. same) fiber tracts, we obtain the segmentation by incorporating spatial and user-specified pairwise relationships into the formulation. Experiments on synthetic data evaluate the sensitivity of our method to image noise and the presence of complex fiber configurations, and show its superior performance compared to alternative segmentation methods. Experiments on phantom and real data demonstrate the accuracy of the proposed method in segmenting simulated fibers, as well as white matter fiber tracts of clinical importance in the human brain. PMID:24108748

Constraining omega from X-ray properties of clusters of galaxies at high redshifts

DEFF Research Database (Denmark)

Sadat, R.; Blanchard, A.; Oukbir, J.

1997-01-01

Properties of high redshift clusters are a fundamental source of information for cosmology. It has been shown by Oukbir and Blanchard (1997) that the combined knowledge of the redshift distribution of X-ray clusters of galaxies and the luminosity-temperature correlation, L-X - T-X, provides a pow...

Beams of mass-selected clusters: realization and first experiments

International Nuclear Information System (INIS)

Kamalou, O.

2007-04-01

The main objective of this work concerns the production of beams of mass-selected clusters of metallic and semiconductor materials. Clusters are produced in magnetron sputtering source combined with a gas aggregation chamber, cooled by liquid nitrogen circulation. Downstream of the cluster source, a Wiley-McLaren time-of-flight setup allows to select a given cluster size or a narrow size range. The pulsed mass-selected cluster ion beam is separated from the continuous neutral one by an electrostatic 90-quadrupole deflector. After the deflector, the density of the pulsed beam amounts to about 10 3 particles/cm 3 . Preliminary deposition experiments of mass-selected copper clusters with a deposition energy of about 0.5 eV/atom have ben performed on highly oriented pyrolytic graphite (HOPG) substrates, indicating that copper clusters are evidently mobile on the HOPG-surface until they reach cleavage steps, dislocation lines or other surface defects. In order to lower the cluster mobility on the HOPG-surface, we have first irradiated HOPG samples with slow highly charged ions (high dose) in order to create superficial defects. In a second step we have deposited mass-selected copper clusters on these pre-irradiated samples. The first analysis by AFM (Atomic Force Microscopy) techniques showed that the copper clusters are trapped on the defects produced by the highly charged ions. (author)

Using NVMe Gen3 PCIe SSD Cards in High-density Servers for High-performance Big Data Transfer Over Multiple Network Channels

Energy Technology Data Exchange (ETDEWEB)

Fang, Chin [SLAC National Accelerator Lab., Menlo Park, CA (United States)

2015-02-07

This Technical Note describes how the Zettar team came up with a data transfer cluster design that convincingly proved the feasibility of using high-density servers for high-performance Big Data transfers. It then outlines the tests, operations, and observations that address a potential over-heating concern regarding the use of Non-Volatile Memory Host Controller Interface Specification (NVMHCI aka NVM Express or NVMe) Gen 3 PCIe SSD cards in high-density servers. Finally, it points out the possibility of developing a new generation of high-performance Science DMZ data transfer system for the data-intensive research community and commercial enterprises.

Optimized Architectural Approaches in Hardware and Software Enabling Very High Performance Shared Storage Systems

CERN Multimedia

CERN. Geneva

2004-01-01

There are issues encountered in high performance storage systems that normally lead to compromises in architecture. Compute clusters tend to have compute phases followed by an I/O phase that must move data from the entire cluster in one operation. That data may then be shared by a large number of clients creating unpredictable read and write patterns. In some cases the aggregate performance of a server cluster must exceed 100 GB/s to minimize the time required for the I/O cycle thus maximizing compute availability. Accessing the same content from multiple points in a shared file system leads to the classical problems of data "hot spots" on the disk drive side and access collisions on the data connectivity side. The traditional method for increasing apparent bandwidth usually includes data replication which is costly in both storage and management. Scaling a model that includes replicated data presents additional management challenges as capacity and bandwidth expand asymmetrically while the system is scaled. ...

The potential of clustering methods to define intersection test scenarios: Assessing real-life performance of AEB.

Science.gov (United States)

Sander, Ulrich; Lubbe, Nils

2018-04-01

Intersection accidents are frequent and harmful. The accident types 'straight crossing path' (SCP), 'left turn across path - oncoming direction' (LTAP/OD), and 'left-turn across path - lateral direction' (LTAP/LD) represent around 95% of all intersection accidents and one-third of all police-reported car-to-car accidents in Germany. The European New Car Assessment Program (Euro NCAP) have announced that intersection scenarios will be included in their rating from 2020; however, how these scenarios are to be tested has not been defined. This study investigates whether clustering methods can be used to identify a small number of test scenarios sufficiently representative of the accident dataset to evaluate Intersection Automated Emergency Braking (AEB). Data from the German In-Depth Accident Study (GIDAS) and the GIDAS-based Pre-Crash Matrix (PCM) from 1999 to 2016, containing 784 SCP and 453 LTAP/OD accidents, were analyzed with principal component methods to identify variables that account for the relevant total variances of the sample. Three different methods for data clustering were applied to each of the accident types, two similarity-based approaches, namely Hierarchical Clustering (HC) and Partitioning Around Medoids (PAM), and the probability-based Latent Class Clustering (LCC). The optimum number of clusters was derived for HC and PAM with the silhouette method. The PAM algorithm was both initiated with random start medoid selection and medoids from HC. For LCC, the Bayesian Information Criterion (BIC) was used to determine the optimal number of clusters. Test scenarios were defined from optimal cluster medoids weighted by their real-life representation in GIDAS. The set of variables for clustering was further varied to investigate the influence of variable type and character. We quantified how accurately each cluster variation represents real-life AEB performance using pre-crash simulations with PCM data and a generic algorithm for AEB intervention. The

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

Science.gov (United States)

Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Directory of Open Access Journals (Sweden)

Simon Fong

2014-01-01

Full Text Available Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Science.gov (United States)

Deb, Suash; Yang, Xin-She

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Cluster dynamics at different cluster size and incident laser wavelengths

International Nuclear Information System (INIS)

Desai, Tara; Bernardinello, Andrea

2002-01-01

X-ray emission spectra from aluminum clusters of diameter -0.4 μm and gold clusters of dia. ∼1.25 μm are experimentally studied by irradiating the cluster foil targets with 1.06 μm laser, 10 ns (FWHM) at an intensity ∼10 12 W/cm 2 . Aluminum clusters show a different spectra compared to bulk material whereas gold cluster evolve towards bulk gold. Experimental data are analyzed on the basis of cluster dimension, laser wavelength and pulse duration. PIC simulations are performed to study the behavior of clusters at higher intensity I≥10 17 W/cm 2 for different size of the clusters irradiated at different laser wavelengths. Results indicate the dependence of cluster dynamics on cluster size and incident laser wavelength

Deployment Strategies and Clustering Protocols Efficiency

Directory of Open Access Journals (Sweden)

Chérif Diallo

2017-06-01

Full Text Available Wireless sensor networks face significant design challenges due to limited computing and storage capacities and, most importantly, dependence on limited battery power. Energy is a critical resource and is often an important issue to the deployment of sensor applications that claim to be omnipresent in the world of future. Thus optimizing the deployment of sensors becomes a major constraint in the design and implementation of a WSN in order to ensure better network operations. In wireless networking, clustering techniques add scalability, reduce the computation complexity of routing protocols, allow data aggregation and then enhance the network performance. The well-known MaxMin clustering algorithm was previously generalized, corrected and validated. Then, in a previous work we have improved MaxMin by proposing a Single- node Cluster Reduction (SNCR mechanism which eliminates single-node clusters and then improve energy efficiency. In this paper, we show that MaxMin, because of its original pathological case, does not support the grid deployment topology, which is frequently used in WSN architectures. The unreliability feature of the wireless links could have negative impacts on Link Quality Indicator (LQI based clustering protocols. So, in the second part of this paper we show how our distributed Link Quality based d- Clustering Protocol (LQI-DCP has good performance in both stable and high unreliable link environments. Finally, performance evaluation results also show that LQI-DCP fully supports the grid deployment topology and is more energy efficient than MaxMin.

Clustering evolving proteins into homologous families.

Science.gov (United States)

Chan, Cheong Xin; Mahbob, Maisarah; Ragan, Mark A

2013-04-08

Clustering sequences into groups of putative homologs (families) is a critical first step in many areas of comparative biology and bioinformatics. The performance of clustering approaches in delineating biologically meaningful families depends strongly on characteristics of the data, including content bias and degree of divergence. New, highly scalable methods have recently been introduced to cluster the very large datasets being generated by next-generation sequencing technologies. However, there has been little systematic investigation of how characteristics of the data impact the performance of these approaches. Using clusters from a manually curated dataset as reference, we examined the performance of a widely used graph-based Markov clustering algorithm (MCL) and a greedy heuristic approach (UCLUST) in delineating protein families coded by three sets of bacterial genomes of different G+C content. Both MCL and UCLUST generated clusters that are comparable to the reference sets at specific parameter settings, although UCLUST tends to under-cluster compositionally biased sequences (G+C content 33% and 66%). Using simulated data, we sought to assess the individual effects of sequence divergence, rate heterogeneity, and underlying G+C content. Performance decreased with increasing sequence divergence, decreasing among-site rate variation, and increasing G+C bias. Two MCL-based methods recovered the simulated families more accurately than did UCLUST. MCL using local alignment distances is more robust across the investigated range of sequence features than are greedy heuristics using distances based on global alignment. Our results demonstrate that sequence divergence, rate heterogeneity and content bias can individually and in combination affect the accuracy with which MCL and UCLUST can recover homologous protein families. For application to data that are more divergent, and exhibit higher among-site rate variation and/or content bias, MCL may often be the better

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

Science.gov (United States)

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

Assessing the performance of dispersionless and dispersion-accounting methods: helium interaction with cluster models of the TiO2(110) surface.

Science.gov (United States)

de Lara-Castells, María Pilar; Stoll, Hermann; Mitrushchenkov, Alexander O

2014-08-21

As a prototypical dispersion-dominated physisorption problem, we analyze here the performance of dispersionless and dispersion-accounting methodologies on the helium interaction with cluster models of the TiO2(110) surface. A special focus has been given to the dispersionless density functional dlDF and the dlDF+Das construction for the total interaction energy (K. Pernal, R. Podeswa, K. Patkowski, and K. Szalewicz, Phys. Rev. Lett. 2009, 109, 263201), where Das is an effective interatomic pairwise functional form for the dispersion. Likewise, the performance of symmetry-adapted perturbation theory (SAPT) method is evaluated, where the interacting monomers are described by density functional theory (DFT) with the dlDF, PBE, and PBE0 functionals. Our benchmarks include CCSD(T)-F12b calculations and comparative analysis on the nuclear bound states supported by the He-cluster potentials. Moreover, intra- and intermonomer correlation contributions to the physisorption interaction are analyzed through the method of increments (H. Stoll, J. Chem. Phys. 1992, 97, 8449) at the CCSD(T) level of theory. This method is further applied in conjunction with a partitioning of the Hartree-Fock interaction energy to estimate individual interaction energy components, comparing them with those obtained using the different SAPT(DFT) approaches. The cluster size evolution of dispersionless and dispersion-accounting energy components is then discussed, revealing the reduced role of the dispersionless interaction and intramonomer correlation when the extended nature of the surface is better accounted for. On the contrary, both post-Hartree-Fock and SAPT(DFT) results clearly demonstrate the high-transferability character of the effective pairwise dispersion interaction whatever the cluster model is. Our contribution also illustrates how the method of increments can be used as a valuable tool not only to achieve the accuracy of CCSD(T) calculations using large cluster models but also to

Dispersed metal cluster catalysts by design. Synthesis, characterization, structure, and performance

Energy Technology Data Exchange (ETDEWEB)

Arslan, Ilke [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Dixon, David A. [Univ. of Alabama, Tuscaloosa, AL (United States); Gates, Bruce C. [Univ. of California, Davis, CA (United States); Katz, Alexander [Univ. of California, Berkeley, CA (United States)

2015-09-30

ligands on the metals and their reactions; EXAFS spectroscopy and high-resolution STEM to determine cluster framework structures and changes resulting from reactant treatment and locations of metal atoms on support surfaces; X-ray diffraction crystallography to determine full structures of cluster-ligand combinations in the absence of a support, and TEM with tomographic methods to observe individual metal atoms and determine three-dimensional structures of catalysts. Electronic structure calculations were used to verify and interpret spectra and extend the understanding of reactivity beyond what is measurable experimentally.

High Performance Molecular Visualization: In-Situ and Parallel Rendering with EGL

Science.gov (United States)

Stone, John E.; Messmer, Peter; Sisneros, Robert; Schulten, Klaus

2016-01-01

Large scale molecular dynamics simulations produce terabytes of data that is impractical to transfer to remote facilities. It is therefore necessary to perform visualization tasks in-situ as the data are generated, or by running interactive remote visualization sessions and batch analyses co-located with direct access to high performance storage systems. A significant challenge for deploying visualization software within clouds, clusters, and supercomputers involves the operating system software required to initialize and manage graphics acceleration hardware. Recently, it has become possible for applications to use the Embedded-system Graphics Library (EGL) to eliminate the requirement for windowing system software on compute nodes, thereby eliminating a significant obstacle to broader use of high performance visualization applications. We outline the potential benefits of this approach in the context of visualization applications used in the cloud, on commodity clusters, and supercomputers. We discuss the implementation of EGL support in VMD, a widely used molecular visualization application, and we outline benefits of the approach for molecular visualization tasks on petascale computers, clouds, and remote visualization servers. We then provide a brief evaluation of the use of EGL in VMD, with tests using developmental graphics drivers on conventional workstations and on Amazon EC2 G2 GPU-accelerated cloud instance types. We expect that the techniques described here will be of broad benefit to many other visualization applications. PMID:27747137

Clustering Millions of Faces by Identity.

Science.gov (United States)

Otto, Charles; Wang, Dayong; Jain, Anil K

2018-02-01

Given a large collection of unlabeled face images, we address the problem of clustering faces into an unknown number of identities. This problem is of interest in social media, law enforcement, and other applications, where the number of faces can be of the order of hundreds of million, while the number of identities (clusters) can range from a few thousand to millions. To address the challenges of run-time complexity and cluster quality, we present an approximate Rank-Order clustering algorithm that performs better than popular clustering algorithms (k-Means and Spectral). Our experiments include clustering up to 123 million face images into over 10 million clusters. Clustering results are analyzed in terms of external (known face labels) and internal (unknown face labels) quality measures, and run-time. Our algorithm achieves an F-measure of 0.87 on the LFW benchmark (13 K faces of 5,749 individuals), which drops to 0.27 on the largest dataset considered (13 K faces in LFW + 123M distractor images). Additionally, we show that frames in the YouTube benchmark can be clustered with an F-measure of 0.71. An internal per-cluster quality measure is developed to rank individual clusters for manual exploration of high quality clusters that are compact and isolated.

Subspace K-means clustering.

Science.gov (United States)

Timmerman, Marieke E; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

2013-12-01

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).

A High-Efficiency Uneven Cluster Deployment Algorithm Based on Network Layered for Event Coverage in UWSNs

Directory of Open Access Journals (Sweden)

Shanen Yu

2016-12-01

Full Text Available Most existing deployment algorithms for event coverage in underwater wireless sensor networks (UWSNs usually do not consider that network communication has non-uniform characteristics on three-dimensional underwater environments. Such deployment algorithms ignore that the nodes are distributed at different depths and have different probabilities for data acquisition, thereby leading to imbalances in the overall network energy consumption, decreasing the network performance, and resulting in poor and unreliable late network operation. Therefore, in this study, we proposed an uneven cluster deployment algorithm based network layered for event coverage. First, according to the energy consumption requirement of the communication load at different depths of the underwater network, we obtained the expected value of deployment nodes and the distribution density of each layer network after theoretical analysis and deduction. Afterward, the network is divided into multilayers based on uneven clusters, and the heterogeneous communication radius of nodes can improve the network connectivity rate. The recovery strategy is used to balance the energy consumption of nodes in the cluster and can efficiently reconstruct the network topology, which ensures that the network has a high network coverage and connectivity rate in a long period of data acquisition. Simulation results show that the proposed algorithm improves network reliability and prolongs network lifetime by significantly reducing the blind movement of overall network nodes while maintaining a high network coverage and connectivity rate.

Similarity transformed equation of motion coupled-cluster theory based on an unrestricted Hartree-Fock reference for applications to high-spin open-shell systems.

Science.gov (United States)

Huntington, Lee M J; Krupička, Martin; Neese, Frank; Izsák, Róbert

2017-11-07

The similarity transformed equation of motion coupled-cluster approach is extended for applications to high-spin open-shell systems, within the unrestricted Hartree-Fock (UHF) formalism. An automatic active space selection scheme has also been implemented such that calculations can be performed in a black-box fashion. It is observed that both the canonical and automatic active space selecting similarity transformed equation of motion (STEOM) approaches perform about as well as the more expensive equation of motion coupled-cluster singles doubles (EOM-CCSD) method for the calculation of the excitation energies of doublet radicals. The automatic active space selecting UHF STEOM approach can therefore be employed as a viable, lower scaling alternative to UHF EOM-CCSD for the calculation of excited states in high-spin open-shell systems.

Similarity transformed equation of motion coupled-cluster theory based on an unrestricted Hartree-Fock reference for applications to high-spin open-shell systems

Science.gov (United States)

Huntington, Lee M. J.; Krupička, Martin; Neese, Frank; Izsák, Róbert

2017-11-01

The similarity transformed equation of motion coupled-cluster approach is extended for applications to high-spin open-shell systems, within the unrestricted Hartree-Fock (UHF) formalism. An automatic active space selection scheme has also been implemented such that calculations can be performed in a black-box fashion. It is observed that both the canonical and automatic active space selecting similarity transformed equation of motion (STEOM) approaches perform about as well as the more expensive equation of motion coupled-cluster singles doubles (EOM-CCSD) method for the calculation of the excitation energies of doublet radicals. The automatic active space selecting UHF STEOM approach can therefore be employed as a viable, lower scaling alternative to UHF EOM-CCSD for the calculation of excited states in high-spin open-shell systems.

Understanding 3D human torso shape via manifold clustering

Science.gov (United States)

Li, Sheng; Li, Peng; Fu, Yun

2013-05-01

Discovering the variations in human torso shape plays a key role in many design-oriented applications, such as suit designing. With recent advances in 3D surface imaging technologies, people can obtain 3D human torso data that provide more information than traditional measurements. However, how to find different human shapes from 3D torso data is still an open problem. In this paper, we propose to use spectral clustering approach on torso manifold to address this problem. We first represent high-dimensional torso data in a low-dimensional space using manifold learning algorithm. Then the spectral clustering method is performed to get several disjoint clusters. Experimental results show that the clusters discovered by our approach can describe the discrepancies in both genders and human shapes, and our approach achieves better performance than the compared clustering method.

A combined heating cooling stage for cluster thermalization in the gas phase

International Nuclear Information System (INIS)

Ievlev, D.N.; Kuester, A.; Enders, A.; Malinowski, N.; Schaber, H.; Kern, K.

2003-01-01

We report on the design and performance of a combined heating/cooling stage for the thermalization of clusters in a gas phase time-of-flight mass spectrometer. With this setup the cluster temperature can sensitively be adjusted within the range from 100 up to 800 K and higher. The unique combination of a heating stage with a subsequent cooling stage allows us to perform thermodynamic investigations on clusters at very high temperatures without quality losses in the spectra due to delayed fragmentation in the drift tube of the mass spectrometer. The performance of the setup is demonstrated by the example of (C 60 ) n clusters

Performance of amplify-and-forward multihop transmission over relay clusters with different routing strategies

KAUST Repository

Yilmaz, Ferkan; Khan, Fahd Ahmed; Alouini, Mohamed-Slim

2014-01-01

We consider a multihop relay network in which two terminals are communicating with each other via a number of cluster of relays. Performance of such networks depends on the routing protocols employed. In this paper, we find the expressions for the Average Symbol Error Probability (ASEP) performance of Amplify-and-Forward (AF) multihop transmission for the simplest routing protocol in which the relay transmits using the channel having the best Signal to Noise Ratio (SNR). The ASEP performance of a better protocol proposed in Gui et al. (2009) known as the adhoc protocol is also analyzed. The derived expressions for the performance are a convenient tool to analyze the performance of AF multihop transmission over relay clusters. Monte-Carlo simulations verify the correctness of the proposed formulation and are in agreement with analytical results. Furthermore, we propose new generalized protocols termed as last-nhop selection protocol, the dual path protocol, the forward-backward last-n-hop selection protocol and the forward-backward dual path protocol, to get improved ASEP performances. The ASEP performance of these proposed schemes is analysed by computer simulations. It is shown that close to optimal performance can be achieved by using the last-n-hop selection protocol and its forwardbackward variant. The complexity of the protocols is also studied. Copyright © 2014 Inderscience Enterprises Ltd.

Perspective: Size selected clusters for catalysis and electrochemistry

Science.gov (United States)

Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; Vajda, Stefan

2018-03-01

Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster-support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.

Towards the development of run times leveraging virtualization for high performance computing

International Nuclear Information System (INIS)

Diakhate, F.

2010-12-01

In recent years, there has been a growing interest in using virtualization to improve the efficiency of data centers. This success is rooted in virtualization's excellent fault tolerance and isolation properties, in the overall flexibility it brings, and in its ability to exploit multi-core architectures efficiently. These characteristics also make virtualization an ideal candidate to tackle issues found in new compute cluster architectures. However, in spite of recent improvements in virtualization technology, overheads in the execution of parallel applications remain, which prevent its use in the field of high performance computing. In this thesis, we propose a virtual device dedicated to message passing between virtual machines, so as to improve the performance of parallel applications executed in a cluster of virtual machines. We also introduce a set of techniques facilitating the deployment of virtualized parallel applications. These functionalities have been implemented as part of a runtime system which allows to benefit from virtualization's properties in a way that is as transparent as possible to the user while minimizing performance overheads. (author)

High performance data acquisition with InfiniBand

International Nuclear Information System (INIS)

Adamczewski, Joern; Essel, Hans G.; Kurz, Nikolaus; Linev, Sergey

2008-01-01

For the new experiments at FAIR new concepts of data acquisition systems have to be developed like the distribution of self-triggered, time stamped data streams over high performance networks for event building. In this concept any data filtering is done behind the network. Therefore the network must achieve up to 1 GByte/s bi-directional data transfer per node. Detailed simulations have been done to optimize scheduling mechanisms for such event building networks. For real performance tests InfiniBand has been chosen as one of the fastest available network technology. The measurements of network event building have been performed on different Linux clusters from four to over hundred nodes. Several InfiniBand libraries have been tested like uDAPL, Verbs, or MPI. The tests have been integrated in the data acquisition backbone core software DABC, a general purpose data acquisition library. Detailed results are presented. In the worst cases (over hundred nodes) 50% of the required bandwidth can be already achieved. It seems possible to improve these results by further investigations

Evaluation of a micro-scale wind model's performance over realistic building clusters using wind tunnel experiments

Science.gov (United States)

Zhang, Ning; Du, Yunsong; Miao, Shiguang; Fang, Xiaoyi

2016-08-01

The simulation performance over complex building clusters of a wind simulation model (Wind Information Field Fast Analysis model, WIFFA) in a micro-scale air pollutant dispersion model system (Urban Microscale Air Pollution dispersion Simulation model, UMAPS) is evaluated using various wind tunnel experimental data including the CEDVAL (Compilation of Experimental Data for Validation of Micro-Scale Dispersion Models) wind tunnel experiment data and the NJU-FZ experiment data (Nanjing University-Fang Zhuang neighborhood wind tunnel experiment data). The results show that the wind model can reproduce the vortexes triggered by urban buildings well, and the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of the building shapes and the determination of the key zone sizes. The computational efficiencies of different cases are also discussed in this paper. The model has a high computational efficiency compared to traditional numerical models that solve the Navier-Stokes equations, and can produce very high-resolution (1-5 m) wind fields of a complex neighborhood scale urban building canopy (~ 1 km ×1 km) in less than 3 min when run on a personal computer.

Clustering predicts memory performance in networks of spiking and non-spiking neurons

Directory of Open Access Journals (Sweden)

Weiliang eChen

2011-03-01

Full Text Available The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

A comparison of hierarchical cluster analysis and league table rankings as methods for analysis and presentation of district health system performance data in Uganda.

Science.gov (United States)

Tashobya, Christine K; Dubourg, Dominique; Ssengooba, Freddie; Speybroeck, Niko; Macq, Jean; Criel, Bart

2016-03-01

In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance data and compares this approach with the use of the league table in Uganda. Ministry of Health and district plans and reports, and published documents were used to provide information on the development and utilization of the Uganda district league table. Quantitative data were accessed from the Ministry of Health databases. Statistical analysis using SPSS version 20 and hierarchical cluster analysis, utilizing Wards' method was used. The hierarchical cluster analysis was conducted on the basis of seven clusters determined for each year from 2003 to 2010, ranging from a cluster of good through moderate-to-poor performers. The characteristics and membership of clusters varied from year to year and were determined by the identity and magnitude of performance of the individual variables. Criticisms of the league table include: perceived unfairness, as it did not take into consideration district peculiarities; and being oversummarized and not adequately informative. Clustering organizes the many data points into clusters of similar entities according to an agreed set of indicators and can provide the beginning point for identifying factors behind the observed performance of districts. Although league table ranking emphasize summation and external control, clustering has the potential to encourage a formative, learning approach. More research is required to shed more light on factors behind observed performance of the different clusters. Other countries especially low-income countries that share many similarities with Uganda can learn from these experiences. © The Author 2015

Homemade Buckeye-Pi: A Learning Many-Node Platform for High-Performance Parallel Computing

Science.gov (United States)

Amooie, M. A.; Moortgat, J.

2017-12-01

We report on the "Buckeye-Pi" cluster, the supercomputer developed in The Ohio State University School of Earth Sciences from 128 inexpensive Raspberry Pi (RPi) 3 Model B single-board computers. Each RPi is equipped with fast Quad Core 1.2GHz ARMv8 64bit processor, 1GB of RAM, and 32GB microSD card for local storage. Therefore, the cluster has a total RAM of 128GB that is distributed on the individual nodes and a flash capacity of 4TB with 512 processors, while it benefits from low power consumption, easy portability, and low total cost. The cluster uses the Message Passing Interface protocol to manage the communications between each node. These features render our platform the most powerful RPi supercomputer to date and suitable for educational applications in high-performance-computing (HPC) and handling of large datasets. In particular, we use the Buckeye-Pi to implement optimized parallel codes in our in-house simulator for subsurface media flows with the goal of achieving a massively-parallelized scalable code. We present benchmarking results for the computational performance across various number of RPi nodes. We believe our project could inspire scientists and students to consider the proposed unconventional cluster architecture as a mainstream and a feasible learning platform for challenging engineering and scientific problems.

A new experimental setup for high-pressure catalytic activity measurements on surface deposited mass-selected Pt clusters

International Nuclear Information System (INIS)

Watanabe, Yoshihide; Isomura, Noritake

2009-01-01

A new experimental setup to study catalytic and electronic properties of size-selected clusters on metal oxide substrates from the viewpoint of cluster-support interaction and to formulate a method for the development of heterogeneous catalysts such as automotive exhaust catalysts has been developed. The apparatus consists of a size-selected cluster source, a photoemission spectrometer, a scanning tunneling microscope (STM), and a high-pressure reaction cell. The high-pressure reaction cell measurements provided information on catalytic properties in conditions close to practical use. The authors investigated size-selected platinum clusters deposited on a TiO 2 (110) surface using a reaction cell and STM. Catalytic activity measurements showed that the catalytic activities have a cluster-size dependency.

Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

Science.gov (United States)

Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

2016-01-01

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

Behavioral Health Risk Profiles of Undergraduate University Students in England, Wales, and Northern Ireland: A Cluster Analysis.

Science.gov (United States)

El Ansari, Walid; Ssewanyana, Derrick; Stock, Christiane

2018-01-01

Limited research has explored clustering of lifestyle behavioral risk factors (BRFs) among university students. This study aimed to explore clustering of BRFs, composition of clusters, and the association of the clusters with self-rated health and perceived academic performance. We assessed (BRFs), namely tobacco smoking, physical inactivity, alcohol consumption, illicit drug use, unhealthy nutrition, and inadequate sleep, using a self-administered general Student Health Survey among 3,706 undergraduates at seven UK universities. A two-step cluster analysis generated: Cluster 1 (the high physically active and health conscious) with very high health awareness/consciousness, good nutrition, and physical activity (PA), and relatively low alcohol, tobacco, and other drug (ATOD) use. Cluster 2 (the abstinent) had very low ATOD use, high health awareness, good nutrition, and medium high PA. Cluster 3 (the moderately health conscious) included the highest regard for healthy eating, second highest fruit/vegetable consumption, and moderately high ATOD use. Cluster 4 (the risk taking) showed the highest ATOD use, were the least health conscious, least fruit consuming, and attached the least importance on eating healthy. Compared to the healthy cluster (Cluster 1), students in other clusters had lower self-rated health, and particularly, students in the risk taking cluster (Cluster 4) reported lower academic performance. These associations were stronger for men than for women. Of the four clusters, Cluster 4 had the youngest students. Our results suggested that prevention among university students should address multiple BRFs simultaneously, with particular focus on the younger students.

Herd Clustering: A synergistic data clustering approach using collective intelligence

KAUST Repository

Wong, Kachun

2014-10-01

Traditional data mining methods emphasize on analytical abilities to decipher data, assuming that data are static during a mining process. We challenge this assumption, arguing that we can improve the analysis by vitalizing data. In this paper, this principle is used to develop a new clustering algorithm. Inspired by herd behavior, the clustering method is a synergistic approach using collective intelligence called Herd Clustering (HC). The novel part is laid in its first stage where data instances are represented by moving particles. Particles attract each other locally and form clusters by themselves as shown in the case studies reported. To demonstrate its effectiveness, the performance of HC is compared to other state-of-the art clustering methods on more than thirty datasets using four performance metrics. An application for DNA motif discovery is also conducted. The results support the effectiveness of HC and thus the underlying philosophy. © 2014 Elsevier B.V.

Energy band dispersion in photoemission spectra of argon clusters

International Nuclear Information System (INIS)

Foerstel, Marko; Mucke, Melanie; Arion, Tiberiu; Lischke, Toralf; Barth, Silko; Ulrich, Volker; Ohrwall, Gunnar; Bjoerneholm, Olle; Hergenhahn, Uwe; Bradshaw, Alex M.

2011-01-01

Using photoemission we have investigated free argon clusters from a supersonic nozzle expansion in the photon energy range from threshold up to 28 eV. Measurements were performed both at high resolution with a hemispherical electrostatic energy analyser and at lower resolution with a magnetic bottle device. The latter experiments were performed for various mean cluster sizes. In addition to the ∼1.5 eV broad 3p-derived valence band seen in previous work, there is a sharper feature at ∼15 eV binding energy. Surprisingly for non-oriented clusters, this peak shifts smoothly in binding energy over the narrow photon energy range 15.5-17.7 eV, indicating energy band dispersion. The onset of this bulk band-like behaviour could be determined from the cluster size dependence.

Cluster Analysis of Clinical Data Identifies Fibromyalgia Subgroups

Science.gov (United States)

Docampo, Elisa; Collado, Antonio; Escaramís, Geòrgia; Carbonell, Jordi; Rivera, Javier; Vidal, Javier; Alegre, José

2013-01-01

Introduction Fibromyalgia (FM) is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. Material and Methods 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. Results Variables clustered into three independent dimensions: “symptomatology”, “comorbidities” and “clinical scales”. Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1), high symptomatology and comorbidities (Cluster 2), and high symptomatology but low comorbidities (Cluster 3), showing differences in measures of disease severity. Conclusions We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment. PMID:24098674

Cluster analysis of clinical data identifies fibromyalgia subgroups.

Directory of Open Access Journals (Sweden)

Elisa Docampo

Full Text Available INTRODUCTION: Fibromyalgia (FM is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. MATERIAL AND METHODS: 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. RESULTS: VARIABLES CLUSTERED INTO THREE INDEPENDENT DIMENSIONS: "symptomatology", "comorbidities" and "clinical scales". Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1, high symptomatology and comorbidities (Cluster 2, and high symptomatology but low comorbidities (Cluster 3, showing differences in measures of disease severity. CONCLUSIONS: We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment.

MixSim : An R Package for Simulating Data to Study Performance of Clustering Algorithms

Directory of Open Access Journals (Sweden)

Volodymyr Melnykov

2012-11-01

Full Text Available The R package MixSim is a new tool that allows simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of MixSim, there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. All features of the package are illustrated in great detail. The utility of the package is highlighted through a small comparison study of several popular clustering algorithms.

Deconstructing Bipolar Disorder and Schizophrenia: A cross-diagnostic cluster analysis of cognitive phenotypes.

Science.gov (United States)

Lee, Junghee; Rizzo, Shemra; Altshuler, Lori; Glahn, David C; Miklowitz, David J; Sugar, Catherine A; Wynn, Jonathan K; Green, Michael F

2017-02-01

Bipolar disorder (BD) and schizophrenia (SZ) show substantial overlap. It has been suggested that a subgroup of patients might contribute to these overlapping features. This study employed a cross-diagnostic cluster analysis to identify subgroups of individuals with shared cognitive phenotypes. 143 participants (68 BD patients, 39 SZ patients and 36 healthy controls) completed a battery of EEG and performance assessments on perception, nonsocial cognition and social cognition. A K-means cluster analysis was conducted with all participants across diagnostic groups. Clinical symptoms, functional capacity, and functional outcome were assessed in patients. A two-cluster solution across 3 groups was the most stable. One cluster including 44 BD patients, 31 controls and 5 SZ patients showed better cognition (High cluster) than the other cluster with 24 BD patients, 35 SZ patients and 5 controls (Low cluster). BD patients in the High cluster performed better than BD patients in the Low cluster across cognitive domains. Within each cluster, participants with different clinical diagnoses showed different profiles across cognitive domains. All patients are in the chronic phase and out of mood episode at the time of assessment and most of the assessment were behavioral measures. This study identified two clusters with shared cognitive phenotype profiles that were not proxies for clinical diagnoses. The finding of better social cognitive performance of BD patients than SZ patients in the Lowe cluster suggest that relatively preserved social cognition may be important to identify disease process distinct to each disorder. Copyright © 2016 Elsevier B.V. All rights reserved.

Clustering performance comparison using K-means and expectation maximization algorithms.

Science.gov (United States)

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Performance Evaluation of a Cluster-Based Service Discovery Protocol for Heterogeneous Wireless Sensor Networks

NARCIS (Netherlands)

Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

2006-01-01

Abstract—This paper evaluates the performance in terms of resource consumption of a service discovery protocol proposed for heterogeneous Wireless Sensor Networks (WSNs). The protocol is based on a clustering structure, which facilitates the construction of a distributed directory. Nodes with higher

Designing a Scalable Fault Tolerance Model for High Performance Computational Chemistry: A Case Study with Coupled Cluster Perturbative Triples.

Science.gov (United States)

van Dam, Hubertus J J; Vishnu, Abhinav; de Jong, Wibe A

2011-01-11

In the past couple of decades, the massive computational power provided by the most modern supercomputers has resulted in simulation of higher-order computational chemistry methods, previously considered intractable. As the system sizes continue to increase, the computational chemistry domain continues to escalate this trend using parallel computing with programming models such as Message Passing Interface (MPI) and Partitioned Global Address Space (PGAS) programming models such as Global Arrays. The ever increasing scale of these supercomputers comes at a cost of reduced Mean Time Between Failures (MTBF), currently on the order of days and projected to be on the order of hours for upcoming extreme scale systems. While traditional disk-based check pointing methods are ubiquitous for storing intermediate solutions, they suffer from high overhead of writing and recovering from checkpoints. In practice, checkpointing itself often brings the system down. Clearly, methods beyond checkpointing are imperative to handling the aggravating issue of reducing MTBF. In this paper, we address this challenge by designing and implementing an efficient fault tolerant version of the Coupled Cluster (CC) method with NWChem, using in-memory data redundancy. We present the challenges associated with our design, including an efficient data storage model, maintenance of at least one consistent data copy, and the recovery process. Our performance evaluation without faults shows that the current design exhibits a small overhead. In the presence of a simulated fault, the proposed design incurs negligible overhead in comparison to the state of the art implementation without faults.

Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

Science.gov (United States)

Syakur, M. A.; Khotimah, B. K.; Rochman, E. M. S.; Satoto, B. D.

2018-04-01

Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.

Clustering performances in the NBA according to players' anthropometric attributes and playing experience.

Science.gov (United States)

Zhang, Shaoliang; Lorenzo, Alberto; Gómez, Miguel-Angel; Mateus, Nuno; Gonçalves, Bruno; Sampaio, Jaime

2018-04-20

The aim of this study was: (i) to group basketball players into similar clusters based on a combination of anthropometric characteristics and playing experience; and (ii) explore the distribution of players (included starters and non-starters) from different levels of teams within the obtained clusters. The game-related statistics from 699 regular season balanced games were analyzed using a two-step cluster model and a discriminant analysis. The clustering process allowed identifying five different player profiles: Top height and weight (HW) with low experience, TopHW-LowE; Middle HW with middle experience, MiddleHW-MiddleE; Middle HW with top experience, MiddleHW-TopE; Low HW with low experience, LowHW-LowE; Low HW with middle experience, LowHW-MiddleE. Discriminant analysis showed that TopHW-LowE group was highlighted by two-point field goals made and missed, offensive and defensive rebounds, blocks, and personal fouls; whereas the LowHW-LowE group made fewest passes and touches. The players from weaker teams were mostly distributed in LowHW-LowE group, whereas players from stronger teams were mainly grouped in LowHW-MiddleE group; and players that participated in the finals were allocated in the MiddleHW-MiddleE group. These results provide alternative references for basketball staff concerning the process of evaluating performance.

Efficient coupling of high intensity short laser pulses into snow clusters

Science.gov (United States)

Palchan, T.; Pecker, S.; Henis, Z.; Eisenmann, S.; Zigler, A.

2007-01-01

Measurements of energy absorption of high intensity laser pulses in snow clusters are reported. Targets consisting of sapphire coated with snow nanoparticles were found to absorb more than 95% of the incident light compared to 50% absorption in flat sapphire targets.

Ionization of water clusters by fast Highly Charged Ions: Stability, fragmentation, energetics and charge mobility

International Nuclear Information System (INIS)

Legendre, S; Maisonny, R; Capron, M; Bernigaud, V; Cassimi, A; Gervais, B; Grandin, J-P; Huber, B A; Manil, B; Rousseau, P; Tarisien, M; Adoui, L; Lopez-Tarifa, P; AlcamI, M; MartIn, F; Politis, M-F; Penhoat, M A Herve du; Vuilleumier, R; Gaigeot, M-P; Tavernelli, I

2009-01-01

We study dissociative ionization of water clusters by impact of fast Ni ions. Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) is used to obtain information about stability, energetics and charge mobility of the ionized clusters. An unusual stability of the (H 2 O) 4 H ''+ ion is observed, which could be the signature of the so called ''Eigen'' structure in gas phase water clusters. High charge mobility, responsible for the formation of protonated water clusters that dominate the mass spectrum, is evidenced. These results are supported by CPMD and TDDFT simulations, which also reveal the mechanisms of such mobility.

Performance analysis of clustering techniques over microarray data: A case study

Science.gov (United States)

Dash, Rasmita; Misra, Bijan Bihari

2018-03-01

Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

Behavioral Health Risk Profiles of Undergraduate University Students in England, Wales, and Northern Ireland: A Cluster Analysis

Directory of Open Access Journals (Sweden)

Walid El Ansari

2018-05-01

Full Text Available BackgroundLimited research has explored clustering of lifestyle behavioral risk factors (BRFs among university students. This study aimed to explore clustering of BRFs, composition of clusters, and the association of the clusters with self-rated health and perceived academic performance.MethodWe assessed (BRFs, namely tobacco smoking, physical inactivity, alcohol consumption, illicit drug use, unhealthy nutrition, and inadequate sleep, using a self-administered general Student Health Survey among 3,706 undergraduates at seven UK universities.ResultsA two-step cluster analysis generated: Cluster 1 (the high physically active and health conscious with very high health awareness/consciousness, good nutrition, and physical activity (PA, and relatively low alcohol, tobacco, and other drug (ATOD use. Cluster 2 (the abstinent had very low ATOD use, high health awareness, good nutrition, and medium high PA. Cluster 3 (the moderately health conscious included the highest regard for healthy eating, second highest fruit/vegetable consumption, and moderately high ATOD use. Cluster 4 (the risk taking showed the highest ATOD use, were the least health conscious, least fruit consuming, and attached the least importance on eating healthy. Compared to the healthy cluster (Cluster 1, students in other clusters had lower self-rated health, and particularly, students in the risk taking cluster (Cluster 4 reported lower academic performance. These associations were stronger for men than for women. Of the four clusters, Cluster 4 had the youngest students.ConclusionOur results suggested that prevention among university students should address multiple BRFs simultaneously, with particular focus on the younger students.

Analysis of Fiber Clustering in Composite Materials Using High-Fidelity Multiscale Micromechanics

Science.gov (United States)

Bednarcyk, Brett A.; Aboudi, Jacob; Arnold, Steven M.

2015-01-01

A new multiscale micromechanical approach is developed for the prediction of the behavior of fiber reinforced composites in presence of fiber clustering. The developed method is based on a coupled two-scale implementation of the High-Fidelity Generalized Method of Cells theory, wherein both the local and global scales are represented using this micromechanical method. Concentration tensors and effective constitutive equations are established on both scales and linked to establish the required coupling, thus providing the local fields throughout the composite as well as the global properties and effective nonlinear response. Two nondimensional parameters, in conjunction with actual composite micrographs, are used to characterize the clustering of fibers in the composite. Based on the predicted local fields, initial yield and damage envelopes are generated for various clustering parameters for a polymer matrix composite with both carbon and glass fibers. Nonlinear epoxy matrix behavior is also considered, with results in the form of effective nonlinear response curves, with varying fiber clustering and for two sets of nonlinear matrix parameters.

Discrimination of Black Ball-point Pen Inks by High Performance Liquid Chromatography (HPLC)

International Nuclear Information System (INIS)

Mohamed Izzharif Abdul Halim; Norashikin Saim; Rozita Osman; Halila Jasmani; Nurul Nadhirah Zainal Abidin

2013-01-01

In this study, thirteen types of black ball-point pen inks of three major brands were analyzed using high performance liquid chromatography (HPLC). Separation of the ink components was achieved using Bondapak C-18 column with gradient elution using water, ethanol and ethyl acetate. The chromatographic data obtained at wavelength 254.8 nm was analyzed using agglomerative hierarchical clustering (AHC) and principle component analysis (PCA). AHC was able to group the inks into three clusters. This result was supported by PCA, whereby distinct separation of the three different brands was achieved. Therefore, HPLC in combination with chemometric methods may be a valuable tool for the analysis of black ball-point pen inks for forensic purposes. (author)

GLOBULAR CLUSTER ABUNDANCES FROM HIGH-RESOLUTION, INTEGRATED-LIGHT SPECTROSCOPY. II. EXPANDING THE METALLICITY RANGE FOR OLD CLUSTERS AND UPDATED ANALYSIS TECHNIQUES

Energy Technology Data Exchange (ETDEWEB)

Colucci, Janet E.; Bernstein, Rebecca A.; McWilliam, Andrew [The Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101 (United States)

2017-01-10

We present abundances of globular clusters (GCs) in the Milky Way and Fornax from integrated-light (IL) spectra. Our goal is to evaluate the consistency of the IL analysis relative to standard abundance analysis for individual stars in those same clusters. This sample includes an updated analysis of seven clusters from our previous publications and results for five new clusters that expand the metallicity range over which our technique has been tested. We find that the [Fe/H] measured from IL spectra agrees to ∼0.1 dex for GCs with metallicities as high as [Fe/H] = −0.3, but the abundances measured for more metal-rich clusters may be underestimated. In addition we systematically evaluate the accuracy of abundance ratios, [X/Fe], for Na i, Mg i, Al i, Si i, Ca i, Ti i, Ti ii, Sc ii, V i, Cr i, Mn i, Co i, Ni i, Cu i, Y ii, Zr i, Ba ii, La ii, Nd ii, and Eu ii. The elements for which the IL analysis gives results that are most similar to analysis of individual stellar spectra are Fe i, Ca i, Si i, Ni i, and Ba ii. The elements that show the greatest differences include Mg i and Zr i. Some elements show good agreement only over a limited range in metallicity. More stellar abundance data in these clusters would enable more complete evaluation of the IL results for other important elements.

A Performance-Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

Directory of Open Access Journals (Sweden)

Sergio Briguglio

2003-01-01

Full Text Available A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage and OpenMP (at the intra-node one. The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.

Clustering approaches to improve the performance of low cost air pollution sensors.

Science.gov (United States)

Smith, Katie R; Edwards, Peter M; Evans, Mathew J; Lee, James D; Shaw, Marvin D; Squires, Freya; Wilde, Shona; Lewis, Alastair C

2017-08-24

Low cost air pollution sensors have substantial potential for atmospheric research and for the applied control of pollution in the urban environment, including more localized warnings to the public. The current generation of single-chemical gas sensors experience degrees of interference from other co-pollutants and have sensitivity to environmental factors such as temperature, wind speed and supply voltage. There are uncertainties introduced also because of sensor-to-sensor response variability, although this is less well reported. The sensitivity of Metal Oxide Sensors (MOS) to volatile organic compounds (VOCs) changed with relative humidity (RH) by up to a factor of five over the range of 19-90% RH and with an uncertainty in the correction of a factor of two at any given RH. The short-term (second to minute) stabilities of MOS and electrochemical CO sensor responses were reasonable. During more extended use, inter-sensor quantitative comparability was degraded due to unpredictable variability in individual sensor responses (to either measurand or interference or both) drifting over timescales of several hours to days. For timescales longer than a week identical sensors showed slow, often downwards, drifts in their responses which diverged across six CO sensors by up to 30% after two weeks. The measurement derived from the median sensor within clusters of 6, 8 and up to 21 sensors was evaluated against individual sensor performance and external reference values. The clustered approach maintained the cost competitiveness of a sensor device, but the median concentration from the ensemble of sensor signals largely eliminated the randomised hour-to-day response drift seen in individual sensors and excluded the effects of small numbers of poorly performing sensors that drifted significantly over longer time periods. The results demonstrate that for individual sensors to be optimally comparable to one another, and to reference instruments, they would likely require

Ruprecht 106 - A young metal-poor Galactic globular cluster

International Nuclear Information System (INIS)

Buonanno, R.; Buscema, G.; Fusi Pecci, F.; Richer, H.B.; Fahlman, G.G.

1990-01-01

The first CCD photometric survey in the Galactic globular cluster Ruprecht 106 has been performed. The results show that Ruprecht 106 is a metal-poor cluster with (Fe/H) about -2 located at about 25 kpc from the Galactic center. A sizable, high centrally concentrated population of blue stragglers was detected. Significant differences in the positions of the turnoffs in the color-magnitude diagram are found compared to those in metal-poor clusters. The cluster appears younger than other typical metal-poor Galactic globulars by about 4-5 Gyr; if true, this object would represent the first direct proof of the existence of a significant age spread among old, very metal-poor clusters. 51 refs

Residential High-Rise Clusters as a Contemporary Planning Challenge in Manama

Directory of Open Access Journals (Sweden)

Florian Wiedmann

2015-08-01

Full Text Available This paper analyzes the different roots of current residential high-rise clusters emerging in new city districts along the coast of Bahrain’s capital city Manama, and the resulting urban planning and design challenges. Since the local real-estate markets were liberalized in Bahrain in 2003, the population grew rapidly to more than one million inhabitants. Consequently, the housing demand increased rapidly due to extensive immigration. Many residential developments were however constructed for the upper spectrum of the real-estate market, due to speculative tendencies causing a raise in land value. The emerging high-rise clusters are developed along the various waterfronts of Manama on newly reclaimed land. This paper explores the spatial consequences of the recent boom in construction boom and the various challenges for architects and urban planners to enhance urban qualities.

CHEMICAL ABUNDANCE EVIDENCE OF ENDURING HIGH STAR FORMATION RATES IN AN EARLY-TYPE GALAXY: HIGH [Ca/Fe] IN NGC 5128 GLOBULAR CLUSTERS

International Nuclear Information System (INIS)

Colucci, Janet E.; Durán, María Fernanda; Bernstein, Rebecca A.; McWilliam, Andrew

2013-01-01

We present [Fe/H], ages, and Ca abundances for an initial sample of 10 globular clusters in NGC 5128 obtained from high-resolution, high signal-to-noise ratio echelle spectra of their integrated light. All abundances and ages are obtained using our original technique for high-resolution integrated light abundance analysis of globular clusters. The clusters have a range in [Fe/H] between –1.6 and –0.2. In this sample, the average [Ca/Fe] for clusters with [Fe/H] <–0.4 is +0.37 ± 0.07, while the average [Ca/Fe] in our Milky Way (MW) and M31 GC samples is +0.29 ± 0.09 and +0.24 ± 0.10, respectively. This may imply a more rapid chemical enrichment history for NGC 5128 than for either the MW or M31. This sample provides the first quantitative picture of the chemical history of NGC 5128 that is directly comparable to what is available for the MW. Data presented here were obtained with the MIKE echelle spectrograph on the Magellan Clay Telescope

CHEMICAL ABUNDANCE EVIDENCE OF ENDURING HIGH STAR FORMATION RATES IN AN EARLY-TYPE GALAXY: HIGH [Ca/Fe] IN NGC 5128 GLOBULAR CLUSTERS

Energy Technology Data Exchange (ETDEWEB)

Colucci, Janet E.; Duran, Maria Fernanda; Bernstein, Rebecca A. [Department of Astronomy and Astrophysics, 1156 High Street, UCO/Lick Observatory, University of California, Santa Cruz, CA 95064 (United States); McWilliam, Andrew, E-mail: jcolucci@ucolick.org [Observatories of the Carnegie Institute of Washington, 813 Santa Barbara Street, Pasadena, CA 91101-1292 (United States)

2013-08-20

We present [Fe/H], ages, and Ca abundances for an initial sample of 10 globular clusters in NGC 5128 obtained from high-resolution, high signal-to-noise ratio echelle spectra of their integrated light. All abundances and ages are obtained using our original technique for high-resolution integrated light abundance analysis of globular clusters. The clusters have a range in [Fe/H] between -1.6 and -0.2. In this sample, the average [Ca/Fe] for clusters with [Fe/H] <-0.4 is +0.37 {+-} 0.07, while the average [Ca/Fe] in our Milky Way (MW) and M31 GC samples is +0.29 {+-} 0.09 and +0.24 {+-} 0.10, respectively. This may imply a more rapid chemical enrichment history for NGC 5128 than for either the MW or M31. This sample provides the first quantitative picture of the chemical history of NGC 5128 that is directly comparable to what is available for the MW. Data presented here were obtained with the MIKE echelle spectrograph on the Magellan Clay Telescope.

Dense Fe cluster-assembled films by energetic cluster deposition

International Nuclear Information System (INIS)

Peng, D.L.; Yamada, H.; Hihara, T.; Uchida, T.; Sumiyama, K.

2004-01-01

High-density Fe cluster-assembled films were produced at room temperature by an energetic cluster deposition. Though cluster-assemblies are usually sooty and porous, the present Fe cluster-assembled films are lustrous and dense, revealing a soft magnetic behavior. Size-monodispersed Fe clusters with the mean cluster size d=9 nm were synthesized using a plasma-gas-condensation technique. Ionized clusters are accelerated electrically and deposited onto the substrate together with neutral clusters from the same cluster source. Packing fraction and saturation magnetic flux density increase rapidly and magnetic coercivity decreases remarkably with increasing acceleration voltage. The Fe cluster-assembled film obtained at the acceleration voltage of -20 kV has a packing fraction of 0.86±0.03, saturation magnetic flux density of 1.78±0.05 Wb/m 2 , and coercivity value smaller than 80 A/m. The resistivity at room temperature is ten times larger than that of bulk Fe metal

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.

Heuristics for minimizing the maximum within-clusters distance

Directory of Open Access Journals (Sweden)

José Augusto Fioruci

2012-12-01

Full Text Available The clustering problem consists in finding patterns in a data set in order to divide it into clusters with high within-cluster similarity. This paper presents the study of a problem, here called MMD problem, which aims at finding a clustering with a predefined number of clusters that minimizes the largest within-cluster distance (diameter among all clusters. There are two main objectives in this paper: to propose heuristics for the MMD and to evaluate the suitability of the best proposed heuristic results according to the real classification of some data sets. Regarding the first objective, the results obtained in the experiments indicate a good performance of the best proposed heuristic that outperformed the Complete Linkage algorithm (the most used method from the literature for this problem. Nevertheless, regarding the suitability of the results according to the real classification of the data sets, the proposed heuristic achieved better quality results than C-Means algorithm, but worse than Complete Linkage.

High prevalence of clustered tuberculosis cases in Peruvian migrants in Florence, Italy

Directory of Open Access Journals (Sweden)

Lorenzo Zammarchi

2014-12-01

Full Text Available Tuberculosis is a leading cause of morbidity for Peruvian migrants in Florence, Italy, where they account for about 20% of yearly diagnosed cases. A retrospective study on cases notified in Peruvian residents in Florence in the period 2001-2010 was carried out and available Mycobacterium tuberculosis strains were genotyped (MIRU-VNTR-24 and Spoligotyping. One hundred thirty eight cases were retrieved. Genotyping performed in 87 strains revealed that 39 (44.8% belonged to 12 clusters. Assuming that in each cluster the transmission of tuberculosis from the index case took place in Florence, a large proportion of cases could be preventable by improving early diagnosis of contagious cases and contact tracing.

Development of small scale cluster computer for numerical analysis

Science.gov (United States)

Zulkifli, N. H. N.; Sapit, A.; Mohammed, A. N.

2017-09-01

In this study, two units of personal computer were successfully networked together to form a small scale cluster. Each of the processor involved are multicore processor which has four cores in it, thus made this cluster to have eight processors. Here, the cluster incorporate Ubuntu 14.04 LINUX environment with MPI implementation (MPICH2). Two main tests were conducted in order to test the cluster, which is communication test and performance test. The communication test was done to make sure that the computers are able to pass the required information without any problem and were done by using simple MPI Hello Program where the program written in C language. Additional, performance test was also done to prove that this cluster calculation performance is much better than single CPU computer. In this performance test, four tests were done by running the same code by using single node, 2 processors, 4 processors, and 8 processors. The result shows that with additional processors, the time required to solve the problem decrease. Time required for the calculation shorten to half when we double the processors. To conclude, we successfully develop a small scale cluster computer using common hardware which capable of higher computing power when compare to single CPU processor, and this can be beneficial for research that require high computing power especially numerical analysis such as finite element analysis, computational fluid dynamics, and computational physics analysis.

Testing feedback message framing and comparators to address prescribing of high-risk medications in nursing homes: protocol for a pragmatic, factorial, cluster-randomized trial.

Science.gov (United States)

Ivers, Noah M; Desveaux, Laura; Presseau, Justin; Reis, Catherine; Witteman, Holly O; Taljaard, Monica K; McCleary, Nicola; Thavorn, Kednapa; Grimshaw, Jeremy M

2017-07-14

Audit and feedback (AF) interventions that leverage routine administrative data offer a scalable and relatively low-cost method to improve processes of care. AF interventions are usually designed to highlight discrepancies between desired and actual performance and to encourage recipients to act to address such discrepancies. Comparing to a regional average is a common approach, but more recipients would have a discrepancy if compared to a higher-than-average level of performance. In addition, how recipients perceive and respond to discrepancies may depend on how the feedback itself is framed. We aim to evaluate the effectiveness of different comparators and framing in feedback on high-risk prescribing in nursing homes. This is a pragmatic, 2 × 2 factorial, cluster-randomized controlled trial testing variations in the comparator and framing on the effectiveness of quarterly AF in changing high-risk prescribing in nursing homes in Ontario, Canada. We grouped homes that share physicians into clusters and randomized these clusters into the four experimental conditions. Outcomes will be assessed after 6 months; all primary analyses will be by intention-to-treat. The primary outcome (monthly number of high-risk medications received by each patient) will be analysed using a general linear mixed effects regression model. We will present both four-arm and factorial analyses. With 160 clusters and an average of 350 beds per cluster, assuming no interaction and similar effects for each intervention, we anticipate 90% power to detect an absolute mean difference of 0.3 high-risk medications prescribed. A mixed-methods process evaluation will explore potential mechanisms underlying the observed effects, exploring targeted constructs including intention, self-efficacy, outcome expectations, descriptive norms, and goal prioritization. An economic analysis will examine cost-effectiveness analysis from the perspective of the publicly funded health care system. This protocol

Globular Cluster Candidates for Hosting a Central Black Hole

Science.gov (United States)

Noyola, Eva

2009-07-01

We are continuing our study of the dynamical properties of globular clusters and we propose to obtain surface brightness profiles for high concentration clusters. Our results to date show that the distribution of central surface brightness slopes do not conform to standard models. This has important implications for how they form and evolve, and suggest the possible presence of central intermediate-mass black holes. From our previous archival proposals {AR-9542 and AR-10315}, we find that many high concentration globular clusters do not have flat cores or steep central cusps, instead they show weak cusps. Numerical simulations suggest that clusters with weak cusps may harbor intermediate-mass black holes and we have one confirmation of this connection with omega Centauri. This cluster shows a shallow cusp in its surface brightness profile, while kinematical measurements suggest the presence of a black hole in its center. Our goal is to extend these studies to a sample containing 85% of the Galactic globular clusters with concentrations higher than 1.7 and look for objects departing from isothermal behavior. The ACS globular cluster survey {GO-10775} provides enough objects to have an excellent coverage of a wide range of galactic clusters, but it contains only a couple of the ones with high concentration. The proposed sample consists of clusters whose light profile can only be adequately measured from space-based imaging. This would take us close to completeness for the high concentration cases and therefore provide a more complete list of candidates for containing a central black hole. The dataset will also be combined with our existing kinematic measurements and enhanced with future kinematic studies to perform detailed dynamical modeling.

Infrared Extinction Performance of Randomly Oriented Microbial-Clustered Agglomerate Materials.

Science.gov (United States)

Li, Le; Hu, Yihua; Gu, Youlin; Zhao, Xinying; Xu, Shilong; Yu, Lei; Zheng, Zhi Ming; Wang, Peng

2017-11-01

In this study, the spatial structure of randomly distributed clusters of fungi An0429 spores was simulated using a cluster aggregation (CCA) model, and the single scattering parameters of fungi An0429 spores were calculated using the discrete dipole approximation (DDA) method. The transmittance of 10.6 µm infrared (IR) light in the aggregated fungi An0429 spores swarm is simulated by using the Monte Carlo method. Several parameters that affect the transmittance of 10.6 µm IR light, such as the number and radius of original fungi An0429 spores, porosity of aggregated fungi An0429 spores, and density of aggregated fungi An0429 spores of the formation aerosol area were discussed. Finally, the transmittances of microbial materials with different qualities were measured in the dynamic test platform. The simulation results showed that the parameters analyzed were closely connected with the extinction performance of fungi An0429 spores. By controlling the value of the influencing factors, the transmittance could be lower than a certain threshold to meet the requirement of attenuation in application. In addition, the experimental results showed that the Monte Carlo method could well reflect the attenuation law of IR light in fungi An0429 spore agglomerates swarms.

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.

Flocking-based Document Clustering on the Graphics Processing Unit

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL; Patton, Robert M [ORNL; ST Charles, Jesse Lee [ORNL

2008-01-01

Abstract?Analyzing and grouping documents by content is a complex problem. One explored method of solving this problem borrows from nature, imitating the flocking behavior of birds. Each bird represents a single document and flies toward other documents that are similar to it. One limitation of this method of document clustering is its complexity O(n2). As the number of documents grows, it becomes increasingly difficult to receive results in a reasonable amount of time. However, flocking behavior, along with most naturally inspired algorithms such as ant colony optimization and particle swarm optimization, are highly parallel and have found increased performance on expensive cluster computers. In the last few years, the graphics processing unit (GPU) has received attention for its ability to solve highly-parallel and semi-parallel problems much faster than the traditional sequential processor. Some applications see a huge increase in performance on this new platform. The cost of these high-performance devices is also marginal when compared with the price of cluster machines. In this paper, we have conducted research to exploit this architecture and apply its strengths to the document flocking problem. Our results highlight the potential benefit the GPU brings to all naturally inspired algorithms. Using the CUDA platform from NIVIDA? we developed a document flocking implementation to be run on the NIVIDA?GEFORCE 8800. Additionally, we developed a similar but sequential implementation of the same algorithm to be run on a desktop CPU. We tested the performance of each on groups of news articles ranging in size from 200 to 3000 documents. The results of these tests were very significant. Performance gains ranged from three to nearly five times improvement of the GPU over the CPU implementation. This dramatic improvement in runtime makes the GPU a potentially revolutionary platform for document clustering algorithms.

Comparison of five cluster validity indices performance in brain [18 F]FET-PET image segmentation using k-means.

Science.gov (United States)

Abualhaj, Bedor; Weng, Guoyang; Ong, Melissa; Attarwala, Ali Asgar; Molina, Flavia; Büsing, Karen; Glatting, Gerhard

2017-01-01

Dynamic [ 18 F]fluoro-ethyl-L-tyrosine positron emission tomography ([ 18 F]FET-PET) is used to identify tumor lesions for radiotherapy treatment planning, to differentiate glioma recurrence from radiation necrosis and to classify gliomas grading. To segment different regions in the brain k-means cluster analysis can be used. The main disadvantage of k-means is that the number of clusters must be pre-defined. In this study, we therefore compared different cluster validity indices for automated and reproducible determination of the optimal number of clusters based on the dynamic PET data. The k-means algorithm was applied to dynamic [ 18 F]FET-PET images of 8 patients. Akaike information criterion (AIC), WB, I, modified Dunn's and Silhouette indices were compared on their ability to determine the optimal number of clusters based on requirements for an adequate cluster validity index. To check the reproducibility of k-means, the coefficients of variation CVs of the objective function values OFVs (sum of squared Euclidean distances within each cluster) were calculated using 100 random centroid initialization replications RCI 100 for 2 to 50 clusters. k-means was performed independently on three neighboring slices containing tumor for each patient to investigate the stability of the optimal number of clusters within them. To check the independence of the validity indices on the number of voxels, cluster analysis was applied after duplication of a slice selected from each patient. CVs of index values were calculated at the optimal number of clusters using RCI 100 to investigate the reproducibility of the validity indices. To check if the indices have a single extremum, visual inspection was performed on the replication with minimum OFV from RCI 100 . The maximum CV of OFVs was 2.7 × 10 -2 from all patients. The optimal number of clusters given by modified Dunn's and Silhouette indices was 2 or 3 leading to a very poor segmentation. WB and I indices suggested in

Effect of dose and size on defect engineering in carbon cluster implanted silicon wafers

Science.gov (United States)

Okuyama, Ryosuke; Masada, Ayumi; Shigematsu, Satoshi; Kadono, Takeshi; Hirose, Ryo; Koga, Yoshihiro; Okuda, Hidehiko; Kurita, Kazunari

2018-01-01

Carbon-cluster-ion-implanted defects were investigated by high-resolution cross-sectional transmission electron microscopy toward achieving high-performance CMOS image sensors. We revealed that implantation damage formation in the silicon wafer bulk significantly differs between carbon-cluster and monomer ions after implantation. After epitaxial growth, small and large defects were observed in the implanted region of carbon clusters. The electron diffraction pattern of both small and large defects exhibits that from bulk crystalline silicon in the implanted region. On the one hand, we assumed that the silicon carbide structure was not formed in the implanted region, and small defects formed because of the complex of carbon and interstitial silicon. On the other hand, large defects were hypothesized to originate from the recrystallization of the amorphous layer formed by high-dose carbon-cluster implantation. These defects are considered to contribute to the powerful gettering capability required for high-performance CMOS image sensors.

The dynamics of cyclone clustering in re-analysis and a high-resolution climate model

Science.gov (United States)

Priestley, Matthew; Pinto, Joaquim; Dacre, Helen; Shaffrey, Len

2017-04-01

Extratropical cyclones have a tendency to occur in groups (clusters) in the exit of the North Atlantic storm track during wintertime, potentially leading to widespread socioeconomic impacts. The Winter of 2013/14 was the stormiest on record for the UK and was characterised by the recurrent clustering of intense extratropical cyclones. This clustering was associated with a strong, straight and persistent North Atlantic 250 hPa jet with Rossby wave-breaking (RWB) on both flanks, pinning the jet in place. Here, we provide for the first time an analysis of all clustered events in 36 years of the ERA-Interim Re-analysis at three latitudes (45˚ N, 55˚ N, 65˚ N) encompassing various regions of Western Europe. The relationship between the occurrence of RWB and cyclone clustering is studied in detail. Clustering at 55˚ N is associated with an extended and anomalously strong jet flanked on both sides by RWB. However, clustering at 65(45)˚ N is associated with RWB to the south (north) of the jet, deflecting the jet northwards (southwards). A positive correlation was found between the intensity of the clustering and RWB occurrence to the north and south of the jet. However, there is considerable spread in these relationships. Finally, analysis has shown that the relationships identified in the re-analysis are also present in a high-resolution coupled global climate model (HiGEM). In particular, clustering is associated with the same dynamical conditions at each of our three latitudes in spite of the identified biases in frequency and intensity of RWB.

Parallel algorithms and cluster computing

CERN Document Server

Hoffmann, Karl Heinz

2007-01-01

This book presents major advances in high performance computing as well as major advances due to high performance computing. It contains a collection of papers in which results achieved in the collaboration of scientists from computer science, mathematics, physics, and mechanical engineering are presented. From the science problems to the mathematical algorithms and on to the effective implementation of these algorithms on massively parallel and cluster computers we present state-of-the-art methods and technology as well as exemplary results in these fields. This book shows that problems which seem superficially distinct become intimately connected on a computational level.

Performance Analysis of Entropy Methods on K Means in Clustering Process

Science.gov (United States)

Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

2017-12-01

K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

High performance computing system in the framework of the Higgs boson studies

CERN Document Server

Belyaev, Nikita; The ATLAS collaboration

2017-01-01

The Higgs boson physics is one of the most important and promising fields of study in modern High Energy Physics. To perform precision measurements of the Higgs boson properties, the use of fast and efficient instruments of Monte Carlo event simulation is required. Due to the increasing amount of data and to the growing complexity of the simulation software tools, the computing resources currently available for Monte Carlo simulation on the LHC GRID are not sufficient. One of the possibilities to address this shortfall of computing resources is the usage of institutes computer clusters, commercial computing resources and supercomputers. In this paper, a brief description of the Higgs boson physics, the Monte-Carlo generation and event simulation techniques are presented. A description of modern high performance computing systems and tests of their performance are also discussed. These studies have been performed on the Worldwide LHC Computing Grid and Kurchatov Institute Data Processing Center, including Tier...

High performance computing system in the framework of the Higgs boson studies

CERN Document Server

Belyaev, Nikita; The ATLAS collaboration; Velikhov, Vasily; Konoplich, Rostislav

2017-01-01

The Higgs boson physics is one of the most important and promising fields of study in the modern high energy physics. It is important to notice, that GRID computing resources become strictly limited due to increasing amount of statistics, required for physics analyses and unprecedented LHC performance. One of the possibilities to address the shortfall of computing resources is the usage of computer institutes' clusters, commercial computing resources and supercomputers. To perform precision measurements of the Higgs boson properties in these realities, it is also highly required to have effective instruments to simulate kinematic distributions of signal events. In this talk we give a brief description of the modern distribution reconstruction method called Morphing and perform few efficiency tests to demonstrate its potential. These studies have been performed on the WLCG and Kurchatov Institute’s Data Processing Center, including Tier-1 GRID site and supercomputer as well. We also analyze the CPU efficienc...

High-order harmonic generation in clusters irradiated by an infrared laser field of moderate intensity

International Nuclear Information System (INIS)

Zaretsky, D F; Korneev, Ph; Becker, W

2010-01-01

Extending the Lewenstein model of high-order harmonic generation (HHG) in a laser-irradiated atom, a model of HHG in a cluster is formulated. The constituent atoms of the cluster are assumed to be partly ionized. An electron freed through tunnelling may recombine either with its parent ion or with another ion in the vicinity. Harmonics due to the former process are coherent within the same cluster and may be coherent between different clusters, while harmonics due to the latter process are incoherent. Depending on the density of available ions, the incoherent mechanism may dominate the total harmonic yield, and the harmonic spectrum, which extends to higher energies, has a less distinct cutoff and an enhanced low-energy part.

High Performance Circularly Polarized Microstrip Antenna

Science.gov (United States)

Bondyopadhyay, Probir K. (Inventor)

1997-01-01

A microstrip antenna for radiating circularly polarized electromagnetic waves comprising a cluster array of at least four microstrip radiator elements, each of which is provided with dual orthogonal coplanar feeds in phase quadrature relation achieved by connection to an asymmetric T-junction power divider impedance notched at resonance. The dual fed circularly polarized reference element is positioned with its axis at a 45 deg angle with respect to the unit cell axis. The other three dual fed elements in the unit cell are positioned and fed with a coplanar feed structure with sequential rotation and phasing to enhance the axial ratio and impedance matching performance over a wide bandwidth. The centers of the radiator elements are disposed at the corners of a square with each side of a length d in the range of 0.7 to 0.9 times the free space wavelength of the antenna radiation and the radiator elements reside in a square unit cell area of sides equal to 2d and thereby permit the array to be used as a phased array antenna for electronic scanning and is realizable in a high temperature superconducting thin film material for high efficiency.

Chemical Abundance Evidence of Enduring High Star Formation Rates in an Early-type Galaxy: High [Ca/Fe] in NGC 5128 Globular Clusters

Science.gov (United States)

Colucci, Janet E.; Fernanda Durán, María; Bernstein, Rebecca A.; McWilliam, Andrew

2013-08-01

We present [Fe/H], ages, and Ca abundances for an initial sample of 10 globular clusters in NGC 5128 obtained from high-resolution, high signal-to-noise ratio echelle spectra of their integrated light. All abundances and ages are obtained using our original technique for high-resolution integrated light abundance analysis of globular clusters. The clusters have a range in [Fe/H] between -1.6 and -0.2. In this sample, the average [Ca/Fe] for clusters with [Fe/H] <-0.4 is +0.37 ± 0.07, while the average [Ca/Fe] in our Milky Way (MW) and M31 GC samples is +0.29 ± 0.09 and +0.24 ± 0.10, respectively. This may imply a more rapid chemical enrichment history for NGC 5128 than for either the MW or M31. This sample provides the first quantitative picture of the chemical history of NGC 5128 that is directly comparable to what is available for the MW. Data presented here were obtained with the MIKE echelle spectrograph on the Magellan Clay Telescope. This Letter includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

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)

FIRST Bent-Double Radio Sources: Tracers of High-Redshift Clusters

International Nuclear Information System (INIS)

Blanton, E. L.; Gregg, M. D.; Helfand, D. J.; Becker, R. H.; White, R. L.

2000-01-01

Bent-double radio sources can act as tracers for clusters of galaxies. We present imaging and spectroscopic observations of the environments surrounding 10 of these sources (most of them wide-angle tails [WATs]) selected from the VLA FIRST survey. Our results reveal a previously unknown cluster associated with eight of the radio sources with redshifts in the range 0.33< z<0.85; furthermore, we cannot rule out that the other two bent doubles may be associated with clusters at higher redshift. Richness measurements indicate that these clusters are typical of the majority of those found in the Abell catalog, with a range of Abell richness classes from 0 to 2. The line-of-sight velocity dispersions are very different from cluster to cluster, ranging from approximately 300 to 1100 km s-1. At the upper end of these intervals, we may be sampling some of the highest redshift massive clusters known. Alternatively, the large velocity dispersions measured in some of the clusters may indicate that they are merging systems with significant substructure, consistent with recent ideas concerning WAT formation (Burns et al.). (c) 2000 The American Astronomical Society

Characterization of micron-size hydrogen clusters using Mie scattering.

Science.gov (United States)

Jinno, S; Tanaka, H; Matsui, R; Kanasaki, M; Sakaki, H; Kando, M; Kondo, K; Sugiyama, A; Uesaka, M; Kishimoto, Y; Fukuda, Y

2017-08-07

Hydrogen clusters with diameters of a few micrometer range, composed of 10 8-10 hydrogen molecules, have been produced for the first time in an expansion of supercooled, high-pressure hydrogen gas into a vacuum through a conical nozzle connected to a cryogenic pulsed solenoid valve. The size distribution of the clusters has been evaluated by measuring the angular distribution of laser light scattered from the clusters. The data were analyzed based on the Mie scattering theory combined with the Tikhonov regularization method including the instrumental functions, the validity of which was assessed by performing a calibration study using a reference target consisting of standard micro-particles with two different sizes. The size distribution of the clusters was found discrete peaked at 0.33 ± 0.03, 0.65 ± 0.05, 0.81 ± 0.06, 1.40 ± 0.06 and 2.00 ± 0.13 µm in diameter. The highly reproducible and impurity-free nature of the micron-size hydrogen clusters can be a promising target for laser-driven multi-MeV proton sources with the currently available high power lasers.

The interaction of super-intense ultra-short laser pulse and micro-clusters with large atomic clusters

International Nuclear Information System (INIS)

Miao Jingwei; Yang Chaowen; An Zhu; Yuan Xuedong; Sun Weiguo; Luo Xiaobing; Wang Hu; Bai Lixing; Shi Miangong; Miao Lei; Zhen Zhijian; Gu Yuqin; Liu Hongjie; Zhu Zhouseng; Sun Liwei; Liao Xuehua

2007-01-01

The fusion mechanism of large deuterium clusters (100-1000 Atoms/per cluster) in super-intense ultra-short laser pulse field, Coulomb explosions of micro-cluster in solids, gases and Large-size clusters have been studied using the interaction of a high-intensity femtosecond laser pulses with large deuterium clusters, collision of high-quality beam of micro-cluster from 2.5 MV van de Graaff accelerator with solids, gases and large clusters. The experimental advance of the project is reported. (authors)

A Flocking Based algorithm for Document Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

2006-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

Formation of globular cluster candidates in merging proto-galaxies at high redshift: a view from the FIRE cosmological simulations

Science.gov (United States)

Kim, Ji-hoon; Ma, Xiangcheng; Grudić, Michael Y.; Hopkins, Philip F.; Hayward, Christopher C.; Wetzel, Andrew; Faucher-Giguère, Claude-André; Kereš, Dušan; Garrison-Kimmel, Shea; Murray, Norman

2018-03-01

Using a state-of-the-art cosmological simulation of merging proto-galaxies at high redshift from the FIRE project, with explicit treatments of star formation and stellar feedback in the interstellar medium, we investigate the formation of star clusters and examine one of the formation hypotheses of present-day metal-poor globular clusters. We find that frequent mergers in high-redshift proto-galaxies could provide a fertile environment to produce long-lasting bound star clusters. The violent merger event disturbs the gravitational potential and pushes a large gas mass of ≳ 105-6 M⊙ collectively to high density, at which point it rapidly turns into stars before stellar feedback can stop star formation. The high dynamic range of the reported simulation is critical in realizing such dense star-forming clouds with a small dynamical time-scale, tff ≲ 3 Myr, shorter than most stellar feedback time-scales. Our simulation then allows us to trace how clusters could become virialized and tightly bound to survive for up to ˜420 Myr till the end of the simulation. Because the cluster's tightly bound core was formed in one short burst, and the nearby older stars originally grouped with the cluster tend to be preferentially removed, at the end of the simulation the cluster has a small age spread.

Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

Science.gov (United States)

Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

2014-11-01

Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Implications for Primordial Non-Gaussianity ($f_{NL}$) from weak lensing masses of high-z galaxy clusters

CERN Document Server

Jimenez, Raul

2009-01-01

The recent weak lensing measurement of the dark matter mass of the high-redshift galaxy cluster XMMUJ2235.3-2557 of (8.5 +- 1.7) x 10^{14} Msun at z=1.4, indicates that, if the cluster is assumed to be the result of the collapse of dark matter in a primordial gaussian field in the standard LCDM model, then its abundance should be 3-10 if the non-Gaussianity parameter f^local_NL is in the range 150-200. This value is comparable to the limit for f_NL obtained by current constraints from the CMB. We conclude that mass determination of high-redshift, massive clusters can offer a complementary probe of primordial non-gaussianity.

Application of cluster computing in materials science

International Nuclear Information System (INIS)

Kuzmin, A.

2006-01-01

Solution of many problems in materials science requires that high performance computing (HPC) be used. Therefore, a cluster computer, Latvian Super-cluster (LASC), was constructed at the Institute of Solid State Physics of the University of Latvia in 2002. The LASC is used for advanced research in the fields of quantum chemistry, solid state physics and nano materials. In this work we overview currently available computational technologies and exemplify their application by interpretation of x-ray absorption spectra for nano-sized ZnO. (author)

Using Cluster Bootstrapping to Analyze Nested Data with a Few Clusters

Science.gov (United States)

Huang, Francis L.

2018-01-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials…

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

Science.gov (United States)

Chen, Gary K.; Chi, Eric C.; Ranola, John Michael O.; Lange, Kenneth

2015-01-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

Study of parameters of the nearest neighbour shared algorithm on clustering documents

Science.gov (United States)

Mustika Rukmi, Alvida; Budi Utomo, Daryono; Imro’atus Sholikhah, Neni

2018-03-01

Document clustering is one way of automatically managing documents, extracting of document topics and fastly filtering information. Preprocess of clustering documents processed by textmining consists of: keyword extraction using Rapid Automatic Keyphrase Extraction (RAKE) and making the document as concept vector using Latent Semantic Analysis (LSA). Furthermore, the clustering process is done so that the documents with the similarity of the topic are in the same cluster, based on the preprocesing by textmining performed. Shared Nearest Neighbour (SNN) algorithm is a clustering method based on the number of "nearest neighbors" shared. The parameters in the SNN Algorithm consist of: k nearest neighbor documents, ɛ shared nearest neighbor documents and MinT minimum number of similar documents, which can form a cluster. Characteristics The SNN algorithm is based on shared ‘neighbor’ properties. Each cluster is formed by keywords that are shared by the documents. SNN algorithm allows a cluster can be built more than one keyword, if the value of the frequency of appearing keywords in document is also high. Determination of parameter values on SNN algorithm affects document clustering results. The higher parameter value k, will increase the number of neighbor documents from each document, cause similarity of neighboring documents are lower. The accuracy of each cluster is also low. The higher parameter value ε, caused each document catch only neighbor documents that have a high similarity to build a cluster. It also causes more unclassified documents (noise). The higher the MinT parameter value cause the number of clusters will decrease, since the number of similar documents can not form clusters if less than MinT. Parameter in the SNN Algorithm determine performance of clustering result and the amount of noise (unclustered documents ). The Silhouette coeffisient shows almost the same result in many experiments, above 0.9, which means that SNN algorithm works well

Merged consensus clustering to assess and improve class discovery with microarray data

Directory of Open Access Journals (Sweden)

Jarman Andrew P

2010-12-01

Full Text Available Abstract Background One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a large number of methods available to perform clustering, but it is often unclear which method is best suited to the data and how to quantify the quality of the classifications produced. Results Here we describe an R package containing methods to analyse the consistency of clustering results from any number of different clustering methods using resampling statistics. These methods allow the identification of the the best supported clusters and additionally rank cluster members by their fidelity within the cluster. These metrics allow us to compare the performance of different clustering algorithms under different experimental conditions and to select those that produce the most reliable clustering structures. We show the application of this method to simulated data, canonical gene expression experiments and our own novel analysis of genes involved in the specification of the peripheral nervous system in the fruitfly, Drosophila melanogaster. Conclusions Our package enables users to apply the merged consensus clustering methodology conveniently within the R programming environment, providing both analysis and graphical display functions for exploring clustering approaches. It extends the basic principle of consensus clustering by allowing the merging of results between different methods to provide an averaged clustering robustness. We show that this extension is useful in correcting for the tendency of clustering algorithms to treat outliers differently within datasets. The R package, clusterCons, is freely available at CRAN and sourceforge under the GNU public licence.

"K"-Means May Perform as well as Mixture Model Clustering but May Also Be Much Worse: Comment on Steinley and Brusco (2011)

Science.gov (United States)

Vermunt, Jeroen K.

2011-01-01

Steinley and Brusco (2011) presented the results of a huge simulation study aimed at evaluating cluster recovery of mixture model clustering (MMC) both for the situation where the number of clusters is known and is unknown. They derived rather strong conclusions on the basis of this study, especially with regard to the good performance of…

PGHPF – An Optimizing High Performance Fortran Compiler for Distributed Memory Machines

Directory of Open Access Journals (Sweden)

Zeki Bozkus

1997-01-01

Full Text Available High Performance Fortran (HPF is the first widely supported, efficient, and portable parallel programming language for shared and distributed memory systems. HPF is realized through a set of directive-based extensions to Fortran 90. It enables application developers and Fortran end-users to write compact, portable, and efficient software that will compile and execute on workstations, shared memory servers, clusters, traditional supercomputers, or massively parallel processors. This article describes a production-quality HPF compiler for a set of parallel machines. Compilation techniques such as data and computation distribution, communication generation, run-time support, and optimization issues are elaborated as the basis for an HPF compiler implementation on distributed memory machines. The performance of this compiler on benchmark programs demonstrates that high efficiency can be achieved executing HPF code on parallel architectures.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

Directory of Open Access Journals (Sweden)

Jiayi Wu

Full Text Available Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM. We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

Science.gov (United States)

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Cluster Physics with Merging Galaxy Clusters

Directory of Open Access Journals (Sweden)

Sandor M. Molnar

2016-02-01

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

Diametrical clustering for identifying anti-correlated gene clusters.

Science.gov (United States)

Dhillon, Inderjit S; Marcotte, Edward M; Roshan, Usman

2003-09-01

Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

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.

A Dissimilarity Measure for Clustering High- and Infinite Dimensional Data that Satisfies the Triangle Inequality

Science.gov (United States)

Socolovsky, Eduardo A.; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

The cosine or correlation measures of similarity used to cluster high dimensional data are interpreted as projections, and the orthogonal components are used to define a complementary dissimilarity measure to form a similarity-dissimilarity measure pair. Using a geometrical approach, a number of properties of this pair is established. This approach is also extended to general inner-product spaces of any dimension. These properties include the triangle inequality for the defined dissimilarity measure, error estimates for the triangle inequality and bounds on both measures that can be obtained with a few floating-point operations from previously computed values of the measures. The bounds and error estimates for the similarity and dissimilarity measures can be used to reduce the computational complexity of clustering algorithms and enhance their scalability, and the triangle inequality allows the design of clustering algorithms for high dimensional distributed data.

High-Intensity Femtosecond Laser Interaction with Rare Gas Clusters

Institute of Scientific and Technical Information of China (English)

林亚风; 钟钦; 曾淳; 陈哲

2001-01-01

With a 45 fs multiterawatt 790 nm laser system and jets of argon and krypton atomic clusters, a study of the interaction of fs intense laser pulses with large size rare gas dusters was conducted. The maximum laser intensity of about 7 × 1016 W/cm2 and dusters composed of thousands of atoms which were determined through Rayleigh scattering measurements were involved inthe experiments. On the one hand, the results indicate that the interaction is strongly cluster size dependent. The stronger the interaction, the larger the clusters are. On the other hand, a saturation followed by a drop of the energy of ions ejected from the interaction will occur when the laser intensity exceeds a definite value for clusters of a certain size.

APEnet+: high bandwidth 3D torus direct network for petaflops scale commodity clusters

International Nuclear Information System (INIS)

Ammendola, R; Salamon, A; Salina, G; Biagioni, A; Prezza, O; Cicero, F Lo; Lonardo, A; Paolucci, P S; Rossetti, D; Tosoratto, L; Vicini, P; Simula, F

2011-01-01

We describe herein the APElink+ board, a PCIe interconnect adapter featuring the latest advances in wire speed and interface technology plus hardware support for a RDMA programming model and experimental acceleration of GPU networking; this design allows us to build a low latency, high bandwidth PC cluster, the APEnet+ network, the new generation of our cost-effective, tens-of-thousands-scalable cluster network architecture. Some test results and characterization of data transmission of a complete testbench, based on a commercial development card mounting an Altera ® FPGA, are provided.

APEnet+: high bandwidth 3D torus direct network for petaflops scale commodity clusters

Energy Technology Data Exchange (ETDEWEB)

Ammendola, R; Salamon, A; Salina, G [INFN Tor Vergata, Roma (Italy); Biagioni, A; Prezza, O; Cicero, F Lo; Lonardo, A; Paolucci, P S; Rossetti, D; Tosoratto, L; Vicini, P [INFN Roma, Roma (Italy); Simula, F [Sapienza Universita di Roma, Roma (Italy)

2011-12-23

We describe herein the APElink+ board, a PCIe interconnect adapter featuring the latest advances in wire speed and interface technology plus hardware support for a RDMA programming model and experimental acceleration of GPU networking; this design allows us to build a low latency, high bandwidth PC cluster, the APEnet+ network, the new generation of our cost-effective, tens-of-thousands-scalable cluster network architecture. Some test results and characterization of data transmission of a complete testbench, based on a commercial development card mounting an Altera{sup Registered-Sign} FPGA, are provided.

Cluster observations of the high-latitude magnetopause and cusp: initial results from the CIS ion instruments

Directory of Open Access Journals (Sweden)

J. M. Bosqued

2001-09-01

Full Text Available Launched on an elliptical high inclination orbit (apogee: 19.6 RE since January 2001 the Cluster satellites have been conducting the first detailed three-dimensional studies of the high-latitude dayside magnetosphere, including the exterior cusp, neighbouring boundary layers and magnetopause regions. Cluster satellites carry the CIS ion spectrometers that provide high-precision, 3D distributions of low-energy (<35 keV/e ions every 4 s. This paper presents the first two observations of the cusp and/or magnetopause behaviour made under different interplanetary magnetic field (IMF conditions. Flow directions, 3D distribution functions, density profiles and ion composition profiles are analyzed to demonstrate the high variability of high-latitude regions. In the first crossing analyzed (26 January 2001, dusk side, IMF-BZ < 0, multiple, isolated boundary layer, magnetopause and magnetosheath encounters clearly occurred on a quasi-steady basis for ~ 2 hours. CIS ion instruments show systematic accelerated flows in the current layer and adjacent boundary layers on the Earthward side of the magnetopause. Multi-point analysis of the magnetopause, combining magnetic and plasma data from the four Cluster spacecraft, demonstrates that oscillatory outward-inward motions occur with a normal speed of the order of ± 40 km/s; the thickness of the high-latitude current layer is evaluated to be of the order of 900–1000 km. Alfvénic accelerated flows and D-shaped distributions are convincing signatures of a magnetic reconnection occurring equatorward of the Cluster satellites. Moreover, the internal magnetic and plasma structure of a flux transfer event (FTE is analyzed in detail; its size along the magnetopause surface is ~ 12 000 km and it convects with a velocity of ~ 200 km/s. The second event analyzed (2 February 2001 corresponds to the first Cluster pass within the cusp when the IMF-BZ component was northward directed. The analysis of relevant CIS plasma

Sparse subspace clustering for data with missing entries and high-rank matrix completion.

Science.gov (United States)

Fan, Jicong; Chow, Tommy W S

2017-09-01

Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Board characteristics, governance objectives, and hospital performance

DEFF Research Database (Denmark)

Thiel, Andrea; Winter, Vera; Büchner, Vera Antonia

2018-01-01

membership relates to board characteristics and financial performance. METHODOLOGY: Using factor analysis, we identify latent classes of governance objectives and use hierarchical cluster analysis to detect distinct clusters with varying emphasis on the classes. We then use multinomial regression to explore...... the associations between cluster membership and board characteristics (size, gender diversity, and occupational diversity) and examine the associations between clusters and financial performance using OLS regression. RESULTS: Classes of objectives reflecting three governance theories-agency theory, stewardship...... and hospital financial performance, with two of three groups performing significantly better than the reference group. CONCLUSION: High performance in hospitals can be the result of governance logics, which, compared to simple board characteristics, are associated with better financial outcomes. PRACTICE...

Bioinformatics and Astrophysics Cluster (BinAc)

Science.gov (United States)

Krüger, Jens; Lutz, Volker; Bartusch, Felix; Dilling, Werner; Gorska, Anna; Schäfer, Christoph; Walter, Thomas

2017-09-01

BinAC provides central high performance computing capacities for bioinformaticians and astrophysicists from the state of Baden-Württemberg. The bwForCluster BinAC is part of the implementation concept for scientific computing for the universities in Baden-Württemberg. Community specific support is offered through the bwHPC-C5 project.

SAIL: Summation-bAsed Incremental Learning for Information-Theoretic Text Clustering.

Science.gov (United States)

Cao, Jie; Wu, Zhiang; Wu, Junjie; Xiong, Hui

2013-04-01

Information-theoretic clustering aims to exploit information-theoretic measures as the clustering criteria. A common practice on this topic is the so-called Info-Kmeans, which performs K-means clustering with KL-divergence as the proximity function. While expert efforts on Info-Kmeans have shown promising results, a remaining challenge is to deal with high-dimensional sparse data such as text corpora. Indeed, it is possible that the centroids contain many zero-value features for high-dimensional text vectors, which leads to infinite KL-divergence values and creates a dilemma in assigning objects to centroids during the iteration process of Info-Kmeans. To meet this challenge, in this paper, we propose a Summation-bAsed Incremental Learning (SAIL) algorithm for Info-Kmeans clustering. Specifically, by using an equivalent objective function, SAIL replaces the computation of KL-divergence by the incremental computation of Shannon entropy. This can avoid the zero-feature dilemma caused by the use of KL-divergence. To improve the clustering quality, we further introduce the variable neighborhood search scheme and propose the V-SAIL algorithm, which is then accelerated by a multithreaded scheme in PV-SAIL. Our experimental results on various real-world text collections have shown that, with SAIL as a booster, the clustering performance of Info-Kmeans can be significantly improved. Also, V-SAIL and PV-SAIL indeed help improve the clustering quality at a lower cost of computation.

A clustering approach to segmenting users of internet-based risk calculators.

Science.gov (United States)

Harle, C A; Downs, J S; Padman, R

2011-01-01

Risk calculators are widely available Internet applications that deliver quantitative health risk estimates to consumers. Although these tools are known to have varying effects on risk perceptions, little is known about who will be more likely to accept objective risk estimates. To identify clusters of online health consumers that help explain variation in individual improvement in risk perceptions from web-based quantitative disease risk information. A secondary analysis was performed on data collected in a field experiment that measured people's pre-diabetes risk perceptions before and after visiting a realistic health promotion website that provided quantitative risk information. K-means clustering was performed on numerous candidate variable sets, and the different segmentations were evaluated based on between-cluster variation in risk perception improvement. Variation in responses to risk information was best explained by clustering on pre-intervention absolute pre-diabetes risk perceptions and an objective estimate of personal risk. Members of a high-risk overestimater cluster showed large improvements in their risk perceptions, but clusters of both moderate-risk and high-risk underestimaters were much more muted in improving their optimistically biased perceptions. Cluster analysis provided a unique approach for segmenting health consumers and predicting their acceptance of quantitative disease risk information. These clusters suggest that health consumers were very responsive to good news, but tended not to incorporate bad news into their self-perceptions much. These findings help to quantify variation among online health consumers and may inform the targeted marketing of and improvements to risk communication tools on the Internet.

Scalable Density-Based Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2011-01-01

For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering...... method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real...... and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well....

Analysis of genetic association using hierarchical clustering and cluster validation indices.

Science.gov (United States)

Pagnuco, Inti A; Pastore, Juan I; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L

2017-10-01

It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, based on some criteria of similarity. This task is usually performed by clustering algorithms, where the genes are clustered into meaningful groups based on their expression values in a set of experiment. In this work, we propose a method to find sets of co-expressed genes, based on cluster validation indices as a measure of similarity for individual gene groups, and a combination of variants of hierarchical clustering to generate the candidate groups. We evaluated its ability to retrieve significant sets on simulated correlated and real genomics data, where the performance is measured based on its detection ability of co-regulated sets against a full search. Additionally, we analyzed the quality of the best ranked groups using an online bioinformatics tool that provides network information for the selected genes. Copyright © 2017 Elsevier Inc. All rights reserved.

Groups and clusters of galaxies

International Nuclear Information System (INIS)

Bijleveld, W.

1984-01-01

In this thesis, a correlative study is performed with respect to the radio and X-ray parameters of galaxy clusters and groups of galaxies (Msub(v)-Psub(1.4); Msub(v)-Lsub(x); Lsub(x)-Psub(1.4); R-Msub(v) correlations). Special attention is paid to correlations with cD and elliptical galaxies. It is concluded that in rich clusters massive cD galaxies form; massive galaxies are able to bind a large X-ray halo; strong X-ray emitters fuel their central radio sources at a high rate; the total gas content of groups is low, which implies that the contribution of groups to the total matter density in the universe is small. (Auth.)

Industrial clusters and social networks and their impact on the performance of micro- and small-scale enterprises: evidence from the handloom sector in Ethiopia

NARCIS (Netherlands)

Ali, M.A.

2012-01-01

This study empirically investigates how clustering and social networks affect the performance of micro- and small-scale enterprises by looking at the evidence from Ethiopia. By contrasting the performance of clustered micro enterprises with that of dispersed ones, it was first shown that

Interaction of rare gas clusters in intense laser field

International Nuclear Information System (INIS)

Dobosz, Sandrine

1998-01-01

Rare gas cluster jet targets have only been scarcely studied in strong laser fields. This is surprising since their properties are particularly appealing. Although considered as a gas phase target, the local density within clusters is comparable to that of the bulk. Intense irradiation of clusters produces a plasma thereby giving rise to strong collisional heating. This explains, in particular, the observation of very high fragment charge states and the generation of X-rays in the keV energy range. The complete set of our experimental results shows that the intra-cluster atoms are first ionised by tunnel ionisation followed by massive electron impact ionisation. Thus, for Xenon clusters, we have observed up to 30-fold charged. The most energetic electrons leave the cluster which contributes to a positive charge build-up on the cluster surface. The plasma expands under the combined action of the Coulomb and kinetic pressures. The contribution of each pressure depends on the cluster size and we show that the Coulomb pressure is prevailing for the smallest sizes. This scenario explains the ejection of fragments with energies of up to lMeV. We have also performed a high resolution X-ray study to explore in situ the properties of the plasma. These studies underline the importance of electron-ion collisions and allow to deterrnine the mean charge states of the emitting ions. Finally, we have developed a model, describing the cluster expansion, which confirms our experimental observations. (author) [fr

HDclassif : An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

Directory of Open Access Journals (Sweden)

Laurent Berge

2012-01-01

Full Text Available This paper presents the R package HDclassif which is devoted to the clustering and the discriminant analysis of high-dimensional data. The classification methods proposed in the package result from a new parametrization of the Gaussian mixture model which combines the idea of dimension reduction and model constraints on the covariance matrices. The supervised classification method using this parametrization is called high dimensional discriminant analysis (HDDA. In a similar manner, the associated clustering method iscalled high dimensional data clustering (HDDC and uses the expectation-maximization algorithm for inference. In order to correctly t the data, both methods estimate the specific subspace and the intrinsic dimension of the groups. Due to the constraints on the covariance matrices, the number of parameters to estimate is significantly lower than other model-based methods and this allows the methods to be stable and efficient in high dimensions. Two introductory examples illustrated with R codes allow the user to discover the hdda and hddc functions. Experiments on simulated and real datasets also compare HDDC and HDDA with existing classification methods on high-dimensional datasets. HDclassif is a free software and distributed under the general public license, as part of the R software project.

A neural network clustering algorithm for the ATLAS silicon pixel detector

CERN Document Server

Aad, Georges; Abdallah, Jalal; Abdel Khalek, Samah; Abdinov, Ovsat; Aben, Rosemarie; Abi, Babak; Abolins, Maris; AbouZeid, Ossama; Abramowicz, Halina; Abreu, Henso; Abreu, Ricardo; Abulaiti, Yiming; Acharya, Bobby Samir; Adamczyk, Leszek; Adams, David; Adelman, Jahred; Adomeit, Stefanie; Adye, Tim; Agatonovic-Jovin, Tatjana; Aguilar-Saavedra, Juan Antonio; Agustoni, Marco; Ahlen, Steven; Ahmadov, Faig; Aielli, Giulio; Akerstedt, Henrik; Åkesson, Torsten Paul Ake; Akimoto, Ginga; Akimov, Andrei; Alberghi, Gian Luigi; Albert, Justin; Albrand, Solveig; Alconada Verzini, Maria Josefina; Aleksa, Martin; Aleksandrov, Igor; Alexa, Calin; Alexander, Gideon; Alexandre, Gauthier; Alexopoulos, Theodoros; Alhroob, Muhammad; Alimonti, Gianluca; Alio, Lion; Alison, John; Allbrooke, Benedict; Allison, Lee John; Allport, Phillip; Almond, John; Aloisio, Alberto; Alonso, Alejandro; Alonso, Francisco; Alpigiani, Cristiano; Altheimer, Andrew David; Alvarez Gonzalez, Barbara; Alviggi, Mariagrazia; Amako, Katsuya; Amaral Coutinho, Yara; Amelung, Christoph; Amidei, Dante; Amor Dos Santos, Susana Patricia; Amorim, Antonio; Amoroso, Simone; Amram, Nir; Amundsen, Glenn; Anastopoulos, Christos; Ancu, Lucian Stefan; Andari, Nansi; Andeen, Timothy; Anders, Christoph Falk; Anders, Gabriel; Anderson, Kelby; Andreazza, Attilio; Andrei, George Victor; Anduaga, Xabier; Angelidakis, Stylianos; Angelozzi, Ivan; Anger, Philipp; Angerami, Aaron; Anghinolfi, Francis; Anisenkov, Alexey; Anjos, Nuno; Annovi, Alberto; Antonaki, Ariadni; Antonelli, Mario; Antonov, Alexey; Antos, Jaroslav; Anulli, Fabio; Aoki, Masato; Aperio Bella, Ludovica; Apolle, Rudi; Arabidze, Giorgi; Aracena, Ignacio; Arai, Yasuo; Araque, Juan Pedro; Arce, Ayana; Arguin, Jean-Francois; Argyropoulos, Spyridon; Arik, Metin; Armbruster, Aaron James; Arnaez, Olivier; Arnal, Vanessa; Arnold, Hannah; Arratia, Miguel; Arslan, Ozan; Artamonov, Andrei; Artoni, Giacomo; Asai, Shoji; Asbah, Nedaa; Ashkenazi, Adi; Åsman, Barbro; Asquith, Lily; Assamagan, Ketevi; Astalos, Robert; Atkinson, Markus; Atlay, Naim Bora; Auerbach, Benjamin; Augsten, Kamil; Aurousseau, Mathieu; Avolio, Giuseppe; Azuelos, Georges; Azuma, Yuya; Baak, Max; Baas, Alessandra; Bacci, Cesare; Bachacou, Henri; Bachas, Konstantinos; Backes, Moritz; Backhaus, Malte; Backus Mayes, John; Badescu, Elisabeta; Bagiacchi, Paolo; Bagnaia, Paolo; Bai, Yu; Bain, Travis; Baines, John; Baker, Oliver Keith; Balek, Petr; Balli, Fabrice; Banas, Elzbieta; Banerjee, Swagato; Bannoura, Arwa A E; Bansal, Vikas; Bansil, Hardeep Singh; Barak, Liron; Baranov, Sergei; Barberio, Elisabetta Luigia; Barberis, Dario; Barbero, Marlon; Barillari, Teresa; Barisonzi, Marcello; Barklow, Timothy; Barlow, Nick; Barnett, Bruce; Barnett, Michael; Barnovska, Zuzana; Baroncelli, Antonio; Barone, Gaetano; Barr, Alan; Barreiro, Fernando; Barreiro Guimarães da Costa, João; Bartoldus, Rainer; Barton, Adam Edward; Bartos, Pavol; Bartsch, Valeria; Bassalat, Ahmed; Basye, Austin; Bates, Richard; Batkova, Lucia; Batley, Richard; Battaglia, Marco; Battistin, Michele; Bauer, Florian; Bawa, Harinder Singh; Beau, Tristan; Beauchemin, Pierre-Hugues; Beccherle, Roberto; Bechtle, Philip; Beck, Hans Peter; Becker, Anne Kathrin; Becker, Sebastian; Beckingham, Matthew; Becot, Cyril; Beddall, Andrew; Beddall, Ayda; Bedikian, Sourpouhi; Bednyakov, Vadim; Bee, Christopher; Beemster, Lars; Beermann, Thomas; Begel, Michael; Behr, Katharina; Belanger-Champagne, Camille; Bell, Paul; Bell, William; Bella, Gideon; Bellagamba, Lorenzo; Bellerive, Alain; Bellomo, Massimiliano; Belotskiy, Konstantin; Beltramello, Olga; Benary, Odette; Benchekroun, Driss; Bendtz, Katarina; Benekos, Nektarios; Benhammou, Yan; Benhar Noccioli, Eleonora; Benitez Garcia, Jorge-Armando; Benjamin, Douglas; Bensinger, James; Benslama, Kamal; Bentvelsen, Stan; Berge, David; Bergeaas Kuutmann, Elin; Berger, Nicolas; Berghaus, Frank; Beringer, Jürg; Bernard, Clare; Bernat, Pauline; Bernius, Catrin; Bernlochner, Florian Urs; Berry, Tracey; Berta, Peter; Bertella, Claudia; Bertoli, Gabriele; Bertolucci, Federico; Bertsche, David; Besana, Maria Ilaria; Besjes, Geert-Jan; Bessidskaia, Olga; Bessner, Martin Florian; Besson, Nathalie; Betancourt, Christopher; Bethke, Siegfried; Bhimji, Wahid; Bianchi, Riccardo-Maria; Bianchini, Louis; Bianco, Michele; Biebel, Otmar; Bieniek, Stephen Paul; Bierwagen, Katharina; Biesiada, Jed; Biglietti, Michela; Bilbao De Mendizabal, Javier; Bilokon, Halina; Bindi, Marcello; Binet, Sebastien; Bingul, Ahmet; Bini, Cesare; Black, Curtis; Black, James; Black, Kevin; Blackburn, Daniel; Blair, Robert; Blanchard, Jean-Baptiste; Blazek, Tomas; Bloch, Ingo; Blocker, Craig; Blum, Walter; Blumenschein, Ulrike; Bobbink, Gerjan; Bobrovnikov, Victor; Bocchetta, Simona Serena; Bocci, Andrea; Bock, Christopher; Boddy, Christopher Richard; Boehler, Michael; Boek, Thorsten Tobias; Bogaerts, Joannes Andreas; Bogdanchikov, Alexander; Bogouch, Andrei; Bohm, Christian; Bohm, Jan; Boisvert, Veronique; Bold, Tomasz; Boldea, Venera; Boldyrev, Alexey; Bomben, Marco; Bona, Marcella; Boonekamp, Maarten; Borisov, Anatoly; Borissov, Guennadi; Borri, Marcello; Borroni, Sara; Bortfeldt, Jonathan; Bortolotto, Valerio; Bos, Kors; Boscherini, Davide; Bosman, Martine; Boterenbrood, Hendrik; Boudreau, Joseph; Bouffard, Julian; Bouhova-Thacker, Evelina Vassileva; Boumediene, Djamel Eddine; Bourdarios, Claire; Bousson, Nicolas; Boutouil, Sara; Boveia, Antonio; Boyd, James; Boyko, Igor; Bracinik, Juraj; Brandt, Andrew; Brandt, Gerhard; Brandt, Oleg; Bratzler, Uwe; Brau, Benjamin; Brau, James; Braun, Helmut; Brazzale, Simone Federico; Brelier, Bertrand; Brendlinger, Kurt; Brennan, Amelia Jean; Brenner, Richard; Bressler, Shikma; Bristow, Kieran; Bristow, Timothy Michael; Britton, Dave; Brochu, Frederic; Brock, Ian; Brock, Raymond; Bromberg, Carl; Bronner, Johanna; Brooijmans, Gustaaf; Brooks, Timothy; Brooks, William; Brosamer, Jacquelyn; Brost, Elizabeth; Brown, Jonathan; Bruckman de Renstrom, Pawel; Bruncko, Dusan; Bruneliere, Renaud; Brunet, Sylvie; Bruni, Alessia; Bruni, Graziano; Bruschi, Marco; Bryngemark, Lene; Buanes, Trygve; Buat, Quentin; Bucci, Francesca; Buchholz, Peter; Buckingham, Ryan; Buckley, Andrew; Buda, Stelian Ioan; Budagov, Ioulian; Buehrer, Felix; Bugge, Lars; Bugge, Magnar Kopangen; Bulekov, Oleg; Bundock, Aaron Colin; Burckhart, Helfried; Burdin, Sergey; Burghgrave, Blake; Burke, Stephen; Burmeister, Ingo; Busato, Emmanuel; Büscher, Daniel; Büscher, Volker; Bussey, Peter; Buszello, Claus-Peter; Butler, Bart; Butler, John; Butt, Aatif Imtiaz; Buttar, Craig; Butterworth, Jonathan; Butti, Pierfrancesco; Buttinger, William; Buzatu, Adrian; Byszewski, Marcin; Cabrera Urbán, Susana; Caforio, Davide; Cakir, Orhan; Calafiura, Paolo; Calandri, Alessandro; Calderini, Giovanni; Calfayan, Philippe; Calkins, Robert; Caloba, Luiz; Calvet, David; Calvet, Samuel; Camacho Toro, Reina; Camarda, Stefano; Cameron, David; Caminada, Lea Michaela; Caminal Armadans, Roger; Campana, Simone; Campanelli, Mario; Campoverde, Angel; Canale, Vincenzo; Canepa, Anadi; Cano Bret, Marc; Cantero, Josu; Cantrill, Robert; Cao, Tingting; Capeans Garrido, Maria Del Mar; Caprini, Irinel; Caprini, Mihai; Capua, Marcella; Caputo, Regina; Cardarelli, Roberto; Carli, Tancredi; Carlino, Gianpaolo; Carminati, Leonardo; Caron, Sascha; Carquin, Edson; Carrillo-Montoya, German D; Carter, Janet; Carvalho, João; Casadei, Diego; Casado, Maria Pilar; Casolino, Mirkoantonio; Castaneda-Miranda, Elizabeth; Castelli, Angelantonio; Castillo Gimenez, Victoria; Castro, Nuno Filipe; Catastini, Pierluigi; Catinaccio, Andrea; Catmore, James; Cattai, Ariella; Cattani, Giordano; Caughron, Seth; Cavaliere, Viviana; Cavalli, Donatella; Cavalli-Sforza, Matteo; Cavasinni, Vincenzo; Ceradini, Filippo; Cerio, Benjamin; Cerny, Karel; Santiago Cerqueira, Augusto; Cerri, Alessandro; Cerrito, Lucio; Cerutti, Fabio; Cerv, Matevz; Cervelli, Alberto; Cetin, Serkant Ali; Chafaq, Aziz; Chakraborty, Dhiman; Chalupkova, Ina; Chang, Philip; Chapleau, Bertrand; Chapman, John Derek; Charfeddine, Driss; Charlton, Dave; Chau, Chav Chhiv; Chavez Barajas, Carlos Alberto; Cheatham, Susan; Chegwidden, Andrew; Chekanov, Sergei; Chekulaev, Sergey; Chelkov, Gueorgui; Chelstowska, Magda Anna; Chen, Chunhui; Chen, Hucheng; Chen, Karen; Chen, Liming; Chen, Shenjian; Chen, Xin; Chen, Yujiao; Cheng, Hok Chuen; Cheng, Yangyang; Cheplakov, Alexander; Cherkaoui El Moursli, Rajaa; Chernyatin, Valeriy; Cheu, Elliott; Chevalier, Laurent; Chiarella, Vitaliano; Chiefari, Giovanni; Childers, John Taylor; Chilingarov, Alexandre; Chiodini, Gabriele; Chisholm, Andrew; Chislett, Rebecca Thalatta; Chitan, Adrian; Chizhov, Mihail; Chouridou, Sofia; Chow, Bonnie Kar Bo; Chromek-Burckhart, Doris; Chu, Ming-Lee; Chudoba, Jiri; Chwastowski, Janusz; Chytka, Ladislav; Ciapetti, Guido; Ciftci, Abbas Kenan; Ciftci, Rena; Cinca, Diane; Cindro, Vladimir; Ciocio, Alessandra; Cirkovic, Predrag; Citron, Zvi Hirsh; Citterio, Mauro; Ciubancan, Mihai; Clark, Allan G; Clark, Philip James; Clarke, Robert; Cleland, Bill; Clemens, Jean-Claude; Clement, Christophe; Coadou, Yann; Cobal, Marina; Coccaro, Andrea; Cochran, James H; Coffey, Laurel; Cogan, Joshua Godfrey; Coggeshall, James; Cole, Brian; Cole, Stephen; Colijn, Auke-Pieter; Collot, Johann; Colombo, Tommaso; Colon, German; Compostella, Gabriele; Conde Muiño, Patricia; Coniavitis, Elias; Conidi, Maria Chiara; Connell, Simon Henry; Connelly, Ian; Consonni, Sofia Maria; Consorti, Valerio; Constantinescu, Serban; Conta, Claudio; Conti, Geraldine; Conventi, Francesco; Cooke, Mark; Cooper, Ben; Cooper-Sarkar, Amanda; Cooper-Smith, Neil; Copic, Katherine; Cornelissen, Thijs; Corradi, Massimo; Corriveau, Francois; Corso-Radu, Alina; Cortes-Gonzalez, Arely; Cortiana, Giorgio; Costa, Giuseppe; Costa, María José; Costanzo, Davide; Côté, David; Cottin, Giovanna; Cowan, Glen; Cox, Brian; Cranmer, Kyle; Cree, Graham; Crépé-Renaudin, Sabine; Crescioli, Francesco; Cribbs, Wayne Allen; Crispin Ortuzar, Mireia; Cristinziani, Markus; Croft, Vince; Crosetti, Giovanni; Cuciuc, Constantin-Mihai; Cuhadar Donszelmann, Tulay; Cummings, Jane; Curatolo, Maria; Cuthbert, Cameron; Czirr, Hendrik; Czodrowski, Patrick; Czyczula, Zofia; D'Auria, Saverio; D'Onofrio, Monica; Da Cunha Sargedas De Sousa, Mario Jose; Da Via, Cinzia; Dabrowski, Wladyslaw; Dafinca, Alexandru; Dai, Tiesheng; Dale, Orjan; Dallaire, Frederick; Dallapiccola, Carlo; Dam, Mogens; Daniells, Andrew Christopher; Dano Hoffmann, Maria; Dao, Valerio; Darbo, Giovanni; Darmora, Smita; Dassoulas, James; Dattagupta, Aparajita; Davey, Will; David, Claire; Davidek, Tomas; Davies, Eleanor; Davies, Merlin; Davignon, Olivier; Davison, Adam; Davison, Peter; Davygora, Yuriy; Dawe, Edmund; Dawson, Ian; Daya-Ishmukhametova, Rozmin; De, Kaushik; de Asmundis, Riccardo; De Castro, Stefano; De Cecco, Sandro; De Groot, Nicolo; de Jong, Paul; De la Torre, Hector; De Lorenzi, Francesco; De Nooij, Lucie; De Pedis, Daniele; De Salvo, Alessandro; De Sanctis, Umberto; De Santo, Antonella; De Vivie De Regie, Jean-Baptiste; Dearnaley, William James; Debbe, Ramiro; Debenedetti, Chiara; Dechenaux, Benjamin; Dedovich, Dmitri; Deigaard, Ingrid; Del Peso, Jose; Del Prete, Tarcisio; Deliot, Frederic; Delitzsch, Chris Malena; Deliyergiyev, Maksym; Dell'Acqua, Andrea; Dell'Asta, Lidia; Dell'Orso, Mauro; Della Pietra, Massimo; della Volpe, Domenico; Delmastro, Marco; Delsart, Pierre-Antoine; Deluca, Carolina; Demers, Sarah; Demichev, Mikhail; Demilly, Aurelien; Denisov, Sergey; Derendarz, Dominik; Derkaoui, Jamal Eddine; Derue, Frederic; Dervan, Paul; Desch, Klaus Kurt; Deterre, Cecile; Deviveiros, Pier-Olivier; Dewhurst, Alastair; Dhaliwal, Saminder; Di Ciaccio, Anna; Di Ciaccio, Lucia; Di Domenico, Antonio; Di Donato, Camilla; Di Girolamo, Alessandro; Di Girolamo, Beniamino; Di Mattia, Alessandro; Di Micco, Biagio; Di Nardo, Roberto; Di Simone, Andrea; Di Sipio, Riccardo; Di Valentino, David; Dias, Flavia; Diaz, Marco Aurelio; Diehl, Edward; Dietrich, Janet; Dietzsch, Thorsten; Diglio, Sara; Dimitrievska, Aleksandra; Dingfelder, Jochen; Dionisi, Carlo; Dita, Petre; Dita, Sanda; Dittus, Fridolin; Djama, Fares; Djobava, Tamar; Barros do Vale, Maria Aline; Do Valle Wemans, André; Doan, Thi Kieu Oanh; Dobos, Daniel; Doglioni, Caterina; Doherty, Tom; Dohmae, Takeshi; Dolejsi, Jiri; Dolezal, Zdenek; Dolgoshein, Boris; Donadelli, Marisilvia; Donati, Simone; Dondero, Paolo; Donini, Julien; Dopke, Jens; Doria, Alessandra; Dova, Maria-Teresa; Doyle, Tony; Dris, Manolis; Dubbert, Jörg; Dube, Sourabh; Dubreuil, Emmanuelle; Duchovni, Ehud; Duckeck, Guenter; Ducu, Otilia Anamaria; Duda, Dominik; Dudarev, Alexey; Dudziak, Fanny; Duflot, Laurent; Duguid, Liam; Dührssen, Michael; Dunford, Monica; Duran Yildiz, Hatice; Düren, Michael; Durglishvili, Archil; Dwuznik, Michal; Dyndal, Mateusz; Ebke, Johannes; Edson, William; Edwards, Nicholas Charles; Ehrenfeld, Wolfgang; Eifert, Till; Eigen, Gerald; Einsweiler, Kevin; Ekelof, Tord; El Kacimi, Mohamed; Ellert, Mattias; Elles, Sabine; Ellinghaus, Frank; Ellis, Nicolas; Elmsheuser, Johannes; Elsing, Markus; Emeliyanov, Dmitry; Enari, Yuji; Endner, Oliver Chris; Endo, Masaki; Engelmann, Roderich; Erdmann, Johannes; Ereditato, Antonio; Eriksson, Daniel; Ernis, Gunar; Ernst, Jesse; Ernst, Michael; Ernwein, Jean; Errede, Deborah; Errede, Steven; Ertel, Eugen; Escalier, Marc; Esch, Hendrik; Escobar, Carlos; Esposito, Bellisario; Etienvre, Anne-Isabelle; Etzion, Erez; Evans, Hal; Ezhilov, Alexey; Fabbri, Laura; Facini, Gabriel; Fakhrutdinov, Rinat; Falciano, Speranza; Falla, Rebecca Jane; Faltova, Jana; Fang, Yaquan; Fanti, Marcello; Farbin, Amir; Farilla, Addolorata; Farooque, Trisha; Farrell, Steven; Farrington, Sinead; Farthouat, Philippe; Fassi, Farida; Fassnacht, Patrick; Fassouliotis, Dimitrios; Favareto, Andrea; Fayard, Louis; Federic, Pavol; Fedin, Oleg; Fedorko, Wojciech; Fehling-Kaschek, Mirjam; Feigl, Simon; Feligioni, Lorenzo; Feng, Cunfeng; Feng, Eric; Feng, Haolu; Fenyuk, Alexander; Fernandez Perez, Sonia; Ferrag, Samir; Ferrando, James; Ferrari, Arnaud; Ferrari, Pamela; Ferrari, Roberto; Ferreira de Lima, Danilo Enoque; Ferrer, Antonio; Ferrere, Didier; Ferretti, Claudio; Ferretto Parodi, Andrea; Fiascaris, Maria; Fiedler, Frank; Filipčič, Andrej; Filipuzzi, Marco; Filthaut, Frank; Fincke-Keeler, Margret; Finelli, Kevin Daniel; Fiolhais, Miguel; Fiorini, Luca; Firan, Ana; Fischer, Adam; Fischer, Julia; Fisher, Wade Cameron; Fitzgerald, Eric Andrew; Flechl, Martin; Fleck, Ivor; Fleischmann, Philipp; Fleischmann, Sebastian; Fletcher, Gareth Thomas; Fletcher, Gregory; Flick, Tobias; Floderus, Anders; Flores Castillo, Luis; Florez Bustos, Andres Carlos; Flowerdew, Michael; Formica, Andrea; Forti, Alessandra; Fortin, Dominique; Fournier, Daniel; Fox, Harald; Fracchia, Silvia; Francavilla, Paolo; Franchini, Matteo; Franchino, Silvia; Francis, David; Franklin, Melissa; Franz, Sebastien; Fraternali, Marco; French, Sky; Friedrich, Conrad; Friedrich, Felix; Froidevaux, Daniel; Frost, James; Fukunaga, Chikara; Fullana Torregrosa, Esteban; Fulsom, Bryan Gregory; Fuster, Juan; Gabaldon, Carolina; Gabizon, Ofir; Gabrielli, Alessandro; Gabrielli, Andrea; Gadatsch, Stefan; Gadomski, Szymon; Gagliardi, Guido; Gagnon, Pauline; Galea, Cristina; Galhardo, Bruno; Gallas, Elizabeth; Gallo, Valentina Santina; Gallop, Bruce; Gallus, Petr; Galster, Gorm Aske Gram Krohn; Gan, KK; Gandrajula, Reddy Pratap; Gao, Jun; Gao, Yongsheng; Garay Walls, Francisca; Garberson, Ford; García, Carmen; García Navarro, José Enrique; Garcia-Sciveres, Maurice; Gardner, Robert; Garelli, Nicoletta; Garonne, Vincent; Gatti, Claudio; Gaudio, Gabriella; Gaur, Bakul; Gauthier, Lea; Gauzzi, Paolo; Gavrilenko, Igor; Gay, Colin; Gaycken, Goetz; Gazis, Evangelos; Ge, Peng; Gecse, Zoltan; Gee, Norman; Geerts, Daniël Alphonsus Adrianus; Geich-Gimbel, Christoph; Gellerstedt, Karl; Gemme, Claudia; Gemmell, Alistair; Genest, Marie-Hélène; Gentile, Simonetta; George, Matthias; George, Simon; Gerbaudo, Davide; Gershon, Avi; Ghazlane, Hamid; Ghodbane, Nabil; Giacobbe, Benedetto; Giagu, Stefano; Giangiobbe, Vincent; Giannetti, Paola; Gianotti, Fabiola; Gibbard, Bruce; Gibson, Stephen; Gilchriese, Murdock; Gillam, Thomas; Gillberg, Dag; Gilles, Geoffrey; Gingrich, Douglas; Giokaris, Nikos; Giordani, MarioPaolo; Giordano, Raffaele; Giorgi, Filippo Maria; Giorgi, Francesco Michelangelo; Giraud, Pierre-Francois; Giugni, Danilo; Giuliani, Claudia; Giulini, Maddalena; Gjelsten, Børge Kile; Gkaitatzis, Stamatios; Gkialas, Ioannis; Gladilin, Leonid; Glasman, Claudia; Glatzer, Julian; Glaysher, Paul; Glazov, Alexandre; Glonti, George; Goblirsch-Kolb, Maximilian; Goddard, Jack Robert; Godfrey, Jennifer; Godlewski, Jan; Goeringer, Christian; Goldfarb, Steven; Golling, Tobias; Golubkov, Dmitry; Gomes, Agostinho; Gomez Fajardo, Luz Stella; Gonçalo, Ricardo; Goncalves Pinto Firmino Da Costa, Joao; Gonella, Laura; González de la Hoz, Santiago; Gonzalez Parra, Garoe; Gonzalez-Sevilla, Sergio; Goossens, Luc; Gorbounov, Petr Andreevich; Gordon, Howard; Gorelov, Igor; Gorini, Benedetto; Gorini, Edoardo; Gorišek, Andrej; Gornicki, Edward; Goshaw, Alfred; Gössling, Claus; Gostkin, Mikhail Ivanovitch; Gouighri, Mohamed; Goujdami, Driss; Goulette, Marc Phillippe; Goussiou, Anna; Goy, Corinne; Gozpinar, Serdar; Grabas, Herve Marie Xavier; Graber, Lars; Grabowska-Bold, Iwona; Grafström, Per; Grahn, Karl-Johan; Gramling, Johanna; Gramstad, Eirik; Grancagnolo, Sergio; Grassi, Valerio; Gratchev, Vadim; Gray, Heather; Graziani, Enrico; Grebenyuk, Oleg; Greenwood, Zeno Dixon; Gregersen, Kristian; Gregor, Ingrid-Maria; Grenier, Philippe; Griffiths, Justin; Grillo, Alexander; Grimm, Kathryn; Grinstein, Sebastian; Gris, Philippe Luc Yves; Grishkevich, Yaroslav; Grivaz, Jean-Francois; Grohs, Johannes Philipp; Grohsjean, Alexander; Gross, Eilam; Grosse-Knetter, Joern; Grossi, Giulio Cornelio; Groth-Jensen, Jacob; Grout, Zara Jane; Guan, Liang; Guescini, Francesco; Guest, Daniel; Gueta, Orel; Guicheney, Christophe; Guido, Elisa; Guillemin, Thibault; Guindon, Stefan; Gul, Umar; Gumpert, Christian; Gunther, Jaroslav; Guo, Jun; Gupta, Shaun; Gutierrez, Phillip; Gutierrez Ortiz, Nicolas Gilberto; Gutschow, Christian; Guttman, Nir; Guyot, Claude; Gwenlan, Claire; Gwilliam, Carl; Haas, Andy; Haber, Carl; Hadavand, Haleh Khani; Haddad, Nacim; Haefner, Petra; Hageböck, Stephan; Hajduk, Zbigniew; Hakobyan, Hrachya; Haleem, Mahsana; Hall, David; Halladjian, Garabed; Hamacher, Klaus; Hamal, Petr; Hamano, Kenji; Hamer, Matthias; Hamilton, Andrew; Hamilton, Samuel; Hamnett, Phillip George; Han, Liang; Hanagaki, Kazunori; Hanawa, Keita; Hance, Michael; Hanke, Paul; Hanna, Remie; Hansen, Jørgen Beck; Hansen, Jorn Dines; Hansen, Peter Henrik; Hara, Kazuhiko; Hard, Andrew; Harenberg, Torsten; Hariri, Faten; Harkusha, Siarhei; Harper, Devin; Harrington, Robert; Harris, Orin; Harrison, Paul Fraser; Hartjes, Fred; Hasegawa, Satoshi; Hasegawa, Yoji; Hasib, A; Hassani, Samira; Haug, Sigve; Hauschild, Michael; Hauser, Reiner; Havranek, Miroslav; Hawkes, Christopher; Hawkings, Richard John; Hawkins, Anthony David; Hayashi, Takayasu; Hayden, Daniel; Hays, Chris; Hayward, Helen; Haywood, Stephen; Head, Simon; Heck, Tobias; Hedberg, Vincent; Heelan, Louise; Heim, Sarah; Heim, Timon; Heinemann, Beate; Heinrich, Lukas; Hejbal, Jiri; Helary, Louis; Heller, Claudio; Heller, Matthieu; Hellman, Sten; Hellmich, Dennis; Helsens, Clement; Henderson, James; Henderson, Robert; Heng, Yang; Hengler, Christopher; Henrichs, Anna; Henriques Correia, Ana Maria; Henrot-Versille, Sophie; Hensel, Carsten; Herbert, Geoffrey Henry; Hernández Jiménez, Yesenia; Herrberg-Schubert, Ruth; Herten, Gregor; Hertenberger, Ralf; Hervas, Luis; Hesketh, Gavin Grant; Hessey, Nigel; Hickling, Robert; Higón-Rodriguez, Emilio; Hill, Ewan; Hill, John; Hiller, Karl Heinz; Hillert, Sonja; Hillier, Stephen; Hinchliffe, Ian; Hines, Elizabeth; Hirose, Minoru; Hirschbuehl, Dominic; Hobbs, John; Hod, Noam; Hodgkinson, Mark; Hodgson, Paul; Hoecker, Andreas; Hoeferkamp, Martin; Hoffman, Julia; Hoffmann, Dirk; Hofmann, Julia Isabell; Hohlfeld, Marc; Holmes, Tova Ray; Hong, Tae Min; Hooft van Huysduynen, Loek; Hostachy, Jean-Yves; Hou, Suen; Hoummada, Abdeslam; Howard, Jacob; Howarth, James; Hrabovsky, Miroslav; Hristova, Ivana; Hrivnac, Julius; Hryn'ova, Tetiana; Hsu, Catherine; Hsu, Pai-hsien Jennifer; Hsu, Shih-Chieh; Hu, Diedi; Hu, Xueye; Huang, Yanping; Hubacek, Zdenek; Hubaut, Fabrice; Huegging, Fabian; Huffman, Todd Brian; Hughes, Emlyn; Hughes, Gareth; Huhtinen, Mika; Hülsing, Tobias Alexander; Hurwitz, Martina; Huseynov, Nazim; Huston, Joey; Huth, John; Iacobucci, Giuseppe; Iakovidis, Georgios; Ibragimov, Iskander; Iconomidou-Fayard, Lydia; Ideal, Emma; Iengo, Paolo; Igonkina, Olga; Iizawa, Tomoya; Ikegami, Yoichi; Ikematsu, Katsumasa; Ikeno, Masahiro; Ilchenko, Iurii; Iliadis, Dimitrios; Ilic, Nikolina; Inamaru, Yuki; Ince, Tayfun; Ioannou, Pavlos; Iodice, Mauro; Iordanidou, Kalliopi; Ippolito, Valerio; Irles Quiles, Adrian; Isaksson, Charlie; Ishino, Masaya; Ishitsuka, Masaki; Ishmukhametov, Renat; Issever, Cigdem; Istin, Serhat; Iturbe Ponce, Julia Mariana; Iuppa, Roberto; Ivarsson, Jenny; Iwanski, Wieslaw; Iwasaki, Hiroyuki; Izen, Joseph; Izzo, Vincenzo; Jackson, Brett; Jackson, Matthew; Jackson, Paul; Jaekel, Martin; Jain, Vivek; Jakobs, Karl; Jakobsen, Sune; Jakoubek, Tomas; Jakubek, Jan; Jamin, David Olivier; Jana, Dilip; Jansen, Eric; Jansen, Hendrik; Janssen, Jens; Janus, Michel; Jarlskog, Göran; Javadov, Namig; Javůrek, Tomáš; Jeanty, Laura; Jejelava, Juansher; Jeng, Geng-yuan; Jennens, David; Jenni, Peter; Jentzsch, Jennifer; Jeske, Carl; Jézéquel, Stéphane; Ji, Haoshuang; Ji, Weina; Jia, Jiangyong; Jiang, Yi; Jimenez Belenguer, Marcos; Jin, Shan; Jinaru, Adam; Jinnouchi, Osamu; Joergensen, Morten Dam; Johansson, Erik; Johansson, Per; Johns, Kenneth; Jon-And, Kerstin; Jones, Graham; Jones, Roger; Jones, Tim; Jongmanns, Jan; Jorge, Pedro; Joshi, Kiran Daniel; Jovicevic, Jelena; Ju, Xiangyang; Jung, Christian; Jungst, Ralph Markus; Jussel, Patrick; Juste Rozas, Aurelio; Kaci, Mohammed; Kaczmarska, Anna; Kado, Marumi; Kagan, Harris; Kagan, Michael; Kajomovitz, Enrique; Kalderon, Charles William; Kama, Sami; Kamenshchikov, Andrey; Kanaya, Naoko; Kaneda, Michiru; Kaneti, Steven; Kantserov, Vadim; Kanzaki, Junichi; Kaplan, Benjamin; Kapliy, Anton; Kar, Deepak; Karakostas, Konstantinos; Karastathis, Nikolaos; Karnevskiy, Mikhail; Karpov, Sergey; Karpova, Zoya; Karthik, Krishnaiyengar; Kartvelishvili, Vakhtang; Karyukhin, Andrey; Kashif, Lashkar; Kasieczka, Gregor; Kass, Richard; Kastanas, Alex; Kataoka, Yousuke; Katre, Akshay; Katzy, Judith; Kaushik, Venkatesh; Kawagoe, Kiyotomo; Kawamoto, Tatsuo; Kawamura, Gen; Kazama, Shingo; Kazanin, Vassili; Kazarinov, Makhail; Keeler, Richard; Kehoe, Robert; Keil, Markus; Keller, John; Kempster, Jacob Julian; Keoshkerian, Houry; Kepka, Oldrich; Kerševan, Borut Paul; Kersten, Susanne; Kessoku, Kohei; Keung, Justin; Khalil-zada, Farkhad; Khandanyan, Hovhannes; Khanov, Alexander; Khodinov, Alexander; Khomich, Andrei; Khoo, Teng Jian; Khoriauli, Gia; Khoroshilov, Andrey; Khovanskiy, Valery; Khramov, Evgeniy; Khubua, Jemal; Kim, Hee Yeun; Kim, Hyeon Jin; Kim, Shinhong; Kimura, Naoki; Kind, Oliver; King, Barry; King, Matthew; King, Robert Steven Beaufoy; King, Samuel Burton; Kirk, Julie; Kiryunin, Andrey; Kishimoto, Tomoe; Kisielewska, Danuta; Kiss, Florian; Kittelmann, Thomas; Kiuchi, Kenji; Kladiva, Eduard; Klein, Max; Klein, Uta; Kleinknecht, Konrad; Klimek, Pawel; Klimentov, Alexei; Klingenberg, Reiner; Klinger, Joel Alexander; Klioutchnikova, Tatiana; Klok, Peter; Kluge, Eike-Erik; Kluit, Peter; Kluth, Stefan; Kneringer, Emmerich; Knoops, Edith; Knue, Andrea; Kobayashi, Dai; Kobayashi, Tomio; Kobel, Michael; Kocian, Martin; Kodys, Peter; Koevesarki, Peter; Koffas, Thomas; Koffeman, Els; Kogan, Lucy Anne; Kohlmann, Simon; Kohout, Zdenek; Kohriki, Takashi; Koi, Tatsumi; Kolanoski, Hermann; Koletsou, Iro; Koll, James; Komar, Aston; Komori, Yuto; Kondo, Takahiko; Kondrashova, Nataliia; Köneke, Karsten; König, Adriaan; König, Sebastian; Kono, Takanori; Konoplich, Rostislav; Konstantinidis, Nikolaos; Kopeliansky, Revital; Koperny, Stefan; Köpke, Lutz; Kopp, Anna Katharina; Korcyl, Krzysztof; Kordas, Kostantinos; Korn, Andreas; Korol, Aleksandr; Korolkov, Ilya; Korolkova, Elena; Korotkov, Vladislav; Kortner, Oliver; Kortner, Sandra; Kostyukhin, Vadim; Kotov, Vladislav; Kotwal, Ashutosh; Kourkoumelis, Christine; Kouskoura, Vasiliki; Koutsman, Alex; Kowalewski, Robert Victor; Kowalski, Tadeusz; Kozanecki, Witold; Kozhin, Anatoly; Kral, Vlastimil; Kramarenko, Viktor; Kramberger, Gregor; Krasnopevtsev, Dimitriy; Krasny, Mieczyslaw Witold; Krasznahorkay, Attila; Kraus, Jana; Kravchenko, Anton; Kreiss, Sven; Kretz, Moritz; Kretzschmar, Jan; Kreutzfeldt, Kristof; Krieger, Peter; Kroeninger, Kevin; Kroha, Hubert; Kroll, Joe; Kroseberg, Juergen; Krstic, Jelena; Kruchonak, Uladzimir; Krüger, Hans; Kruker, Tobias; Krumnack, Nils; Krumshteyn, Zinovii; Kruse, Amanda; Kruse, Mark; Kruskal, Michael; Kubota, Takashi; Kuday, Sinan; Kuehn, Susanne; Kugel, Andreas; Kuhl, Andrew; Kuhl, Thorsten; Kukhtin, Victor; Kulchitsky, Yuri; Kuleshov, Sergey; Kuna, Marine; Kunkle, Joshua; Kupco, Alexander; Kurashige, Hisaya; Kurochkin, Yurii; Kurumida, Rie; Kus, Vlastimil; Kuwertz, Emma Sian; Kuze, Masahiro; Kvita, Jiri; La Rosa, Alessandro; La Rotonda, Laura; Lacasta, Carlos; Lacava, Francesco; Lacey, James; Lacker, Heiko; Lacour, Didier; Lacuesta, Vicente Ramón; Ladygin, Evgueni; Lafaye, Remi; Laforge, Bertrand; Lagouri, Theodota; Lai, Stanley; Laier, Heiko; Lambourne, Luke; Lammers, Sabine; Lampen, Caleb; Lampl, Walter; Lançon, Eric; Landgraf, Ulrich; Landon, Murrough; Lang, Valerie Susanne; Lankford, Andrew; Lanni, Francesco; Lantzsch, Kerstin; Laplace, Sandrine; Lapoire, Cecile; Laporte, Jean-Francois; Lari, Tommaso; Lassnig, Mario; Laurelli, Paolo; Lavrijsen, Wim; Law, Alexander; Laycock, Paul; Le, Bao Tran; Le Dortz, Olivier; Le Guirriec, Emmanuel; Le Menedeu, Eve; LeCompte, Thomas; Ledroit-Guillon, Fabienne Agnes Marie; Lee, Claire Alexandra; Lee, Hurng-Chun; Lee, Jason; Lee, Shih-Chang; Lee, Lawrence; Lefebvre, Guillaume; Lefebvre, Michel; Legger, Federica; Leggett, Charles; Lehan, Allan; Lehmacher, Marc; Lehmann Miotto, Giovanna; Lei, Xiaowen; Leight, William Axel; Leisos, Antonios; Leister, Andrew Gerard; Leite, Marco Aurelio Lisboa; Leitner, Rupert; Lellouch, Daniel; Lemmer, Boris; Leney, Katharine; Lenz, Tatjana; Lenzen, Georg; Lenzi, Bruno; Leone, Robert; Leone, Sandra; Leonhardt, Kathrin; Leonidopoulos, Christos; Leontsinis, Stefanos; Leroy, Claude; Lester, Christopher; Lester, Christopher Michael; Levchenko, Mikhail; Levêque, Jessica; Levin, Daniel; Levinson, Lorne; Levy, Mark; Lewis, Adrian; Lewis, George; Leyko, Agnieszka; Leyton, Michael; Li, Bing; Li, Bo; Li, Haifeng; Li, Ho Ling; Li, Lei; Li, Liang; Li, Shu; Li, Yichen; Liang, Zhijun; Liao, Hongbo; Liberti, Barbara; Lichard, Peter; Lie, Ki; Liebal, Jessica; Liebig, Wolfgang; Limbach, Christian; Limosani, Antonio; Lin, Simon; Lin, Tai-Hua; Linde, Frank; Lindquist, Brian Edward; Linnemann, James; Lipeles, Elliot; Lipniacka, Anna; Lisovyi, Mykhailo; Liss, Tony; Lissauer, David; Lister, Alison; Litke, Alan; Liu, Bo; Liu, Dong; Liu, Jianbei; Liu, Kun; Liu, Lulu; Liu, Miaoyuan; Liu, Minghui; Liu, Yanwen; Livan, Michele; Livermore, Sarah; Lleres, Annick; Llorente Merino, Javier; Lloyd, Stephen; Lo Sterzo, Francesco; Lobodzinska, Ewelina; Loch, Peter; Lockman, William; Loddenkoetter, Thomas; Loebinger, Fred; Loevschall-Jensen, Ask Emil; Loginov, Andrey; Loh, Chang Wei; Lohse, Thomas; Lohwasser, Kristin; Lokajicek, Milos; Lombardo, Vincenzo Paolo; Long, Brian Alexander; Long, Jonathan; Long, Robin Eamonn; Lopes, Lourenco; Lopez Mateos, David; Lopez Paredes, Brais; Lopez Paz, Ivan; Lorenz, Jeanette; Lorenzo Martinez, Narei; Losada, Marta; Loscutoff, Peter; Lou, XinChou; Lounis, Abdenour; Love, Jeremy; Love, Peter; Lowe, Andrew; Lu, Feng; Lubatti, Henry; Luci, Claudio; Lucotte, Arnaud; Luehring, Frederick; Lukas, Wolfgang; Luminari, Lamberto; Lundberg, Olof; Lund-Jensen, Bengt; Lungwitz, Matthias; Lynn, David; Lysak, Roman; Lytken, Else; Ma, Hong; Ma, Lian Liang; Maccarrone, Giovanni; Macchiolo, Anna; Machado Miguens, Joana; Macina, Daniela; Madaffari, Daniele; Madar, Romain; Maddocks, Harvey Jonathan; Mader, Wolfgang; Madsen, Alexander; Maeno, Mayuko; Maeno, Tadashi; Magradze, Erekle; Mahboubi, Kambiz; Mahlstedt, Joern; Mahmoud, Sara; Maiani, Camilla; Maidantchik, Carmen; Maier, Andreas Alexander; Maio, Amélia; Majewski, Stephanie; Makida, Yasuhiro; Makovec, Nikola; Mal, Prolay; Malaescu, Bogdan; Malecki, Pawel; Maleev, Victor; Malek, Fairouz; Mallik, Usha; Malon, David; Malone, Caitlin; Maltezos, Stavros; Malyshev, Vladimir; Malyukov, Sergei; Mamuzic, Judita; Mandelli, Beatrice; Mandelli, Luciano; Mandić, Igor; Mandrysch, Rocco; Maneira, José; Manfredini, Alessandro; Manhaes de Andrade Filho, Luciano; Manjarres Ramos, Joany Andreina; Mann, Alexander; Manning, Peter; Manousakis-Katsikakis, Arkadios; Mansoulie, Bruno; Mantifel, Rodger; Mapelli, Livio; March, Luis; Marchand, Jean-Francois; Marchiori, Giovanni; Marcisovsky, Michal; Marino, Christopher; Marjanovic, Marija; Marques, Carlos; Marroquim, Fernando; Marsden, Stephen Philip; Marshall, Zach; Marti, Lukas Fritz; Marti-Garcia, Salvador; Martin, Brian; Martin, Brian Thomas; Martin, Tim; Martin, Victoria Jane; Martin dit Latour, Bertrand; Martinez, Homero; Martinez, Mario; Martin-Haugh, Stewart; Martyniuk, Alex; Marx, Marilyn; Marzano, Francesco; Marzin, Antoine; Masetti, Lucia; Mashimo, Tetsuro; Mashinistov, Ruslan; Masik, Jiri; Maslennikov, Alexey; Massa, Ignazio; Massol, Nicolas; Mastrandrea, Paolo; Mastroberardino, Anna; Masubuchi, Tatsuya; Mättig, Peter; Mattmann, Johannes; Maurer, Julien; Maxfield, Stephen; Maximov, Dmitriy; Mazini, Rachid; Mazzaferro, Luca; Mc Goldrick, Garrin; Mc Kee, Shawn Patrick; McCarn, Allison; McCarthy, Robert; McCarthy, Tom; McCubbin, Norman; McFarlane, Kenneth; Mcfayden, Josh; Mchedlidze, Gvantsa; McMahon, Steve; McPherson, Robert; Meade, Andrew; Mechnich, Joerg; Medinnis, Michael; Meehan, Samuel; Mehlhase, Sascha; Mehta, Andrew; Meier, Karlheinz; Meineck, Christian; Meirose, Bernhard; Melachrinos, Constantinos; Mellado Garcia, Bruce Rafael; Meloni, Federico; Mengarelli, Alberto; Menke, Sven; Meoni, Evelin; Mercurio, Kevin Michael; Mergelmeyer, Sebastian; Meric, Nicolas; Mermod, Philippe; Merola, Leonardo; Meroni, Chiara; Merritt, Frank; Merritt, Hayes; Messina, Andrea; Metcalfe, Jessica; Mete, Alaettin Serhan; Meyer, Carsten; Meyer, Christopher; Meyer, Jean-Pierre; Meyer, Jochen; Middleton, Robin; Migas, Sylwia; Mijović, Liza; Mikenberg, Giora; Mikestikova, Marcela; Mikuž, Marko; Milic, Adriana; Miller, David; Mills, Corrinne; Milov, Alexander; Milstead, David; Milstein, Dmitry; Minaenko, Andrey; Minashvili, Irakli; Mincer, Allen; Mindur, Bartosz; Mineev, Mikhail; Ming, Yao; Mir, Lluisa-Maria; Mirabelli, Giovanni; Mitani, Takashi; Mitrevski, Jovan; Mitsou, Vasiliki A; Mitsui, Shingo; Miucci, Antonio; Miyagawa, Paul; Mjörnmark, Jan-Ulf; Moa, Torbjoern; Mochizuki, Kazuya; Mohapatra, Soumya; Mohr, Wolfgang; Molander, Simon; Moles-Valls, Regina; Mönig, Klaus; Monini, Caterina; Monk, James; Monnier, Emmanuel; Montejo Berlingen, Javier; Monticelli, Fernando; Monzani, Simone; Moore, Roger; Moraes, Arthur; Morange, Nicolas; Moreno, Deywis; Moreno Llácer, María; Morettini, Paolo; Morgenstern, Marcus; Morii, Masahiro; Moritz, Sebastian; Morley, Anthony Keith; Mornacchi, Giuseppe; Morris, John; Morvaj, Ljiljana; Moser, Hans-Guenther; Mosidze, Maia; Moss, Josh; Motohashi, Kazuki; Mount, Richard; Mountricha, Eleni; Mouraviev, Sergei; Moyse, Edward; Muanza, Steve; Mudd, Richard; Mueller, Felix; Mueller, James; Mueller, Klemens; Mueller, Thibaut; Mueller, Timo; Muenstermann, Daniel; Munwes, Yonathan; Murillo Quijada, Javier Alberto; Murray, Bill; Musheghyan, Haykuhi; Musto, Elisa; Myagkov, Alexey; Myska, Miroslav; Nackenhorst, Olaf; Nadal, Jordi; Nagai, Koichi; Nagai, Ryo; Nagai, Yoshikazu; Nagano, Kunihiro; Nagarkar, Advait; Nagasaka, Yasushi; Nagel, Martin; Nairz, Armin Michael; Nakahama, Yu; Nakamura, Koji; Nakamura, Tomoaki; Nakano, Itsuo; Namasivayam, Harisankar; Nanava, Gizo; Narayan, Rohin; Nattermann, Till; Naumann, Thomas; Navarro, Gabriela; Nayyar, Ruchika; Neal, Homer; Nechaeva, Polina; Neep, Thomas James; Nef, Pascal Daniel; Negri, Andrea; Negri, Guido; Negrini, Matteo; Nektarijevic, Snezana; Nelson, Andrew; Nelson, Timothy Knight; Nemecek, Stanislav; Nemethy, Peter; Nepomuceno, Andre Asevedo; Nessi, Marzio; Neubauer, Mark; Neumann, Manuel; Neves, Ricardo; Nevski, Pavel; Newman, Paul; Nguyen, Duong Hai; Nickerson, Richard; Nicolaidou, Rosy; Nicquevert, Bertrand; Nielsen, Jason; Nikiforou, Nikiforos; Nikiforov, Andriy; Nikolaenko, Vladimir; Nikolic-Audit, Irena; Nikolics, Katalin; Nikolopoulos, Konstantinos; Nilsson, Paul; Ninomiya, Yoichi; Nisati, Aleandro; Nisius, Richard; Nobe, Takuya; Nodulman, Lawrence; Nomachi, Masaharu; Nomidis, Ioannis; Norberg, Scarlet; Nordberg, Markus; Novgorodova, Olga; Nowak, Sebastian; Nozaki, Mitsuaki; Nozka, Libor; Ntekas, Konstantinos; Nunes Hanninger, Guilherme; Nunnemann, Thomas; Nurse, Emily; Nuti, Francesco; O'Brien, Brendan Joseph; O'grady, Fionnbarr; O'Neil, Dugan; O'Shea, Val; Oakham, Gerald; Oberlack, Horst; Obermann, Theresa; Ocariz, Jose; Ochi, Atsuhiko; Ochoa, Ines; Oda, Susumu; Odaka, Shigeru; Ogren, Harold; Oh, Alexander; Oh, Seog; Ohm, Christian; Ohman, Henrik; Ohshima, Takayoshi; Okamura, Wataru; Okawa, Hideki; Okumura, Yasuyuki; Okuyama, Toyonobu; Olariu, Albert; Olchevski, Alexander; Olivares Pino, Sebastian Andres; Oliveira Damazio, Denis; Oliver Garcia, Elena; Olszewski, Andrzej; Olszowska, Jolanta; Onofre, António; Onyisi, Peter; Oram, Christopher; Oreglia, Mark; Oren, Yona; Orestano, Domizia; Orlando, Nicola; Oropeza Barrera, Cristina; Orr, Robert; Osculati, Bianca; Ospanov, Rustem; Otero y Garzon, Gustavo; Otono, Hidetoshi; Ouchrif, Mohamed; Ouellette, Eric; Ould-Saada, Farid; Ouraou, Ahmimed; Oussoren, Koen Pieter; Ouyang, Qun; Ovcharova, Ana; Owen, Mark; Ozcan, Veysi Erkcan; Ozturk, Nurcan; Pachal, Katherine; Pacheco Pages, Andres; Padilla Aranda, Cristobal; Pagáčová, Martina; Pagan Griso, Simone; Paganis, Efstathios; Pahl, Christoph; Paige, Frank; Pais, Preema; Pajchel, Katarina; Palacino, Gabriel; Palestini, Sandro; Palka, Marek; Pallin, Dominique; Palma, Alberto; Palmer, Jody; Pan, Yibin; Panagiotopoulou, Evgenia; Panduro Vazquez, William; Pani, Priscilla; Panikashvili, Natalia; Panitkin, Sergey; Pantea, Dan; Paolozzi, Lorenzo; Papadopoulou, Theodora; Papageorgiou, Konstantinos; Paramonov, Alexander; Paredes Hernandez, Daniela; Parker, Michael Andrew; Parodi, Fabrizio; Parsons, John; Parzefall, Ulrich; Pasqualucci, Enrico; Passaggio, Stefano; Passeri, Antonio; Pastore, Fernanda; Pastore, Francesca; Pásztor, Gabriella; Pataraia, Sophio; Patel, Nikhul; Pater, Joleen; Patricelli, Sergio; Pauly, Thilo; Pearce, James; Pedersen, Maiken; Pedraza Lopez, Sebastian; Pedro, Rute; Peleganchuk, Sergey; Pelikan, Daniel; Peng, Haiping; Penning, Bjoern; Penwell, John; Perepelitsa, Dennis; Perez Codina, Estel; Pérez García-Estañ, María Teresa; Perez Reale, Valeria; Perini, Laura; Pernegger, Heinz; Perrino, Roberto; Peschke, Richard; Peshekhonov, Vladimir; Peters, Krisztian; Peters, Yvonne; Petersen, Brian; Petersen, Troels; Petit, Elisabeth; Petridis, Andreas; Petridou, Chariclia; Petrolo, Emilio; Petrucci, Fabrizio; Pettersson, Nora Emilia; Pezoa, Raquel; Phillips, Peter William; Piacquadio, Giacinto; Pianori, Elisabetta; Picazio, Attilio; Piccaro, Elisa; Piccinini, Maurizio; Piegaia, Ricardo; Pignotti, David; Pilcher, James; Pilkington, Andrew; Pina, João Antonio; Pinamonti, Michele; Pinder, Alex; Pinfold, James; Pingel, Almut; Pinto, Belmiro; Pires, Sylvestre; Pitt, Michael; Pizio, Caterina; Plazak, Lukas; Pleier, Marc-Andre; Pleskot, Vojtech; Plotnikova, Elena; Plucinski, Pawel; Poddar, Sahill; Podlyski, Fabrice; Poettgen, Ruth; Poggioli, Luc; Pohl, David-leon; Pohl, Martin; Polesello, Giacomo; Policicchio, Antonio; Polifka, Richard; Polini, Alessandro; Pollard, Christopher Samuel; Polychronakos, Venetios; Pommès, Kathy; Pontecorvo, Ludovico; Pope, Bernard; Popeneciu, Gabriel Alexandru; Popovic, Dragan; Poppleton, Alan; Portell Bueso, Xavier; Pospisil, Stanislav; Potamianos, Karolos; Potrap, Igor; Potter, Christina; Potter, Christopher; Poulard, Gilbert; Poveda, Joaquin; Pozdnyakov, Valery; Pralavorio, Pascal; Pranko, Aliaksandr; Prasad, Srivas; Pravahan, Rishiraj; Prell, Soeren; Price, Darren; Price, Joe; Price, Lawrence; Prieur, Damien; Primavera, Margherita; Proissl, Manuel; Prokofiev, Kirill; Prokoshin, Fedor; Protopapadaki, Eftychia-sofia; Protopopescu, Serban; Proudfoot, James; Przybycien, Mariusz; Przysiezniak, Helenka; Ptacek, Elizabeth; Puddu, Daniele; Pueschel, Elisa; Puldon, David; Purohit, Milind; Puzo, Patrick; Qian, Jianming; Qin, Gang; Qin, Yang; Quadt, Arnulf; Quarrie, David; Quayle, William; Queitsch-Maitland, Michaela; Quilty, Donnchadha; Qureshi, Anum; Radeka, Veljko; Radescu, Voica; Radhakrishnan, Sooraj Krishnan; Radloff, Peter; Rados, Pere; Ragusa, Francesco; Rahal, Ghita; Rajagopalan, Srinivasan; Rammensee, Michael; Randle-Conde, Aidan Sean; Rangel-Smith, Camila; Rao, Kanury; Rauscher, Felix; Rave, Tobias Christian; Ravenscroft, Thomas; Raymond, Michel; Read, Alexander Lincoln; Readioff, Nathan Peter; Rebuzzi, Daniela; Redelbach, Andreas; Redlinger, George; Reece, Ryan; Reeves, Kendall; Rehnisch, Laura; Reisin, Hernan; Relich, Matthew; Rembser, Christoph; Ren, Huan; Ren, Zhongliang; Renaud, Adrien; Rescigno, Marco; Resconi, Silvia; Rezanova, Olga; Reznicek, Pavel; Rezvani, Reyhaneh; Richter, Robert; Ridel, Melissa; Rieck, Patrick; Rieger, Julia; Rijssenbeek, Michael; Rimoldi, Adele; Rinaldi, Lorenzo; Ritsch, Elmar; Riu, Imma; Rizatdinova, Flera; Rizvi, Eram; Robertson, Steven; Robichaud-Veronneau, Andree; Robinson, Dave; Robinson, James; Robson, Aidan; Roda, Chiara; Rodrigues, Luis; Roe, Shaun; Røhne, Ole; Rolli, Simona; Romaniouk, Anatoli; Romano, Marino; Romero Adam, Elena; Rompotis, Nikolaos; Roos, Lydia; Ros, Eduardo; Rosati, Stefano; Rosbach, Kilian; Rose, Matthew; Rosendahl, Peter Lundgaard; Rosenthal, Oliver; Rossetti, Valerio; Rossi, Elvira; Rossi, Leonardo Paolo; Rosten, Rachel; Rotaru, Marina; Roth, Itamar; Rothberg, Joseph; Rousseau, David; Royon, Christophe; Rozanov, Alexandre; Rozen, Yoram; Ruan, Xifeng; Rubbo, Francesco; Rubinskiy, Igor; Rud, Viacheslav; Rudolph, Christian; Rudolph, Matthew Scott; Rühr, Frederik; Ruiz-Martinez, Aranzazu; Rurikova, Zuzana; Rusakovich, Nikolai; Ruschke, Alexander; Rutherfoord, John; Ruthmann, Nils; Ryabov, Yury; Rybar, Martin; Rybkin, Grigori; Ryder, Nick; Saavedra, Aldo; Sacerdoti, Sabrina; Saddique, Asif; Sadeh, Iftach; Sadrozinski, Hartmut; Sadykov, Renat; Safai Tehrani, Francesco; Sakamoto, Hiroshi; Sakurai, Yuki; Salamanna, Giuseppe; Salamon, Andrea; Saleem, Muhammad; Salek, David; Sales De Bruin, Pedro Henrique; Salihagic, Denis; Salnikov, Andrei; Salt, José; Salvachua Ferrando, Belén; Salvatore, Daniela; Salvatore, Pasquale Fabrizio; Salvucci, Antonio; Salzburger, Andreas; Sampsonidis, Dimitrios; Sanchez, Arturo; Sánchez, Javier; Sanchez Martinez, Victoria; Sandaker, Heidi; Sandbach, Ruth Laura; Sander, Heinz Georg; Sanders, Michiel; Sandhoff, Marisa; Sandoval, Tanya; Sandoval, Carlos; Sandstroem, Rikard; Sankey, Dave; Sansoni, Andrea; Santoni, Claudio; Santonico, Rinaldo; Santos, Helena; Santoyo Castillo, Itzebelt; Sapp, Kevin; Sapronov, Andrey; Saraiva, João; Sarrazin, Bjorn; Sartisohn, Georg; Sasaki, Osamu; Sasaki, Yuichi; Sauvage, Gilles; Sauvan, Emmanuel; Savard, Pierre; Savu, Dan Octavian; Sawyer, Craig; Sawyer, Lee; Saxon, David; Saxon, James; Sbarra, Carla; Sbrizzi, Antonio; Scanlon, Tim; Scannicchio, Diana; Scarcella, Mark; Scarfone, Valerio; Schaarschmidt, Jana; Schacht, Peter; Schaefer, Douglas; Schaefer, Ralph; Schaepe, Steffen; Schaetzel, Sebastian; Schäfer, Uli; Schaffer, Arthur; Schaile, Dorothee; Schamberger, R. Dean; Scharf, Veit; Schegelsky, Valery; Scheirich, Daniel; Schernau, Michael; Scherzer, Max; Schiavi, Carlo; Schieck, Jochen; Schillo, Christian; Schioppa, Marco; Schlenker, Stefan; Schmidt, Evelyn; Schmieden, Kristof; Schmitt, Christian; Schmitt, Christopher; Schmitt, Sebastian; Schneider, Basil; Schnellbach, Yan Jie; Schnoor, Ulrike; Schoeffel, Laurent; Schoening, Andre; Schoenrock, Bradley Daniel; Schorlemmer, Andre Lukas; Schott, Matthias; Schouten, Doug; Schovancova, Jaroslava; Schramm, Steven; Schreyer, Manuel; Schroeder, Christian; Schuh, Natascha; Schultens, Martin Johannes; Schultz-Coulon, Hans-Christian; Schulz, Holger; Schumacher, Markus; Schumm, Bruce; Schune, Philippe; Schwanenberger, Christian; Schwartzman, Ariel; Schwegler, Philipp; Schwemling, Philippe; Schwienhorst, Reinhard; Schwindling, Jerome; Schwindt, Thomas; Schwoerer, Maud; Sciacca, Gianfranco; Scifo, Estelle; Sciolla, Gabriella; Scott, Bill; Scuri, Fabrizio; Scutti, Federico; Searcy, Jacob; Sedov, George; Sedykh, Evgeny; Seidel, Sally; Seiden, Abraham; Seifert, Frank; Seixas, José; Sekhniaidze, Givi; Sekula, Stephen; Selbach, Karoline Elfriede; Seliverstov, Dmitry; Sellers, Graham; Semprini-Cesari, Nicola; Serfon, Cedric; Serin, Laurent; Serkin, Leonid; Serre, Thomas; Seuster, Rolf; Severini, Horst; Sfiligoj, Tina; Sforza, Federico; Sfyrla, Anna; Shabalina, Elizaveta; Shamim, Mansoora; Shan, Lianyou; Shang, Ruo-yu; Shank, James; Shapiro, Marjorie; Shatalov, Pavel; Shaw, Kate; Shehu, Ciwake Yusufu; Sherwood, Peter; Shi, Liaoshan; Shimizu, Shima; Shimmin, Chase Owen; Shimojima, Makoto; Shiyakova, Mariya; Shmeleva, Alevtina; Shochet, Mel; Short, Daniel; Shrestha, Suyog; Shulga, Evgeny; Shupe, Michael; Shushkevich, Stanislav; Sicho, Petr; Sidiropoulou, Ourania; Sidorov, Dmitri; Sidoti, Antonio; Siegert, Frank; Sijacki, Djordje; Silva, José; Silver, Yiftah; Silverstein, Daniel; Silverstein, Samuel; Simak, Vladislav; Simard, Olivier; Simic, Ljiljana; Simion, Stefan; Simioni, Eduard; Simmons, Brinick; Simoniello, Rosa; Simonyan, Margar; Sinervo, Pekka; Sinev, Nikolai; Sipica, Valentin; Siragusa, Giovanni; Sircar, Anirvan; Sisakyan, Alexei; Sivoklokov, Serguei; Sjölin, Jörgen; Sjursen, Therese; Skottowe, Hugh Philip; Skovpen, Kirill; Skubic, Patrick; Slater, Mark; Slavicek, Tomas; Sliwa, Krzysztof; Smakhtin, Vladimir; Smart, Ben; Smestad, Lillian; Smirnov, Sergei; Smirnov, Yury; Smirnova, Lidia; Smirnova, Oxana; Smith, Kenway; Smizanska, Maria; Smolek, Karel; Snesarev, Andrei; Snidero, Giacomo; Snyder, Scott; Sobie, Randall; Socher, Felix; Soffer, Abner; Soh, Dart-yin; Solans, Carlos; Solar, Michael; Solc, Jaroslav; Soldatov, Evgeny; Soldevila, Urmila; Solfaroli Camillocci, Elena; Solodkov, Alexander; Soloshenko, Alexei; Solovyanov, Oleg; Solovyev, Victor; Sommer, Philip; Song, Hong Ye; Soni, Nitesh; Sood, Alexander; Sopczak, Andre; Sopko, Bruno; Sopko, Vit; Sorin, Veronica; Sosebee, Mark; Soualah, Rachik; Soueid, Paul; Soukharev, Andrey; South, David; Spagnolo, Stefania; Spanò, Francesco; Spearman, William Robert; Spettel, Fabian; Spighi, Roberto; Spigo, Giancarlo; Spousta, Martin; Spreitzer, Teresa; Spurlock, Barry; St Denis, Richard Dante; Staerz, Steffen; Stahlman, Jonathan; Stamen, Rainer; Stanecka, Ewa; Stanek, Robert; Stanescu, Cristian; Stanescu-Bellu, Madalina; Stanitzki, Marcel Michael; Stapnes, Steinar; Starchenko, Evgeny; Stark, Jan; Staroba, Pavel; Starovoitov, Pavel; Staszewski, Rafal; Stavina, Pavel; Steinberg, Peter; Stelzer, Bernd; Stelzer, Harald Joerg; Stelzer-Chilton, Oliver; Stenzel, Hasko; Stern, Sebastian; Stewart, Graeme; Stillings, Jan Andre; Stockton, Mark; Stoebe, Michael; Stoicea, Gabriel; Stolte, Philipp; Stonjek, Stefan; Stradling, Alden; Straessner, Arno; Stramaglia, Maria Elena; Strandberg, Jonas; Strandberg, Sara; Strandlie, Are; Strauss, Emanuel; Strauss, Michael; Strizenec, Pavol; Ströhmer, Raimund; Strom, David; Stroynowski, Ryszard; Stucci, Stefania Antonia; Stugu, Bjarne; Styles, Nicholas Adam; Su, Dong; Su, Jun; Subramania, Halasya Siva; Subramaniam, Rajivalochan; Succurro, Antonella; Sugaya, Yorihito; Suhr, Chad; Suk, Michal; Sulin, Vladimir; Sultansoy, Saleh; Sumida, Toshi; Sun, Xiaohu; Sundermann, Jan Erik; Suruliz, Kerim; Susinno, Giancarlo; Sutton, Mark; Suzuki, Yu; Svatos, Michal; Swedish, Stephen; Swiatlowski, Maximilian; Sykora, Ivan; Sykora, Tomas; Ta, Duc; Taccini, Cecilia; Tackmann, Kerstin; Taenzer, Joe; Taffard, Anyes; Tafirout, Reda; Taiblum, Nimrod; Takahashi, Yuta; Takai, Helio; Takashima, Ryuichi; Takeda, Hiroshi; Takeshita, Tohru; Takubo, Yosuke; Talby, Mossadek; Talyshev, Alexey; Tam, Jason; Tan, Kong Guan; Tanaka, Junichi; Tanaka, Reisaburo; Tanaka, Satoshi; Tanaka, Shuji; Tanasijczuk, Andres Jorge; Tannenwald, Benjamin Bordy; Tannoury, Nancy; Tapprogge, Stefan; Tarem, Shlomit; Tarrade, Fabien; Tartarelli, Giuseppe Francesco; Tas, Petr; Tasevsky, Marek; Tashiro, Takuya; Tassi, Enrico; Tavares Delgado, Ademar; Tayalati, Yahya; Taylor, Frank; Taylor, Geoffrey; Taylor, Wendy; Teischinger, Florian Alfred; Teixeira Dias Castanheira, Matilde; Teixeira-Dias, Pedro; Temming, Kim Katrin; Ten Kate, Herman; Teng, Ping-Kun; Teoh, Jia Jian; Terada, Susumu; Terashi, Koji; Terron, Juan; Terzo, Stefano; Testa, Marianna; Teuscher, Richard; Therhaag, Jan; Theveneaux-Pelzer, Timothée; Thomas, Juergen; Thomas-Wilsker, Joshuha; Thompson, Emily; Thompson, Paul; Thompson, Peter; Thompson, Stan; Thomsen, Lotte Ansgaard; Thomson, Evelyn; Thomson, Mark; Thong, Wai Meng; Thun, Rudolf; Tian, Feng; Tibbetts, Mark James; Tikhomirov, Vladimir; Tikhonov, Yury; Timoshenko, Sergey; Tiouchichine, Elodie; Tipton, Paul; Tisserant, Sylvain; Todorov, Theodore; Todorova-Nova, Sharka; Toggerson, Brokk; Tojo, Junji; Tokár, Stanislav; Tokushuku, Katsuo; Tollefson, Kirsten; Tomlinson, Lee; Tomoto, Makoto; Tompkins, Lauren; Toms, Konstantin; Topilin, Nikolai; Torrence, Eric; Torres, Heberth; Torró Pastor, Emma; Toth, Jozsef; Touchard, Francois; Tovey, Daniel; Tran, Huong Lan; Trefzger, Thomas; Tremblet, Louis; Tricoli, Alessandro; Trigger, Isabel Marian; Trincaz-Duvoid, Sophie; Tripiana, Martin; Triplett, Nathan; Trischuk, William; Trocmé, Benjamin; Troncon, Clara; Trottier-McDonald, Michel; Trovatelli, Monica; True, Patrick; Trzebinski, Maciej; Trzupek, Adam; Tsarouchas, Charilaos; Tseng, Jeffrey; Tsiareshka, Pavel; Tsionou, Dimitra; Tsipolitis, Georgios; Tsirintanis, Nikolaos; Tsiskaridze, Shota; Tsiskaridze, Vakhtang; Tskhadadze, Edisher; Tsukerman, Ilya; Tsulaia, Vakhtang; Tsuno, Soshi; Tsybychev, Dmitri; Tudorache, Alexandra; Tudorache, Valentina; Tuna, Alexander Naip; Tupputi, Salvatore; Turchikhin, Semen; Turecek, Daniel; Turk Cakir, Ilkay; Turra, Ruggero; Tuts, Michael; Tykhonov, Andrii; Tylmad, Maja; Tyndel, Mike; Uchida, Kirika; Ueda, Ikuo; Ueno, Ryuichi; Ughetto, Michael; Ugland, Maren; Uhlenbrock, Mathias; Ukegawa, Fumihiko; Unal, Guillaume; Undrus, Alexander; Unel, Gokhan; Ungaro, Francesca; Unno, Yoshinobu; Urbaniec, Dustin; Urquijo, Phillip; Usai, Giulio; Usanova, Anna; Vacavant, Laurent; Vacek, Vaclav; Vachon, Brigitte; Valencic, Nika; Valentinetti, Sara; Valero, Alberto; Valery, Loic; Valkar, Stefan; Valladolid Gallego, Eva; Vallecorsa, Sofia; Valls Ferrer, Juan Antonio; Van Den Wollenberg, Wouter; Van Der Deijl, Pieter; van der Geer, Rogier; van der Graaf, Harry; Van Der Leeuw, Robin; van der Ster, Daniel; van Eldik, Niels; van Gemmeren, Peter; Van Nieuwkoop, Jacobus; van Vulpen, Ivo; van Woerden, Marius Cornelis; Vanadia, Marco; Vandelli, Wainer; Vanguri, Rami; Vaniachine, Alexandre; Vankov, Peter; Vannucci, Francois; Vardanyan, Gagik; Vari, Riccardo; Varnes, Erich; Varol, Tulin; Varouchas, Dimitris; Vartapetian, Armen; Varvell, Kevin; Vazeille, Francois; Vazquez Schroeder, Tamara; Veatch, Jason; Veloso, Filipe; Veneziano, Stefano; Ventura, Andrea; Ventura, Daniel; Venturi, Manuela; Venturi, Nicola; Venturini, Alessio; Vercesi, Valerio; Verducci, Monica; Verkerke, Wouter; Vermeulen, Jos; Vest, Anja; Vetterli, Michel; Viazlo, Oleksandr; Vichou, Irene; Vickey, Trevor; Vickey Boeriu, Oana Elena; Viehhauser, Georg; Viel, Simon; Vigne, Ralph; Villa, Mauro; Villaplana Perez, Miguel; Vilucchi, Elisabetta; Vincter, Manuella; Vinogradov, Vladimir; Virzi, Joseph; Vivarelli, Iacopo; Vives Vaque, Francesc; Vlachos, Sotirios; Vladoiu, Dan; Vlasak, Michal; Vogel, Adrian; Vogel, Marcelo; Vokac, Petr; Volpi, Guido; Volpi, Matteo; von der Schmitt, Hans; von Radziewski, Holger; von Toerne, Eckhard; Vorobel, Vit; Vorobev, Konstantin; Vos, Marcel; Voss, Rudiger; Vossebeld, Joost; Vranjes, Nenad; Vranjes Milosavljevic, Marija; Vrba, Vaclav; Vreeswijk, Marcel; Vu Anh, Tuan; Vuillermet, Raphael; Vukotic, Ilija; Vykydal, Zdenek; Wagner, Peter; Wagner, Wolfgang; Wahlberg, Hernan; Wahrmund, Sebastian; Wakabayashi, Jun; Walder, James; Walker, Rodney; Walkowiak, Wolfgang; Wall, Richard; Waller, Peter; Walsh, Brian; Wang, Chao; Wang, Chiho; Wang, Fuquan; Wang, Haichen; Wang, Hulin; Wang, Jike; Wang, Jin; Wang, Kuhan; Wang, Rui; Wang, Song-Ming; Wang, Tan; Wang, Xiaoxiao; Wanotayaroj, Chaowaroj; Warburton, Andreas; Ward, Patricia; Wardrope, David Robert; Warsinsky, Markus; Washbrook, Andrew; Wasicki, Christoph; Watkins, Peter; Watson, Alan; Watson, Ian; Watson, Miriam; Watts, Gordon; Watts, Stephen; Waugh, Ben; Webb, Samuel; Weber, Michele; Weber, Stefan Wolf; Webster, Jordan S; Weidberg, Anthony; Weigell, Philipp; Weinert, Benjamin; Weingarten, Jens; Weiser, Christian; Weits, Hartger; Wells, Phillippa; Wenaus, Torre; Wendland, Dennis; Weng, Zhili; Wengler, Thorsten; Wenig, Siegfried; Wermes, Norbert; Werner, Matthias; Werner, Per; Wessels, Martin; Wetter, Jeffrey; Whalen, Kathleen; White, Andrew; White, Martin; White, Ryan; White, Sebastian; Whiteson, Daniel; Wicke, Daniel; Wickens, Fred; Wiedenmann, Werner; Wielers, Monika; Wienemann, Peter; Wiglesworth, Craig; Wiik-Fuchs, Liv Antje Mari; Wijeratne, Peter Alexander; Wildauer, Andreas; Wildt, Martin Andre; Wilkens, Henric George; Will, Jonas Zacharias; Williams, Hugh; Williams, Sarah; Willis, Christopher; Willocq, Stephane; Wilson, Alan; Wilson, John; Wingerter-Seez, Isabelle; Winklmeier, Frank; Winter, Benedict Tobias; Wittgen, Matthias; Wittig, Tobias; Wittkowski, Josephine; Wollstadt, Simon Jakob; Wolter, Marcin Wladyslaw; Wolters, Helmut; Wosiek, Barbara; Wotschack, Jorg; Woudstra, Martin; Wozniak, Krzysztof; Wright, Michael; Wu, Mengqing; Wu, Sau Lan; Wu, Xin; Wu, Yusheng; Wulf, Evan; Wyatt, Terry Richard; Wynne, Benjamin; Xella, Stefania; Xiao, Meng; Xu, Da; Xu, Lailin; Yabsley, Bruce; Yacoob, Sahal; Yamada, Miho; Yamaguchi, Hiroshi; Yamaguchi, Yohei; Yamamoto, Akira; Yamamoto, Kyoko; Yamamoto, Shimpei; Yamamura, Taiki; Yamanaka, Takashi; Yamauchi, Katsuya; Yamazaki, Yuji; Yan, Zhen; Yang, Haijun; Yang, Hongtao; Yang, Un-Ki; Yang, Yi; Yanush, Serguei; Yao, Liwen; Yao, Weiming; Yasu, Yoshiji; Yatsenko, Elena; Yau Wong, Kaven Henry; Ye, Jingbo; Ye, Shuwei; Yen, Andy L; Yildirim, Eda; Yilmaz, Metin; Yoosoofmiya, Reza; Yorita, Kohei; Yoshida, Rikutaro; Yoshihara, Keisuke; Young, Charles; Young, Christopher John; Youssef, Saul; Yu, David Ren-Hwa; Yu, Jaehoon; Yu, Jiaming; Yu, Jie; Yuan, Li; Yurkewicz, Adam; Yusuff, Imran; Zabinski, Bartlomiej; Zaidan, Remi; Zaitsev, Alexander; Zaman, Aungshuman; Zambito, Stefano; Zanello, Lucia; Zanzi, Daniele; Zeitnitz, Christian; Zeman, Martin; Zemla, Andrzej; Zengel, Keith; Zenin, Oleg; Ženiš, Tibor; Zerwas, Dirk; Zevi della Porta, Giovanni; Zhang, Dongliang; Zhang, Fangzhou; Zhang, Huaqiao; Zhang, Jinlong; Zhang, Lei; Zhang, Xueyao; Zhang, Zhiqing; Zhao, Zhengguo; Zhemchugov, Alexey; Zhong, Jiahang; Zhou, Bing; Zhou, Lei; Zhou, Ning; Zhu, Cheng Guang; Zhu, Hongbo; Zhu, Junjie; Zhu, Yingchun; Zhuang, Xuai; Zhukov, Konstantin; Zibell, Andre; Zieminska, Daria; Zimine, Nikolai; Zimmermann, Christoph; Zimmermann, Robert; Zimmermann, Simone; Zimmermann, Stephanie; Zinonos, Zinonas; Ziolkowski, Michael; Zobernig, Georg; Zoccoli, Antonio; zur Nedden, Martin; Zurzolo, Giovanni; Zutshi, Vishnu; Zwalinski, Lukasz

2014-09-15

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.

Collaborative Clustering for Sensor Networks

Science.gov (United States)

Wagstaff. Loro :/; Green Jillian; Lane, Terran

2011-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks.

Science.gov (United States)

Aadil, Farhan; Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

2018-05-03

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Directory of Open Access Journals (Sweden)

Farhan Aadil

2018-05-01

Full Text Available Flying ad-hoc networks (FANETs are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

Directory of Open Access Journals (Sweden)

Olszewski Kellen L

2007-07-01

Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

Cluster analysis in phenotyping a Portuguese population.

Science.gov (United States)

Loureiro, C C; Sa-Couto, P; Todo-Bom, A; Bousquet, J

2015-09-03

Unbiased cluster analysis using clinical parameters has identified asthma phenotypes. Adding inflammatory biomarkers to this analysis provided a better insight into the disease mechanisms. This approach has not yet been applied to asthmatic Portuguese patients. To identify phenotypes of asthma using cluster analysis in a Portuguese asthmatic population treated in secondary medical care. Consecutive patients with asthma were recruited from the outpatient clinic. Patients were optimally treated according to GINA guidelines and enrolled in the study. Procedures were performed according to a standard evaluation of asthma. Phenotypes were identified by cluster analysis using Ward's clustering method. Of the 72 patients enrolled, 57 had full data and were included for cluster analysis. Distribution was set in 5 clusters described as follows: cluster (C) 1, early onset mild allergic asthma; C2, moderate allergic asthma, with long evolution, female prevalence and mixed inflammation; C3, allergic brittle asthma in young females with early disease onset and no evidence of inflammation; C4, severe asthma in obese females with late disease onset, highly symptomatic despite low Th2 inflammation; C5, severe asthma with chronic airflow obstruction, late disease onset and eosinophilic inflammation. In our study population, the identified clusters were mainly coincident with other larger-scale cluster analysis. Variables such as age at disease onset, obesity, lung function, FeNO (Th2 biomarker) and disease severity were important for cluster distinction. Copyright © 2015. Published by Elsevier España, S.L.U.

Comparative Investigation of Shared Filesystems for the LHCb Online Cluster

International Nuclear Information System (INIS)

Vijay Kartik, S; Neufeld, Niko

2012-01-01

This paper describes the investigative study undertaken to evaluate shared filesystem performance and suitability in the LHCb Online environment. Particular focus is given to the measurements and field tests designed and performed on an in-house OpenAFS setup; related comparisons with NFSv4 and GPFS (a clustered filesystem from IBM) are presented. The motivation for the investigation and the test setup arises from the need to serve common user-space like home directories, experiment software and control areas, and clustered log areas. Since the operational requirements on such user-space are stringent in terms of read-write operations (in frequency and access speed) and unobtrusive data relocation, test results are presented with emphasis on file-level performance, stability and “high-availability” of the shared filesystems. Use cases specific to the experiment operation in LHCb, including the specific handling of shared filesystems served to a cluster of 1500 diskless nodes, are described. Issues of prematurely expiring authenticated sessions are explicitly addressed, keeping in mind long-running analysis jobs on the Online cluster. In addition, quantitative test results are also presented with alternatives including NFSv4. Comparative measurements of filesystem performance benchmarks are presented, which are seen to be used as reference for decisions on potential migration of the current storage solution deployed in the LHCb online cluster.

ESPRIT-Forest: Parallel clustering of massive amplicon sequence data in subquadratic time.

Science.gov (United States)

Cai, Yunpeng; Zheng, Wei; Yao, Jin; Yang, Yujie; Mai, Volker; Mao, Qi; Sun, Yijun

2017-04-01

The rapid development of sequencing technology has led to an explosive accumulation of genomic sequence data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, it is currently computationally expensive to perform hierarchical clustering of extremely large sequence datasets due to its quadratic time and space complexities. In this paper we developed a new algorithm called ESPRIT-Forest for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method. The basic idea is to organize sequences into a pseudo-metric based partitioning tree for sub-linear time searching of nearest neighbors, and then use a new multiple-pair merging criterion to construct clusters in parallel using multiple threads. The new algorithm was tested on the human microbiome project (HMP) dataset, currently one of the largest published microbial 16S rRNA sequence dataset. Our experiment demonstrated that with the power of parallel computing it is now compu- tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences. The software is available at http://www.acsu.buffalo.edu/∼yijunsun/lab/ESPRIT-Forest.html.

Nuclear cluster states

International Nuclear Information System (INIS)

Rae, W.D.M.; Merchant, A.C.

1993-01-01

We review clustering in light nuclei including molecular resonances in heavy ion reactions. In particular we study the systematics, paying special attention to the relationships between cluster states and superdeformed configurations. We emphasise the selection rules which govern the formation and decay of cluster states. We review some recent experimental results from Daresbury and elsewhere. In particular we report on the evidence for a 7-α chain state in 28 Si in experiments recently performed at the NSF, Daresbury. Finally we begin to address theoretically the important question of the lifetimes of cluster states as deduced from the experimental energy widths of the resonances. (Author)

Mobile clusters of single board computers: an option for providing resources to student projects and researchers.

Science.gov (United States)

Baun, Christian

2016-01-01

Clusters usually consist of servers, workstations or personal computers as nodes. But especially for academic purposes like student projects or scientific projects, the cost for purchase and operation can be a challenge. Single board computers cannot compete with the performance or energy-efficiency of higher-value systems, but they are an option to build inexpensive cluster systems. Because of the compact design and modest energy consumption, it is possible to build clusters of single board computers in a way that they are mobile and can be easily transported by the users. This paper describes the construction of such a cluster, useful applications and the performance of the single nodes. Furthermore, the clusters' performance and energy-efficiency is analyzed by executing the High Performance Linpack benchmark with a different number of nodes and different proportion of the systems total main memory utilized.

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data.

Directory of Open Access Journals (Sweden)

Ali Seyed Shirkhorshidi

Full Text Available Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.

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.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

Science.gov (United States)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION

Directory of Open Access Journals (Sweden)

X. Lin

2017-09-01

Full Text Available Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM to find latent features from high dimensional urban sensing data by “learning” via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

A case study in application I/O on Linux clusters

International Nuclear Information System (INIS)

Ross, R.; Nurmi, D.; Cheng, A.; Zingale, M.

2001-01-01

A critical but often ignored component of system performance is the I/O system. Today's applications expect a great deal from underlying storage systems and software, and both high performance distributed storage and high level interfaces have been developed to fill these needs. In this paper they discuss the I/O performance of a parallel scientific application on a Linux cluster, the FLASH astrophysics code. This application relies on three I/O software components to provide high performance parallel I/O on Linux clusters: the Parallel Virtual File System (PVFS), the ROMIO MPI-IO implementation, and the Hierarchical Data Format (HDF5) library. First they discuss the roles played by each of these components in providing an I/O solution. Next they discuss the FLASH I/O benchmark and point out its relevance. Following this they examine the performance of the benchmark, and through instrumentation of both the application and underlying system software code they discover the location of major software bottlenecks. They work around the most inhibiting of these bottlenecks, showing substantial performance improvement. Finally they point out similarities between the inefficiencies found here and those found in message passing systems, indicating that research in the message passing field could be leveraged to solve similar problems in high-level I/O interfaces

A study on the effect of varying sequence of lab performance skills on lab performance of high school physics students

Science.gov (United States)

Bournia-Petrou, Ethel A.

The main goal of this investigation was to study how student rank in class, student gender and skill sequence affect high school students' performance on the lab skills involved in a laboratory-based inquiry task in physics. The focus of the investigation was the effect of skill sequence as determined by the particular task. The skills considered were: Hypothesis, Procedure, Planning, Data, Graph, Calculations and Conclusion. Three physics lab tasks based on the simple pendulum concept were administered to 282 Regents physics high school students. The reliability of the designed tasks was high. Student performance was evaluated on individual student written responses and a scoring rubric. The tasks had high discrimination power and were of moderate difficulty (65%). It was found that, student performance was weak on Conclusion (42%), Hypothesis (48%), and Procedure (51%), where the numbers in parentheses represent the mean as a percentage of the maximum possible score. Student performance was strong on Calculations (91%), Data (82%), Graph (74%) and Plan (68%). Out of all seven skills, Procedure had the strongest correlation (.73) with the overall task performance. Correlation analysis revealed some strong relationships among the seven skills which were grouped in two distinct clusters: Hypothesis, Procedure and Plan belong to one, and Data, Graph, Calculations, and Conclusion belong to the other. This distinction may indicate different mental processes at play within each skill cluster. The effect of student rank was not statistically significant according to the MANOVA results due to the large variation of rank levels among the participating schools. The effect of gender was significant on the entire test because of performance differences on Calculations and Graph, where male students performed better than female students. Skill sequence had a significant effect on the skills of Procedure, Plan, Data and Conclusion. Students are rather weak in proposing a

Quality Evaluation of Potentilla fruticosa L. by High Performance Liquid Chromatography Fingerprinting Associated with Chemometric Methods.

Science.gov (United States)

Liu, Wei; Wang, Dongmei; Liu, Jianjun; Li, Dengwu; Yin, Dongxue

2016-01-01

The present study was performed to assess the quality of Potentilla fruticosa L. sampled from distinct regions of China using high performance liquid chromatography (HPLC) fingerprinting coupled with a suite of chemometric methods. For this quantitative analysis, the main active phytochemical compositions and the antioxidant activity in P. fruticosa were also investigated. Considering the high percentages and antioxidant activities of phytochemicals, P. fruticosa samples from Kangding, Sichuan were selected as the most valuable raw materials. Similarity analysis (SA) of HPLC fingerprints, hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA) were further employed to provide accurate classification and quality estimates of P. fruticosa. Two principal components (PCs) were collected by PCA. PC1 separated samples from Kangding, Sichuan, capturing 57.64% of the variance, whereas PC2 contributed to further separation, capturing 18.97% of the variance. Two kinds of discriminant functions with a 100% discrimination ratio were constructed. The results strongly supported the conclusion that the eight samples from different regions were clustered into three major groups, corresponding with their morphological classification, for which HPLC analysis confirmed the considerable variation in phytochemical compositions and that P. fruticosa samples from Kangding, Sichuan were of high quality. The results of SA, HCA, PCA, and DA were in agreement and performed well for the quality assessment of P. fruticosa. Consequently, HPLC fingerprinting coupled with chemometric techniques provides a highly flexible and reliable method for the quality evaluation of traditional Chinese medicines.

Quality Evaluation of Potentilla fruticosa L. by High Performance Liquid Chromatography Fingerprinting Associated with Chemometric Methods

Science.gov (United States)

Liu, Wei; Wang, Dongmei; Liu, Jianjun; Li, Dengwu; Yin, Dongxue

2016-01-01

The present study was performed to assess the quality of Potentilla fruticosa L. sampled from distinct regions of China using high performance liquid chromatography (HPLC) fingerprinting coupled with a suite of chemometric methods. For this quantitative analysis, the main active phytochemical compositions and the antioxidant activity in P. fruticosa were also investigated. Considering the high percentages and antioxidant activities of phytochemicals, P. fruticosa samples from Kangding, Sichuan were selected as the most valuable raw materials. Similarity analysis (SA) of HPLC fingerprints, hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA) were further employed to provide accurate classification and quality estimates of P. fruticosa. Two principal components (PCs) were collected by PCA. PC1 separated samples from Kangding, Sichuan, capturing 57.64% of the variance, whereas PC2 contributed to further separation, capturing 18.97% of the variance. Two kinds of discriminant functions with a 100% discrimination ratio were constructed. The results strongly supported the conclusion that the eight samples from different regions were clustered into three major groups, corresponding with their morphological classification, for which HPLC analysis confirmed the considerable variation in phytochemical compositions and that P. fruticosa samples from Kangding, Sichuan were of high quality. The results of SA, HCA, PCA, and DA were in agreement and performed well for the quality assessment of P. fruticosa. Consequently, HPLC fingerprinting coupled with chemometric techniques provides a highly flexible and reliable method for the quality evaluation of traditional Chinese medicines. PMID:26890416

A High-precision Trigonometric Parallax to an Ancient Metal-poor Globular Cluster

Science.gov (United States)

Brown, T. M.; Casertano, S.; Strader, J.; Riess, A.; VandenBerg, D. A.; Soderblom, D. R.; Kalirai, J.; Salinas, R.

2018-03-01

Using the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST), we have obtained a direct trigonometric parallax for the nearest metal-poor globular cluster, NGC 6397. Although trigonometric parallaxes have been previously measured for many nearby open clusters, this is the first parallax for an ancient metal-poor population—one that is used as a fundamental template in many stellar population studies. This high-precision measurement was enabled by the HST/WFC3 spatial-scanning mode, providing hundreds of astrometric measurements for dozens of stars in the cluster and also for Galactic field stars along the same sightline. We find a parallax of 0.418 ± 0.013 ± 0.018 mas (statistical, systematic), corresponding to a true distance modulus of 11.89 ± 0.07 ± 0.09 mag (2.39 ± 0.07 ± 0.10 kpc). The V luminosity at the stellar main-sequence turnoff implies an absolute cluster age of 13.4 ± 0.7 ± 1.2 Gyr. Based on observations made with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with programs GO-13817, GO-14336, and GO-14773.

Cluster-state quantum computing enhanced by high-fidelity generalized measurements.

Science.gov (United States)

Biggerstaff, D N; Kaltenbaek, R; Hamel, D R; Weihs, G; Rudolph, T; Resch, K J

2009-12-11

We introduce and implement a technique to extend the quantum computational power of cluster states by replacing some projective measurements with generalized quantum measurements (POVMs). As an experimental demonstration we fully realize an arbitrary three-qubit cluster computation by implementing a tunable linear-optical POVM, as well as fast active feedforward, on a two-qubit photonic cluster state. Over 206 different computations, the average output fidelity is 0.9832+/-0.0002; furthermore the error contribution from our POVM device and feedforward is only of O(10(-3)), less than some recent thresholds for fault-tolerant cluster computing.

Neuro- and social-cognitive clustering highlights distinct profiles in adults with anorexia nervosa.

Science.gov (United States)

Renwick, Beth; Musiat, Peter; Lose, Anna; DeJong, Hannah; Broadbent, Hannah; Kenyon, Martha; Loomes, Rachel; Watson, Charlotte; Ghelani, Shreena; Serpell, Lucy; Richards, Lorna; Johnson-Sabine, Eric; Boughton, Nicky; Treasure, Janet; Schmidt, Ulrike

2015-01-01

This study aimed to explore the neuro- and social-cognitive profile of a consecutive series of adult outpatients with anorexia nervosa (AN) when compared with widely available age and gender matched historical control data. The relationship between performance profiles, clinical characteristics, service utilization, and treatment adherence was also investigated. Consecutively recruited outpatients with a broad diagnosis of AN (restricting subtype AN-R: n = 44, binge-purge subtype AN-BP: n = 33 or Eating Disorder Not Otherwise Specified-AN subtype EDNOS-AN: n = 23) completed a comprehensive set of neurocognitive (set-shifting, central coherence) and social-cognitive measures (Emotional Theory of Mind). Data were subjected to hierarchical cluster analysis and a discriminant function analysis. Three separate, meaningful clusters emerged. Cluster 1 (n = 45) showed overall average to high average neuro- and social- cognitive performance, Cluster 2 (n = 38) showed mixed performance characterized by distinct strengths and weaknesses, and Cluster 3 (n = 17) showed poor overall performance (Autism Spectrum disorder (ASD) like cluster). The three clusters did not differ in terms of eating disorder symptoms, comorbid features or service utilization and treatment adherence. A discriminant function analysis confirmed that the clusters were best characterized by performance in perseveration and set-shifting measures. The findings suggest that considerable neuro- and social-cognitive heterogeneity exists in patients with AN, with a subset showing ASD-like features. The value of this method of profiling in predicting longer term patient outcomes and in guiding development of etiologically targeted treatments remains to be seen. © 2014 Wiley Periodicals, Inc.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

Directory of Open Access Journals (Sweden)

Ashlock Daniel

2009-08-01

Full Text Available Abstract Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering.

Science.gov (United States)

Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu

2009-08-22

Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium

Science.gov (United States)

Schran, Christoph; Uhl, Felix; Behler, Jörg; Marx, Dominik

2018-03-01

The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to the rather weak but strongly anisotropic nature of the interactions. We show that this challenge can be met by using a combination of an effective pair potential for the He-He interactions and a flexible high-dimensional neural network potential (NNP) for describing the complex interaction between helium and the solute in a pairwise additive manner. This approach yields an excellent agreement with a mean absolute deviation as small as 0.04 kJ mol-1 for the interaction energy between helium and both hydronium and Zundel cations compared with coupled cluster reference calculations with an energetically converged basis set. The construction and improvement of the potential can be performed in a highly automated way, which opens the door for applications to a variety of reactive molecules to study the effect of solvation on the solute as well as the solute-induced structuring of the solvent. Furthermore, we show that this NNP approach yields very convincing agreement with the coupled cluster reference for properties like many-body spatial and radial distribution functions. This holds for the microsolvation of the protonated water monomer and dimer by a few helium atoms up to their solvation in bulk helium as obtained from path integral simulations at about 1 K.

High-resolution tomography of positron emitters with clustered pinhole SPECT

Energy Technology Data Exchange (ETDEWEB)

Goorden, Marlies C; Beekman, Freek J [Section of Radiation Detection and Medical Imaging, Applied Sciences, Delft University of Technology, Mekelweg 15, 2629 JB Delft (Netherlands)], E-mail: m.c.goorden@tudelft.nl

2010-03-07

State-of-the-art small-animal single photon emission computed tomography (SPECT) enables sub-half-mm resolution imaging of radio-labelled molecules. Due to severe photon penetration through pinhole edges, current multi-pinhole SPECT is not suitable for high-resolution imaging of photons with high energies, such as the annihilation photons emitted by positron emitting tracers (511 keV). To deal with this edge penetration, we introduce here clustered multi-pinhole SPECT (CMP): each pinhole in a cluster has a narrow opening angle to reduce photon penetration. Using simulations, CMP is compared with (i) a collimator with traditional pinholes that is currently used for sub-half-mm imaging of SPECT isotopes (U-SPECT-II), and (ii), like (i) but with collimator thickness adapted to image high-energy photons (traditional multi-pinhole SPECT, TMP). At 511 keV, U-SPECT-II is able to resolve the 0.9 mm rods of an iteratively reconstructed Jaszczak-like capillary hot rod phantom, and while TMP only leads to small improvements, CMP can resolve rods as small as 0.7 mm. Using a digital tumour phantom, we show that CMP resolves many details not assessable with standard USPECT-II and TMP collimators. Furthermore, CMP makes it possible to visualize uptake of positron emitting tracers in sub-compartments of a digital mouse striatal brain phantom. This may open up unique possibilities for analysing processes such as those underlying the function of neurotransmitter systems. Additional potential of CMP may include (i) the imaging of other high-energy single-photon emitters (e.g. I-131) and (ii) localized imaging of positron emitting tracers simultaneously with single photon emitters, with an even better resolution than coincidence PET.

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

Science.gov (United States)

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

New Heterogeneous Clustering Protocol for Prolonging Wireless Sensor Networks Lifetime

Directory of Open Access Journals (Sweden)

Md. Golam Rashed

2014-06-01

Full Text Available Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. The network characteristics of existing classical clustering protocols for wireless sensor network are homogeneous. Clustering protocols fail to maintain the stability of the system, especially when nodes are heterogeneous. We have seen that the behavior of Heterogeneous-Hierarchical Energy Aware Routing Protocol (H-HEARP becomes very unstable once the first node dies, especially in the presence of node heterogeneity. In this paper we assume a new clustering protocol whose network characteristics is heterogeneous for prolonging of network lifetime. The computer simulation results demonstrate that the proposed clustering algorithm outperforms than other clustering algorithms in terms of the time interval before the death of the first node (we refer to as stability period. The simulation results also show the high performance of the proposed clustering algorithm for higher values of extra energy brought by more powerful nodes.

Clustering on Membranes

DEFF Research Database (Denmark)

Johannes, Ludger; Pezeshkian, Weria; Ipsen, John H

2018-01-01

Clustering of extracellular ligands and proteins on the plasma membrane is required to perform specific cellular functions, such as signaling and endocytosis. Attractive forces that originate in perturbations of the membrane's physical properties contribute to this clustering, in addition to direct...... protein-protein interactions. However, these membrane-mediated forces have not all been equally considered, despite their importance. In this review, we describe how line tension, lipid depletion, and membrane curvature contribute to membrane-mediated clustering. Additional attractive forces that arise...... from protein-induced perturbation of a membrane's fluctuations are also described. This review aims to provide a survey of the current understanding of membrane-mediated clustering and how this supports precise biological functions....

High Speed White Dwarf Asteroseismology with the Herty Hall Cluster

Science.gov (United States)

Gray, Aaron; Kim, A.

2012-01-01

Asteroseismology is the process of using observed oscillations of stars to infer their interior structure. In high speed asteroseismology, we complete that by quickly computing hundreds of thousands of models to match the observed period spectra. Each model on a single processor takes five to ten seconds to run. Therefore, we use a cluster of sixteen Dell Workstations with dual-core processors. The computers use the Ubuntu operating system and Apache Hadoop software to manage workloads.

Cluster observations of the high-latitude magnetopause and cusp: initial results from the CIS ion instruments

Directory of Open Access Journals (Sweden)

J. M. Bosqued

Full Text Available Launched on an elliptical high inclination orbit (apogee: 19.6 R_E since January 2001 the Cluster satellites have been conducting the first detailed three-dimensional studies of the high-latitude dayside magnetosphere, including the exterior cusp, neighbouring boundary layers and magnetopause regions. Cluster satellites carry the CIS ion spectrometers that provide high-precision, 3D distributions of low-energy (<35 keV/e ions every 4 s. This paper presents the first two observations of the cusp and/or magnetopause behaviour made under different interplanetary magnetic field (IMF conditions. Flow directions, 3D distribution functions, density profiles and ion composition profiles are analyzed to demonstrate the high variability of high-latitude regions. In the first crossing analyzed (26 January 2001, dusk side, IMF-BZ < 0, multiple, isolated boundary layer, magnetopause and magnetosheath encounters clearly occurred on a quasi-steady basis for ~ 2 hours. CIS ion instruments show systematic accelerated flows in the current layer and adjacent boundary layers on the Earthward side of the magnetopause. Multi-point analysis of the magnetopause, combining magnetic and plasma data from the four Cluster spacecraft, demonstrates that oscillatory outward-inward motions occur with a normal speed of the order of ± 40 km/s; the thickness of the high-latitude current layer is evaluated to be of the order of 900–1000 km. Alfvénic accelerated flows and D-shaped distributions are convincing signatures of a magnetic reconnection occurring equatorward of the Cluster satellites. Moreover, the internal magnetic and plasma structure of a flux transfer event (FTE is analyzed in detail; its size along the magnetopause surface is ~ 12 000 km and it convects with a velocity of ~ 200 km/s. The second event analyzed (2 February 2001 corresponds to the first Cluster pass within the cusp when the IMF-BZ component was northward directed. The analysis of