Science.gov

Sample records for large sharing networks

  1. Fixed Access Network Sharing

    NASA Astrophysics Data System (ADS)

    Cornaglia, Bruno; Young, Gavin; Marchetta, Antonio

    2015-12-01

    Fixed broadband network deployments are moving inexorably to the use of Next Generation Access (NGA) technologies and architectures. These NGA deployments involve building fiber infrastructure increasingly closer to the customer in order to increase the proportion of fiber on the customer's access connection (Fibre-To-The-Home/Building/Door/Cabinet… i.e. FTTx). This increases the speed of services that can be sold and will be increasingly required to meet the demands of new generations of video services as we evolve from HDTV to "Ultra-HD TV" with 4k and 8k lines of video resolution. However, building fiber access networks is a costly endeavor. It requires significant capital in order to cover any significant geographic coverage. Hence many companies are forming partnerships and joint-ventures in order to share the NGA network construction costs. One form of such a partnership involves two companies agreeing to each build to cover a certain geographic area and then "cross-selling" NGA products to each other in order to access customers within their partner's footprint (NGA coverage area). This is tantamount to a bi-lateral wholesale partnership. The concept of Fixed Access Network Sharing (FANS) is to address the possibility of sharing infrastructure with a high degree of flexibility for all network operators involved. By providing greater configuration control over the NGA network infrastructure, the service provider has a greater ability to define the network and hence to define their product capabilities at the active layer. This gives the service provider partners greater product development autonomy plus the ability to differentiate from each other at the active network layer.

  2. Expansible quantum secret sharing network

    NASA Astrophysics Data System (ADS)

    Sun, Ying; Xu, Sheng-Wei; Chen, Xiu-Bo; Niu, Xin-Xin; Yang, Yi-Xian

    2013-08-01

    In the practical applications, member expansion is a usual demand during the development of a secret sharing network. However, there are few consideration and discussion on network expansibility in the existing quantum secret sharing schemes. We propose an expansible quantum secret sharing scheme with relatively simple and economical quantum resources and show how to split and reconstruct the quantum secret among an expansible user group in our scheme. Its trait, no requirement of any agent's assistant during the process of member expansion, can help to prevent potential menaces of insider cheating. We also give a discussion on the security of this scheme from three aspects.

  3. Sharing Writing through Computer Networking.

    ERIC Educational Resources Information Center

    Fey, Marion H.

    1997-01-01

    Suggests computer networking can support the essential purposes of the collaborative-writing movement, offering opportunities for sharing writing. Notes that literacy teachers are exploring the connectivity of computer networking through numerous designs that use either real-time or asynchronous communication. Discusses new roles for students and…

  4. Data sharing and intellectual property in a genomic epidemiology network: policies for large-scale research collaboration.

    PubMed Central

    Chokshi, Dave A.; Parker, Michael; Kwiatkowski, Dominic P.

    2006-01-01

    Genomic epidemiology is a field of research that seeks to improve the prevention and management of common diseases through an understanding of their molecular origins. It involves studying thousands of individuals, often from different populations, with exacting techniques. The scale and complexity of such research has required the formation of research consortia. Members of these consortia need to agree on policies for managing shared resources and handling genetic data. Here we consider data-sharing and intellectual property policies for an international research consortium working on the genomic epidemiology of malaria. We outline specific guidelines governing how samples and data are transferred among its members; how results are released into the public domain; when to seek protection for intellectual property; and how intellectual property should be managed. We outline some pragmatic solutions founded on the basic principles of promoting innovation and access. PMID:16710548

  5. Analysis and improvement of vehicle information sharing networks

    NASA Astrophysics Data System (ADS)

    Gong, Hang; He, Kun; Qu, Yingchun; Wang, Pu

    2016-06-01

    Based on large-scale mobile phone data, mobility demand was estimated and locations of vehicles were inferred in the Boston area. Using the spatial distribution of vehicles, we analyze the vehicle information sharing network generated by the vehicle-to-vehicle (V2V) communications. Although a giant vehicle cluster is observed, the coverage and the efficiency of the information sharing network remain limited. Consequently, we propose a method to extend the information sharing network's coverage by adding long-range connections between targeted vehicle clusters. Furthermore, we employ the optimal design strategy discovered in square lattice to improve the efficiency of the vehicle information sharing network.

  6. Knowledge Searching and Sharing on Virtual Networks.

    ERIC Educational Resources Information Center

    Helokunnas, Tuija; Herrala, Juha

    2001-01-01

    Describes searching and sharing of knowledge on virtual networks, based on experiences gained when hosting virtual knowledge networks at Tampere University of Technology in Finland. Discusses information and knowledge management studies; role of information technology in knowledge searching and sharing; implementation and experiences of the…

  7. Designing a Networked-Sharing Construction Environment.

    ERIC Educational Resources Information Center

    Lin, Sunny; Sun, Chuen-Tsai; Kao, Gloria

    2002-01-01

    Discusses group learning on the Internet and problems of unequal participation and describes the development of a Web-based learning system called NetShare (Networked Sharing Construction Environment) that uses a cooperative-competitive learning strategy to facilitate participants' equal contributions. Reports preliminary results of a study with…

  8. Traffic sharing algorithms for hybrid mobile networks

    NASA Technical Reports Server (NTRS)

    Arcand, S.; Murthy, K. M. S.; Hafez, R.

    1995-01-01

    In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

  9. Sharing Clouds: Showing, Distributing, and Sharing Large Point Datasets

    NASA Astrophysics Data System (ADS)

    Grigsby, S.

    2012-12-01

    Sharing large data sets with colleagues and the general public presents a unique technological challenge for scientists. In addition to large data volumes, there are significant challenges in representing data that is often irregular, multidimensional and spatial in nature. For derived data products, additional challenges exist in displaying and providing provenance data. For this presentation, several open source technologies are demonstrated for the remote display and access of large irregular point data sets. These technologies and techniques include the remote viewing of point data using HTML5 and OpenGL, which provides a highly accessible preview of the data sets for a range of audiences. Intermediate levels of accessibility and high levels of interactivity are accomplished with technologies such as wevDAV, which allows collaborators to run analysis on local clients, using data stored and administered on remote servers. Remote processing and analysis, including provenance tracking, will be discussed at the workgroup level. The data sets used for this presentation include data acquired from the NSF funded National Center for Airborne Laser Mapping (NCALM), and data acquired for research and instructional use in NASA's Student Airborne Research Program (SARP). These datasets include Light Ranging And Detection (LiDAR) point clouds ranging in size from several hundred thousand to several hundred million data points; the techniques and technologies discussed are applicable to other forms of irregular point data.

  10. Network Adaptive Deadband: NCS Data Flow Control for Shared Networks

    PubMed Central

    Díaz-Cacho, Miguel; Delgado, Emma; Prieto, José A. G.; López, Joaquín

    2012-01-01

    This paper proposes a new middleware solution called Network Adaptive Deadband (NAD) for long time operation of Networked Control Systems (NCS) through the Internet or any shared network based on IP technology. The proposed middleware takes into account the network status and the NCS status, to improve the global system performance and to share more effectively the network by several NCS and sensor/actuator data flows. Relationship between network status and NCS status is solved with a TCP-friendly transport flow control protocol and the deadband concept, relating deadband value and transmission throughput. This creates a deadband-based flow control solution. Simulation and experiments in shared networks show that the implemented network adaptive deadband has better performance than an optimal constant deadband solution in the same circumstances. PMID:23208556

  11. Data sharing in the Undiagnosed Diseases Network

    PubMed Central

    Brownstein, Catherine A.; Holm, Ingrid A.; Ramoni, Rachel; Goldstein, David B.

    2015-01-01

    The Undiagnosed Diseases Network (UDN), builds on the successes of the Undiagnosed Diseases Program at the National Institutes of Health (NIH UDP). Through support from the NIH Common Fund, a coordinating center, six additional clinical sites, and two sequencing cores comprise the UDN. The objectives of the UDN are to: (1) improve the level of diagnosis and care for patients with undiagnosed diseases through the development of common protocols designed by an enlarged community of investigators across the Network; (2) facilitate research into the etiology of undiagnosed diseases, by collecting and sharing standardized, high-quality clinical and laboratory data including genotyping, phenotyping, and environmental exposure data; and (3) create an integrated and collaborative research community across multiple clinical sites, and among laboratory and clinical investigators, to investigate the pathophysiology of these rare diseases and to identify options for patient management. Broad-based data sharing is at the core of achieving these objectives, and the UDN is establishing the policies and governance structure to support broad data sharing. PMID:26220576

  12. 47 CFR 27.1305 - Shared wireless broadband network.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 47 Telecommunication 2 2012-10-01 2012-10-01 false Shared wireless broadband network. 27.1305... MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES 700 MHz Public/Private Partnership § 27.1305 Shared wireless broadband network. The Shared Wireless Broadband Network developed by the 700 MHz Public/Private...

  13. 47 CFR 90.1405 - Shared wireless broadband network.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 47 Telecommunication 5 2012-10-01 2012-10-01 false Shared wireless broadband network. 90.1405... SERVICES PRIVATE LAND MOBILE RADIO SERVICES 700 MHz Public/Private Partnership § 90.1405 Shared wireless broadband network. The Shared Wireless Broadband Network developed by the 700 MHz Public/Private...

  14. 47 CFR 27.1305 - Shared wireless broadband network.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 47 Telecommunication 2 2011-10-01 2011-10-01 false Shared wireless broadband network. 27.1305... MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES 700 MHz Public/Private Partnership § 27.1305 Shared wireless broadband network. The Shared Wireless Broadband Network developed by the 700 MHz Public/Private...

  15. 47 CFR 90.1405 - Shared wireless broadband network.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 47 Telecommunication 5 2011-10-01 2011-10-01 false Shared wireless broadband network. 90.1405... SERVICES PRIVATE LAND MOBILE RADIO SERVICES 700 MHz Public/Private Partnership § 90.1405 Shared wireless broadband network. The Shared Wireless Broadband Network developed by the 700 MHz Public/Private...

  16. 47 CFR 90.1410 - Network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 47 Telecommunication 5 2011-10-01 2011-10-01 false Network sharing agreement. 90.1410 Section 90... PRIVATE LAND MOBILE RADIO SERVICES 700 MHz Public/Private Partnership § 90.1410 Network sharing agreement... related entities as the Commission may require or allow will be governed by the Network Sharing...

  17. 47 CFR 90.1410 - Network sharing agreement.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 47 Telecommunication 5 2010-10-01 2010-10-01 false Network sharing agreement. 90.1410 Section 90... PRIVATE LAND MOBILE RADIO SERVICES 700 MHz Public/Private Partnership § 90.1410 Network sharing agreement... related entities as the Commission may require or allow will be governed by the Network Sharing...

  18. 47 CFR 27.1305 - Shared wireless broadband network.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 47 Telecommunication 2 2010-10-01 2010-10-01 false Shared wireless broadband network. 27.1305... broadband network. The Shared Wireless Broadband Network developed by the 700 MHz Public/Private Partnership must be designed to meet requirements associated with a nationwide, public safety broadband network....

  19. 47 CFR 90.1405 - Shared wireless broadband network.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 47 Telecommunication 5 2010-10-01 2010-10-01 false Shared wireless broadband network. 90.1405... broadband network. The Shared Wireless Broadband Network developed by the 700 MHz Public/Private Partnership must be designed to meet requirements associated with a nationwide, public safety broadband network....

  20. Secure Peer-to-Peer Networks for Scientific Information Sharing

    NASA Technical Reports Server (NTRS)

    Karimabadi, Homa

    2012-01-01

    The most common means of remote scientific collaboration today includes the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. With the growth of broadband Internet, there has been a desire to share large files (movies, files, scientific data files) over the Internet. Email has limits on the size of files that can be attached and transmitted. FTP is often used to share large files, but this requires the user to set up an FTP site for which it is hard to set group privileges, it is not straightforward for everyone, and the content is not searchable. Peer-to-peer technology (P2P), which has been overwhelmingly successful in popular content distribution, is the basis for development of a scientific collaboratory called Scientific Peer Network (SciPerNet). This technology combines social networking with P2P file sharing. SciPerNet will be a standalone application, written in Java and Swing, thus insuring portability to a number of different platforms. Some of the features include user authentication, search capability, seamless integration with a data center, the ability to create groups and social networks, and on-line chat. In contrast to P2P networks such as Gnutella, Bit Torrent, and others, SciPerNet incorporates three design elements that are critical to application of P2P for scientific purposes: User authentication, Data integrity validation, Reliable searching SciPerNet also provides a complementary solution to virtual observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase scientific returns from NASA missions. As such, SciPerNet can serve a two-fold purpose for NASA: a cost-savings software as well as a productivity tool for scientists working with data from NASA missions.

  1. Querying Large Biological Network Datasets

    ERIC Educational Resources Information Center

    Gulsoy, Gunhan

    2013-01-01

    New experimental methods has resulted in increasing amount of genetic interaction data to be generated every day. Biological networks are used to store genetic interaction data gathered. Increasing amount of data available requires fast large scale analysis methods. Therefore, we address the problem of querying large biological network datasets.…

  2. 47 CFR 27.1310 - Network sharing agreement.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 47 Telecommunication 2 2010-10-01 2010-10-01 false Network sharing agreement. 27.1310 Section 27... MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES 700 MHz Public/Private Partnership § 27.1310 Network sharing..., and related entities as the Commission may require or allow will be governed by the Network...

  3. Learning Theory for Collaborative Large Shared Digital Spaces

    ERIC Educational Resources Information Center

    McGivern, Daniela; Morgan, Michael; Butler, Matthew

    2012-01-01

    This research applies Socio-Cultural theory and Distributed Cognition/Activity theory to conceptualize the design of collaborative learning activities in large shared digital spaces. The paper begins by providing a summary of previous work in the creation of a technology platform for large shared digital spaces. It then details how Socio-Cultural…

  4. Shared governance in an integrated health care network.

    PubMed

    Batson, Vicki

    2004-09-01

    The nursing practice model of shared governance is a key strategy for enhancing work environments in Magnet hospitals. Shared governance is a dynamic process that promotes collaboration, shared decision making, and accountability for practice through workforce empowerment. Although the principles of shared governance are universal, structure and process generally follow the needs of an organization based on its core values, mission, vision, and philosophy. This article details the shared governance structure of one health care network.

  5. Shared Access Optical Networks For The Local Loop

    NASA Astrophysics Data System (ADS)

    Payne, D. B.; Stern, J. R.

    1988-09-01

    The application of single mode fibre to the local network environment opens up major opportunities for service provision via shared access networks. Previous technologies (copper pair, coaxial cable and multimode fibre) had bandwidth limitation problems that placed a severe restriction on both the level of resource sharing and the service package that could be delivered. The enormous bandwidth capability of single mode fibre can be used to provide significant resource sharing without incurring fundamental restrictions on the capacity of the services carried. The paper briefly outlines some of the activities within British Telecom on shared access systems. Early systems concepts were either based on fibre feeders to remote multiplexers for the delivery of telephony and data services to large customers or the use of advanced wavelength multiplexing techniques over passive optical networks for the transmission of wideband services to business and residential customers. Recently activity has concentrated on a passive optical network that shows good potential for the economic provision of telephony services. The structure of the network allows the later addition of broadband services via additional wavelengths without disturbing existing telephony/data customers. The basic network has a fibre feeder from the exchange to passive optical splitters housed at the Cabinet and Distribution Points (DP). Each customer receives a fibre from DP and via this a TDM multiplex broadcast from the exchange which carries the customer's traffic. The customer equipment accesses the time slots destined for the customer and delivers the data via a suitable interface to provide the services required. Customers transmit back to the exchange in a time multiplex synchronised by a ranging protocol that sets an appropriate delay in the customer equipment to avoid collisions at the optical combiners in the DPs and Cabinet. Present studies are considering a total optical split of 128 ways with a

  6. 47 CFR 27.1310 - Network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES 700 MHz Public/Private Partnership § 27.1310 Network sharing... Commission for prior approval. All other modifications must be submitted to the Chiefs of the Wireless... valuation methodology to determine the fair market value of the shared wireless broadband network assets....

  7. Shared Service Center vs. Shared Service Network: A Multiple Case Study Analysis of Factors Impacting on Shared Service Configurations

    NASA Astrophysics Data System (ADS)

    Becker, Jörg; Niehaves, Björn; Krause, Andreas

    Shared services have proven to be a key element when it comes to increasing government efficiency by collaboration. Here, we seek to investigate into the shared services phenomenon in the context of government reforms. For this purpose, an interview and document analysis-based multiple case study has been conducted in Germany. The qualitative analysis covers two shared service implementations on the local government level and identifies important preconditions for shared service emergence, namely cost pressure as motive, the existence of key actors promoting the topic and the existence of prior cooperation. Moreover, it is shown that the structure of such previous cooperation determines, if shared services are being organised in a centralised (shared service centre) or decentralised format (shared service network).

  8. 77 FR 50712 - Information Collection: Southern Alaska Sharing Network and Subsistence Study; Proposed...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-08-22

    ... Bureau of Ocean Energy Management Information Collection: Southern Alaska Sharing Network and Subsistence... in Alaska, ``Southern Alaska Sharing Network and Subsistence Study.'' DATES: Submit written comments.... Title: Southern Alaska Sharing Network and Subsistence Study. Abstract: The Bureau of Ocean...

  9. Complex Dynamics in Information Sharing Networks

    NASA Astrophysics Data System (ADS)

    Cronin, Bruce

    This study examines the roll-out of an electronic knowledge base in a medium-sized professional services firm over a six year period. The efficiency of such implementation is a key business problem in IT systems of this type. Data from usage logs provides the basis for analysis of the dynamic evolution of social networks around the depository during this time. The adoption pattern follows an "s-curve" and usage exhibits something of a power law distribution, both attributable to network effects, and network position is associated with organisational performance on a number of indicators. But periodicity in usage is evident and the usage distribution displays an exponential cut-off. Further analysis provides some evidence of mathematical complexity in the periodicity. Some implications of complex patterns in social network data for research and management are discussed. The study provides a case study demonstrating the utility of the broad methodological approach.

  10. Food-Sharing Networks in Lamalera, Indonesia: Status, Sharing, and Signaling

    PubMed Central

    Nolin, David A.

    2012-01-01

    Costly signaling has been proposed as a possible mechanism to explain food sharing in foraging populations. This sharing-as-signaling hypothesis predicts an association between sharing and status. Using exponential random graph modeling (ERGM), this prediction is tested on a social network of between-household food-sharing relationships in the fishing and sea-hunting village of Lamalera, Indonesia. Previous analyses (Nolin 2010) have shown that most sharing in Lamalera is consistent with reciprocal altruism. The question addressed here is whether any additional variation may be explained as sharing-as-signaling by high-status households. The results show that high-status households both give and receive more than other households, a pattern more consistent with reciprocal altruism than costly signaling. However, once the propensity to reciprocate and household productivity are controlled, households of men holding leadership positions show greater odds of unreciprocated giving when compared to households of non-leaders. This pattern of excessive giving by leaders is consistent with the sharing-as-signaling hypothesis. Wealthy households show the opposite pattern, giving less and receiving more than other households. These households may reciprocate in a currency other than food or their wealth may attract favor-seeking behavior from others. Overall, status covariates explain little variation in the sharing network as a whole, and much of the sharing observed by high-status households is best explained by the same factors that explain sharing by other households. This pattern suggests that multiple mechanisms may operate simultaneously to promote sharing in Lamalera and that signaling may motivate some sharing by some individuals even within sharing regimes primarily maintained by other mechanisms. PMID:22822299

  11. Information Sharing in a Nonprofit Network

    ERIC Educational Resources Information Center

    Stoll, Jennifer

    2012-01-01

    The civil rights and other social justice movements, neighborhood watches, local garden cooperatives, and so forth are examples of a grassroots context that is largely understudied in CSCW. In recent history, movements to fight child sex trafficking, end hunger in New York City, advocate for financial reform, or even overthrow governments have…

  12. Network discovery with large DCMs

    PubMed Central

    Seghier, Mohamed L.; Friston, Karl J.

    2013-01-01

    In this work, we address the problem of using dynamic causal modelling (DCM) to estimate the coupling parameters (effective connectivity) of large models with many regions. This is a potentially important problem because meaningful graph theoretic analyses of effective connectivity rest upon the statistics of the connections (edges). This calls for characterisations of networks with an appreciable number of regions (nodes). The problem here is that the number of coupling parameters grows quadratically with the number of nodes—leading to severe conditional dependencies among their estimates and a computational load that quickly becomes unsustainable. Here, we describe a simple solution, in which we use functional connectivity to provide prior constraints that bound the effective number of free parameters. In brief, we assume that priors over connections between individual nodes can be replaced by priors over connections between modes (patterns over nodes). By using a small number of modes, we can reduce the dimensionality of the problem in an informed way. The modes we use are the principal components or eigenvectors of the functional connectivity matrix. However, this approach begs the question of how many modes to use. This question can be addressed using Bayesian model comparison to optimise the number of modes. We imagine that this form of prior – over the extrinsic (endogenous) connections in large DCMs – may be useful for people interested in applying graph theory to distributed networks in the brain or to characterise connectivity beyond the subgraphs normally examined in DCM. PMID:23246991

  13. User Expectations for Media Sharing Practices in Open Display Networks.

    PubMed

    Jose, Rui; Cardoso, Jorge C S; Hong, Jason

    2015-07-06

    Open Display Networks have the potential to allow many content creators to publish their media to an open-ended set of screen displays. However, this raises the issue of how to match that content to the right displays. In this study, we aim to understand how the perceived utility of particular media sharing scenarios is affected by three independent variables, more specifically: (a) the locativeness of the content being shared; (b) how personal that content is and (c) the scope in which it is being shared. To assess these effects, we composed a set of 24 media sharing scenarios embedded with different treatments of our three independent variables. We then asked 100 participants to express their perception of the relevance of those scenarios. The results suggest a clear preference for scenarios where content is both local and directly related to the person that is publishing it. This is in stark contrast to the types of content that are commonly found in public displays, and confirms the opportunity that open displays networks may represent a new media for self-expression. This novel understanding may inform the design of new publication paradigms that will enable people to share media across the display networks.

  14. User Expectations for Media Sharing Practices in Open Display Networks

    PubMed Central

    Jose, Rui; Cardoso, Jorge C. S.; Hong, Jason

    2015-01-01

    Open Display Networks have the potential to allow many content creators to publish their media to an open-ended set of screen displays. However, this raises the issue of how to match that content to the right displays. In this study, we aim to understand how the perceived utility of particular media sharing scenarios is affected by three independent variables, more specifically: (a) the locativeness of the content being shared; (b) how personal that content is and (c) the scope in which it is being shared. To assess these effects, we composed a set of 24 media sharing scenarios embedded with different treatments of our three independent variables. We then asked 100 participants to express their perception of the relevance of those scenarios. The results suggest a clear preference for scenarios where content is both local and directly related to the person that is publishing it. This is in stark contrast to the types of content that are commonly found in public displays, and confirms the opportunity that open displays networks may represent a new media for self-expression. This novel understanding may inform the design of new publication paradigms that will enable people to share media across the display networks. PMID:26153770

  15. A physical layer perspective on access network sharing

    NASA Astrophysics Data System (ADS)

    Pfeiffer, Thomas

    2015-12-01

    Unlike in copper or wireless networks, there is no sharing of resources in fiber access networks yet, other than bit stream access or cable sharing, in which the fibers of a cable are let to one or multiple operators. Sharing optical resources on a single fiber among multiple operators or different services has not yet been applied. While this would allow for a better exploitation of installed infrastructures, there are operational issues which still need to be resolved, before this sharing model can be implemented in networks. Operating multiple optical systems and services over a common fiber plant, autonomously and independently from each other, can result in mutual distortions on the physical layer. These distortions will degrade the performance of the involved systems, unless precautions are taken in the infrastructure hardware to eliminate or to reduce them to an acceptable level. Moreover, the infrastructure needs to be designed such as to support different system technologies and to ensure a guaranteed quality of the end-to-end connections. In this paper, suitable means are proposed to be introduced in fiber access infrastructures that will allow for shared utilization of the fibers while safeguarding the operational needs and business interests of the involved parties.

  16. Distributed simulation using a real-time shared memory network

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Mattern, Duane L.; Wong, Edmond; Musgrave, Jeffrey L.

    1993-01-01

    The Advanced Control Technology Branch of the NASA Lewis Research Center performs research in the area of advanced digital controls for aeronautic and space propulsion systems. This work requires the real-time implementation of both control software and complex dynamical models of the propulsion system. We are implementing these systems in a distributed, multi-vendor computer environment. Therefore, a need exists for real-time communication and synchronization between the distributed multi-vendor computers. A shared memory network is a potential solution which offers several advantages over other real-time communication approaches. A candidate shared memory network was tested for basic performance. The shared memory network was then used to implement a distributed simulation of a ramjet engine. The accuracy and execution time of the distributed simulation was measured and compared to the performance of the non-partitioned simulation. The ease of partitioning the simulation, the minimal time required to develop for communication between the processors and the resulting execution time all indicate that the shared memory network is a real-time communication technique worthy of serious consideration.

  17. esyN: network building, sharing and publishing.

    PubMed

    Bean, Daniel M; Heimbach, Joshua; Ficorella, Lorenzo; Micklem, Gos; Oliver, Stephen G; Favrin, Giorgio

    2014-01-01

    The construction and analysis of networks is increasingly widespread in biological research. We have developed esyN ("easy networks") as a free and open source tool to facilitate the exchange of biological network models between researchers. esyN acts as a searchable database of user-created networks from any field. We have developed a simple companion web tool that enables users to view and edit networks using data from publicly available databases. Both normal interaction networks (graphs) and Petri nets can be created. In addition to its basic tools, esyN contains a number of logical templates that can be used to create models more easily. The ability to use previously published models as building blocks makes esyN a powerful tool for the construction of models and network graphs. Users are able to save their own projects online and share them either publicly or with a list of collaborators. The latter can be given the ability to edit the network themselves, allowing online collaboration on network construction. esyN is designed to facilitate unrestricted exchange of this increasingly important type of biological information. Ultimately, the aim of esyN is to bring the advantages of Open Source software development to the construction of biological networks. PMID:25181461

  18. Development of Resource Sharing Networks. Study No. 1. Life Sciences Information Network.

    ERIC Educational Resources Information Center

    National Library of Australia, Canberra.

    A life sciences network which will be one of ten Australian resource sharing networks to be developed by the Australian National Scientific and Technical Library (ANSTEL) is proposed. The study outlines major elements and concepts of the system, including technical considerations, costing, management, and an implementation plan for the network.…

  19. Four health data networks illustrate the potential for a shared national multipurpose big-data network.

    PubMed

    Curtis, Lesley H; Brown, Jeffrey; Platt, Richard

    2014-07-01

    Information in electronic health data that are drawn from large populations of patients is transforming health care, public health practice, and clinical research. This article describes our experience in developing data networks that repurpose electronic health records and administrative data. The four programs we feature are the Food and Drug Administration's Mini-Sentinel program (which focuses on medical product safety), the National Patient-Centered Clinical Research Network (PCORnet, comparative effectiveness research), the National Institutes of Health's Health Care Systems Research Collaboratory Distributed Research Network (biomedical research), and ESPnet (public health surveillance). Challenges to these uses of electronic health data include understanding the factors driving the collection, coding, and preservation of the data; the extensive customization of different systems that collect similar data; the fragmentation of the US health care delivery system and its records; and privacy and proprietary considerations. We view these four programs as examples of the first stage in the development of a shared national big-data resource that leverages the investments of many agencies and organizations for the benefit of multiple networks and users. PMID:25006144

  20. Four health data networks illustrate the potential for a shared national multipurpose big-data network.

    PubMed

    Curtis, Lesley H; Brown, Jeffrey; Platt, Richard

    2014-07-01

    Information in electronic health data that are drawn from large populations of patients is transforming health care, public health practice, and clinical research. This article describes our experience in developing data networks that repurpose electronic health records and administrative data. The four programs we feature are the Food and Drug Administration's Mini-Sentinel program (which focuses on medical product safety), the National Patient-Centered Clinical Research Network (PCORnet, comparative effectiveness research), the National Institutes of Health's Health Care Systems Research Collaboratory Distributed Research Network (biomedical research), and ESPnet (public health surveillance). Challenges to these uses of electronic health data include understanding the factors driving the collection, coding, and preservation of the data; the extensive customization of different systems that collect similar data; the fragmentation of the US health care delivery system and its records; and privacy and proprietary considerations. We view these four programs as examples of the first stage in the development of a shared national big-data resource that leverages the investments of many agencies and organizations for the benefit of multiple networks and users.

  1. To share or not to share: Drivers and barriers for sharing data via online amateur weather networks

    NASA Astrophysics Data System (ADS)

    Gharesifard, Mohammad; Wehn, Uta

    2016-04-01

    Increasing attention is being paid to the importance and potential of crowd-sourced data to complement current environmental data-streams (i.e. in-situ observations and RS data). In parallel, the diffusion of Information Communication Technologies (ICTs) that are interactive and easy to use have provided a way forward in facing extreme climatic events and the threatening hazards resulting from those. The combination of these two trends is referred to as ICT-enabled 'citizen observatories' of the environment. Nevertheless, the success of these citizen observatories hinges on the continued involvement of citizens as central actors of these initiatives. Developing strategies to (further) engage citizens requires in-depth understanding of the behavioral determinants that encourage or impede individuals to collect and share environment-related data. This paper takes the case of citizen-sensed weather data using Personal Weather Stations (PWSs) and looks at the drivers and barriers for sharing such data via online amateur weather networks. This is done employing a behavioral science lens that considers data sharing a decision and systematically investigates the influential factors that affect this decision. The analysis and findings are based on qualitative empirical research carried out in the Netherlands, United Kingdom and Italy. Subsequently, a model was developed that depicts the main drivers and barriers for citizen participation in weather observatories. This resulting model can be utilized as a tool to develop strategies for further enhancing ICT-enabled citizen participation in climatic observations and, consequently, in environmental management.

  2. Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness

    PubMed Central

    Buchler, Norbou; Fitzhugh, Sean M.; Marusich, Laura R.; Ungvarsky, Diane M.; Lebiere, Christian; Gonzalez, Cleotilde

    2016-01-01

    A common assumption in organizations is that information sharing improves situation awareness and ultimately organizational effectiveness. The sheer volume and rapid pace of information and communications received and readily accessible through computer networks, however, can overwhelm individuals, resulting in data overload from a combination of diverse data sources, multiple data formats, and large data volumes. The current conceptual framework of network enabled operations (NEO) posits that robust networking and information sharing act as a positive feedback loop resulting in greater situation awareness and mission effectiveness in military operations (Alberts and Garstka, 2004). We test this assumption in a large-scale, 2-week military training exercise. We conducted a social network analysis of email communications among the multi-echelon Mission Command staff (one Division and two sub-ordinate Brigades) and assessed the situational awareness of every individual. Results from our exponential random graph models challenge the aforementioned assumption, as increased email output was associated with lower individual situation awareness. It emerged that higher situation awareness was associated with a lower probability of out-ties, so that broadly sending many messages decreased the likelihood of attaining situation awareness. This challenges the hypothesis that increased information sharing improves situation awareness, at least for those doing the bulk of the sharing. In addition, we observed two trends that reflect a compartmentalizing of networked information sharing as email links were more commonly formed among members of the command staff with both similar functions and levels of situation awareness, than between two individuals with dissimilar functions and levels of situation awareness; both those findings can be interpreted to reflect effects of homophily. Our results have major implications that challenge the current conceptual framework of NEO. In

  3. Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness.

    PubMed

    Buchler, Norbou; Fitzhugh, Sean M; Marusich, Laura R; Ungvarsky, Diane M; Lebiere, Christian; Gonzalez, Cleotilde

    2016-01-01

    A common assumption in organizations is that information sharing improves situation awareness and ultimately organizational effectiveness. The sheer volume and rapid pace of information and communications received and readily accessible through computer networks, however, can overwhelm individuals, resulting in data overload from a combination of diverse data sources, multiple data formats, and large data volumes. The current conceptual framework of network enabled operations (NEO) posits that robust networking and information sharing act as a positive feedback loop resulting in greater situation awareness and mission effectiveness in military operations (Alberts and Garstka, 2004). We test this assumption in a large-scale, 2-week military training exercise. We conducted a social network analysis of email communications among the multi-echelon Mission Command staff (one Division and two sub-ordinate Brigades) and assessed the situational awareness of every individual. Results from our exponential random graph models challenge the aforementioned assumption, as increased email output was associated with lower individual situation awareness. It emerged that higher situation awareness was associated with a lower probability of out-ties, so that broadly sending many messages decreased the likelihood of attaining situation awareness. This challenges the hypothesis that increased information sharing improves situation awareness, at least for those doing the bulk of the sharing. In addition, we observed two trends that reflect a compartmentalizing of networked information sharing as email links were more commonly formed among members of the command staff with both similar functions and levels of situation awareness, than between two individuals with dissimilar functions and levels of situation awareness; both those findings can be interpreted to reflect effects of homophily. Our results have major implications that challenge the current conceptual framework of NEO. In

  4. Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness.

    PubMed

    Buchler, Norbou; Fitzhugh, Sean M; Marusich, Laura R; Ungvarsky, Diane M; Lebiere, Christian; Gonzalez, Cleotilde

    2016-01-01

    A common assumption in organizations is that information sharing improves situation awareness and ultimately organizational effectiveness. The sheer volume and rapid pace of information and communications received and readily accessible through computer networks, however, can overwhelm individuals, resulting in data overload from a combination of diverse data sources, multiple data formats, and large data volumes. The current conceptual framework of network enabled operations (NEO) posits that robust networking and information sharing act as a positive feedback loop resulting in greater situation awareness and mission effectiveness in military operations (Alberts and Garstka, 2004). We test this assumption in a large-scale, 2-week military training exercise. We conducted a social network analysis of email communications among the multi-echelon Mission Command staff (one Division and two sub-ordinate Brigades) and assessed the situational awareness of every individual. Results from our exponential random graph models challenge the aforementioned assumption, as increased email output was associated with lower individual situation awareness. It emerged that higher situation awareness was associated with a lower probability of out-ties, so that broadly sending many messages decreased the likelihood of attaining situation awareness. This challenges the hypothesis that increased information sharing improves situation awareness, at least for those doing the bulk of the sharing. In addition, we observed two trends that reflect a compartmentalizing of networked information sharing as email links were more commonly formed among members of the command staff with both similar functions and levels of situation awareness, than between two individuals with dissimilar functions and levels of situation awareness; both those findings can be interpreted to reflect effects of homophily. Our results have major implications that challenge the current conceptual framework of NEO. In

  5. Power-aware provisioning strategy with shared path protection in optical WDM networks

    NASA Astrophysics Data System (ADS)

    Bao, Ning-Hai; Li, Le-Min; Yu, Hong-Fang; Zhang, Zhi-Zhong; Luo, Hong-Bin

    2012-03-01

    As the Internet continues to grow, the power consumption of telecommunication networks is rising at a considerable speed, which seriously increases the operational expenditure and greenhouse gas emission. Since optical Wavelength Division Multiplexing (WDM) networks are currently the most promising network infrastructures, power saving issue on these networks has received more attention in recent years. In traditional optical WDM networks, a large amount of power is drained by the redundant idle resources and reserved backup resources although these powered on resources do not carry traffic in most of the time. In order to reduce the network power consumption, turning off the corresponding network components or switching them to a low-power, standby state (or called sleep mode) is a promising greening approach. In this paper, we study the power-aware provisioning strategies and propose a sleep mode based Power-Aware Shared Path Protection (PASPP) heuristic algorithm to achieve the power efficiency of optical WDM networks. By jointly utilizing link-cost and fiber-cost in path routing, resource assignment, and resource release, PASPP makes working paths and backup paths converge on different fibers as much as possible, and switch idle and backup components to sleep mode to realize power saving. Simulation results show that our PASPP can obtain notable power saving and achieve satisfactory tradeoff between power efficiency and blocking probability with respect to Power-Unaware Shared Path Protection (PUSPP).

  6. Online Community Detection for Large Complex Networks

    PubMed Central

    Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

    2014-01-01

    Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

  7. Large-scale quantum networks based on graphs

    NASA Astrophysics Data System (ADS)

    Epping, Michael; Kampermann, Hermann; Bruß, Dagmar

    2016-05-01

    Society relies and depends increasingly on information exchange and communication. In the quantum world, security and privacy is a built-in feature for information processing. The essential ingredient for exploiting these quantum advantages is the resource of entanglement, which can be shared between two or more parties. The distribution of entanglement over large distances constitutes a key challenge for current research and development. Due to losses of the transmitted quantum particles, which typically scale exponentially with the distance, intermediate quantum repeater stations are needed. Here we show how to generalise the quantum repeater concept to the multipartite case, by describing large-scale quantum networks, i.e. network nodes and their long-distance links, consistently in the language of graphs and graph states. This unifying approach comprises both the distribution of multipartite entanglement across the network, and the protection against errors via encoding. The correspondence to graph states also provides a tool for optimising the architecture of quantum networks.

  8. Network Analysis of an Emergent Massively Collaborative Creation on Video Sharing Website

    NASA Astrophysics Data System (ADS)

    Hamasaki, Masahiro; Takeda, Hideaki; Nishimura, Takuichi

    The Web technology enables numerous people to collaborate in creation. We designate it as massively collaborative creation via the Web. As an example of massively collaborative creation, we particularly examine video development on Nico Nico Douga, which is a video sharing website that is popular in Japan. We specifically examine videos on Hatsune Miku, a version of a singing synthesizer application software that has inspired not only song creation but also songwriting, illustration, and video editing. As described herein, creators of interact to create new contents through their social network. In this paper, we analyzed the process of developing thousands of videos based on creators' social networks and investigate relationships among creation activity and social networks. The social network reveals interesting features. Creators generate large and sparse social networks including some centralized communities, and such centralized community's members shared special tags. Different categories of creators have different roles in evolving the network, e.g., songwriters gather more links than other categories, implying that they are triggers to network evolution.

  9. Refuge sharing network predicts ectoparasite load in a lizard.

    PubMed

    Leu, Stephan T; Kappeler, Peter M; Bull, C Michael

    2010-09-01

    Living in social groups facilitates cross-infection by parasites. However, empirical studies on indirect transmission within wildlife populations are scarce. We investigated whether asynchronous overnight refuge sharing among neighboring sleepy lizards, Tiliqua rugosa, facilitates indirect transmission of its ectoparasitic tick, Amblyomma limbatum. We fitted 18 neighboring lizards with GPS recorders, observed their overnight refuge use each night over 3 months, and counted their ticks every fortnight. We constructed a transmission network to estimate the cross-infection risk based on asynchronous refuge sharing frequencies among all lizards and the life history traits of the tick. Although self-infection was possible, the network provided a powerful predictor of measured tick loads. Highly connected lizards that frequently used their neighbors' refuges were characterized by higher tick loads. Thus, indirect contact had a major influence on transmission pathways and parasite loads. Furthermore, lizards that used many different refuges had lower cross- and self-infection risks and lower tick loads than individuals that used relatively fewer refuges. Increasing the number of refuges used by a lizard may be an important defense mechanism against ectoparasite transmission in this species. Our study provides important empirical data to further understand how indirectly transmitted parasites move through host populations and influence individual parasite loads.

  10. Fair sharing of resources in a supply network with constraints

    NASA Astrophysics Data System (ADS)

    Carvalho, Rui; Buzna, Lubos; Just, Wolfram; Helbing, Dirk; Arrowsmith, David K.

    2012-04-01

    This paper investigates the effect of network topology on the fair allocation of network resources among a set of agents, an all-important issue for the efficiency of transportation networks all around us. We analyze a generic mechanism that distributes network capacity fairly among existing flow demands. The problem can be solved by semianalytical methods on a nearest-neighbor graph with one source and sink pair, when transport occurs over shortest paths. For this setup, we uncover a broad range of patterns of intersecting shortest paths as a function of the distance between the source and the sink. When the number of intersections is the maximum and the distance between the source and the sink is large, we find that a fair allocation implies a decrease of at least 50% from the maximum throughput. We also find that the histogram of the flow allocations assigned to the agents decays as a power law with exponent -1. Our semianalytical framework suggests possible explanations for the well-known reduction of the throughput in fair allocations. It also suggests that the combination of network topology and routing rules can lead to highly uneven (but fair) distributions of resources, a remark of caution to network designers.

  11. Information sharing and relationships on social networking sites.

    PubMed

    Steijn, Wouter M P; Schouten, Alexander P

    2013-08-01

    This article investigates the relationship between sharing personal information and relationship development in the context of social networking sites (SNSs). Information disclosed on these sites could affect relationships in a different manner compared to more traditional interactions, such as instant messaging or face-to-face interaction. Respondents in the age range of 12 to 83 were surveyed about experiences of relationship development as a consequence of contact through Facebook or Hyves-the most popular Dutch SNSs. Results showed a primarily positive effect of information sharing on SNSs on our relationships. Furthermore, relationship development mainly occurs among acquaintances and friends, and public posts are most strongly related to relationship development. These findings suggest that SNSs might affect relationships in a distinct fashion as acquaintances and friends gain access to public self-disclosures that might normally only be reserved for close friends and family. Overall, this study provides an insight into some of the positive aspects of the public nature of SNSs in contrast with the general negative associations.

  12. Information sharing and relationships on social networking sites.

    PubMed

    Steijn, Wouter M P; Schouten, Alexander P

    2013-08-01

    This article investigates the relationship between sharing personal information and relationship development in the context of social networking sites (SNSs). Information disclosed on these sites could affect relationships in a different manner compared to more traditional interactions, such as instant messaging or face-to-face interaction. Respondents in the age range of 12 to 83 were surveyed about experiences of relationship development as a consequence of contact through Facebook or Hyves-the most popular Dutch SNSs. Results showed a primarily positive effect of information sharing on SNSs on our relationships. Furthermore, relationship development mainly occurs among acquaintances and friends, and public posts are most strongly related to relationship development. These findings suggest that SNSs might affect relationships in a distinct fashion as acquaintances and friends gain access to public self-disclosures that might normally only be reserved for close friends and family. Overall, this study provides an insight into some of the positive aspects of the public nature of SNSs in contrast with the general negative associations. PMID:23659723

  13. 47 CFR 90.1415 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... the network sharing agreement. 90.1415 Section 90.1415 Telecommunication FEDERAL COMMUNICATIONS.../Private Partnership § 90.1415 Establishment, execution, and application of the network sharing agreement... to the NSA must also include the Upper 700 MHz D Block licensee, the Network Assets Holder, and...

  14. 47 CFR 90.1415 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... the network sharing agreement. 90.1415 Section 90.1415 Telecommunication FEDERAL COMMUNICATIONS.../Private Partnership § 90.1415 Establishment, execution, and application of the network sharing agreement... to the NSA must also include the Upper 700 MHz D Block licensee, the Network Assets Holder, and...

  15. 47 CFR 27.1315 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... the network sharing agreement. 27.1315 Section 27.1315 Telecommunication FEDERAL COMMUNICATIONS.../Private Partnership § 27.1315 Establishment, execution, and application of the network sharing agreement... Upper 700 MHz D Block licensee, the Network Assets Holder, and the Operating Company, as these...

  16. Large-Scale Unsupervised Hashing with Shared Structure Learning.

    PubMed

    Liu, Xianglong; Mu, Yadong; Zhang, Danchen; Lang, Bo; Li, Xuelong

    2015-09-01

    Hashing methods are effective in generating compact binary signatures for images and videos. This paper addresses an important open issue in the literature, i.e., how to learn compact hash codes by enhancing the complementarity among different hash functions. Most of prior studies solve this problem either by adopting time-consuming sequential learning algorithms or by generating the hash functions which are subject to some deliberately-designed constraints (e.g., enforcing hash functions orthogonal to one another). We analyze the drawbacks of past works and propose a new solution to this problem. Our idea is to decompose the feature space into a subspace shared by all hash functions and its complementary subspace. On one hand, the shared subspace, corresponding to the common structure across different hash functions, conveys most relevant information for the hashing task. Similar to data de-noising, irrelevant information is explicitly suppressed during hash function generation. On the other hand, in case that the complementary subspace also contains useful information for specific hash functions, the final form of our proposed hashing scheme is a compromise between these two kinds of subspaces. To make hash functions not only preserve the local neighborhood structure but also capture the global cluster distribution of the whole data, an objective function incorporating spectral embedding loss, binary quantization loss, and shared subspace contribution is introduced to guide the hash function learning. We propose an efficient alternating optimization method to simultaneously learn both the shared structure and the hash functions. Experimental results on three well-known benchmarks CIFAR-10, NUS-WIDE, and a-TRECVID demonstrate that our approach significantly outperforms state-of-the-art hashing methods.

  17. [Study on network architecture of a tele-medical information sharing platform].

    PubMed

    Pan, Lin; Yu, Lun; Chen, Jin-xiong

    2006-07-01

    In the article,a plan of network construction which satisfies the demand of applications for a telemedical information sharing platform is proposed. We choice network access plans in view of user actual situation, through the analysis of the service demand and many kinds of network access technologies. Hospital servers that locate in LAN link sharing platform with node servers, should separate from the broadband network of sharing platform in order to ensure the security of the internal hospital network and the administration management. We use the VPN technology to realize the safe transmission of information in the platform network. Preliminary experiments have proved the plan is practicable.

  18. Social Network Analysis of Patient Sharing Among Hospitals in Orange County, California

    PubMed Central

    McGlone, Sarah M.; Song, Yeohan; Avery, Taliser R.; Eubank, Stephen; Chang, Chung-Chou; Bailey, Rachel R.; Wagener, Diane K.; Burke, Donald S.; Platt, Richard; Huang, Susan S.

    2011-01-01

    Objectives. We applied social network analyses to determine how hospitals within Orange County, California, are interconnected by patient sharing, a system which may have numerous public health implications. Methods. Our analyses considered 2 general patient-sharing networks: uninterrupted patient sharing (UPS; i.e., direct interhospital transfers) and total patient sharing (TPS; i.e., all interhospital patient sharing, including patients with intervening nonhospital stays). We considered these networks at 3 thresholds of patient sharing: at least 1, at least 10, and at least 100 patients shared. Results. Geographically proximate hospitals were somewhat more likely to share patients, but many hospitals shared patients with distant hospitals. Number of patient admissions and percentage of cancer patients were associated with greater connectivity across the system. The TPS network revealed numerous connections not seen in the UPS network, meaning that direct transfers only accounted for a fraction of total patient sharing. Conclusions. Our analysis demonstrated that Orange County's 32 hospitals were highly and heterogeneously interconnected by patient sharing. Different hospital populations had different levels of influence over the patient-sharing network. PMID:21330578

  19. Information Sharing Challenges For Large Multi-Jurisdiction Issues

    SciTech Connect

    Millard, W. David; Carter, Richard J.

    2002-07-29

    In order for decision-makers to effectively handle the complexity of the multi-jurisdiction issues, automation tools must be provided that can operate effectively in a multi-jurisdictional environment. Decision support systems that span multiple users, agencies, and jurisdictions should provide users with the information needed to make decisions and the ability to disseminate those decisions. To do so, such systems need the capability to 1) control the ownership of information, 2) control how information is shared, 3) display highly dynamic geographic information from multiple jurisdictions, 4) disseminate dynamic status information between jurisdictions, and 5) notify appropriate users across jurisdictions of changes to decisions and information

  20. Shared and Distributed Memory Parallel Security Analysis of Large-Scale Source Code and Binary Applications

    SciTech Connect

    Quinlan, D; Barany, G; Panas, T

    2007-08-30

    Many forms of security analysis on large scale applications can be substantially automated but the size and complexity can exceed the time and memory available on conventional desktop computers. Most commercial tools are understandably focused on such conventional desktop resources. This paper presents research work on the parallelization of security analysis of both source code and binaries within our Compass tool, which is implemented using the ROSE source-to-source open compiler infrastructure. We have focused on both shared and distributed memory parallelization of the evaluation of rules implemented as checkers for a wide range of secure programming rules, applicable to desktop machines, networks of workstations and dedicated clusters. While Compass as a tool focuses on source code analysis and reports violations of an extensible set of rules, the binary analysis work uses the exact same infrastructure but is less well developed into an equivalent final tool.

  1. Bibliographic Automation of Large Library Operations Using a Time-Sharing System: Phase I. Final Report.

    ERIC Educational Resources Information Center

    Epstein, A. H.; And Others

    The first phase of an ongoing library automation project at Stanford University is described. Project BALLOTS (Bibliographic Automation of Large Library Operations Using a Time-Sharing System) seeks to automate the acquisition and cataloging functions of a large library using an on-line time-sharing computer. The main objectives are to control…

  2. Data Validation and Sharing in a Large Research Program

    EPA Science Inventory

    Appropriate data handling practices are important in the support of large research teams with shifting and competing priorities. Determining those best practices is an ongoing effort for the US EPA’s National Aquatic Resource Surveys. We focus on the well understood data ...

  3. Cultural Differences in an Interorganizational Network: Shared Public Relations Firms among Japanese and American Companies.

    ERIC Educational Resources Information Center

    Jang, Ha-Yong

    1997-01-01

    Investigates impact of national culture on interorganizational relationships among organizations. Matches 35 Japanese and American companies by their business types. Reveals that the network of shared public relations firms was loosely connected--American companies were more central. Indicates the network structure of shared public relations firms…

  4. The Development and Evaluation of a Network for Producing and Sharing Video Presentations

    ERIC Educational Resources Information Center

    Sadik, Alaa

    2014-01-01

    This paper describes the technology and methodology used in the development and evaluation of an online network to help the instructors to produce and share video presentations in a new and innovative way. The network offers an application and platform for recording and sharing video presentations. The application allows instructors to narrate and…

  5. Draft Proposal for the Development of an Australian Bibliographic Network. Development of Resource Sharing Networks. Networks Study No. 15.

    ERIC Educational Resources Information Center

    National Library of Australia, Canberra.

    As part of its responsibility for the development of bibliographic services in Australia, the Australian National Library presents this draft proposal for a national online shared cataloging facility to be known as the Australian Bibliographic Network or ABN. A preface describes ABN's historical background and relationship to BIBDATA, a previously…

  6. The Double-Stranded DNA Virosphere as a Modular Hierarchical Network of Gene Sharing

    PubMed Central

    Iranzo, Jaime

    2016-01-01

    ABSTRACT Virus genomes are prone to extensive gene loss, gain, and exchange and share no universal genes. Therefore, in a broad-scale study of virus evolution, gene and genome network analyses can complement traditional phylogenetics. We performed an exhaustive comparative analysis of the genomes of double-stranded DNA (dsDNA) viruses by using the bipartite network approach and found a robust hierarchical modularity in the dsDNA virosphere. Bipartite networks consist of two classes of nodes, with nodes in one class, in this case genomes, being connected via nodes of the second class, in this case genes. Such a network can be partitioned into modules that combine nodes from both classes. The bipartite network of dsDNA viruses includes 19 modules that form 5 major and 3 minor supermodules. Of these modules, 11 include tailed bacteriophages, reflecting the diversity of this largest group of viruses. The module analysis quantitatively validates and refines previously proposed nontrivial evolutionary relationships. An expansive supermodule combines the large and giant viruses of the putative order “Megavirales” with diverse moderate-sized viruses and related mobile elements. All viruses in this supermodule share a distinct morphogenetic tool kit with a double jelly roll major capsid protein. Herpesviruses and tailed bacteriophages comprise another supermodule, held together by a distinct set of morphogenetic proteins centered on the HK97-like major capsid protein. Together, these two supermodules cover the great majority of currently known dsDNA viruses. We formally identify a set of 14 viral hallmark genes that comprise the hubs of the network and account for most of the intermodule connections. PMID:27486193

  7. Latest developments in advanced network management and cross-sharing of next-generation flux stations

    NASA Astrophysics Data System (ADS)

    Burba, George; Johnson, Dave; Velgersdyk, Michael; Begashaw, Israel; Allyn, Douglas

    2016-04-01

    be merged into a single quality-control file (v) Multiple flux stations can be linked into an automated time-synchronized network (vi) Flux network managers, or PI's, can see all stations in real-time, including fluxes, supporting data, automated reports, and email alerts (vii) PI's can assign rights, allow or restrict access to stations and data: selected stations can be shared via rights-managed access internally or with external institutions (viii) Researchers without stations could form "virtual networks" for specific projects by collaborating with PIs from different actual networks This presentation provides detailed examples of FluxSuite currently utilized to manage two large flux networks in China (National Academy of Sciences and Agricultural Academy of Sciences), and smaller networks with stations in the USA, Germany, Ireland, Malaysia and other locations around the globe. Very latest 2016 developments and expanded functionality are also discussed.

  8. Bounded link prediction in very large networks

    NASA Astrophysics Data System (ADS)

    Cui, Wei; Pu, Cunlai; Xu, Zhongqi; Cai, Shimin; Yang, Jian; Michaelson, Andrew

    2016-09-01

    Evaluating link prediction methods is a hard task in very large complex networks due to the prohibitive computational cost. However, if we consider the lower bound of node pairs' similarity scores, this task can be greatly optimized. In this paper, we study CN index in the bounded link prediction framework, which is applicable to enormous heterogeneous networks. Specifically, we propose a fast algorithm based on the parallel computing scheme to obtain all node pairs with CN values larger than the lower bound. Furthermore, we propose a general measurement, called self-predictability, to quantify the performance of similarity indices in link prediction, which can also indicate the link predictability of networks with respect to given similarity indices.

  9. Future large broadband switched satellite communications networks

    NASA Technical Reports Server (NTRS)

    Staelin, D. H.; Harvey, R. R.

    1979-01-01

    Critical technical, market, and policy issues relevant to future large broadband switched satellite networks are summarized. Our market projections for the period 1980 to 2000 are compared. Clusters of switched satellites, in lieu of large platforms, etc., are shown to have significant advantages. Analysis of an optimum terrestrial network architecture suggests the proper densities of ground stations and that link reliabilities 99.99% may entail less than a 10% cost premium for diversity protection at 20/30 GHz. These analyses suggest that system costs increase as the 0.6 power of traffic. Cost estimates for nominal 20/30 GHz satellite and ground facilities suggest optimum system configurations might employ satellites with 285 beams, multiple TDMA bands each carrying 256 Mbps, and 16 ft ground station antennas. A nominal development program is outlined.

  10. Coupled cluster algorithms for networks of shared memory parallel processors

    NASA Astrophysics Data System (ADS)

    Bentz, Jonathan L.; Olson, Ryan M.; Gordon, Mark S.; Schmidt, Michael W.; Kendall, Ricky A.

    2007-05-01

    As the popularity of using SMP systems as the building blocks for high performance supercomputers increases, so too increases the need for applications that can utilize the multiple levels of parallelism available in clusters of SMPs. This paper presents a dual-layer distributed algorithm, using both shared-memory and distributed-memory techniques to parallelize a very important algorithm (often called the "gold standard") used in computational chemistry, the single and double excitation coupled cluster method with perturbative triples, i.e. CCSD(T). The algorithm is presented within the framework of the GAMESS [M.W. Schmidt, K.K. Baldridge, J.A. Boatz, S.T. Elbert, M.S. Gordon, J.J. Jensen, S. Koseki, N. Matsunaga, K.A. Nguyen, S. Su, T.L. Windus, M. Dupuis, J.A. Montgomery, General atomic and molecular electronic structure system, J. Comput. Chem. 14 (1993) 1347-1363]. (General Atomic and Molecular Electronic Structure System) program suite and the Distributed Data Interface [M.W. Schmidt, G.D. Fletcher, B.M. Bode, M.S. Gordon, The distributed data interface in GAMESS, Comput. Phys. Comm. 128 (2000) 190]. (DDI), however, the essential features of the algorithm (data distribution, load-balancing and communication overhead) can be applied to more general computational problems. Timing and performance data for our dual-level algorithm is presented on several large-scale clusters of SMPs.

  11. A Networked NMR Spectrometer: Configuring a Shared Instrument

    ERIC Educational Resources Information Center

    Alonso, David; Mutch, G. William; Wong, Peter; Warren, Steven; Barot, Bal; Kosinski, Jan; Sinton, Mark

    2005-01-01

    A model for a shared nuclear magnetic resonance spectroscopy (NMR) facility between a private university and two local community colleges is presented. The discussion of the components required for the shared facility, modes of data distribution, and overall effect on the curriculum is presented.

  12. Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks

    NASA Astrophysics Data System (ADS)

    Wu, Zhengping; Wu, Hao

    With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.

  13. Sharing from Scratch: How To Network CD-ROM.

    ERIC Educational Resources Information Center

    Doering, David

    1998-01-01

    Examines common CD-ROM networking architectures: via existing operating systems (OS), thin server towers, and dedicated servers. Discusses digital video disc (DVD) and non-CD/DVD optical storage solutions and presents case studies of networks that work. (PEN)

  14. Local Area Networks: Vehicles for Connecting and Sharing Information.

    ERIC Educational Resources Information Center

    Lipman, Art

    1993-01-01

    Describes local area networks (LANs) and discusses advantages of their use in schools for students and teachers, including networking in labs, media centers, and classrooms. Roles of the network supervisor and/or technician are explained, including making decisions about the rights of users and instruction and assistance. (LRW)

  15. Sharing by Design: Understanding and Supporting Personal Health Information Sharing and Collaboration within Social Networks

    ERIC Educational Resources Information Center

    Skeels, Meredith McLain

    2010-01-01

    Friends, family, and community provide important support and help to patients who face an illness. Unfortunately, keeping a social network informed about a patient's health status and needs takes effort, making it difficult for people who are sick and exhausted from illness. Members of a patient's social network are often eager to help, but can be…

  16. Empirical Models of Social Learning in a Large, Evolving Network

    PubMed Central

    Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

    2016-01-01

    This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals’ access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends. PMID:27701430

  17. Potential Parasite Transmission in Multi-Host Networks Based on Parasite Sharing

    PubMed Central

    Pilosof, Shai; Morand, Serge; Krasnov, Boris R.; Nunn, Charles L.

    2015-01-01

    Epidemiological networks are commonly used to explore dynamics of parasite transmission among individuals in a population of a given host species. However, many parasites infect multiple host species, and thus multi-host networks may offer a better framework for investigating parasite dynamics. We investigated the factors that influence parasite sharing – and thus potential transmission pathways – among rodent hosts in Southeast Asia. We focused on differences between networks of a single host species and networks that involve multiple host species. In host-parasite networks, modularity (the extent to which the network is divided into subgroups of rodents that interact with similar parasites) was higher in the multi-species than in the single-species networks. This suggests that phylogeny affects patterns of parasite sharing, which was confirmed in analyses showing that it predicted affiliation of individuals to modules. We then constructed “potential transmission networks” based on the host-parasite networks, in which edges depict the similarity between a pair of individuals in the parasites they share. The centrality of individuals in these networks differed between multi- and single-species networks, with species identity and individual characteristics influencing their position in the networks. Simulations further revealed that parasite dynamics differed between multi- and single-species networks. We conclude that multi-host networks based on parasite sharing can provide new insights into the potential for transmission among hosts in an ecological community. In addition, the factors that determine the nature of parasite sharing (i.e. structure of the host-parasite network) may impact transmission patterns. PMID:25748947

  18. Good Samaritans in Networks: An Experiment on How Networks Influence Egalitarian Sharing and the Evolution of Inequality.

    PubMed

    Chiang, Yen-Sheng

    2015-01-01

    The fact that the more resourceful people are sharing with the poor to mitigate inequality-egalitarian sharing-is well documented in the behavioral science research. How inequality evolves as a result of egalitarian sharing is determined by the structure of "who gives whom". While most prior experimental research investigates allocation of resources in dyads and groups, the paper extends the research of egalitarian sharing to networks for a more generalized structure of social interaction. An agent-based model is proposed to predict how actors, linked in networks, share their incomes with neighbors. A laboratory experiment with human subjects further shows that income distributions evolve to different states in different network topologies. Inequality is significantly reduced in networks where the very rich and the very poor are connected so that income discrepancy is salient enough to motivate the rich to share their incomes with the poor. The study suggests that social networks make a difference in how egalitarian sharing influences the evolution of inequality.

  19. Decision Support System in the Management of Resource-Sharing Networks.

    ERIC Educational Resources Information Center

    Dubey, Yogendra P.

    1984-01-01

    Reports on emergence of decision support system (DSS) as a practical approach for applying computers and information to problems facing management. Information processing and decision making in organizations, simulation-model-based DSS in management of library resource sharing networks, and a resource-sharing simulation system are highlighted.…

  20. Oklahoma Library Technology Network Plan for Information Sharing and Telecommunications.

    ERIC Educational Resources Information Center

    Oklahoma State Dept. of Libraries, Oklahoma City.

    This plan sets forth approaches for state-level assistance for Oklahoma libraries to exchange information and to share or acquire machine-readable information from public and private sources through telecommunications, as well as for access to these libraries for existing and future state informational databases. Objectives and requirements are…

  1. Synchronization of coupled large-scale Boolean networks

    SciTech Connect

    Li, Fangfei

    2014-03-15

    This paper investigates the complete synchronization and partial synchronization of two large-scale Boolean networks. First, the aggregation algorithm towards large-scale Boolean network is reviewed. Second, the aggregation algorithm is applied to study the complete synchronization and partial synchronization of large-scale Boolean networks. Finally, an illustrative example is presented to show the efficiency of the proposed results.

  2. Good Samaritans in Networks: An Experiment on How Networks Influence Egalitarian Sharing and the Evolution of Inequality

    PubMed Central

    Chiang, Yen-Sheng

    2015-01-01

    The fact that the more resourceful people are sharing with the poor to mitigate inequality—egalitarian sharing—is well documented in the behavioral science research. How inequality evolves as a result of egalitarian sharing is determined by the structure of “who gives whom”. While most prior experimental research investigates allocation of resources in dyads and groups, the paper extends the research of egalitarian sharing to networks for a more generalized structure of social interaction. An agent-based model is proposed to predict how actors, linked in networks, share their incomes with neighbors. A laboratory experiment with human subjects further shows that income distributions evolve to different states in different network topologies. Inequality is significantly reduced in networks where the very rich and the very poor are connected so that income discrepancy is salient enough to motivate the rich to share their incomes with the poor. The study suggests that social networks make a difference in how egalitarian sharing influences the evolution of inequality. PMID:26061642

  3. 78 FR 3447 - Information Collection: Southern Alaska Sharing Network and Subsistence Study; Submitted for OMB...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-01-16

    ... notice (77 FR 50712) announcing that we would submit this ICR to OMB for approval. The notice provided... Bureau of Ocean Energy Management Information Collection: Southern Alaska Sharing Network and Subsistence... networks in coastal Alaska. This notice provides the public a second opportunity to comment on...

  4. Sharing, Privacy and Trust in Our Networked World. A Report to the OCLC Membership

    ERIC Educational Resources Information Center

    Storey, Tom, Ed.

    2007-01-01

    The practice of using a social network to establish and enhance relationships based on some common ground--shared interests, related skills, or a common geographic location--is as old as human societies, but social networking has flourished due to the ease of connecting on the Web. This OCLC membership report explores this web of social…

  5. Horizontal Evaluation: Fostering Knowledge Sharing and Program Improvement within a Network

    ERIC Educational Resources Information Center

    Thiele, Graham; Devaux, Andre; Velasco, Claudio; Horton, Douglas

    2007-01-01

    Horizontal evaluation combines self-assessment and external evaluation by peers. Papa Andina, a regional network that works to reduce rural poverty in the Andean region by fostering innovation in potato production and marketing, has used horizontal evaluations to improve the work of local project teams and to share knowledge within the network. In…

  6. Categorical Structure among Shared Features in Networks of Early-Learned Nouns

    ERIC Educational Resources Information Center

    Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

    2009-01-01

    The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

  7. Enhancing topology adaptation in information-sharing social networks

    NASA Astrophysics Data System (ADS)

    Cimini, Giulio; Chen, Duanbing; Medo, Matúš; Lü, Linyuan; Zhang, Yi-Cheng; Zhou, Tao

    2012-04-01

    The advent of the Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. It has thus become important to address issues like who gets followed and how to allow people to discover new and better information sources. In this paper we conduct an empirical analysis of different online social networking sites and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. These rules create networks which are effective in information diffusion and resemble structures resulting from real social systems.

  8. HydroShare: Applying professional software engineering to a new NSF-funded large software project

    NASA Astrophysics Data System (ADS)

    Idaszak, R.; Tarboton, D. G.; Ames, D.; Saleem Arrigo, J. A.; Band, L. E.; Bedig, A.; Castronova, A. M.; Christopherson, L.; Coposky, J.; Couch, A.; Dash, P.; Gan, T.; Goodall, J.; Gustafson, K.; Heard, J.; Hooper, R. P.; Horsburgh, J. S.; Jackson, S.; Johnson, H.; Maidment, D. R.; Mbewe, P.; Merwade, V.; Miles, B.; Reeder, S.; Russell, T.; Song, C.; Taylor, A.; Thakur, S.; Valentine, D. W.; Whiteaker, T. L.

    2013-12-01

    HydroShare is an online, collaborative system being developed for sharing hydrologic data and models as part of the NSF's Software Infrastructure for Sustained Innovation (SI2) program (NSF collaborative award numbers 1148453 and 1148090). HydroShare involves a large software development effort requiring cooperative research and distributed software development between domain scientists, professional software engineers (here 'professional' denotes previous commercial experience in the application of modern software engineering), and university software developers. HydroShare expands upon the data sharing capabilities of the Hydrologic Information System of the Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI) by broadening the classes of data accommodated, expanding capability to include the sharing of models and model components, and taking advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. With a goal of enabling better science concomitant with improved sustainable software practices, we will describe our approach, experiences, and lessons learned thus-far in applying professional software engineering to a large NSF-funded software project from the project's onset.

  9. Freedom and Sharing in the Global Network Society

    ERIC Educational Resources Information Center

    Lankshear, Colin

    2006-01-01

    This article focuses on some ideas from social and political philosophy concerning the ideal of freedom that may be useful for thinking about issues associated with the rise of network societies. The tendency for "freedom" to mean very different things to different people has carried over to the context of thinking about issues associated with new…

  10. Knowledge Sharing via Social Networking Platforms in Organizations

    ERIC Educational Resources Information Center

    Kettles, Degan

    2012-01-01

    Knowledge Management Systems have been actively promoted for decades within organizations but have frequently failed to be used. Recently, deployments of enterprise social networking platforms used for knowledge management have become commonplace. These platforms help harness the knowledge of workers by serving as repositories of knowledge as well…

  11. Library Resource-Sharing in the Network-Centric World.

    ERIC Educational Resources Information Center

    McGee, Rob

    This paper discusses changes in services, technology, and organization as libraries prepare to enter the "network-centric library world." Part 1 addresses the transition from the analog era to the digital age, and the convergence of libraries and education, including opportunities for library leadership in Internet access, digital literacy, and…

  12. A subthreshold MOS circuit for the Lotka-Volterra neural network producing the winners-share-all solution.

    PubMed

    Asai, T; Fukai, T; Tanaka, S

    1999-03-01

    An analog MOS circuit is proposed for implementing a Lotka-Volterra (LV) competitive neural network which produces winners-share-all solutions. The solutions give multiple winners receiving large inputs and are particularly useful for selecting a set of inputs through "decision by majority". We show that the LV network can easily be implemented using subthreshold MOS transistors. Results of extensive circuit simulations prove that the proposed circuit does exhibit a reliable selection compared with winner-take-all circuits, in the possible presence of device mismatches. These results pave a way to future implementation on a real device.

  13. Networking Biology: The Origins of Sequence-Sharing Practices in Genomics.

    PubMed

    Stevens, Hallam

    2015-10-01

    The wide sharing of biological data, especially nucleotide sequences, is now considered to be a key feature of genomics. Historians and sociologists have attempted to account for the rise of this sharing by pointing to precedents in model organism communities and in natural history. This article supplements these approaches by examining the role that electronic networking technologies played in generating the specific forms of sharing that emerged in genomics. The links between early computer users at the Stanford Artificial Intelligence Laboratory in the 1960s, biologists using local computer networks in the 1970s, and GenBank in the 1980s, show how networking technologies carried particular practices of communication, circulation, and data distribution from computing into biology. In particular, networking practices helped to transform sequences themselves into objects that had value as a community resource. PMID:26593711

  14. Networking Biology: The Origins of Sequence-Sharing Practices in Genomics.

    PubMed

    Stevens, Hallam

    2015-10-01

    The wide sharing of biological data, especially nucleotide sequences, is now considered to be a key feature of genomics. Historians and sociologists have attempted to account for the rise of this sharing by pointing to precedents in model organism communities and in natural history. This article supplements these approaches by examining the role that electronic networking technologies played in generating the specific forms of sharing that emerged in genomics. The links between early computer users at the Stanford Artificial Intelligence Laboratory in the 1960s, biologists using local computer networks in the 1970s, and GenBank in the 1980s, show how networking technologies carried particular practices of communication, circulation, and data distribution from computing into biology. In particular, networking practices helped to transform sequences themselves into objects that had value as a community resource.

  15. Developing A Large-Scale, Collaborative, Productive Geoscience Education Network

    NASA Astrophysics Data System (ADS)

    Manduca, C. A.; Bralower, T. J.; Egger, A. E.; Fox, S.; Ledley, T. S.; Macdonald, H.; Mcconnell, D. A.; Mogk, D. W.; Tewksbury, B. J.

    2012-12-01

    Over the past 15 years, the geoscience education community has grown substantially and developed broad and deep capacity for collaboration and dissemination of ideas. While this community is best viewed as emergent from complex interactions among changing educational needs and opportunities, we highlight the role of several large projects in the development of a network within this community. In the 1990s, three NSF projects came together to build a robust web infrastructure to support the production and dissemination of on-line resources: On The Cutting Edge (OTCE), Earth Exploration Toolbook, and Starting Point: Teaching Introductory Geoscience. Along with the contemporaneous Digital Library for Earth System Education, these projects engaged geoscience educators nationwide in exploring professional development experiences that produced lasting on-line resources, collaborative authoring of resources, and models for web-based support for geoscience teaching. As a result, a culture developed in the 2000s in which geoscience educators anticipated that resources for geoscience teaching would be shared broadly and that collaborative authoring would be productive and engaging. By this time, a diverse set of examples demonstrated the power of the web infrastructure in supporting collaboration, dissemination and professional development . Building on this foundation, more recent work has expanded both the size of the network and the scope of its work. Many large research projects initiated collaborations to disseminate resources supporting educational use of their data. Research results from the rapidly expanding geoscience education research community were integrated into the Pedagogies in Action website and OTCE. Projects engaged faculty across the nation in large-scale data collection and educational research. The Climate Literacy and Energy Awareness Network and OTCE engaged community members in reviewing the expanding body of on-line resources. Building Strong

  16. Comparative analysis of articulated and behavioural social networks in a social news sharing website

    NASA Astrophysics Data System (ADS)

    Kaltenbrunner, Andreas; Gonzalez, Gustavo; Ruiz De Querol, Ricard; Volkovich, Yana

    2011-12-01

    This study analyses and contrasts the explicit (articulated) and implicit (behavioural) social networks on the Spanish Digg-like social news website meneame.net. The explicit network is given in the form of declared but not necessarily bidirectional friendship links; the behavioural network is extracted from conversations through comments to the shared links. These two directed social networks and their intersection are analysed and described in detail, which leads to some important conclusions about user behaviour on link sharing websites and online conversation habits in general. We find that reply interactions are more likely to occur between non-friends and that these interactions are (if bidirectional) also more balanced in the case of non-friends. A k-core decomposition of the networks reveals a fundamental difference in the practice of establishing behavioural and articulated links.

  17. Accelerated Training for Large Feedforward Neural Networks

    NASA Technical Reports Server (NTRS)

    Stepniewski, Slawomir W.; Jorgensen, Charles C.

    1998-01-01

    In this paper we introduce a new training algorithm, the scaled variable metric (SVM) method. Our approach attempts to increase the convergence rate of the modified variable metric method. It is also combined with the RBackprop algorithm, which computes the product of the matrix of second derivatives (Hessian) with an arbitrary vector. The RBackprop method allows us to avoid computationally expensive, direct line searches. In addition, it can be utilized in the new, 'predictive' updating technique of the inverse Hessian approximation. We have used directional slope testing to adjust the step size and found that this strategy works exceptionally well in conjunction with the Rbackprop algorithm. Some supplementary, but nevertheless important enhancements to the basic training scheme such as improved setting of a scaling factor for the variable metric update and computationally more efficient procedure for updating the inverse Hessian approximation are presented as well. We summarize by comparing the SVM method with four first- and second- order optimization algorithms including a very effective implementation of the Levenberg-Marquardt method. Our tests indicate promising computational speed gains of the new training technique, particularly for large feedforward networks, i.e., for problems where the training process may be the most laborious.

  18. Survivable VON mapping with ambiguity similitude for differentiable maximum shared capacity in elastic optical networks

    NASA Astrophysics Data System (ADS)

    Yang, Hui; Zhu, Xiaoxu; Bai, Wei; Zhao, Yongli; Zhang, Jie; Liu, Zhu; Zhou, Ziguan; Ou, Qinghai

    2016-09-01

    Virtualization is considered to be a promising solution to support various emerging applications. This paper illustrates the problem of virtual mapping from a new perspective, and mainly focuses on survivable mapping of virtual networks and the potential trade-off between spectral resource usage effectiveness and failure resilience level. We design an optimum shared protection mapping (OSPM) scheme in elastic optical networks. A differentiable maximum shared capacity of each frequency slot is defined to more efficiently shared protection resource. In order to satisfy various assessment standards, a metric called ambiguity similitude is defined for the first time to give insight on the optimizing difficulty. Simulation results are presented to compare the outcome of the novel OSPM algorithm with traditional dedicated link protection and maximum shared protection mapping. By synthetic analysis, OSPM outperforms the other two schemes in terms of striking a perfect balance among blocking probability, resources utilization, protective success rate, and spectrum redundancy.

  19. Validating Large Scale Networks Using Temporary Local Scale Networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The USDA NRCS Soil Climate Analysis Network and NOAA Climate Reference Networks are nationwide meteorological and land surface data networks with soil moisture measurements in the top layers of soil. There is considerable interest in scaling these point measurements to larger scales for validating ...

  20. User Generated Content Consumption and Social Networking in Knowledge-Sharing OSNs

    NASA Astrophysics Data System (ADS)

    Lussier, Jake T.; Raeder, Troy; Chawla, Nitesh V.

    Knowledge-sharing online social networks are becoming increasingly pervasive and popular. While the user-to-user interactions in these networks have received substantial attention, the consumption of user generated content has not been studied extensively. In this work, we use data gathered from digg.com to present novel findings and draw important sociological conclusions regarding the intimate relationship between consumption and social networking. We first demonstrate that individuals' consumption habits influence their friend networks, consistent with the concept of homophily. We then show that one's social network can also influence the consumption of a submission through the activation of an extended friend network. Finally, we investigate the level of reciprocity, or balance, in the network and uncover relationships that are significantly less balanced than expected.

  1. Analyzing large biological datasets with association networks

    SciTech Connect

    Karpinets, T. V.; Park, B. H.; Uberbacher, E. C.

    2012-05-25

    Due to advances in high throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches for timely processing of the collected data into new knowledge. In this study we address this problem by developing a new approach for discovering modular structure, relationships and regularities in complex data. These goals are achieved by converting records of biological annotations of an object, like organism, gene, chemical, sequence, into networks (Anets) and rules (Arules) of the associated annotations. Anets are based on similarity of annotation profiles of objects and can be further analyzed and visualized providing a compact birds-eye view of most significant relationships in the collected data and a way of their clustering and classification. Arules are generated by Apriori considering each record of annotations as a transaction and augmenting each annotation item by its type. Arules provide a way to validate relationships discovered by Anets producing comprehensive statistics on frequently associated annotations and specific confident relationships among them. A combination of Anets and Arules represents condensed information on associations among the collected data, helping to discover new knowledge and generate hypothesis. As an example we have applied the approach to analyze bacterial metadata from the Genomes OnLine Database. The analysis allowed us to produce a map of sequenced bacterial and archaeal organisms based on their genomic, metabolic and physiological characteristics with three major clusters of metadata representing bacterial pathogens, environmental isolates, and plant symbionts. A signature profile of clustered annotations of environmental bacteria if compared with pathogens linked the aerobic respiration, the high GC content and the large genome size to diversity of metabolic activities and physiological features of the organisms.

  2. Control of Large-Scale Boolean Networks via Network Aggregation.

    PubMed

    Zhao, Yin; Ghosh, Bijoy K; Cheng, Daizhan

    2016-07-01

    A major challenge to solve problems in control of Boolean networks is that the computational cost increases exponentially when the number of nodes in the network increases. We consider the problem of controllability and stabilizability of Boolean control networks, address the increasing cost problem by partitioning the network graph into several subnetworks, and analyze the subnetworks separately. Easily verifiable necessary conditions for controllability and stabilizability are proposed for a general aggregation structure. For acyclic aggregation, we develop a sufficient condition for stabilizability. It dramatically reduces the computational complexity if the number of nodes in each block of the acyclic aggregation is small enough compared with the number of nodes in the entire Boolean network.

  3. Heuristic approaches for energy-efficient shared restoration in WDM networks

    NASA Astrophysics Data System (ADS)

    Alilou, Shahab

    In recent years, there has been ongoing research on the design of energy-efficient Wavelength Division Multiplexing (WDM) networks. The explosive growth of Internet traffic has led to increased power consumption of network components. Network survivability has also been a relevant research topic, as it plays a crucial role in assuring continuity of service with no disruption, regardless of network component failure. Network survivability mechanisms tend to utilize considerable resources such as spare capacity in order to protect and restore information. This thesis investigates techniques for reducing energy demand and enhancing energy efficiency in the context of network survivability. We propose two novel heuristic energy-efficient shared protection approaches for WDM networks. These approaches intend to save energy by setting on sleep mode devices that are not being used while providing shared backup paths to satisfy network survivability. The first approach exploits properties of a math series in order to assign weight to the network links. It aims at reducing power consumption at the network indirectly by aggregating traffic on a set of nodes and links with high traffic load level. Routing traffic on links and nodes that are already under utilization makes it possible for the links and nodes with no load to be set on sleep mode. The second approach is intended to dynamically route traffic through nodes and links with high traffic load level. Similar to the first approach, this approach computes a pair of paths for every newly arrived demand. It computes these paths for every new demand by comparing the power consumption of nodes and links in the network before the demand arrives with their potential power consumption if they are chosen along the paths of this demand. Simulations of two different networks were used to compare the total network power consumption obtained using the proposed techniques against a standard shared-path restoration scheme. Shared

  4. Network Computing Infrastructure to Share Tools and Data in Global Nuclear Energy Partnership

    NASA Astrophysics Data System (ADS)

    Kim, Guehee; Suzuki, Yoshio; Teshima, Naoya

    CCSE/JAEA (Center for Computational Science and e-Systems/Japan Atomic Energy Agency) integrated a prototype system of a network computing infrastructure for sharing tools and data to support the U.S. and Japan collaboration in GNEP (Global Nuclear Energy Partnership). We focused on three technical issues to apply our information process infrastructure, which are accessibility, security, and usability. In designing the prototype system, we integrated and improved both network and Web technologies. For the accessibility issue, we adopted SSL-VPN (Security Socket Layer-Virtual Private Network) technology for the access beyond firewalls. For the security issue, we developed an authentication gateway based on the PKI (Public Key Infrastructure) authentication mechanism to strengthen the security. Also, we set fine access control policy to shared tools and data and used shared key based encryption method to protect tools and data against leakage to third parties. For the usability issue, we chose Web browsers as user interface and developed Web application to provide functions to support sharing tools and data. By using WebDAV (Web-based Distributed Authoring and Versioning) function, users can manipulate shared tools and data through the Windows-like folder environment. We implemented the prototype system in Grid infrastructure for atomic energy research: AEGIS (Atomic Energy Grid Infrastructure) developed by CCSE/JAEA. The prototype system was applied for the trial use in the first period of GNEP.

  5. Distributed generation of shared RSA keys in mobile ad hoc networks

    NASA Astrophysics Data System (ADS)

    Liu, Yi-Liang; Huang, Qin; Shen, Ying

    2005-12-01

    Mobile Ad Hoc Networks is a totally new concept in which mobile nodes are able to communicate together over wireless links in an independent manner, independent of fixed physical infrastructure and centralized administrative infrastructure. However, the nature of Ad Hoc Networks makes them very vulnerable to security threats. Generation and distribution of shared keys for CA (Certification Authority) is challenging for security solution based on distributed PKI(Public-Key Infrastructure)/CA. The solutions that have been proposed in the literature and some related issues are discussed in this paper. The solution of a distributed generation of shared threshold RSA keys for CA is proposed in the present paper. During the process of creating an RSA private key share, every CA node only has its own private security. Distributed arithmetic is used to create the CA's private share locally, and that the requirement of centralized management institution is eliminated. Based on fully considering the Mobile Ad Hoc network's characteristic of self-organization, it avoids the security hidden trouble that comes by holding an all private security share of CA, with which the security and robustness of system is enhanced.

  6. Confucius Institutes: Distributed Leadership and Knowledge Sharing in a Worldwide Network

    ERIC Educational Resources Information Center

    Li, Hsi Chang; Mirmirani, Sam; Ilacqua, Joseph A.

    2009-01-01

    Purpose: The purpose of this paper is to focus on Confucius Institutes and assess the applicability of theories of leadership and knowledge sharing to multinational organizations and worldwide networks. Growth of multinational trade and decrease in international tension have facilitated the globalization of both profit-seeking and non-profit…

  7. More than a Master: Developing, Sharing, and Using Knowledge in School-University Research Networks

    ERIC Educational Resources Information Center

    Cornelissen, Frank; Daly, Alan J.; Liou, Yi-Hwa; van Swet, Jacqueline; Beijaard, Douwe; Bergen, Theo C. M.

    2014-01-01

    Postgraduate master's programs for in-service teachers may be a promising new avenue in developing research partnership networks that link schools and university and enable collaborative development, sharing and use of knowledge of teacher research. This study explores the way these knowledge processes originating from master's…

  8. Situated and Socially Shared Cognition in Practice. Designing a Collaborative Network Learning Experience for Adult Learners.

    ERIC Educational Resources Information Center

    Korhonen, Vesa

    The main purpose of this paper is to draw attention to assumptions that guide the instructional design process when implementing and organizing network-based learning environments in practice. In this case, the situated and socially shared cognition model and participation metaphor create the guiding paradigm for collaborative learning action,…

  9. Social Networking Privacy Control: Exploring University Variables Related to Young Adults' Sharing of Personally Identifiable Information

    ERIC Educational Resources Information Center

    Zimmerman, Melisa S.

    2014-01-01

    The growth of the Internet, and specifically social networking sites (SNSs) like Facebook, create opportunities for individuals to share private and identifiable information with a closed or open community. Internet crime has been on the rise and research has shown that criminals are using individuals' personal information pulled from social…

  10. World Bank's Global Development Learning Network: Sharing Knowledge Electronically between Nations To "Fight Poverty."

    ERIC Educational Resources Information Center

    Lorenzo, George

    2002-01-01

    Describes the Global Development Learning Network (GDLN), a satellite-driven global communication system developed by the World Bank to help developing countries fight poverty and share in a global exchange of information. Explains Distance Learning Centers that are used by private and public organizations and institutions for distance education…

  11. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

    PubMed

    Wang, Mingxun; Carver, Jeremy J; Phelan, Vanessa V; Sanchez, Laura M; Garg, Neha; Peng, Yao; Nguyen, Don Duy; Watrous, Jeramie; Kapono, Clifford A; Luzzatto-Knaan, Tal; Porto, Carla; Bouslimani, Amina; Melnik, Alexey V; Meehan, Michael J; Liu, Wei-Ting; Crüsemann, Max; Boudreau, Paul D; Esquenazi, Eduardo; Sandoval-Calderón, Mario; Kersten, Roland D; Pace, Laura A; Quinn, Robert A; Duncan, Katherine R; Hsu, Cheng-Chih; Floros, Dimitrios J; Gavilan, Ronnie G; Kleigrewe, Karin; Northen, Trent; Dutton, Rachel J; Parrot, Delphine; Carlson, Erin E; Aigle, Bertrand; Michelsen, Charlotte F; Jelsbak, Lars; Sohlenkamp, Christian; Pevzner, Pavel; Edlund, Anna; McLean, Jeffrey; Piel, Jörn; Murphy, Brian T; Gerwick, Lena; Liaw, Chih-Chuang; Yang, Yu-Liang; Humpf, Hans-Ulrich; Maansson, Maria; Keyzers, Robert A; Sims, Amy C; Johnson, Andrew R; Sidebottom, Ashley M; Sedio, Brian E; Klitgaard, Andreas; Larson, Charles B; Boya P, Cristopher A; Torres-Mendoza, Daniel; Gonzalez, David J; Silva, Denise B; Marques, Lucas M; Demarque, Daniel P; Pociute, Egle; O'Neill, Ellis C; Briand, Enora; Helfrich, Eric J N; Granatosky, Eve A; Glukhov, Evgenia; Ryffel, Florian; Houson, Hailey; Mohimani, Hosein; Kharbush, Jenan J; Zeng, Yi; Vorholt, Julia A; Kurita, Kenji L; Charusanti, Pep; McPhail, Kerry L; Nielsen, Kristian Fog; Vuong, Lisa; Elfeki, Maryam; Traxler, Matthew F; Engene, Niclas; Koyama, Nobuhiro; Vining, Oliver B; Baric, Ralph; Silva, Ricardo R; Mascuch, Samantha J; Tomasi, Sophie; Jenkins, Stefan; Macherla, Venkat; Hoffman, Thomas; Agarwal, Vinayak; Williams, Philip G; Dai, Jingqui; Neupane, Ram; Gurr, Joshua; Rodríguez, Andrés M C; Lamsa, Anne; Zhang, Chen; Dorrestein, Kathleen; Duggan, Brendan M; Almaliti, Jehad; Allard, Pierre-Marie; Phapale, Prasad; Nothias, Louis-Felix; Alexandrov, Theodore; Litaudon, Marc; Wolfender, Jean-Luc; Kyle, Jennifer E; Metz, Thomas O; Peryea, Tyler; Nguyen, Dac-Trung; VanLeer, Danielle; Shinn, Paul; Jadhav, Ajit; Müller, Rolf; Waters, Katrina M; Shi, Wenyuan; Liu, Xueting; Zhang, Lixin; Knight, Rob; Jensen, Paul R; Palsson, Bernhard Ø; Pogliano, Kit; Linington, Roger G; Gutiérrez, Marcelino; Lopes, Norberto P; Gerwick, William H; Moore, Bradley S; Dorrestein, Pieter C; Bandeira, Nuno

    2016-08-01

    The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

  12. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

    PubMed

    Wang, Mingxun; Carver, Jeremy J; Phelan, Vanessa V; Sanchez, Laura M; Garg, Neha; Peng, Yao; Nguyen, Don Duy; Watrous, Jeramie; Kapono, Clifford A; Luzzatto-Knaan, Tal; Porto, Carla; Bouslimani, Amina; Melnik, Alexey V; Meehan, Michael J; Liu, Wei-Ting; Crüsemann, Max; Boudreau, Paul D; Esquenazi, Eduardo; Sandoval-Calderón, Mario; Kersten, Roland D; Pace, Laura A; Quinn, Robert A; Duncan, Katherine R; Hsu, Cheng-Chih; Floros, Dimitrios J; Gavilan, Ronnie G; Kleigrewe, Karin; Northen, Trent; Dutton, Rachel J; Parrot, Delphine; Carlson, Erin E; Aigle, Bertrand; Michelsen, Charlotte F; Jelsbak, Lars; Sohlenkamp, Christian; Pevzner, Pavel; Edlund, Anna; McLean, Jeffrey; Piel, Jörn; Murphy, Brian T; Gerwick, Lena; Liaw, Chih-Chuang; Yang, Yu-Liang; Humpf, Hans-Ulrich; Maansson, Maria; Keyzers, Robert A; Sims, Amy C; Johnson, Andrew R; Sidebottom, Ashley M; Sedio, Brian E; Klitgaard, Andreas; Larson, Charles B; Boya P, Cristopher A; Torres-Mendoza, Daniel; Gonzalez, David J; Silva, Denise B; Marques, Lucas M; Demarque, Daniel P; Pociute, Egle; O'Neill, Ellis C; Briand, Enora; Helfrich, Eric J N; Granatosky, Eve A; Glukhov, Evgenia; Ryffel, Florian; Houson, Hailey; Mohimani, Hosein; Kharbush, Jenan J; Zeng, Yi; Vorholt, Julia A; Kurita, Kenji L; Charusanti, Pep; McPhail, Kerry L; Nielsen, Kristian Fog; Vuong, Lisa; Elfeki, Maryam; Traxler, Matthew F; Engene, Niclas; Koyama, Nobuhiro; Vining, Oliver B; Baric, Ralph; Silva, Ricardo R; Mascuch, Samantha J; Tomasi, Sophie; Jenkins, Stefan; Macherla, Venkat; Hoffman, Thomas; Agarwal, Vinayak; Williams, Philip G; Dai, Jingqui; Neupane, Ram; Gurr, Joshua; Rodríguez, Andrés M C; Lamsa, Anne; Zhang, Chen; Dorrestein, Kathleen; Duggan, Brendan M; Almaliti, Jehad; Allard, Pierre-Marie; Phapale, Prasad; Nothias, Louis-Felix; Alexandrov, Theodore; Litaudon, Marc; Wolfender, Jean-Luc; Kyle, Jennifer E; Metz, Thomas O; Peryea, Tyler; Nguyen, Dac-Trung; VanLeer, Danielle; Shinn, Paul; Jadhav, Ajit; Müller, Rolf; Waters, Katrina M; Shi, Wenyuan; Liu, Xueting; Zhang, Lixin; Knight, Rob; Jensen, Paul R; Palsson, Bernhard Ø; Pogliano, Kit; Linington, Roger G; Gutiérrez, Marcelino; Lopes, Norberto P; Gerwick, William H; Moore, Bradley S; Dorrestein, Pieter C; Bandeira, Nuno

    2016-08-01

    The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data. PMID:27504778

  13. A comparative analysis of the statistical properties of large mobile phone calling networks.

    PubMed

    Li, Ming-Xia; Jiang, Zhi-Qiang; Xie, Wen-Jie; Miccichè, Salvatore; Tumminello, Michele; Zhou, Wei-Xing; Mantegna, Rosario N

    2014-05-30

    Mobile phone calling is one of the most widely used communication methods in modern society. The records of calls among mobile phone users provide us a valuable proxy for the understanding of human communication patterns embedded in social networks. Mobile phone users call each other forming a directed calling network. If only reciprocal calls are considered, we obtain an undirected mutual calling network. The preferential communication behavior between two connected users can be statistically tested and it results in two Bonferroni networks with statistically validated edges. We perform a comparative analysis of the statistical properties of these four networks, which are constructed from the calling records of more than nine million individuals in Shanghai over a period of 110 days. We find that these networks share many common structural properties and also exhibit idiosyncratic features when compared with previously studied large mobile calling networks. The empirical findings provide us an intriguing picture of a representative large social network that might shed new lights on the modelling of large social networks.

  14. A comparative analysis of the statistical properties of large mobile phone calling networks

    NASA Astrophysics Data System (ADS)

    Li, Ming-Xia; Jiang, Zhi-Qiang; Xie, Wen-Jie; Miccichè, Salvatore; Tumminello, Michele; Zhou, Wei-Xing; Mantegna, Rosario N.

    2014-05-01

    Mobile phone calling is one of the most widely used communication methods in modern society. The records of calls among mobile phone users provide us a valuable proxy for the understanding of human communication patterns embedded in social networks. Mobile phone users call each other forming a directed calling network. If only reciprocal calls are considered, we obtain an undirected mutual calling network. The preferential communication behavior between two connected users can be statistically tested and it results in two Bonferroni networks with statistically validated edges. We perform a comparative analysis of the statistical properties of these four networks, which are constructed from the calling records of more than nine million individuals in Shanghai over a period of 110 days. We find that these networks share many common structural properties and also exhibit idiosyncratic features when compared with previously studied large mobile calling networks. The empirical findings provide us an intriguing picture of a representative large social network that might shed new lights on the modelling of large social networks.

  15. Molecular analysis reveals high compartmentalization in aphid-primary parasitoid networks and low parasitoid sharing between crop and noncrop habitats.

    PubMed

    Derocles, Stephane A P; Le Ralec, Anne; Besson, Mathilde M; Maret, Marion; Walton, Alan; Evans, Darren M; Plantegenest, Manuel

    2014-08-01

    The ecosystem service of insect pest regulation by natural enemies, such as primary parasitoids, may be enhanced by the presence of uncultivated, semi-natural habitats within agro-ecosystems, although quantifying such host-parasitoid interactions is difficult. Here, we use rRNA 16S gene sequencing to assess both the level of parasitism by Aphidiinae primary parasitoids and parasitoid identity on a large sample of aphids collected in cultivated and uncultivated agricultural habitats in Western France. We used these data to construct ecological networks to assess the level of compartmentalization between aphid and parasitoid food webs of cultivated and uncultivated habitats. We evaluated the extent to which uncultivated margins provided a resource for parasitoids shared between pest and nonpest aphids. We compared the observed quantitative ecological network described by our molecular approach to an empirical qualitative network based on aphid-parasitoid interactions from traditional rearing data found in the literature. We found that the molecular network was highly compartmentalized and that parasitoid sharing is relatively rare between aphids, especially between crop and noncrop compartments. Moreover, the few cases of putative shared generalist parasitoids were questionable and could be due to the lack of discrimination of cryptic species or from intraspecific host specialization. Our results suggest that apparent competition mediated by Aphidiinae parasitoids is probably rare in agricultural areas and that the contribution of field margins as a source of these biocontrol agents is much more limited than expected. Further large-scale (spatial and temporal) studies on other crops and noncrop habitats are needed to confirm this.

  16. A Scalable Long-Reach Wavelength-Division Multiplexing Access Network Sharing Both Fiber Protection and Broadcasting Services

    NASA Astrophysics Data System (ADS)

    Feng, Chen; Gan, Chaoqin; Gao, Ziyue; Guo, Su; Li, Wei; Fang, Yiqin

    2014-07-01

    A novel scalable wavelength-division multiplexing access network is proposed in this article. By newly designing the remote node, this network can not only support the long-reach transmission and broadcasting services, it can also have flexible scalability and the ability of sharing fiber protection. These make this network have great resilient capability. Also, this scheme still has the characteristic of Rayleigh backscattering mitigation and shared-seeding light of upstream signals. The simulation results indicate this network has good performance.

  17. Comparison of large networks with sub-sampling strategies

    NASA Astrophysics Data System (ADS)

    Ali, Waqar; Wegner, Anatol E.; Gaunt, Robert E.; Deane, Charlotte M.; Reinert, Gesine

    2016-07-01

    Networks are routinely used to represent large data sets, making the comparison of networks a tantalizing research question in many areas. Techniques for such analysis vary from simply comparing network summary statistics to sophisticated but computationally expensive alignment-based approaches. Most existing methods either do not generalize well to different types of networks or do not provide a quantitative similarity score between networks. In contrast, alignment-free topology based network similarity scores empower us to analyse large sets of networks containing different types and sizes of data. Netdis is such a score that defines network similarity through the counts of small sub-graphs in the local neighbourhood of all nodes. Here, we introduce a sub-sampling procedure based on neighbourhoods which links naturally with the framework of network comparisons through local neighbourhood comparisons. Our theoretical arguments justify basing the Netdis statistic on a sample of similar-sized neighbourhoods. Our tests on empirical and synthetic datasets indicate that often only 10% of the neighbourhoods of a network suffice for optimal performance, leading to a drastic reduction in computational requirements. The sampling procedure is applicable even when only a small sample of the network is known, and thus provides a novel tool for network comparison of very large and potentially incomplete datasets.

  18. Comparison of large networks with sub-sampling strategies

    PubMed Central

    Ali, Waqar; Wegner, Anatol E.; Gaunt, Robert E.; Deane, Charlotte M.; Reinert, Gesine

    2016-01-01

    Networks are routinely used to represent large data sets, making the comparison of networks a tantalizing research question in many areas. Techniques for such analysis vary from simply comparing network summary statistics to sophisticated but computationally expensive alignment-based approaches. Most existing methods either do not generalize well to different types of networks or do not provide a quantitative similarity score between networks. In contrast, alignment-free topology based network similarity scores empower us to analyse large sets of networks containing different types and sizes of data. Netdis is such a score that defines network similarity through the counts of small sub-graphs in the local neighbourhood of all nodes. Here, we introduce a sub-sampling procedure based on neighbourhoods which links naturally with the framework of network comparisons through local neighbourhood comparisons. Our theoretical arguments justify basing the Netdis statistic on a sample of similar-sized neighbourhoods. Our tests on empirical and synthetic datasets indicate that often only 10% of the neighbourhoods of a network suffice for optimal performance, leading to a drastic reduction in computational requirements. The sampling procedure is applicable even when only a small sample of the network is known, and thus provides a novel tool for network comparison of very large and potentially incomplete datasets. PMID:27380992

  19. Displacement and deformation measurement for large structures by camera network

    NASA Astrophysics Data System (ADS)

    Shang, Yang; Yu, Qifeng; Yang, Zhen; Xu, Zhiqiang; Zhang, Xiaohu

    2014-03-01

    A displacement and deformation measurement method for large structures by a series-parallel connection camera network is presented. By taking the dynamic monitoring of a large-scale crane in lifting operation as an example, a series-parallel connection camera network is designed, and the displacement and deformation measurement method by using this series-parallel connection camera network is studied. The movement range of the crane body is small, and that of the crane arm is large. The displacement of the crane body, the displacement of the crane arm relative to the body and the deformation of the arm are measured. Compared with a pure series or parallel connection camera network, the designed series-parallel connection camera network can be used to measure not only the movement and displacement of a large structure but also the relative movement and deformation of some interesting parts of the large structure by a relatively simple optical measurement system.

  20. Food-Sharing Networks in Lamalera, Indonesia: Reciprocity, Kinship, and Distance

    PubMed Central

    Nolin, David A.

    2010-01-01

    Exponential random graph modeling (ERGM) is used here to test hypotheses derived from human behavioral ecology about the adaptive nature of human food sharing. Respondents in all (n=317) households in the fishing and sea-hunting village of Lamalera, Indonesia were asked to name those households to whom they had more frequently given (and from whom they had more frequently received) food during the preceding sea-hunting season. The responses were used to construct a social network of between-household food-sharing relationships in the village. The results show that kinship, proximity, and reciprocal sharing all strongly increase the probability of giving food to a household. The effects of kinship and distance are relatively independent of each other, while reciprocity is more common among residentially and genealogically close households. The results show support for reciprocal altruism as a motivation for food sharing while kinship and distance appear to be important partner-choice criteria. PMID:21218145

  1. Tractable Analysis for Large Social Networks

    ERIC Educational Resources Information Center

    Zhang, Bin

    2012-01-01

    Social scientists usually are more interested in consumers' dichotomous choice, such as purchase a product or not, adopt a technology or not, etc. However, up to date, there is nearly no model can help us solve the problem of multi-network effects comparison with a dichotomous dependent variable. Furthermore, the study of multi-network…

  2. Visualization techniques and tools for large geo-physical networks

    NASA Astrophysics Data System (ADS)

    Nocke, Thomas; Buschmann, Stefan; Donges, Jonathan F.; Marwan, Norbert

    2016-04-01

    Network analysis is an important approach in studying complex phenomena within geophysical observation and simulation data. This field produces increasing numbers of large geo-referenced networks to be analyzed. Particular focus lies on the network analysis of the complex statistical interrelationship structure within climatological fields. The typical procedure for such network analyzes is the extraction of network measures in combination with static standard visualization methods. To analyze the visualization challenges within this field, we performed a questionnaire with climate and complex system scientists, and identified a strong requirement for solutions visualizing large and very large geo-referenced networks by providing alternative mappings for static plots and allowing for interactive visualization for networks with 100.000 or even millions of edges. In addition, the questionnaire revealed, that existing interactive visualization methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. Within this presentation, we illustrate how interactive visual analytics methods in combination with geo-visualisation can be tailored for visual large climate network investigation (see as well Nocke et al. 2015). Therefore, we present a survey of requirements of network analysts and the related challenges and, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualization techniques and tools, underpinned with concrete examples from climate network research and innovative solutions (e.g. alternative projections, 3D layered networks) implemented within the network visualization system GTX. References: Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H.-J. Schulz, and C. Tominski: Review: Visual analytics of climate networks, Nonlinear Processes in Geophysics, 22, 545-570, doi:10.5194/npg-22-545-2015, 2015

  3. Measuring structural similarity in large online networks.

    PubMed

    Shi, Yongren; Macy, Michael

    2016-09-01

    Structural similarity based on bipartite graphs can be used to detect meaningful communities, but the networks have been tiny compared to massive online networks. Scalability is important in applications involving tens of millions of individuals with highly skewed degree distributions. Simulation analysis holding underlying similarity constant shows that two widely used measures - Jaccard index and cosine similarity - are biased by the distribution of out-degree in web-scale networks. However, an alternative measure, the Standardized Co-incident Ratio (SCR), is unbiased. We apply SCR to members of Congress, musical artists, and professional sports teams to show how massive co-following on Twitter can be used to map meaningful affiliations among cultural entities, even in the absence of direct connections to one another. Our results show how structural similarity can be used to map cultural alignments and demonstrate the potential usefulness of social media data in the study of culture, politics, and organizations across the social and behavioral sciences. PMID:27480374

  4. Sharing and Specificity of Co-expression Networks across 35 Human Tissues.

    PubMed

    Pierson, Emma; Koller, Daphne; Battle, Alexis; Mostafavi, Sara; Ardlie, Kristin G; Getz, Gad; Wright, Fred A; Kellis, Manolis; Volpi, Simona; Dermitzakis, Emmanouil T

    2015-05-01

    To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.

  5. Does motor imagery share neural networks with executed movement: a multivariate fMRI analysis

    PubMed Central

    Sharma, Nikhil; Baron, Jean-Claude

    2013-01-01

    Introduction: Motor imagery (MI) is the mental rehearsal of a motor first person action-representation. There is interest in using MI to access the motor network after stroke. Conventional fMRI modeling has shown that MI and executed movement (EM) activate similar cortical areas but it remains unknown whether they share cortical networks. Proving this is central to using MI to access the motor network and as a form of motor training. Here we use multivariate analysis (tensor independent component analysis-TICA) to map the array of neural networks involved during MI and EM. Methods: Fifteen right-handed healthy volunteers (mean-age 28.4 years) were recruited and screened for their ability to carry out MI (Chaotic MI Assessment). fMRI consisted of an auditory-paced (1 Hz) right hand finger-thumb opposition sequence (2,3,4,5; 2…) with two separate runs acquired (MI & rest and EM & rest: block design). No distinction was made between MI and EM until the final stage of processing. This allowed TICA to identify independent-components (IC) that are common or distinct to both tasks with no prior assumptions. Results: TICA defined 52 ICs. Non-significant ICs and those representing artifact were excluded. Components in which the subject scores were significantly different to zero (for either EM or MI) were included. Seven IC remained. There were IC's shared between EM and MI involving the contralateral BA4, PMd, parietal areas and SMA. IC's exclusive to EM involved the contralateral BA4, S1 and ipsilateral cerebellum whereas the IC related exclusively to MI involved ipsilateral BA4 and PMd. Conclusion: In addition to networks specific to each task indicating a degree of independence, we formally demonstrate here for the first time that MI and EM share cortical networks. This significantly strengthens the rationale for using MI to access the motor networks, but the results also highlight important differences. PMID:24062666

  6. Cross-species transcriptional network analysis defines shared inflammatory responses in murine and human lupus nephritis.

    PubMed

    Berthier, Celine C; Bethunaickan, Ramalingam; Gonzalez-Rivera, Tania; Nair, Viji; Ramanujam, Meera; Zhang, Weijia; Bottinger, Erwin P; Segerer, Stephan; Lindenmeyer, Maja; Cohen, Clemens D; Davidson, Anne; Kretzler, Matthias

    2012-07-15

    Lupus nephritis (LN) is a serious manifestation of systemic lupus erythematosus. Therapeutic studies in mouse LN models do not always predict outcomes of human therapeutic trials, raising concerns about the human relevance of these preclinical models. In this study, we used an unbiased transcriptional network approach to define, in molecular terms, similarities and differences among three lupus models and human LN. Genome-wide gene-expression networks were generated using natural language processing and automated promoter analysis and compared across species via suboptimal graph matching. The three murine models and human LN share both common and unique features. The 20 commonly shared network nodes reflect the key pathologic processes of immune cell infiltration/activation, endothelial cell activation/injury, and tissue remodeling/fibrosis, with macrophage/dendritic cell activation as a dominant cross-species shared transcriptional pathway. The unique nodes reflect differences in numbers and types of infiltrating cells and degree of remodeling among the three mouse strains. To define mononuclear phagocyte-derived pathways in human LN, gene sets activated in isolated NZB/W renal mononuclear cells were compared with human LN kidney profiles. A tissue compartment-specific macrophage-activation pattern was seen, with NF-κB1 and PPARγ as major regulatory nodes in the tubulointerstitial and glomerular networks, respectively. Our study defines which pathologic processes in murine models of LN recapitulate the key transcriptional processes active in human LN and suggests that there are functional differences between mononuclear phagocytes infiltrating different renal microenvironments.

  7. Incentive Mechanism for P2P Content Sharing over Heterogenous Access Networks

    NASA Astrophysics Data System (ADS)

    Sato, Kenichiro; Hashimoto, Ryo; Yoshino, Makoto; Shinkuma, Ryoichi; Takahashi, Tatsuro

    In peer-to-peer (P2P) content sharing, users can share their content by contributing their own resources to one another. However, since there is no incentive for contributing contents or resources to others, users may attempt to obtain content without any contribution. To motivate users to contribute their resources to the service, incentive-rewarding mechanisms have been proposed. On the other hand, emerging wireless technologies, such as IEEE 802.11 wireless local area networks, beyond third generation (B3G) cellular networks and mobile WiMAX, provide high-speed Internet access for wireless users. Using these high-speed wireless access, wireless users can use P2P services and share their content with other wireless users and with fixed users. However, this diversification of access networks makes it difficult to appropriately assign rewards to each user according to their contributions. This is because the cost necessary for contribution is different in different access networks. In this paper, we propose a novel incentive-rewarding mechanism called EMOTIVER that can assign rewards to users appropriately. The proposed mechanism uses an external evaluator and interactive learning agents. We also investigate a way of appropriately controlling rewards based on the system service's quality and managing policy.

  8. LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

    SciTech Connect

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.

  9. Advances in Exponential Random Graph (p*) Models Applied to a Large Social Network

    PubMed Central

    Goodreau, Steven M.

    2007-01-01

    Recent advances in statistical network analysis based on the family of exponential random graph (ERG) models have greatly improved our ability to conduct inference on dependence in large social networks (Snijders 2002, Pattison and Robins 2002, Handcock 2002, Handcock 2003, Snijders et al. 2006, Hunter et al. 2005, Goodreau et al. 2005, previous papers this issue). This paper applies advances in both model parameterizations and computational algorithms to an examination of the structure observed in an adolescent friendship network of 1,681 actors from the National Longitudinal Study of Adolescent Health (AddHealth). ERG models of social network structure are fit using the R package statnet, and their adequacy assessed through comparison of model predictions with the observed data for higher-order network statistics. For this friendship network, the commonly used model of Markov dependence leads to the problems of degeneracy discussed by Handcock (2002, 2003). On the other hand, model parameterizations introduced by Snijders et al (2006) and Hunter and Handcock (2006) avoid degeneracy and provide reasonable fit to the data. Degree-only models did a poor job of capturing observed network structure; those that did best included terms both for heterogeneous mixing on exogenous attributes (grade and self-reported race) as well as endogenous clustering. Networks simulated from this model were largely consistent with the observed network on multiple higher-order network statistics, including the number of triangles, the size of the largest component, the overall reachability, the distribution of geodesic distances, the degree distribution, and the shared partner distribution. The ability to fit such models to large datasets and to make inference about the underling processes generating the network represents a major advance in the field of statistical network analysis. PMID:18449326

  10. Multisector Health Policy Networks in 15 Large US Cities

    PubMed Central

    Leider, J. P.; Carothers, Bobbi J.; Castrucci, Brian C.; Hearne, Shelley

    2016-01-01

    Context: Local health departments (LHDs) have historically not prioritized policy development, although it is one of the 3 core areas they address. One strategy that may influence policy in LHD jurisdictions is the formation of partnerships across sectors to work together on local public health policy. Design: We used a network approach to examine LHD local health policy partnerships across 15 large cities from the Big Cities Health Coalition. Setting/Participants: We surveyed the health departments and their partners about their working relationships in 5 policy areas: core local funding, tobacco control, obesity and chronic disease, violence and injury prevention, and infant mortality. Outcome Measures: Drawing on prior literature linking network structures with performance, we examined network density, transitivity, centralization and centrality, member diversity, and assortativity of ties. Results: Networks included an average of 21.8 organizations. Nonprofits and government agencies made up the largest proportions of the networks, with 28.8% and 21.7% of network members, whereas for-profits and foundations made up the smallest proportions in all of the networks, with just 1.2% and 2.4% on average. Mean values of density, transitivity, diversity, assortativity, centralization, and centrality showed similarity across policy areas and most LHDs. The tobacco control and obesity/chronic disease networks were densest and most diverse, whereas the infant mortality policy networks were the most centralized and had the highest assortativity. Core local funding policy networks had lower scores than other policy area networks by most network measures. Conclusion: Urban LHDs partner with organizations from diverse sectors to conduct local public health policy work. Network structures are similar across policy areas jurisdictions. Obesity and chronic disease, tobacco control, and infant mortality networks had structures consistent with higher performing networks, whereas

  11. Country food sharing networks, household structure, and implications for understanding food insecurity in Arctic Canada.

    PubMed

    Collings, Peter; Marten, Meredith G; Pearce, Tristan; Young, Alyson G

    2016-01-01

    We examine the cultural context of food insecurity among Inuit in Ulukhaktok, Northwest Territories, Canada. An analysis of the social network of country food exchanges among 122 households in the settlement reveals that a household's betweenness centrality-a measure of brokerage-in the country food network is predicted by the age of the household. The households of married couples were better positioned within the sharing network than were the households of single females or single males. Households with an active hunter or elder were also better positioned in the network. The households of single men and women appear to experience limited access to country food, a considerable problem given the increasing number of single-adult households over time. We conclude that the differences between how single women and single men experience constrained access to country foods may partially account for previous findings that single women in arctic settlements appear to be at particular risk for food insecurity. PMID:26595315

  12. A Group Based Key Sharing and Management Algorithm for Vehicular Ad Hoc Networks

    PubMed Central

    Moharram, Mohammed Morsi; Azam, Farzana

    2014-01-01

    Vehicular ad hoc networks (VANETs) are one special type of ad hoc networks that involves vehicles on roads. Typically like ad hoc networks, broadcast approach is used for data dissemination. Blind broadcast to each and every node results in exchange of useless and irrelevant messages and hence creates an overhead. Unicasting is not preferred in ad-hoc networks due to the dynamic topology and the resource requirements as compared to broadcasting. Simple broadcasting techniques create several problems on privacy, disturbance, and resource utilization. In this paper, we propose media mixing algorithm to decide what information should be provided to each user and how to provide such information. Results obtained through simulation show that fewer number of keys are needed to share compared to simple broadcasting. Privacy is also enhanced through this approach. PMID:24587749

  13. Extracting and Utilizing Social Networks from Log Files of Shared Workspaces

    NASA Astrophysics Data System (ADS)

    Nasirifard, Peyman; Peristeras, Vassilios; Hayes, Conor; Decker, Stefan

    Log files of online shared workspaces contain rich information that can be further analyzed. In this paper, log-file information is used to extract object-centric and user-centric social networks. The object-centric social networks are used as a means for assigning concept-based expertise elements to users based on the documents that they created, revised or read. The user-centric social networks are derived from users working on common documents. Weights, called the Cooperation Index, are assigned to links between users in a user-centric social network, which indicates how closely two people have collaborated together, based on their history. We also present a set of tools that was developed to realize our approach.

  14. Country food sharing networks, household structure, and implications for understanding food insecurity in Arctic Canada.

    PubMed

    Collings, Peter; Marten, Meredith G; Pearce, Tristan; Young, Alyson G

    2016-01-01

    We examine the cultural context of food insecurity among Inuit in Ulukhaktok, Northwest Territories, Canada. An analysis of the social network of country food exchanges among 122 households in the settlement reveals that a household's betweenness centrality-a measure of brokerage-in the country food network is predicted by the age of the household. The households of married couples were better positioned within the sharing network than were the households of single females or single males. Households with an active hunter or elder were also better positioned in the network. The households of single men and women appear to experience limited access to country food, a considerable problem given the increasing number of single-adult households over time. We conclude that the differences between how single women and single men experience constrained access to country foods may partially account for previous findings that single women in arctic settlements appear to be at particular risk for food insecurity.

  15. A group based key sharing and management algorithm for vehicular ad hoc networks.

    PubMed

    Khan, Zeeshan Shafi; Moharram, Mohammed Morsi; Alaraj, Abdullah; Azam, Farzana

    2014-01-01

    Vehicular ad hoc networks (VANETs) are one special type of ad hoc networks that involves vehicles on roads. Typically like ad hoc networks, broadcast approach is used for data dissemination. Blind broadcast to each and every node results in exchange of useless and irrelevant messages and hence creates an overhead. Unicasting is not preferred in ad-hoc networks due to the dynamic topology and the resource requirements as compared to broadcasting. Simple broadcasting techniques create several problems on privacy, disturbance, and resource utilization. In this paper, we propose media mixing algorithm to decide what information should be provided to each user and how to provide such information. Results obtained through simulation show that fewer number of keys are needed to share compared to simple broadcasting. Privacy is also enhanced through this approach.

  16. On-demand Overlay Networks for Large Scientific Data Transfers

    SciTech Connect

    Ramakrishnan, Lavanya; Guok, Chin; Jackson, Keith; Kissel, Ezra; Swany, D. Martin; Agarwal, Deborah

    2009-10-12

    Large scale scientific data transfers are central to scientific processes. Data from large experimental facilities have to be moved to local institutions for analysis or often data needs to be moved between local clusters and large supercomputing centers. In this paper, we propose and evaluate a network overlay architecture to enable highthroughput, on-demand, coordinated data transfers over wide-area networks. Our work leverages Phoebus and On-demand Secure Circuits and AdvanceReservation System (OSCARS) to provide high performance wide-area network connections. OSCARS enables dynamic provisioning of network paths with guaranteed bandwidth and Phoebus enables the coordination and effective utilization of the OSCARS network paths. Our evaluation shows that this approach leads to improved end-to-end data transfer throughput with minimal overheads. The achievedthroughput using our overlay was limited only by the ability of the end hosts to sink the data.

  17. Towards Online Multiresolution Community Detection in Large-Scale Networks

    PubMed Central

    Huang, Jianbin; Sun, Heli; Liu, Yaguang; Song, Qinbao; Weninger, Tim

    2011-01-01

    The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks. PMID:21887325

  18. Truthful Channel Sharing for Self Coexistence of Overlapping Medical Body Area Networks

    PubMed Central

    Dutkiewicz, Eryk; Zheng, Guanglou

    2016-01-01

    As defined by IEEE 802.15.6 standard, channel sharing is a potential method to coordinate inter-network interference among Medical Body Area Networks (MBANs) that are close to one another. However, channel sharing opens up new vulnerabilities as selfish MBANs may manipulate their online channel requests to gain unfair advantage over others. In this paper, we address this issue by proposing a truthful online channel sharing algorithm and a companion protocol that allocates channel efficiently and truthfully by punishing MBANs for misreporting their channel request parameters such as time, duration and bid for the channel. We first present an online channel sharing scheme for unit-length channel requests and prove that it is truthful. We then generalize our model to settings with variable-length channel requests, where we propose a critical value based channel pricing and preemption scheme. A bid adjustment procedure prevents unbeneficial preemption by artificially raising the ongoing winner’s bid controlled by a penalty factor λ. Our scheme can efficiently detect selfish behaviors by monitoring a trust parameter α of each MBAN and punish MBANs from cheating by suspending their requests. Our extensive simulation results show our scheme can achieve a total profit that is more than 85% of the offline optimum method in the typical MBAN settings. PMID:26844888

  19. Optimization of communication network topology for navigation sharing among distributed satellites

    NASA Astrophysics Data System (ADS)

    Dang, Zhaohui; Zhang, Yulin

    2013-01-01

    Navigation sharing among distributed satellites is quite important for coordinated motion and collision avoidance. This paper proposes optimization methods of the communication network topology to achieve navigation sharing. The whole communication network constructing by inter-satellite links are considered as a topology graph. The aim of this paper is to find the communication network topology with minimum communication connections' number (MCCN) in different conditions. It has found that the communication capacity and the number of channels are two key parameters affecting the results. The model of MCCN topology for navigation sharing is established and corresponding method is designed. Two main scenarios, viz., homogeneous case and heterogeneous case, are considered. For the homogeneous case where each member has the same communication capacity, it designs a construction method (Algorithm 1) to find the MCCN topology. For the heterogeneous case, it introduces a modified genetic algorithm (Algorithm 2) to find the MCCN topology. When considering the fact that the number of channels is limited, the Algorithm 2 is further modified by adding a penalized term in the fitness function. The effectiveness of these algorithms is all proved in theoretical. Three examples are further tested to illustrate the methods developed in this paper.

  20. Simulation of large systems with neural networks

    SciTech Connect

    Paez, T.L.

    1994-09-01

    Artificial neural networks (ANNs) have been shown capable of simulating the behavior of complex, nonlinear, systems, including structural systems. Under certain circumstances, it is desirable to simulate structures that are analyzed with the finite element method. For example, when we perform a probabilistic analysis with the Monte Carlo method, we usually perform numerous (hundreds or thousands of) repetitions of a response simulation with different input and system parameters to estimate the chance of specific response behaviors. In such applications, efficiency in computation of response is critical, and response simulation with ANNs can be valuable. However, finite element analyses of complex systems involve the use of models with tens or hundreds of thousands of degrees of freedom, and ANNs are practically limited to simulations that involve far fewer variables. This paper develops a technique for reducing the amount of information required to characterize the response of a general structure. We show how the reduced information can be used to train a recurrent ANN. Then the trained ANN can be used to simulate the reduced behavior of the original system, and the reduction transformation can be inverted to provide a simulation of the original system. A numerical example is presented.

  1. When Sharing Is a Bad Idea: The Effects of Online Social Network Engagement and Sharing Passwords with Friends on Cyberbullying Involvement.

    PubMed

    Meter, Diana J; Bauman, Sheri

    2015-08-01

    Every day, children and adolescents communicate online via social networking sites (SNSs). They also report sharing passwords with peers and friends, a potentially risky behavior in regard to cyber safety. This longitudinal study tested the hypotheses that social network engagement in multiple settings would predict more cyberbullying involvement over time, and that youth who reported sharing passwords would also experience an increase in cyberbullying involvement. Data were collected at two time points one year apart from 1,272 third through eighth grade students. In line with the first study hypothesis, participating in more online SNSs was associated with increased cyberbullying involvement over time, as well as sharing passwords over time. Cyberbullying involvement at T1 predicted decreases in sharing passwords over time, suggesting that youth become aware of the dangers of sharing passwords as a result of their experience. Sharing passwords at T1 was unrelated to cyberbullying involvement at T2. Although it seems that youth may be learning from their previous mistakes, due to the widespread use of social media and normality of sharing passwords among young people, it is important to continue to educate youth about cyber safety and risky online behavior.

  2. When Sharing Is a Bad Idea: The Effects of Online Social Network Engagement and Sharing Passwords with Friends on Cyberbullying Involvement.

    PubMed

    Meter, Diana J; Bauman, Sheri

    2015-08-01

    Every day, children and adolescents communicate online via social networking sites (SNSs). They also report sharing passwords with peers and friends, a potentially risky behavior in regard to cyber safety. This longitudinal study tested the hypotheses that social network engagement in multiple settings would predict more cyberbullying involvement over time, and that youth who reported sharing passwords would also experience an increase in cyberbullying involvement. Data were collected at two time points one year apart from 1,272 third through eighth grade students. In line with the first study hypothesis, participating in more online SNSs was associated with increased cyberbullying involvement over time, as well as sharing passwords over time. Cyberbullying involvement at T1 predicted decreases in sharing passwords over time, suggesting that youth become aware of the dangers of sharing passwords as a result of their experience. Sharing passwords at T1 was unrelated to cyberbullying involvement at T2. Although it seems that youth may be learning from their previous mistakes, due to the widespread use of social media and normality of sharing passwords among young people, it is important to continue to educate youth about cyber safety and risky online behavior. PMID:26252928

  3. Predictive coding accounts of shared representations in parieto-insular networks.

    PubMed

    Ishida, Hiroaki; Suzuki, Keisuke; Grandi, Laura Clara

    2015-04-01

    The discovery of mirror neurons in the ventral premotor cortex (area F5) and inferior parietal cortex (area PFG) in the macaque monkey brain has provided the physiological evidence for direct matching of the intrinsic motor representations of the self and the visual image of the actions of others. The existence of mirror neurons implies that the brain has mechanisms reflecting shared self and other action representations. This may further imply that the neural basis self-body representations may also incorporate components that are shared with other-body representations. It is likely that such a mechanism is also involved in predicting other's touch sensations and emotions. However, the neural basis of shared body representations has remained unclear. Here, we propose a neural basis of body representation of the self and of others in both human and non-human primates. We review a series of behavioral and physiological findings which together paint a picture that the systems underlying such shared representations require integration of conscious exteroception and interoception subserved by a cortical sensory-motor network involving parieto-inner perisylvian circuits (the ventral intraparietal area [VIP]/inferior parietal area [PFG]-secondary somatosensory cortex [SII]/posterior insular cortex [pIC]/anterior insular cortex [aIC]). Based on these findings, we propose a computational mechanism of the shared body representation in the predictive coding (PC) framework. Our mechanism proposes that processes emerging from generative models embedded in these specific neuronal circuits play a pivotal role in distinguishing a self-specific body representation from a shared one. The model successfully accounts for normal and abnormal shared body phenomena such as mirror-touch synesthesia and somatoparaphrenia. In addition, it generates a set of testable experimental predictions. PMID:25447372

  4. Learning Topology and Dynamics of Large Recurrent Neural Networks

    NASA Astrophysics Data System (ADS)

    She, Yiyuan; He, Yuejia; Wu, Dapeng

    2014-11-01

    Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms. This paper studies how to identify all authentic connections and estimate system parameters of a recurrent network, given a sequence of node observations. This task becomes extremely challenging in modern network applications, because the available observations are usually very noisy and limited, and the associated dynamical system is strongly nonlinear. By formulating the problem as multivariate sparse sigmoidal regression, we develop simple-to-implement network learning algorithms, with rigorous convergence guarantee in theory, for a variety of sparsity-promoting penalty forms. A quantile variant of progressive recurrent network screening is proposed for efficient computation and allows for direct cardinality control of network topology in estimation. Moreover, we investigate recurrent network stability conditions in Lyapunov's sense, and integrate such stability constraints into sparse network learning. Experiments show excellent performance of the proposed algorithms in network topology identification and forecasting.

  5. Assessment on knowledge network sharing capability of industrial cluster based on dempster-shafer theory of evidence.

    PubMed

    Dai, Shengli; Zhang, Hailin

    2014-01-01

    Based on Theory of Evidence and reviewing research papers concerned, a concept model of knowledge sharing network among industrial cluster firms, which can be applied to assess knowledge sharing capacity, has been built. Next, the authors create a set of assessment index systems including twelve subindexes under four principle indexes. In this study, ten experts in the same field were invited to score all the indexes of knowledge sharing capacity concerning one certain industrial cluster. The research result shows relatively high knowledge network sharing capacity among the certain industrial cluster firms. Another conclusion is that the assessment method with Theory of Evidence is feasible to conduct such a research.

  6. The development and achievement of a healthy cities network in Taiwan: sharing leadership and partnership building.

    PubMed

    Hu, Susan C; Kuo, Hsien-Wen

    2016-03-01

    The World Health Organization (WHO) Healthy Cities (HC) projects are the best known of the settings-based approaches to health promotion. They engage local governments in health development through a process of political commitment, institutional change, capacity-building, partnership-based planning and innovative projects. Many cities have promoted HC projects in Taiwan since 2002. In 2008, the Taiwan Alliance for Healthy Cities (TAHC) was launched to assist local governments in effectively establishing, operating and promoting HC projects. In this article, we share our experiences of establishing a platform and network to promote the HC program in Taiwan. Based on individual city profiles and governance in Taiwan, the TAHC developed a well-organized framework and model to encourage strong leadership in local governments and to promote participation and engagement in their communities. In the last 6 years, leaders from Taiwan's local governments in HC networks have integrated the HC concepts into their governance models, actively engaging and combining various resources with practical expertise and private sectors. The network of health in Taiwan allows each city to develop its unique perspective on the HC projects. Using this method, not only local government meets its needs, but also increases governance efficiency and effectiveness, resulting in the promotion of its citizens' overall sustainable urban health development. This HC network in Taiwan has partnerships with government and non-governmental organizations (NGOs), with academic support and citizen involvement, a dynamic data collection system and demonstrated leadership in the sharing of information in the Asian region. PMID:27199013

  7. Large-scale structure of time evolving citation networks

    NASA Astrophysics Data System (ADS)

    Leicht, E. A.; Clarkson, G.; Shedden, K.; Newman, M. E. J.

    2007-09-01

    In this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape.

  8. One dimensional modeling of blood flow in large networks

    NASA Astrophysics Data System (ADS)

    Wang, Xiaofei; Lagree, Pierre-Yves; Fullana, Jose-Maria; Lorthois, Sylvie; Institut de Mecanique des Fluides de Toulouse Collaboration

    2014-11-01

    A fast and valid simulation of blood flow in large networks of vessels can be achieved with a one-dimensional viscoelastic model. In this paper, we developed a parallel code with this model and computed several networks: a circle of arteries, a human systemic network with 55 arteries and a vascular network of mouse kidney with more than one thousand segments. The numerical results were verified and the speedup of parallel computing was tested on multi-core computers. The evolution of pressure distributions in all the networks were visualized and we can see clearly the propagation patterns of the waves. This provides us a convenient tool to simulate blood flow in networks.

  9. Distributed Spectrum Sharing for Video Streaming in Cognitive Radio Ad Hoc Networks

    NASA Astrophysics Data System (ADS)

    Ding, Lei; Pudlewski, Scott; Melodia, Tommaso; Batalama, Stella; Matyjas, John D.; Medley, Michael J.

    A distributed joint routing and spectrum sharing algorithm for video streaming applications over cognitive radio ad hoc networks is proposed in this article. The proposed cross-layer control scheme dynamically allocates routes, spectrum and power to maximize the network throughput under the constraints posed by delay-sensitive video applications. The algorithm evaluates the expected delay of competing flows in single-hop and two-hop networks considering the time-varying spectrum condition and occupancy, traffic characteristics, and the condition of queues at intermediate nodes. Simulation results show that the proposed algorithm significantly reduces the packet loss rate and improves the average peak signal-to-noise ratio (PSNR) of the received video streams.

  10. Benefit of adaptive FEC in shared backup path protected elastic optical network.

    PubMed

    Guo, Hong; Dai, Hua; Wang, Chao; Li, Yongcheng; Bose, Sanjay K; Shen, Gangxiang

    2015-07-27

    We apply an adaptive forward error correction (FEC) allocation strategy to an Elastic Optical Network (EON) operated with shared backup path protection (SBPP). To maximize the protected network capacity that can be carried, an Integer Linear Programing (ILP) model and a spectrum window plane (SWP)-based heuristic algorithm are developed. Simulation results show that the FEC coding overhead required by the adaptive FEC scheme is significantly lower than that needed by a fixed FEC allocation strategy resulting in higher network capacity for the adaptive strategy. The adaptive FEC allocation strategy can also significantly outperform the fixed FEC allocation strategy both in terms of the spare capacity redundancy and the average FEC coding overhead needed per optical channel. The proposed heuristic algorithm is efficient and not only performs closer to the ILP model but also does much better than the shortest-path algorithm. PMID:26367673

  11. Benefit of adaptive FEC in shared backup path protected elastic optical network.

    PubMed

    Guo, Hong; Dai, Hua; Wang, Chao; Li, Yongcheng; Bose, Sanjay K; Shen, Gangxiang

    2015-07-27

    We apply an adaptive forward error correction (FEC) allocation strategy to an Elastic Optical Network (EON) operated with shared backup path protection (SBPP). To maximize the protected network capacity that can be carried, an Integer Linear Programing (ILP) model and a spectrum window plane (SWP)-based heuristic algorithm are developed. Simulation results show that the FEC coding overhead required by the adaptive FEC scheme is significantly lower than that needed by a fixed FEC allocation strategy resulting in higher network capacity for the adaptive strategy. The adaptive FEC allocation strategy can also significantly outperform the fixed FEC allocation strategy both in terms of the spare capacity redundancy and the average FEC coding overhead needed per optical channel. The proposed heuristic algorithm is efficient and not only performs closer to the ILP model but also does much better than the shortest-path algorithm.

  12. Episodic memory in aspects of large-scale brain networks

    PubMed Central

    Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

    2015-01-01

    Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

  13. Revealing Shared and Distinct Gene Network Organization in Arabidopsis Immune Responses by Integrative Analysis1

    PubMed Central

    Dong, Xiaobao; Jiang, Zhenhong; Peng, You-Liang; Zhang, Ziding

    2015-01-01

    Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are two main plant immune responses to counter pathogen invasion. Genome-wide gene network organizing principles leading to quantitative differences between PTI and ETI have remained elusive. We combined an advanced machine learning method and modular network analysis to systematically characterize the organizing principles of Arabidopsis (Arabidopsis thaliana) PTI and ETI at three network resolutions. At the single network node/edge level, we ranked genes and gene interactions based on their ability to distinguish immune response from normal growth and successfully identified many immune-related genes associated with PTI and ETI. Topological analysis revealed that the top-ranked gene interactions tend to link network modules. At the subnetwork level, we identified a subnetwork shared by PTI and ETI encompassing 1,159 genes and 1,289 interactions. This subnetwork is enriched in interactions linking network modules and is also a hotspot of attack by pathogen effectors. The subnetwork likely represents a core component in the coordination of multiple biological processes to favor defense over development. Finally, we constructed modular network models for PTI and ETI to explain the quantitative differences in the global network architecture. Our results indicate that the defense modules in ETI are organized into relatively independent structures, explaining the robustness of ETI to genetic mutations and effector attacks. Taken together, the multiscale comparisons of PTI and ETI provide a systems biology perspective on plant immunity and emphasize coordination among network modules to establish a robust immune response. PMID:25614062

  14. Impact of heuristics in clustering large biological networks.

    PubMed

    Shafin, Md Kishwar; Kabir, Kazi Lutful; Ridwan, Iffatur; Anannya, Tasmiah Tamzid; Karim, Rashid Saadman; Hoque, Mohammad Mozammel; Rahman, M Sohel

    2015-12-01

    Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising. PMID:26386663

  15. Uncovering disassortativity in large scale-free networks

    NASA Astrophysics Data System (ADS)

    Litvak, Nelly; van der Hofstad, Remco

    2013-02-01

    Mixing patterns in large self-organizing networks, such as the Internet, the World Wide Web, and social and biological networks, are often characterized by degree-degree dependencies between neighboring nodes. In this paper, we propose a new way of measuring degree-degree dependencies. One of the problems with the commonly used assortativity coefficient is that in disassortative networks its magnitude decreases with the network size. We mathematically explain this phenomenon and validate the results on synthetic graphs and real-world network data. As an alternative, we suggest to use rank correlation measures such as Spearman's ρ. Our experiments convincingly show that Spearman's ρ produces consistent values in graphs of different sizes but similar structure, and it is able to reveal strong (positive or negative) dependencies in large graphs. In particular, we discover much stronger negative degree-degree dependencies in Web graphs than was previously thought. Rank correlations allow us to compare the assortativity of networks of different sizes, which is impossible with the assortativity coefficient due to its genuine dependence on the network size. We conclude that rank correlations provide a suitable and informative method for uncovering network mixing patterns.

  16. Making Large-Scale Networks from fMRI Data

    PubMed Central

    Schmittmann, Verena D.; Jahfari, Sara; Borsboom, Denny; Savi, Alexander O.; Waldorp, Lourens J.

    2015-01-01

    Pairwise correlations are currently a popular way to estimate a large-scale network (> 1000 nodes) from functional magnetic resonance imaging data. However, this approach generally results in a poor representation of the true underlying network. The reason is that pairwise correlations cannot distinguish between direct and indirect connectivity. As a result, pairwise correlation networks can lead to fallacious conclusions; for example, one may conclude that a network is a small-world when it is not. In a simulation study and an application to resting-state fMRI data, we compare the performance of pairwise correlations in large-scale networks (2000 nodes) against three other methods that are designed to filter out indirect connections. Recovery methods are evaluated in four simulated network topologies (small world or not, scale-free or not) in scenarios where the number of observations is very small compared to the number of nodes. Simulations clearly show that pairwise correlation networks are fragmented into separate unconnected components with excessive connectedness within components. This often leads to erroneous estimates of network metrics, like small-world structures or low betweenness centrality, and produces too many low-degree nodes. We conclude that using partial correlations, informed by a sparseness penalty, results in more accurate networks and corresponding metrics than pairwise correlation networks. However, even with these methods, the presence of hubs in the generating network can be problematic if the number of observations is too small. Additionally, we show for resting-state fMRI that partial correlations are more robust than correlations to different parcellation sets and to different lengths of time-series. PMID:26325185

  17. Unbreakable distributed storage with quantum key distribution network and password-authenticated secret sharing

    PubMed Central

    Fujiwara, M.; Waseda, A.; Nojima, R.; Moriai, S.; Ogata, W.; Sasaki, M.

    2016-01-01

    Distributed storage plays an essential role in realizing robust and secure data storage in a network over long periods of time. A distributed storage system consists of a data owner machine, multiple storage servers and channels to link them. In such a system, secret sharing scheme is widely adopted, in which secret data are split into multiple pieces and stored in each server. To reconstruct them, the data owner should gather plural pieces. Shamir’s (k, n)-threshold scheme, in which the data are split into n pieces (shares) for storage and at least k pieces of them must be gathered for reconstruction, furnishes information theoretic security, that is, even if attackers could collect shares of less than the threshold k, they cannot get any information about the data, even with unlimited computing power. Behind this scenario, however, assumed is that data transmission and authentication must be perfectly secure, which is not trivial in practice. Here we propose a totally information theoretically secure distributed storage system based on a user-friendly single-password-authenticated secret sharing scheme and secure transmission using quantum key distribution, and demonstrate it in the Tokyo metropolitan area (≤90 km). PMID:27363566

  18. Unbreakable distributed storage with quantum key distribution network and password-authenticated secret sharing.

    PubMed

    Fujiwara, M; Waseda, A; Nojima, R; Moriai, S; Ogata, W; Sasaki, M

    2016-01-01

    Distributed storage plays an essential role in realizing robust and secure data storage in a network over long periods of time. A distributed storage system consists of a data owner machine, multiple storage servers and channels to link them. In such a system, secret sharing scheme is widely adopted, in which secret data are split into multiple pieces and stored in each server. To reconstruct them, the data owner should gather plural pieces. Shamir's (k, n)-threshold scheme, in which the data are split into n pieces (shares) for storage and at least k pieces of them must be gathered for reconstruction, furnishes information theoretic security, that is, even if attackers could collect shares of less than the threshold k, they cannot get any information about the data, even with unlimited computing power. Behind this scenario, however, assumed is that data transmission and authentication must be perfectly secure, which is not trivial in practice. Here we propose a totally information theoretically secure distributed storage system based on a user-friendly single-password-authenticated secret sharing scheme and secure transmission using quantum key distribution, and demonstrate it in the Tokyo metropolitan area (≤90 km). PMID:27363566

  19. Unbreakable distributed storage with quantum key distribution network and password-authenticated secret sharing

    NASA Astrophysics Data System (ADS)

    Fujiwara, M.; Waseda, A.; Nojima, R.; Moriai, S.; Ogata, W.; Sasaki, M.

    2016-07-01

    Distributed storage plays an essential role in realizing robust and secure data storage in a network over long periods of time. A distributed storage system consists of a data owner machine, multiple storage servers and channels to link them. In such a system, secret sharing scheme is widely adopted, in which secret data are split into multiple pieces and stored in each server. To reconstruct them, the data owner should gather plural pieces. Shamir’s (k, n)-threshold scheme, in which the data are split into n pieces (shares) for storage and at least k pieces of them must be gathered for reconstruction, furnishes information theoretic security, that is, even if attackers could collect shares of less than the threshold k, they cannot get any information about the data, even with unlimited computing power. Behind this scenario, however, assumed is that data transmission and authentication must be perfectly secure, which is not trivial in practice. Here we propose a totally information theoretically secure distributed storage system based on a user-friendly single-password-authenticated secret sharing scheme and secure transmission using quantum key distribution, and demonstrate it in the Tokyo metropolitan area (≤90 km).

  20. Unbreakable distributed storage with quantum key distribution network and password-authenticated secret sharing.

    PubMed

    Fujiwara, M; Waseda, A; Nojima, R; Moriai, S; Ogata, W; Sasaki, M

    2016-07-01

    Distributed storage plays an essential role in realizing robust and secure data storage in a network over long periods of time. A distributed storage system consists of a data owner machine, multiple storage servers and channels to link them. In such a system, secret sharing scheme is widely adopted, in which secret data are split into multiple pieces and stored in each server. To reconstruct them, the data owner should gather plural pieces. Shamir's (k, n)-threshold scheme, in which the data are split into n pieces (shares) for storage and at least k pieces of them must be gathered for reconstruction, furnishes information theoretic security, that is, even if attackers could collect shares of less than the threshold k, they cannot get any information about the data, even with unlimited computing power. Behind this scenario, however, assumed is that data transmission and authentication must be perfectly secure, which is not trivial in practice. Here we propose a totally information theoretically secure distributed storage system based on a user-friendly single-password-authenticated secret sharing scheme and secure transmission using quantum key distribution, and demonstrate it in the Tokyo metropolitan area (≤90 km).

  1. Managing ISR sharing policies at the network edge using Controlled English

    NASA Astrophysics Data System (ADS)

    Parizas, Christos; Pizzocaro, Diego; Preece, Alun; Zerfos, Petros

    2013-05-01

    In domains such as emergency response and military operations the sharing of Intelligence, Surveillance and Reconnaissance (ISR) assets among different coalition partners is regulated through policies. Traditionally, poli­ cies are created at the center of a coalitions network by high-level decision makers and expressed in low-level policy languages (e.g. Common Information Model SPL) by technical personnel, which makes them difficult to be understood by non-technical users at the edge of the network. Moreover, policies must often be modified by negotiation among coalition partners, typically in rapid response to the changing operational situation. Com­ monly, the users who must cope first with situational changes are those on the edge, so it would be very effective if they were able to create and negotiate policies themselves. We investigate the use of Controlled English (CE) as a means to define a policy representation that is both human-friendly and machine processable. We show how a CE model can capture a variety of policy types, including those based on a traditional asset ownership model, and those defining team-based asset sharing across a coalition. The use of CE is intended to benefit coalition networks by bridging the gap between technical and non-technical users in terms of policy creation and negoti­ ation, while at the same time being directly processable by a policy-checking system without transformation to any other technical representation.

  2. The Structure of Spatial Networks and Communities in Bicycle Sharing Systems

    PubMed Central

    Zaltz Austwick, Martin; O’Brien, Oliver; Strano, Emanuele; Viana, Matheus

    2013-01-01

    Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models. PMID:24040320

  3. Spectrum sharing between a surveillance radar and secondary Wi-Fi networks

    NASA Astrophysics Data System (ADS)

    Hessar, Farzad; Roy, Sumit

    2016-06-01

    Co-existence between unlicensed networks that share spectrum spatio-temporally with terrestrial (e.g. Air Traffic Control) and shipborne radars in 3-GHz band is attracting significant interest. Similar to every primary-secondary coexistence scenario, interference from unlicensed devices to a primary receiver must be within acceptable bounds. In this work, we formulate the spectrum sharing problem between a pulsed, search radar (primary) and 802.11 WLAN as the secondary. We compute the protection region for such a search radar for a) a single secondary user (initially) as well as b) a random spatial distribution of multiple secondary users. Furthermore, we also analyze the interference to the WiFi devices from the radar's transmissions to estimate the impact on achievable WLAN throughput as a function of distance to the primary radar.

  4. Implicit and explicit social mentalizing: dual processes driven by a shared neural network

    PubMed Central

    Van Overwalle, Frank; Vandekerckhove, Marie

    2013-01-01

    Recent social neuroscientific evidence indicates that implicit and explicit inferences on the mind of another person (i.e., intentions, attributions or traits), are subserved by a shared mentalizing network. Under both implicit and explicit instructions, ERP studies reveal that early inferences occur at about the same time, and fMRI studies demonstrate an overlap in core mentalizing areas, including the temporo-parietal junction (TPJ) and the medial prefrontal cortex (mPFC). These results suggest a rapid shared implicit intuition followed by a slower explicit verification processes (as revealed by additional brain activation during explicit vs. implicit inferences). These data provide support for a default-adjustment dual-process framework of social mentalizing. PMID:24062663

  5. Experimental performance evaluation of software defined networking (SDN) based data communication networks for large scale flexi-grid optical networks.

    PubMed

    Zhao, Yongli; He, Ruiying; Chen, Haoran; Zhang, Jie; Ji, Yuefeng; Zheng, Haomian; Lin, Yi; Wang, Xinbo

    2014-04-21

    Software defined networking (SDN) has become the focus in the current information and communication technology area because of its flexibility and programmability. It has been introduced into various network scenarios, such as datacenter networks, carrier networks, and wireless networks. Optical transport network is also regarded as an important application scenario for SDN, which is adopted as the enabling technology of data communication networks (DCN) instead of general multi-protocol label switching (GMPLS). However, the practical performance of SDN based DCN for large scale optical networks, which is very important for the technology selection in the future optical network deployment, has not been evaluated up to now. In this paper we have built a large scale flexi-grid optical network testbed with 1000 virtual optical transport nodes to evaluate the performance of SDN based DCN, including network scalability, DCN bandwidth limitation, and restoration time. A series of network performance parameters including blocking probability, bandwidth utilization, average lightpath provisioning time, and failure restoration time have been demonstrated under various network environments, such as with different traffic loads and different DCN bandwidths. The demonstration in this work can be taken as a proof for the future network deployment. PMID:24787842

  6. Experimental performance evaluation of software defined networking (SDN) based data communication networks for large scale flexi-grid optical networks.

    PubMed

    Zhao, Yongli; He, Ruiying; Chen, Haoran; Zhang, Jie; Ji, Yuefeng; Zheng, Haomian; Lin, Yi; Wang, Xinbo

    2014-04-21

    Software defined networking (SDN) has become the focus in the current information and communication technology area because of its flexibility and programmability. It has been introduced into various network scenarios, such as datacenter networks, carrier networks, and wireless networks. Optical transport network is also regarded as an important application scenario for SDN, which is adopted as the enabling technology of data communication networks (DCN) instead of general multi-protocol label switching (GMPLS). However, the practical performance of SDN based DCN for large scale optical networks, which is very important for the technology selection in the future optical network deployment, has not been evaluated up to now. In this paper we have built a large scale flexi-grid optical network testbed with 1000 virtual optical transport nodes to evaluate the performance of SDN based DCN, including network scalability, DCN bandwidth limitation, and restoration time. A series of network performance parameters including blocking probability, bandwidth utilization, average lightpath provisioning time, and failure restoration time have been demonstrated under various network environments, such as with different traffic loads and different DCN bandwidths. The demonstration in this work can be taken as a proof for the future network deployment.

  7. Congenital blindness is associated with large-scale reorganization of anatomical networks

    PubMed Central

    Hasson, Uri; Andric, Michael; Atilgan, Hicret; Collignon, Olivier

    2016-01-01

    Blindness is a unique model for understanding the role of experience in the development of the brain's functional and anatomical architecture. Documenting changes in the structure of anatomical networks for this population would substantiate the notion that the brain's core network-level organization may undergo neuroplasticity as a result of life-long experience. To examine this issue, we compared whole-brain networks of regional cortical-thickness covariance in early blind and matched sighted individuals. This covariance is thought to reflect signatures of integration between systems involved in similar perceptual/cognitive functions. Using graph-theoretic metrics, we identified a unique mode of anatomical reorganization in the blind that differed from that found for sighted. This was seen in that network partition structures derived from subgroups of blind were more similar to each other than they were to partitions derived from sighted. Notably, after deriving network partitions, we found that language and visual regions tended to reside within separate modules in sighted but showed a pattern of merging into shared modules in the blind. Our study demonstrates that early visual deprivation triggers a systematic large-scale reorganization of whole-brain cortical-thickness networks, suggesting changes in how occipital regions interface with other functional networks in the congenitally blind. PMID:26767944

  8. Yard-Sale exchange on networks: wealth sharing and wealth appropriation

    NASA Astrophysics Data System (ADS)

    Bustos-Guajardo, R.; Moukarzel, Cristian F.

    2012-12-01

    Yard Sale (YS) is a stochastic multiplicative wealth exchange model with two phases: a stable one where wealth is shared, and an unstable one where wealth condenses onto one agent. YS is here studied numerically on 1D rings, 2D square lattices, and random graphs with variable average coordination, comparing its properties with those in mean field (MF). Equilibrium properties in the stable phase are almost unaffected by the introduction of a network. Measurement of decorrelation times in the stable phase allows us to determine the critical interface with very good precision, and it turns out to be the same, for all networks analyzed, as the one that can be analytically derived in MF. In the unstable phase, on the other hand, dynamical as well as asymptotic properties are strongly network dependent. Wealth no longer condenses on a single agent, as in MF, but onto an extensive set of agents, the properties of which depend on the network. Connections with previous studies of coalescence of immobile reactants are discussed, and their analytic predictions are successfully compared with our numerical results.

  9. States of mind: emotions, body feelings, and thoughts share distributed neural networks.

    PubMed

    Oosterwijk, Suzanne; Lindquist, Kristen A; Anderson, Eric; Dautoff, Rebecca; Moriguchi, Yoshiya; Barrett, Lisa Feldman

    2012-09-01

    Scientists have traditionally assumed that different kinds of mental states (e.g., fear, disgust, love, memory, planning, concentration, etc.) correspond to different psychological faculties that have domain-specific correlates in the brain. Yet, growing evidence points to the constructionist hypothesis that mental states emerge from the combination of domain-general psychological processes that map to large-scale distributed brain networks. In this paper, we report a novel study testing a constructionist model of the mind in which participants generated three kinds of mental states (emotions, body feelings, or thoughts) while we measured activity within large-scale distributed brain networks using fMRI. We examined the similarity and differences in the pattern of network activity across these three classes of mental states. Consistent with a constructionist hypothesis, a combination of large-scale distributed networks contributed to emotions, thoughts, and body feelings, although these mental states differed in the relative contribution of those networks. Implications for a constructionist functional architecture of diverse mental states are discussed. PMID:22677148

  10. States of mind: Emotions, body feelings, and thoughts share distributed neural networks

    PubMed Central

    Oosterwijk, Suzanne; Lindquist, Kristen A.; Anderson, Eric; Dautoff, Rebecca; Moriguchi, Yoshiya; Barrett, Lisa Feldman

    2012-01-01

    Scientists have traditionally assumed that different kinds of mental states (e.g., fear, disgust, love, memory, planning, concentration, etc.) correspond to different psychological faculties that have domain-specific correlates in the brain. Yet, growing evidence points to the constructionist hypothesis that mental states emerge from the combination of domain-general psychological processes that map to large-scale distributed brain networks. In this paper, we report a novel study testing a constructionist model of the mind in which participants generated three kinds of mental states (emotions, body feelings, or thoughts) while we measured activity within large-scale distributed brain networks using fMRI. We examined the similarity and differences in the pattern of network activity across these three classes of mental states. Consistent with a constructionist hypothesis, a combination of large-scale distributed networks contributed to emotions, thoughts, and body feelings, although these mental states differed in the relative contribution of those networks. Implications for a constructionist functional architecture of diverse mental states are discussed. PMID:22677148

  11. Diffusion Dynamics of Energy Saving Practices in Large Heterogeneous Online Networks

    PubMed Central

    Mohammadi, Neda; Wang, Qi; Taylor, John E.

    2016-01-01

    Online social networks are today’s fastest growing communications channel and a popular source of information for many, so understanding their contribution to building awareness and shaping public perceptions of climate change is of utmost importance. Today’s online social networks are composed of complex combinations of entities and communication channels and it is not clear which communicators are the most influential, what the patterns of communication flow are, or even whether the widely accepted two-step flow of communication model applies in this new arena. This study examines the diffusion of energy saving practices in a large online social network across organizations, opinion leaders, and the public by tracking 108,771 communications on energy saving practices among 1,084 communicators, then analyzing the flow of information and influence over a 28 day period. Our findings suggest that diffusion networks of messages advocating energy saving practices are predominantly led by the activities of dedicated organizations but their attempts do not result in substantial public awareness, as most of these communications are effectively trapped in organizational loops in which messages are simply shared between organizations. Despite their comparably significant influential values, opinion leaders played a weak role in diffusing energy saving practices to a wider audience. Thus, the two-step flow of communication model does not appear to describe the sharing of energy conservation practices in large online heterogeneous networks. These results shed new light on the underlying mechanisms driving the diffusion of important societal issues such as energy efficiency, particularly in the context of large online social media outlets. PMID:27736912

  12. Spectrum sharing in cognitive radio networks--an auction-based approach.

    PubMed

    Wang, Xinbing; Li, Zheng; Xu, Pengchao; Xu, Youyun; Gao, Xinbo; Chen, Hsiao-Hwa

    2010-06-01

    Cognitive radio is emerging as a promising technique to improve the utilization of the radio frequency spectrum. In this paper, we consider the problem of spectrum sharing among primary (or "licensed") users (PUs) and secondary (or "unlicensed") users (SUs). We formulate the problem based on bandwidth auction, in which each SU makes a bid for the amount of spectrum and each PU may assign the spectrum among the SUs by itself according to the information from the SUs without degrading its own performance. We show that the auction is a noncooperative game and that Nash equilibrium (NE) can be its solution. We first consider a single-PU network to investigate the existence and uniqueness of the NE and further discuss the fairness among the SUs under given conditions. Then, we present a dynamic updating algorithm in which each SU achieves NE in a distributed manner. The stability condition of the dynamic behavior for this spectrum-sharing scheme is studied. The discussion is generalized to the case in which there are multiple PUs in the network, where the properties of the NE are shown under appropriate conditions. Simulations were used to evaluate the system performance and verify the effectiveness of the proposed algorithm.

  13. EPPS: Efficient and Privacy-Preserving Personal Health Information Sharing in Mobile Healthcare Social Networks.

    PubMed

    Jiang, Shunrong; Zhu, Xiaoyan; Wang, Liangmin

    2015-01-01

    Mobile healthcare social networks (MHSNs) have emerged as a promising next-generation healthcare system, which will significantly improve the quality of life. However, there are many security and privacy concerns before personal health information (PHI) is shared with other parities. To ensure patients' full control over their PHI, we propose a fine-grained and scalable data access control scheme based on attribute-based encryption (ABE). Besides, policies themselves for PHI sharing may be sensitive and may reveal information about underlying PHI or about data owners or recipients. In our scheme, we let each attribute contain an attribute name and its value and adopt the Bloom filter to efficiently check attributes before decryption. Thus, the data privacy and policy privacy can be preserved in our proposed scheme. Moreover, considering the fact that the computational cost grows with the complexity of the access policy and the limitation of the resource and energy in a smart phone, we outsource ABE decryption to the cloud while preventing the cloud from learning anything about the content and access policy. The security and performance analysis is carried out to demonstrate that our proposed scheme can achieve fine-grained access policies for PHI sharing in MHSNs. PMID:26404300

  14. EPPS: Efficient and Privacy-Preserving Personal Health Information Sharing in Mobile Healthcare Social Networks

    PubMed Central

    Jiang, Shunrong; Zhu, Xiaoyan; Wang, Liangmin

    2015-01-01

    Mobile healthcare social networks (MHSNs) have emerged as a promising next-generation healthcare system, which will significantly improve the quality of life. However, there are many security and privacy concerns before personal health information (PHI) is shared with other parities. To ensure patients’ full control over their PHI, we propose a fine-grained and scalable data access control scheme based on attribute-based encryption (ABE). Besides, policies themselves for PHI sharing may be sensitive and may reveal information about underlying PHI or about data owners or recipients. In our scheme, we let each attribute contain an attribute name and its value and adopt the Bloom filter to efficiently check attributes before decryption. Thus, the data privacy and policy privacy can be preserved in our proposed scheme. Moreover, considering the fact that the computational cost grows with the complexity of the access policy and the limitation of the resource and energy in a smart phone, we outsource ABE decryption to the cloud while preventing the cloud from learning anything about the content and access policy. The security and performance analysis is carried out to demonstrate that our proposed scheme can achieve fine-grained access policies for PHI sharing in MHSNs. PMID:26404300

  15. EPPS: Efficient and Privacy-Preserving Personal Health Information Sharing in Mobile Healthcare Social Networks.

    PubMed

    Jiang, Shunrong; Zhu, Xiaoyan; Wang, Liangmin

    2015-09-03

    Mobile healthcare social networks (MHSNs) have emerged as a promising next-generation healthcare system, which will significantly improve the quality of life. However, there are many security and privacy concerns before personal health information (PHI) is shared with other parities. To ensure patients' full control over their PHI, we propose a fine-grained and scalable data access control scheme based on attribute-based encryption (ABE). Besides, policies themselves for PHI sharing may be sensitive and may reveal information about underlying PHI or about data owners or recipients. In our scheme, we let each attribute contain an attribute name and its value and adopt the Bloom filter to efficiently check attributes before decryption. Thus, the data privacy and policy privacy can be preserved in our proposed scheme. Moreover, considering the fact that the computational cost grows with the complexity of the access policy and the limitation of the resource and energy in a smart phone, we outsource ABE decryption to the cloud while preventing the cloud from learning anything about the content and access policy. The security and performance analysis is carried out to demonstrate that our proposed scheme can achieve fine-grained access policies for PHI sharing in MHSNs.

  16. DESIGN OF LARGE-SCALE AIR MONITORING NETWORKS

    EPA Science Inventory

    The potential effects of air pollution on human health have received much attention in recent years. In the U.S. and other countries, there are extensive large-scale monitoring networks designed to collect data to inform the public of exposure risks to air pollution. A major crit...

  17. Large-Scale Networked Virtual Environments: Architecture and Applications

    ERIC Educational Resources Information Center

    Lamotte, Wim; Quax, Peter; Flerackers, Eddy

    2008-01-01

    Purpose: Scalability is an important research topic in the context of networked virtual environments (NVEs). This paper aims to describe the ALVIC (Architecture for Large-scale Virtual Interactive Communities) approach to NVE scalability. Design/methodology/approach: The setup and results from two case studies are shown: a 3-D learning environment…

  18. Optical Shared Memory Computing and Multiple Access Protocols for Photonic Networks

    NASA Astrophysics Data System (ADS)

    Li, Kuang-Yu.

    In this research we investigate potential applications of optics in massively parallel computer systems, especially focusing on design issues in three-dimensional optical data storage and free-space photonic networks. An optical implementation of a shared memory uses a single photorefractive crystal and can realize the set of memory modules in a digital shared memory computer. A complete instruction set consists of R sc EAD, W sc RITE, S sc ELECTIVE E sc RASE, and R sc EFRESH, which can be applied to any memory module independent of (and in parallel with) instructions to the other memory modules. In addition, a memory module can execute a sequence of R sc EAD operations simultaneously with the execution of a W sc RITE operation to accommodate differences in optical recording and readout times common to optical volume storage media. An experimental shared memory system is demonstrated and its projected performance is analyzed. A multiplexing technique is presented to significantly reduce both grating- and beam-degeneracy crosstalk in volume holographic systems, by incorporating space, angle, and wavelength as the multiplexing parameters. In this approach, each hologram, which results from the interference between a single input node and an object array, partially overlaps with the other holograms in its neighborhood. This technique can offer improved interconnection density, optical throughput, signal fidelity, and space-bandwidth product utilization. Design principles and numerical simulation results are presented. A free-space photonic cellular hypercube parallel computer, with emphasis on the design of a collisionless multiple access protocol, is presented. This design incorporates wavelength-, space-, and time-multiplexing to achieve multiple access, wavelength reuse, dense connectivity, collisionless communications, and a simple control mechanism. Analytic models based on semi-Markov processes are employed to analyze this protocol. The performance of the

  19. Large Deviations, Dynamics and Phase Transitions in Large Stochastic and Disordered Neural Networks

    NASA Astrophysics Data System (ADS)

    Cabana, Tanguy; Touboul, Jonathan

    2013-10-01

    Neuronal networks are characterized by highly heterogeneous connectivity, and this disorder was recently related experimentally to qualitative properties of the network. The motivation of this paper is to mathematically analyze the role of these disordered connectivities on the large-scale properties of neuronal networks. To this end, we analyze here large-scale limit behaviors of neural networks including, for biological relevance, multiple populations, random connectivities and interaction delays. Due to the randomness of the connectivity, usual mean-field methods (e.g. coupling) cannot be applied, but, similarly to studies developed for spin glasses, we will show that the sequences of empirical measures satisfy a large deviation principle, and converge towards a self-consistent non-Markovian process. From a mathematical viewpoint, the proof differs from previous works in that we are working in infinite-dimensional spaces (interaction delays) and consider multiple cell types. The limit obtained formally characterizes the macroscopic behavior of the network. We propose a dynamical systems approach in order to address the qualitative nature of the solutions of these very complex equations, and apply this methodology to three instances in order to show how non-centered coefficients, interaction delays and multiple populations networks are affected by disorder levels. We identify a number of phase transitions in such systems upon changes in delays, connectivity patterns and dispersion, and particularly focus on the emergence of non-equilibrium states involving synchronized oscillations.

  20. Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

    NASA Technical Reports Server (NTRS)

    Shameldin, Ramez Ahmed; Bowling, Shannon R.

    2010-01-01

    The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

  1. Think locally, act locally: Detection of small, medium-sized, and large communities in large networks

    NASA Astrophysics Data System (ADS)

    Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

    2015-01-01

    It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic

  2. Development of large-scale functional networks over the lifespan.

    PubMed

    Schlee, Winfried; Leirer, Vera; Kolassa, Stephan; Thurm, Franka; Elbert, Thomas; Kolassa, Iris-Tatjana

    2012-10-01

    The development of large-scale functional organization of the human brain across the lifespan is not well understood. Here we used magnetoencephalographic recordings of 53 adults (ages 18-89) to characterize functional brain networks in the resting state. Slow frequencies engage larger networks than higher frequencies and show different development over the lifespan. Networks in the delta (2-4 Hz) frequency range decrease, while networks in the beta/gamma frequency range (> 16 Hz) increase in size with advancing age. Results show that the right frontal lobe and the temporal areas in both hemispheres are important relay stations in the expanding high-frequency networks. Neuropsychological tests confirmed the tendency of cognitive decline with older age. The decrease in visual memory and visuoconstructive functions was strongly associated with the age-dependent enhancement of functional connectivity in both temporal lobes. Using functional network analysis this study elucidates important neuronal principles underlying age-related cognitive decline paving mental deterioration in senescence. PMID:22236372

  3. 77 FR 58416 - Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-09-20

    ... Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO... to the Large Scale Networking (LSN) Coordinating Group (CG). Public Comments: The government...

  4. Effects of a cost-sharing exemption on use of preventive services at one large employer.

    PubMed

    Busch, Susan H; Barry, Colleen L; Vegso, Sally J; Sindelar, Jody L; Cullen, Mark R

    2006-01-01

    In 2004, Alcoa introduced a new health benefit for a portion of its workforce, which eliminated cost sharing for preventive care while increasing cost sharing for many other services. In this era of increased consumerism, Alcoa's benefit redesign constituted an effort to reduce health care costs while preserving use of targeted services. Taking advantage of a unique natural experiment, we find that Alcoa was able to maintain rates of preventive service use. This evidence suggests that differential cost sharing can be used to preserve the use of critical health care services.

  5. Dataworks for GNSS: Software for Supporting Data Sharing and Federation of Geodetic Networks

    NASA Astrophysics Data System (ADS)

    Boler, F. M.; Meertens, C. M.; Miller, M. M.; Wier, S.; Rost, M.; Matykiewicz, J.

    2015-12-01

    Continuously-operating Global Navigation Satellite System (GNSS) networks are increasingly being installed globally for a wide variety of science and societal applications. GNSS enables Earth science research in areas including tectonic plate interactions, crustal deformation in response to loading by tectonics, magmatism, water and ice, and the dynamics of water - and thereby energy transfer - in the atmosphere at regional scale. The many individual scientists and organizations that set up GNSS stations globally are often open to sharing data, but lack the resources or expertise to deploy systems and software to manage and curate data and metadata and provide user tools that would support data sharing. UNAVCO previously gained experience in facilitating data sharing through the NASA-supported development of the Geodesy Seamless Archive Centers (GSAC) open source software. GSAC provides web interfaces and simple web services for data and metadata discovery and access, supports federation of multiple data centers, and simplifies transfer of data and metadata to long-term archives. The NSF supported the dissemination of GSAC to multiple European data centers forming the European Plate Observing System. To expand upon GSAC to provide end-to-end, instrument-to-distribution capability, UNAVCO developed Dataworks for GNSS with NSF funding to the COCONet project, and deployed this software on systems that are now operating as Regional GNSS Data Centers as part of the NSF-funded TLALOCNet and COCONet projects. Dataworks consists of software modules written in Python and Java for data acquisition, management and sharing. There are modules for GNSS receiver control and data download, a database schema for metadata, tools for metadata handling, ingest software to manage file metadata, data file management scripts, GSAC, scripts for mirroring station data and metadata from partner GSACs, and extensive software and operator documentation. UNAVCO plans to provide a cloud VM

  6. Distributed Coordinated Control of Large-Scale Nonlinear Networks

    SciTech Connect

    Kundu, Soumya; Anghel, Marian

    2015-11-08

    We provide a distributed coordinated approach to the stability analysis and control design of largescale nonlinear dynamical systems by using a vector Lyapunov functions approach. In this formulation the large-scale system is decomposed into a network of interacting subsystems and the stability of the system is analyzed through a comparison system. However finding such comparison system is not trivial. In this work, we propose a sum-of-squares based completely decentralized approach for computing the comparison systems for networks of nonlinear systems. Moreover, based on the comparison systems, we introduce a distributed optimal control strategy in which the individual subsystems (agents) coordinate with their immediate neighbors to design local control policies that can exponentially stabilize the full system under initial disturbances.We illustrate the control algorithm on a network of interacting Van der Pol systems.

  7. Distributed Coordinated Control of Large-Scale Nonlinear Networks

    DOE PAGES

    Kundu, Soumya; Anghel, Marian

    2015-11-08

    We provide a distributed coordinated approach to the stability analysis and control design of largescale nonlinear dynamical systems by using a vector Lyapunov functions approach. In this formulation the large-scale system is decomposed into a network of interacting subsystems and the stability of the system is analyzed through a comparison system. However finding such comparison system is not trivial. In this work, we propose a sum-of-squares based completely decentralized approach for computing the comparison systems for networks of nonlinear systems. Moreover, based on the comparison systems, we introduce a distributed optimal control strategy in which the individual subsystems (agents) coordinatemore » with their immediate neighbors to design local control policies that can exponentially stabilize the full system under initial disturbances.We illustrate the control algorithm on a network of interacting Van der Pol systems.« less

  8. Complex modular structure of large-scale brain networks

    NASA Astrophysics Data System (ADS)

    Valencia, M.; Pastor, M. A.; Fernández-Seara, M. A.; Artieda, J.; Martinerie, J.; Chavez, M.

    2009-06-01

    Modular structure is ubiquitous among real-world networks from related proteins to social groups. Here we analyze the modular organization of brain networks at a large scale (voxel level) extracted from functional magnetic resonance imaging signals. By using a random-walk-based method, we unveil the modularity of brain webs and show modules with a spatial distribution that matches anatomical structures with functional significance. The functional role of each node in the network is studied by analyzing its patterns of inter- and intramodular connections. Results suggest that the modular architecture constitutes the structural basis for the coexistence of functional integration of distant and specialized brain areas during normal brain activities at rest.

  9. The Fermi Large Area Telescope Flare Advocate Program: Rapid Sharing of Results with the Community

    NASA Astrophysics Data System (ADS)

    Thompson, David John; Ciprini, Stefano; Gasparrini, Dario; Fermi Large Area Telescope Collaboration

    2015-01-01

    The Fermi Flare Advocate (also known as Gamma-ray Sky Watcher) program provides a quick look and review of the gamma-ray sky observed daily by the Fermi Large Area Telescope (LAT) through on-duty LAT Flare Advocates and high-level software pipelines like the LAT Automatic Science Processing and the Fermi All-sky Variability Analysis. The FA-GSW service provides rapid alerts and communicates to the external scientific community potentially new gamma-ray sources, interesting transients and flares. News items are regularly posted through the Fermi multiwavelength mailing list, Astronomer's Telegrams and Gamma-ray Coordinates Network notices. A weekly digest containing the highlights about the variable LAT gamma-ray sky at E>100 MeV is published on the web ("Fermi Sky Blog"). From July 2008 to September 2014 more than 290 ATels and 90 GCNs have been published by the Fermi LAT Collaboration. Target of opportunity observing programs with other satellites and telescopes have been triggered by Flare Advocates based on gamma-ray flares from blazars and other kinds of sources.

  10. Graph animals, subgraph sampling, and motif search in large networks

    NASA Astrophysics Data System (ADS)

    Baskerville, Kim; Grassberger, Peter; Paczuski, Maya

    2007-09-01

    We generalize a sampling algorithm for lattice animals (connected clusters on a regular lattice) to a Monte Carlo algorithm for “graph animals,” i.e., connected subgraphs in arbitrary networks. As with the algorithm in [N. Kashtan , Bioinformatics 20, 1746 (2004)], it provides a weighted sample, but the computation of the weights is much faster (linear in the size of subgraphs, instead of superexponential). This allows subgraphs with up to ten or more nodes to be sampled with very high statistics, from arbitrarily large networks. Using this together with a heuristic algorithm for rapidly classifying isomorphic graphs, we present results for two protein interaction networks obtained using the tandem affinity purification (TAP) method: one of Escherichia coli with 230 nodes and 695 links, and one for yeast (Saccharomyces cerevisiae) with roughly ten times more nodes and links. We find in both cases that most connected subgraphs are strong motifs ( Z scores >10 ) or antimotifs ( Z scores <-10 ) when the null model is the ensemble of networks with fixed degree sequence. Strong differences appear between the two networks, with dominant motifs in E. coli being (nearly) bipartite graphs and having many pairs of nodes that connect to the same neighbors, while dominant motifs in yeast tend towards completeness or contain large cliques. We also explore a number of methods that do not rely on measurements of Z scores or comparisons with null models. For instance, we discuss the influence of specific complexes like the 26S proteasome in yeast, where a small number of complexes dominate the k cores with large k and have a decisive effect on the strongest motifs with 6-8 nodes. We also present Zipf plots of counts versus rank. They show broad distributions that are not power laws, in contrast to the case when disconnected subgraphs are included.

  11. Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network

    NASA Technical Reports Server (NTRS)

    Cheung, Kar-Ming; Jennings, Esther H.; Sergui, John S.

    2013-01-01

    A large-scale network that supports a large number of users can have an aggregate data rate of hundreds of Mbps at any time. High-fidelity simulation of a large-scale network might be too complicated and memory-intensive for typical commercial-off-the-shelf (COTS) tools. Unlike a large commercial wide-area-network (WAN) that shares diverse network resources among diverse users and has a complex topology that requires routing mechanism and flow control, the ground communication links of a space network operate under the assumption of a guaranteed dedicated bandwidth allocation between specific sparse endpoints in a star-like topology. This work solved the network design problem of estimating the bandwidths of a ground network architecture option that offer different service classes to meet the latency requirements of different user data types. In this work, a top-down analysis and simulation approach was created to size the bandwidths of a store-and-forward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. These techniques were used to estimate the WAN bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network. A new analytical approach, called the "leveling scheme," was developed to model the store-and-forward mechanism of the network data flow. The term "leveling" refers to the spreading of data across a longer time horizon without violating the corresponding latency requirement of the data type. Two versions of the leveling scheme were developed: 1. A straightforward version that simply spreads the data of each data type across the time horizon and doesn't take into account the interactions among data types within a pass, or between data types across overlapping passes at a network node, and is inherently sub-optimal. 2. Two-state Markov leveling scheme that takes into account the second order behavior of

  12. Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy.

    PubMed

    Zhang, Zhiqiang; Liao, Wei; Chen, Huafu; Mantini, Dante; Ding, Ju-Rong; Xu, Qiang; Wang, Zhengge; Yuan, Cuiping; Chen, Guanghui; Jiao, Qing; Lu, Guangming

    2011-10-01

    The human brain is a large-scale integrated network in the functional and structural domain. Graph theoretical analysis provides a novel framework for analysing such complex networks. While previous neuroimaging studies have uncovered abnormalities in several specific brain networks in patients with idiopathic generalized epilepsy characterized by tonic-clonic seizures, little is known about changes in whole-brain functional and structural connectivity networks. Regarding functional and structural connectivity, networks are intimately related and share common small-world topological features. We predict that patients with idiopathic generalized epilepsy would exhibit a decoupling between functional and structural networks. In this study, 26 patients with idiopathic generalized epilepsy characterized by tonic-clonic seizures and 26 age- and sex-matched healthy controls were recruited. Resting-state functional magnetic resonance imaging signal correlations and diffusion tensor image tractography were used to generate functional and structural connectivity networks. Graph theoretical analysis revealed that the patients lost optimal topological organization in both functional and structural connectivity networks. Moreover, the patients showed significant increases in nodal topological characteristics in several cortical and subcortical regions, including mesial frontal cortex, putamen, thalamus and amygdala relative to controls, supporting the hypothesis that regions playing important roles in the pathogenesis of epilepsy may display abnormal hub properties in network analysis. Relative to controls, patients showed further decreases in nodal topological characteristics in areas of the default mode network, such as the posterior cingulate gyrus and inferior temporal gyrus. Most importantly, the degree of coupling between functional and structural connectivity networks was decreased, and exhibited a negative correlation with epilepsy duration in patients. Our findings

  13. Large optical 3D MEMS switches in access networks

    NASA Astrophysics Data System (ADS)

    Madamopoulos, Nicholas; Kaman, Volkan; Yuan, Shifu; Jerphagnon, Olivier; Helkey, Roger; Bowers, John E.

    2007-09-01

    Interest is high among residential customers and businesses for advanced, broadband services such as fast Internet access, electronic commerce, video-on-demand, digital broadcasting, teleconferencing and telemedicine. In order to satisfy such growing demand of end-customers, access technologies such as fiber-to-the-home/building (FTTH/B) are increasingly being deployed. Carriers can reduce maintenance costs, minimize technology obsolescence and introduce new services easily by reducing active elements in the fiber access network. However, having a passive optical network (PON) also introduces operational and maintenance challenges. Increased diagnostic monitoring capability of the network becomes a necessity as more and more fibers are provisioned to deliver services to the end-customers. This paper demonstrates the clear advantages that large 3D optical MEMS switches offer in solving these access network problems. The advantages in preventative maintenance, remote monitoring, test and diagnostic capability are highlighted. The low optical insertion loss for all switch optical connections of the switch enables the monitoring, grooming and serving of a large number of PON lines and customers. Furthermore, the 3D MEMS switch is transparent to optical wavelengths and data formats, thus making it easy to incorporate future upgrades, such higher bit rates or DWDM overlay to a PON.

  14. Large-scale network-level processes during entrainment

    PubMed Central

    Lithari, Chrysa; Sánchez-García, Carolina; Ruhnau, Philipp; Weisz, Nathan

    2016-01-01

    Visual rhythmic stimulation evokes a robust power increase exactly at the stimulation frequency, the so-called steady-state response (SSR). Localization of visual SSRs normally shows a very focal modulation of power in visual cortex and led to the treatment and interpretation of SSRs as a local phenomenon. Given the brain network dynamics, we hypothesized that SSRs have additional large-scale effects on the brain functional network that can be revealed by means of graph theory. We used rhythmic visual stimulation at a range of frequencies (4–30 Hz), recorded MEG and investigated source level connectivity across the whole brain. Using graph theoretical measures we observed a frequency-unspecific reduction of global density in the alpha band “disconnecting” visual cortex from the rest of the network. Also, a frequency-specific increase of connectivity between occipital cortex and precuneus was found at the stimulation frequency that exhibited the highest resonance (30 Hz). In conclusion, we showed that SSRs dynamically re-organized the brain functional network. These large-scale effects should be taken into account not only when attempting to explain the nature of SSRs, but also when used in various experimental designs. PMID:26835557

  15. Large-scale network-level processes during entrainment.

    PubMed

    Lithari, Chrysa; Sánchez-García, Carolina; Ruhnau, Philipp; Weisz, Nathan

    2016-03-15

    Visual rhythmic stimulation evokes a robust power increase exactly at the stimulation frequency, the so-called steady-state response (SSR). Localization of visual SSRs normally shows a very focal modulation of power in visual cortex and led to the treatment and interpretation of SSRs as a local phenomenon. Given the brain network dynamics, we hypothesized that SSRs have additional large-scale effects on the brain functional network that can be revealed by means of graph theory. We used rhythmic visual stimulation at a range of frequencies (4-30 Hz), recorded MEG and investigated source level connectivity across the whole brain. Using graph theoretical measures we observed a frequency-unspecific reduction of global density in the alpha band "disconnecting" visual cortex from the rest of the network. Also, a frequency-specific increase of connectivity between occipital cortex and precuneus was found at the stimulation frequency that exhibited the highest resonance (30 Hz). In conclusion, we showed that SSRs dynamically re-organized the brain functional network. These large-scale effects should be taken into account not only when attempting to explain the nature of SSRs, but also when used in various experimental designs. PMID:26835557

  16. Large-margin classification in infinite neural networks.

    PubMed

    Cho, Youngmin; Saul, Lawrence K

    2010-10-01

    We introduce a new family of positive-definite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural networks with one layer of hidden units. We also show how to derive new kernels, by recursive composition, that may be viewed as mapping their inputs through a series of nonlinear feature spaces. These recursively derived kernels mimic the computation in deep networks with multiple hidden layers. We evaluate SVMs with these kernels on problems designed to illustrate the advantages of deep architectures. Compared to previous benchmarks, we find that on some problems, these SVMs yield state-of-the-art results, beating not only other SVMs but also deep belief nets.

  17. Locating inefficient links in a large-scale transportation network

    NASA Astrophysics Data System (ADS)

    Sun, Li; Liu, Like; Xu, Zhongzhi; Jie, Yang; Wei, Dong; Wang, Pu

    2015-02-01

    Based on data from geographical information system (GIS) and daily commuting origin destination (OD) matrices, we estimated the distribution of traffic flow in the San Francisco road network and studied Braess's paradox in a large-scale transportation network with realistic travel demand. We measured the variation of total travel time Δ T when a road segment is closed, and found that | Δ T | follows a power-law distribution if Δ T < 0 or Δ T > 0. This implies that most roads have a negligible effect on the efficiency of the road network, while the failure of a few crucial links would result in severe travel delays, and closure of a few inefficient links would counter-intuitively reduce travel costs considerably. Generating three theoretical networks, we discovered that the heterogeneously distributed travel demand may be the origin of the observed power-law distributions of | Δ T | . Finally, a genetic algorithm was used to pinpoint inefficient link clusters in the road network. We found that closing specific road clusters would further improve the transportation efficiency.

  18. Supporting large scale applications on networks of workstations

    NASA Technical Reports Server (NTRS)

    Cooper, Robert; Birman, Kenneth P.

    1989-01-01

    Distributed applications on networks of workstations are an increasingly common way to satisfy computing needs. However, existing mechanisms for distributed programming exhibit poor performance and reliability as application size increases. Extension of the ISIS distributed programming system to support large scale distributed applications by providing hierarchical process groups is discussed. Incorporation of hierarchy in the program structure and exploitation of this to limit the communication and storage required in any one component of the distributed system is examined.

  19. Knowledge-Sharing Networks in Hunter-Gatherers and the Evolution of Cumulative Culture.

    PubMed

    Salali, Gul Deniz; Chaudhary, Nikhil; Thompson, James; Grace, Olwen Megan; van der Burgt, Xander M; Dyble, Mark; Page, Abigail E; Smith, Daniel; Lewis, Jerome; Mace, Ruth; Vinicius, Lucio; Migliano, Andrea Bamberg

    2016-09-26

    Humans possess the unique ability for cumulative culture [1, 2]. It has been argued that hunter-gatherer's complex social structure [3-9] has facilitated the evolution of cumulative culture by allowing information exchange among large pools of individuals [10-13]. However, empirical evidence for the interaction between social structure and cultural transmission is scant [14]. Here we examine the reported co-occurrence of plant uses between individuals in dyads (which we define as their "shared knowledge" of plant uses) in BaYaka Pygmies from Congo. We studied reported uses of 33 plants of 219 individuals from four camps. We show that (1) plant uses by BaYaka fall into three main domains: medicinal, foraging, and social norms/beliefs; (2) most medicinal plants have known bioactive properties, and some are positively associated with children's BMI, suggesting that their use is adaptive; (3) knowledge of medicinal plants is mainly shared between spouses and biological and affinal kin; and (4) knowledge of plant uses associated with foraging and social norms is shared more widely among campmates, regardless of relatedness, and is important for camp-wide activities that require cooperation. Our results show the interdependence between social structure and knowledge sharing. We propose that long-term pair bonds, affinal kin recognition, exogamy, and multi-locality create ties between unrelated families, facilitating the transmission of medicinal knowledge and its fitness implications. Additionally, multi-family camps with low inter-relatedness between camp members provide a framework for the exchange of functional information related to cooperative activities beyond the family unit, such as foraging and regulation of social life. PMID:27618264

  20. Knowledge-Sharing Networks in Hunter-Gatherers and the Evolution of Cumulative Culture.

    PubMed

    Salali, Gul Deniz; Chaudhary, Nikhil; Thompson, James; Grace, Olwen Megan; van der Burgt, Xander M; Dyble, Mark; Page, Abigail E; Smith, Daniel; Lewis, Jerome; Mace, Ruth; Vinicius, Lucio; Migliano, Andrea Bamberg

    2016-09-26

    Humans possess the unique ability for cumulative culture [1, 2]. It has been argued that hunter-gatherer's complex social structure [3-9] has facilitated the evolution of cumulative culture by allowing information exchange among large pools of individuals [10-13]. However, empirical evidence for the interaction between social structure and cultural transmission is scant [14]. Here we examine the reported co-occurrence of plant uses between individuals in dyads (which we define as their "shared knowledge" of plant uses) in BaYaka Pygmies from Congo. We studied reported uses of 33 plants of 219 individuals from four camps. We show that (1) plant uses by BaYaka fall into three main domains: medicinal, foraging, and social norms/beliefs; (2) most medicinal plants have known bioactive properties, and some are positively associated with children's BMI, suggesting that their use is adaptive; (3) knowledge of medicinal plants is mainly shared between spouses and biological and affinal kin; and (4) knowledge of plant uses associated with foraging and social norms is shared more widely among campmates, regardless of relatedness, and is important for camp-wide activities that require cooperation. Our results show the interdependence between social structure and knowledge sharing. We propose that long-term pair bonds, affinal kin recognition, exogamy, and multi-locality create ties between unrelated families, facilitating the transmission of medicinal knowledge and its fitness implications. Additionally, multi-family camps with low inter-relatedness between camp members provide a framework for the exchange of functional information related to cooperative activities beyond the family unit, such as foraging and regulation of social life.

  1. Implementation of Cyberinfrastructure and Data Management Workflow for a Large-Scale Sensor Network

    NASA Astrophysics Data System (ADS)

    Jones, A. S.; Horsburgh, J. S.

    2014-12-01

    Monitoring with in situ environmental sensors and other forms of field-based observation presents many challenges for data management, particularly for large-scale networks consisting of multiple sites, sensors, and personnel. The availability and utility of these data in addressing scientific questions relies on effective cyberinfrastructure that facilitates transformation of raw sensor data into functional data products. It also depends on the ability of researchers to share and access the data in useable formats. In addition to addressing the challenges presented by the quantity of data, monitoring networks need practices to ensure high data quality, including procedures and tools for post processing. Data quality is further enhanced if practitioners are able to track equipment, deployments, calibrations, and other events related to site maintenance and associate these details with observational data. In this presentation we will describe the overall workflow that we have developed for research groups and sites conducting long term monitoring using in situ sensors. Features of the workflow include: software tools to automate the transfer of data from field sites to databases, a Python-based program for data quality control post-processing, a web-based application for online discovery and visualization of data, and a data model and web interface for managing physical infrastructure. By automating the data management workflow, the time from collection to analysis is reduced and sharing and publication is facilitated. The incorporation of metadata standards and descriptions and the use of open-source tools enhances the sustainability and reusability of the data. We will describe the workflow and tools that we have developed in the context of the iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) monitoring network. The iUTAH network consists of aquatic and climate sensors deployed in three watersheds to monitor Gradients Along Mountain to Urban

  2. Participants' recall and understanding of genomic research and large-scale data sharing.

    PubMed

    Robinson, Jill Oliver; Slashinski, Melody J; Wang, Tao; Hilsenbeck, Susan G; McGuire, Amy L

    2013-10-01

    As genomic researchers are urged to openly share generated sequence data with other researchers, it is important to examine the utility of informed consent documents and processes, particularly as these relate to participants' engagement with and recall of the information presented to them, their objective or subjective understanding of the key elements of genomic research (e.g., data sharing), as well as how these factors influence or mediate the decisions they make. We conducted a randomized trial of three experimental informed consent documents (ICDs) with participants (n = 229) being recruited to genomic research studies; each document afforded varying control over breadth of release of genetic information. Recall and understanding, their impact on data sharing decisions, and comfort in decision making were assessed in a follow-up structured interview. Over 25% did not remember signing an ICD to participate in a genomic study, and the majority (54%) could not correctly identify with whom they had agreed to share their genomic data. However, participants felt that they understood enough to make an informed decision, and lack of recall did not impact final data sharing decisions or satisfaction with participation. These findings raise questions about the types of information participants need in order to provide valid informed consent, and whether subjective understanding and comfort with decision making are sufficient to satisfy the ethical principle of respect for persons.

  3. Investigating the evolution of Shared Socioeconomic Pathways with a large number of scenarios

    NASA Astrophysics Data System (ADS)

    Schweizer, V. J.; Guivarch, C.; Rozenberg, J.

    2013-12-01

    The new scenario framework for climate change research includes alternative possible trends for socioeconomic development called Shared Socioeconomic Pathways (SSPs). The SSPs bear some similarities to other scenarios used for global change research, but they also have important differences. Like the IPCC Special Report on Emissions Scenarios or the Millennium Ecosystem Assessment, SSPs are defined by a scenario logic consisting of two axes. However, these axes define SSPs with respect to their location in an outcome space for challenges to mitigation and to adaptation rather than by their drivers. Open questions for the SSPs include what their drivers are and how the time dimension could be interpreted with the outcomes space. We present a new analytical approach for addressing both questions by studying large numbers of scenarios produced by an integrated assessment model, IMACLIM-R. We systematically generated 432 scenarios and used the SSP framework to classify them by typology. We then analyzed them dynamically, tracing their evolution through the SSP challenges space at annual time steps over the period 2010-2090. Through this approach, we found that many scenarios do not remain fixed to a particular SSP domain; they drift from one domain to another. In papers describing the framework for new scenarios, SSPs are envisioned as hypothetical (counter-factual) reference scenarios that remain fixed in one domain over some time period of interest. However, we conclude that it may be important to also research scenarios that shift across SSP domains. This is relevant for another open question, which is what scenarios are important to explore given their consequences. Through a data mining technique, we uncovered prominent drivers for scenarios that shift across SSP domains. Scenarios with different challenges for adaptation and mitigation (that is, mitigation and adaptation challenges that are not co-varying) were found to be the least stable, and the following

  4. Tunable self-seeding Fabry-Pérot laser diode for upstream multiwavelength shared ethernet passive optical network

    NASA Astrophysics Data System (ADS)

    Zhu, Min; Xiao, Shilin; Zhou, Zhao; Bi, Meihua

    2011-03-01

    We present a novel upstream multiwavelength shared ethernet passive optical network architecture, based on a proposed self-seeding Fabry-Pérot laser diode (FP-LD) at the optical network unit. The performances of the wavelength and power stability, side-mode suppression ratio, and tuning range for the proposed tunable self-seeding laser module are experimentally investigated. The bit-error-rate measurement is performed with direct modulation on FP-LD of 1.25 Gbps upstream data. The performance benefits from the upstream wavelengths sharing are showed via simulations.

  5. Exploring the use of large clinical data to inform patients for shared decision making.

    PubMed

    Hill, Brent; Proulx, Joshua; Zeng-Treitler, Qing

    2013-01-01

    Barriers to patient participation in the shared decision making process prevent patients from fully participating in evaluating treatment options and treatment selection. Patients who use a decision aid are more informed and engaged in the shared decision making process. Patient decision aids do not use real clinical data for patient information and may not represent the data well. We designed an interface, for a shared decision making aid, that leverages clinical data to inform risk ratios and create patient stories, or vignettes, and present a visual representation of quantified treatment outcomes data. Usability testing was conducted with experts to evaluate the interface and the utility of using real clinical information that patients can explore. The experts' comments were transcribed and coded for themes. Themes were quantified and comments were interpreted for refinement and modification to the patient decision aid interface and data visualization.

  6. CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.

    PubMed

    Masoudi-Nejad, Ali; Ansariola, Mitra; Kashani, Zahra Razaghi Moghadam; Salehzadeh-Yazdi, Ali; Khakabimamaghani, Sahand

    2012-01-01

    Network motifs are small connected sub-graphs that have recently gathered much attention to discover structural behaviors of large and complex networks. Finding motifs with any size is one of the most important problems in complex and large networks. It needs fast and reliable algorithms and tools for achieving this purpose. CytoKavosh is one of the best choices for finding motifs with any given size in any complex network. It relies on a fast algorithm, Kavosh, which makes it faster than other existing tools. Kavosh algorithm applies some well known algorithmic features and includes tricky aspects, which make it an efficient algorithm in this field. CytoKavosh is a Cytoscape plug-in which supports us in finding motifs of given size in a network that is formerly loaded into the Cytoscape work-space (directed or undirected). High performance of CytoKavosh is achieved by dynamically linking highly optimized functions of Kavosh's C++ to the Cytoscape Java program, which makes this plug-in suitable for analyzing large biological networks. Some significant attributes of CytoKavosh is efficiency in time usage and memory and having no limitation related to the implementation in motif size. CytoKavosh is implemented in a visual environment Cytoscape that is convenient for the users to interact and create visual options to analyze the structural behavior of a network. This plug-in can work on any given network and is very simple to use and generates graphical results of discovered motifs with any required details. There is no specific Cytoscape plug-in, specific for finding the network motifs, based on original concept. So, we have introduced for the first time, CytoKavosh as the first plug-in, and we hope that this plug-in can be improved to cover other options to make it the best motif-analyzing tool.

  7. Geometric origin of scaling in large traffic networks.

    PubMed

    Popović, Marko; Štefančić, Hrvoje; Zlatić, Vinko

    2012-11-16

    Large scale traffic networks are an indispensable part of contemporary human mobility and international trade. Networks of airport travel and cargo ship movements are invaluable for the understanding of human mobility patterns [R. Guimera et al., Proc. Natl. Acad. Sci. U.S.A. 102, 7794 (2005))], epidemic spreading [V. Colizza et al., Proc. Natl. Acad. Sci. U.S.A. 103, 2015 (2006)], global trade [International Maritime Organization, http://www.imo.org/], and spread of invasive species [G. M. Ruiz et al., Nature (London) 408, 49 (2000)]. Different studies [M. Barthelemy, Phys. Rept. 499, 1 (2011)] point to the universal character of some of the exponents measured in such networks. Here we show that exponents which relate (i) the strength of nodes to their degree and (ii) weights of links to degrees of nodes that they connect have a geometric origin. We present a simple robust model which exhibits the observed power laws and relates exponents to the dimensionality of 2D space in which traffic networks are embedded. We show that the relation between weight strength and degree is s(k)~k(3/2), the relation between distance strength and degree is s(d)(k)~k(3/2), and the relation between weight of link and degrees of linked nodes is w(ij)~(k(i)k(j))(1/2) on the plane 2D surface. We further analyze the influence of spherical geometry, relevant for the whole planet, on exact values of these exponents. Our model predicts that these exponents should be found in future studies of port networks and it imposes constraints on more refined models of port networks.

  8. High Fidelity Simulations of Large-Scale Wireless Networks

    SciTech Connect

    Onunkwo, Uzoma; Benz, Zachary

    2015-11-01

    The worldwide proliferation of wireless connected devices continues to accelerate. There are 10s of billions of wireless links across the planet with an additional explosion of new wireless usage anticipated as the Internet of Things develops. Wireless technologies do not only provide convenience for mobile applications, but are also extremely cost-effective to deploy. Thus, this trend towards wireless connectivity will only continue and Sandia must develop the necessary simulation technology to proactively analyze the associated emerging vulnerabilities. Wireless networks are marked by mobility and proximity-based connectivity. The de facto standard for exploratory studies of wireless networks is discrete event simulations (DES). However, the simulation of large-scale wireless networks is extremely difficult due to prohibitively large turnaround time. A path forward is to expedite simulations with parallel discrete event simulation (PDES) techniques. The mobility and distance-based connectivity associated with wireless simulations, however, typically doom PDES and fail to scale (e.g., OPNET and ns-3 simulators). We propose a PDES-based tool aimed at reducing the communication overhead between processors. The proposed solution will use light-weight processes to dynamically distribute computation workload while mitigating communication overhead associated with synchronizations. This work is vital to the analytics and validation capabilities of simulation and emulation at Sandia. We have years of experience in Sandia’s simulation and emulation projects (e.g., MINIMEGA and FIREWHEEL). Sandia’s current highly-regarded capabilities in large-scale emulations have focused on wired networks, where two assumptions prevent scalable wireless studies: (a) the connections between objects are mostly static and (b) the nodes have fixed locations.

  9. Network Lasso: Clustering and Optimization in Large Graphs

    PubMed Central

    Hallac, David; Leskovec, Jure; Boyd, Stephen

    2016-01-01

    Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimization solvers do not scale well, and scalable solvers are often specialized to only work on a narrow class of problems. Therefore, there is a need for simple, scalable algorithms that can solve many common optimization problems. In this paper, we introduce the network lasso, a generalization of the group lasso to a network setting that allows for simultaneous clustering and optimization on graphs. We develop an algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in a distributed and scalable manner, which allows for guaranteed global convergence even on large graphs. We also examine a non-convex extension of this approach. We then demonstrate that many types of problems can be expressed in our framework. We focus on three in particular — binary classification, predicting housing prices, and event detection in time series data — comparing the network lasso to baseline approaches and showing that it is both a fast and accurate method of solving large optimization problems. PMID:27398260

  10. High Speed Networking and Large-scale Simulation in Geodynamics

    NASA Technical Reports Server (NTRS)

    Kuang, Weijia; Gary, Patrick; Seablom, Michael; Truszkowski, Walt; Odubiyi, Jide; Jiang, Weiyuan; Liu, Dong

    2004-01-01

    Large-scale numerical simulation has been one of the most important approaches for understanding global geodynamical processes. In this approach, peta-scale floating point operations (pflops) are often required to carry out a single physically-meaningful numerical experiment. For example, to model convective flow in the Earth's core and generation of the geomagnetic field (geodynamo), simulation for one magnetic free-decay time (approximately 15000 years) with a modest resolution of 150 in three spatial dimensions would require approximately 0.2 pflops. If such a numerical model is used to predict geomagnetic secular variation over decades and longer, with e.g. an ensemble Kalman filter assimilation approach, approximately 30 (and perhaps more) independent simulations of similar scales would be needed for one data assimilation analysis. Obviously, such a simulation would require an enormous computing resource that exceeds the capacity of a single facility currently available at our disposal. One solution is to utilize a very fast network (e.g. 10Gb optical networks) and available middleware (e.g. Globus Toolkit) to allocate available but often heterogeneous resources for such large-scale computing efforts. At NASA GSFC, we are experimenting with such an approach by networking several clusters for geomagnetic data assimilation research. We shall present our initial testing results in the meeting.

  11. Integration of land-sharing and land-sparing conservation strategies through regional networking: the Mesoamerican Biological Corridor as a lifeline for carnivores in El Salvador.

    PubMed

    Crespin, Silvio J; García-Villalta, Jorge E

    2014-10-01

    Nations with little remaining natural habitat and small extent are challenged when trying to achieve biodiversity targets. We show that the Central American nation of El Salvador cannot viably sustain populations of 87 % of its extant carnivores, especially in the case of large-bodied species with low population densities. Current land-sparing strategies will not suffice; therefore we propose that land-sharing strategies be implemented in tandem with protected areas to expand current conservation efforts via new regional networks. In Central America such a network can be established by linking international protected area systems in a way that implements the existing vision for the Mesoamerican Biological Corridor. Specifically, we propose a re-envisioning of the Mesoamerican Biological Corridor in which land-sharing practices are adopted throughout the agricultural matrix while ensuring formal protection of the remaining natural habitat. Such an integration of land-sparing and land-sharing could result in the creation of an effective network of protected areas, thereby increasing the probability of safeguarding species with populations that overlap national borders.

  12. Lightning Paths in Sky Share Similarities with Channel Networks on Earth

    NASA Astrophysics Data System (ADS)

    Jain, Sharad K.; Singh, Vijay P.

    2004-06-01

    Lightning in the atmosphere is a transient, high current carrying electric discharge. It occurs when some region of the atmosphere reaches an electric charge sufficiently large so that the electric fields associated with the charge cause an electrical breakdown in the air. Lightning is produced in the cumulonimbus clouds; however, it can also occur in snowstorms and sandstorms. The analysis presented in this article demonstrates that there are many similarities between the celestial networks that are formed during a lighting event and the terrestrial channel networks associated with flowing surface waters in drainage basins. It begins with a preliminary analysis of the paths followed by the cloud-to-ground discharge of electric currents generated in the atmosphere during thunderstorms.

  13. Atypical Behavior Identification in Large Scale Network Traffic

    SciTech Connect

    Best, Daniel M.; Hafen, Ryan P.; Olsen, Bryan K.; Pike, William A.

    2011-10-23

    Cyber analysts are faced with the daunting challenge of identifying exploits and threats within potentially billions of daily records of network traffic. Enterprise-wide cyber traffic involves hundreds of millions of distinct IP addresses and results in data sets ranging from terabytes to petabytes of raw data. Creating behavioral models and identifying trends based on those models requires data intensive architectures and techniques that can scale as data volume increases. Analysts need scalable visualization methods that foster interactive exploration of data and enable identification of behavioral anomalies. Developers must carefully consider application design, storage, processing, and display to provide usability and interactivity with large-scale data. We present an application that highlights atypical behavior in enterprise network flow records. This is accomplished by utilizing data intensive architectures to store the data, aggregation techniques to optimize data access, statistical techniques to characterize behavior, and a visual analytic environment to render the behavioral trends, highlight atypical activity, and allow for exploration.

  14. Measuring Large-Scale Social Networks with High Resolution

    PubMed Central

    Stopczynski, Arkadiusz; Sekara, Vedran; Sapiezynski, Piotr; Cuttone, Andrea; Madsen, Mette My; Larsen, Jakob Eg; Lehmann, Sune

    2014-01-01

    This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection. PMID:24770359

  15. Foundational perspectives on causality in large-scale brain networks

    NASA Astrophysics Data System (ADS)

    Mannino, Michael; Bressler, Steven L.

    2015-12-01

    A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

  16. Foundational perspectives on causality in large-scale brain networks.

    PubMed

    Mannino, Michael; Bressler, Steven L

    2015-12-01

    A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

  17. Dynamical modeling and analysis of large cellular regulatory networks

    NASA Astrophysics Data System (ADS)

    Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.

    2013-06-01

    The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

  18. Predicting protein functions from redundancies in large-scale protein interaction networks

    NASA Technical Reports Server (NTRS)

    Samanta, Manoj Pratim; Liang, Shoudan

    2003-01-01

    Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.

  19. The application of artificial intelligence techniques to large distributed networks

    NASA Technical Reports Server (NTRS)

    Dubyah, R.; Smith, T. R.; Star, J. L.

    1985-01-01

    Data accessibility and transfer of information, including the land resources information system pilot, are structured as large computer information networks. These pilot efforts include the reduction of the difficulty to find and use data, reducing processing costs, and minimize incompatibility between data sources. Artificial Intelligence (AI) techniques were suggested to achieve these goals. The applicability of certain AI techniques are explored in the context of distributed problem solving systems and the pilot land data system (PLDS). The topics discussed include: PLDS and its data processing requirements, expert systems and PLDS, distributed problem solving systems, AI problem solving paradigms, query processing, and distributed data bases.

  20. 78 FR 70076 - Large Scale Networking (LSN)-Middleware and Grid Interagency Coordination (MAGIC) Team

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-11-22

    ... Large Scale Networking (LSN)--Middleware and Grid Interagency Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO... Networking (LSN) Coordinating Group (CG). Public Comments: The government seeks individual input;...

  1. Validating posttransplant hepatocellular carcinoma recurrence data in the United Network for Organ Sharing database.

    PubMed

    Samoylova, Mariya L; Dodge, Jennifer L; Vittinghoff, Eric; Yao, Francis Y; Roberts, John Paul

    2013-12-01

    The Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database is the most comprehensive collection of liver transplantation data, but the quality of these data with respect to hepatocellular carcinoma (HCC) recurrence has not been well assessed. In this study, we compared observed HCC recurrence rates in the UNOS database to expected rates calculated with a hierarchical model for recurrence adjusted for recipient and tumor characteristics. We used the UNOS Standard Transplant Analysis and Research data set for adult transplant patients with an initial exception for an HCC diagnosis granted between January 1, 2006 and September 30, 2010 who underwent transplantation within the same time window. We developed a risk-adjusted Poisson model with patients as the unit of analysis, random effects for transplant centers, and years of follow-up as an offset to predict expected recurrences for each center. To further investigate the possibility of underreporting, we imputed expected recurrences for non-HCC deaths. In all, 5034 HCC liver transplant recipients were identified, and 6.8% experienced recurrence at a median of 1 year after transplantation. The covariate-adjusted shrinkage estimates of the observed/expected HCC recurrence ratios by transplant center ranged from 0.6 to 1.76 (median = 0.97). The 95% confidence intervals for the shrinkage ratios included unity for every center, and this indicated that none could be unambiguously identified as having lower or higher than expected HCC recurrence rates. Imputing outcomes for patients potentially experiencing unreported recurrence changed the center-specific shrinkage ratios to 0.72 to 1.39 (median = 0.98), with no centers having a shrinkage ratio significantly different from 1. The observed HCC recurrence rate was not significantly lower than the expected rate at any center, and this suggests that no systematic underreporting has occurred. This study validates the OPTN HCC

  2. Deep Convolutional Neural Networks for large-scale speech tasks.

    PubMed

    Sainath, Tara N; Kingsbury, Brian; Saon, George; Soltau, Hagen; Mohamed, Abdel-rahman; Dahl, George; Ramabhadran, Bhuvana

    2015-04-01

    Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks.

  3. Large Phased Array Radar Using Networked Small Parabolic Reflectors

    NASA Technical Reports Server (NTRS)

    Amoozegar, Farid

    2006-01-01

    Multifunction phased array systems with radar, telecom, and imaging applications have already been established for flat plate phased arrays of dipoles, or waveguides. In this paper the design trades and candidate options for combining the radar and telecom functions of the Deep Space Network (DSN) into a single large transmit array of small parabolic reflectors will be discussed. In particular the effect of combing the radar and telecom functions on the sizes of individual antenna apertures and the corresponding spacing between the antenna elements of the array will be analyzed. A heterogeneous architecture for the DSN large transmit array is proposed to meet the radar and telecom requirements while considering the budget, scheduling, and strategic planning constrains.

  4. The Shared Health Research Information Network (SHRINE): a prototype federated query tool for clinical data repositories.

    PubMed

    Weber, Griffin M; Murphy, Shawn N; McMurry, Andrew J; Macfadden, Douglas; Nigrin, Daniel J; Churchill, Susanne; Kohane, Isaac S

    2009-01-01

    The authors developed a prototype Shared Health Research Information Network (SHRINE) to identify the technical, regulatory, and political challenges of creating a federated query tool for clinical data repositories. Separate Institutional Review Boards (IRBs) at Harvard's three largest affiliated health centers approved use of their data, and the Harvard Medical School IRB approved building a Query Aggregator Interface that can simultaneously send queries to each hospital and display aggregate counts of the number of matching patients. Our experience creating three local repositories using the open source Informatics for Integrating Biology and the Bedside (i2b2) platform can be used as a road map for other institutions. The authors are actively working with the IRBs and regulatory groups to develop procedures that will ultimately allow investigators to obtain identified patient data and biomaterials through SHRINE. This will guide us in creating a future technical architecture that is scalable to a national level, compliant with ethical guidelines, and protective of the interests of the participating hospitals.

  5. Comparative Study of Multicast Protection Algorithms Using Shared Links in 100GET Transport Network

    NASA Astrophysics Data System (ADS)

    Sulaiman, Samer; Haidine, Abdelfattah; Lehnert, Ralf; Tuerk, Stefan

    In recent years new challenges have emerged in the telecommunications market resulting from the increase of network traffic and strong competition. Because of that, service providers feel constrained to replace expensive and complex IP-routers with a cheap and simple solution which guarantees the requested quality of services (QoS) with low cost. One of these solutions is to use the Ethernet technology as a switching layer, which results in using the cheap Ethernet services (E-Line, E-LAN and E-Tree) and to replace the expensive IP-routers. To achieve this migration step, new algorithms that support the available as well as the future services have to be developed. In this paper, we investigate the multicast protection issue. Three multicast protection algorithms based on the shared capacity between primary and backup solutions are proposed and evaluated. The blocking probability is used to evaluate the performance of the proposed algorithms. The sub-path algorithm resulted in a low blocking probability compared with the other algorithms.

  6. A system for simulating shared memory in heterogeneous distributed-memory networks with specialization for robotics applications

    SciTech Connect

    Jones, J.P.; Bangs, A.L.; Butler, P.L.

    1991-01-01

    Hetero Helix is a programming environment which simulates shared memory on a heterogeneous network of distributed-memory computers. The machines in the network may vary with respect to their native operating systems and internal representation of numbers. Hetero Helix presents a simple programming model to developers, and also considers the needs of designers, system integrators, and maintainers. The key software technology underlying Hetero Helix is the use of a compiler'' which analyzes the data structures in shared memory and automatically generates code which translates data representations from the format native to each machine into a common format, and vice versa. The design of Hetero Helix was motivated in particular by the requirements of robotics applications. Hetero Helix has been used successfully in an integration effort involving 27 CPUs in a heterogeneous network and a body of software totaling roughly 100,00 lines of code. 25 refs., 6 figs.

  7. Non-immunologic predictors of chronic renal allograft failure: data from the United Network of Organ Sharing.

    PubMed

    Chertow, G M; Brenner, B M; Mackenzie, H S; Milford, E L

    1995-12-01

    Experimental evidence and clinical experience suggest that non-immunologic factors are important predictors of long-term renal allograft survival. It has been suggested that chronic allograft failure may in some cases by mediated by non-immunologic factors implicated in the pathobiology of other forms of progressive renal disease. Donor age, sex, and race may influence the "dose" of nephrons delivered in cadaveric renal transplantation. The United Network of Organ Sharing 1994 Public Use Data Tape was used to evaluate these and other risk factors in more than 31,000 recipients of cadaver allografts followed between 1987 and 1992. Female sex and African American race of the donor were important predictors of allograft failure. There was a markedly increased risk of allograft failure at both extremes of donor age. Recipients of large body size had accelerated graft loss. Stratified analyses suggested an interaction between donor and recipient race; nevertheless, all non-immunologic factors examined expressed independent associations with allograft survival. In sum, antigen-independent factors appear to be important determinants of allograft performance. Additional multivariable analyses are required to assess the relative importance of these factors compared with other known immunologic factors, such as HLA antigen mismatch. These findings may have important biomedical and health care policy implications. PMID:8587283

  8. A Multi-core Shared Tree Algorithm Based on Network Coding for Multi-point Optical Multicast

    NASA Astrophysics Data System (ADS)

    Liu, Huanlin; Yang, Yuming; Li, Yuan; Chen, Yong; Huang, Sheng

    2015-03-01

    With the growth of multi-point to multi-point multicast applications, the optical network bandwidth resource consumption is increasing rapidly. It attracted more and more researchers to improve the limited wavelength bandwidth utilization for multicast applications in wavelength division multiplexing (WDM) networks. In the paper, a multi-core shared multicast tree algorithm based on network coding is proposed to minimize the fiber link stress. The proposed algorithm includes three processes: searching the core node candidate set excluding core node loop path, selecting the core nodes from the convergence matrix based on heuristic algorithm, and constructing the multi-core nodes shared trees. The convergence matrix based on the heuristic method is constructed for selecting the core nodes from candidate core node set. To improve the limited wavelength utilization, we introduce network coding into the shared tree to compress the transmitting information. The simulation results show that the proposed algorithm's performance is better than the existing algorithms' performance in terms of link stress and balance degree.

  9. Natural language acquisition in large scale neural semantic networks

    NASA Astrophysics Data System (ADS)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

  10. Liver transplantation in patients with cystic fibrosis: analysis of United Network for Organ Sharing data.

    PubMed

    Mendizabal, Manuel; Reddy, K Rajender; Cassuto, James; Olthoff, Kim M; Faust, Thomas W; Makar, George A; Rand, Elizabeth B; Shaked, Abraham; Abt, Peter L

    2011-03-01

    The improved life expectancy of patients with cystic fibrosis (CF) has led to a change in the impact of liver disease on the prognosis of this population. Liver transplantation has emerged as the procedure of choice for patients with CF and features of hepatic decompensation and for intractable variceal bleeding as a major manifestation. We retrospectively reviewed the United Network for Organ Sharing database to analyze the outcomes of 55 adults and 148 children with CF who underwent liver transplantation, and we compared them to patients who underwent transplantation for other etiologies. We additionally compared the benefits of liver transplantation among patients who underwent transplantation for cystic fibrosis-related liver disease (CFLD) and those who remained on the waiting list. The 5-year survival rates for children and adults undergoing liver transplantation were 85.8% and 72.7%, respectively (P = 0.016). A multivariate Cox regression analysis comparing pediatric and adult CF patients to patients who underwent transplantation for other etiologies noted lower 5-year survival rates (P < 0.0001). However, compared to those remaining on the waiting list, pediatric transplant recipients with CF (hazard ratio = 0.33, 95% confidence interval = 0.16-0.70, P = 0.004) and adult transplant recipients with CF (hazard ratio = 0.25, 95% confidence interval = 0.11-0.57, P = 0.001) gained a significant survival benefit. In conclusion, long-term outcomes in patients with CFLD are acceptable but are inferior in comparison with the outcomes of those undergoing transplantation for other etiologies. Despite such observations, a survival benefit was noted in transplant patients versus those who remained on the waiting list.

  11. High rate of unemployment after liver transplantation: analysis of the United Network for Organ Sharing database.

    PubMed

    Huda, Amina; Newcomer, Robert; Harrington, Charlene; Blegen, Mary G; Keeffe, Emmet B

    2012-01-01

    The goal of liver transplantation (LT) is to maximize the length and quality of a patient's life and facilitate his or her return to full productivity. The aims of this study were (1) to use the United Network for Organ Sharing (UNOS) data set to determine the proportions of recipients who were employed and unemployed within 24 months after LT between 2002 and 2008 and (2) to examine the factors associated with a return to employment. UNOS data that were collected since the adoption of the Model for End-Stage Liver Disease scoring system on February 27, 2002 were analyzed. There were 21,942 transplant recipients who met the inclusion criteria. The employment status of the recipients was analyzed within a 60-day window at the following times after transplantation: 6, 12, and 24 months. Approximately one-quarter of the LT recipients (5360 or 24.4%) were employed within 24 months after transplantation, and the remaining recipients had not returned to work. The demographic variables that were independently associated with posttransplant employment included an age of 18 to 40 years, male sex, a college degree, Caucasian race, and pretransplant employment. Patients with alcoholic liver disease had a significantly lower rate of employment than patients with other etiologies of liver disease. The recipients who were employed after transplantation had significantly better functional status than those who were not employed. In conclusion, the employment rate after LT is low, with only one-quarter of LT recipients employed. New national and individual transplant program policies are needed to assess the root causes of unemployment in recipients who wish to work after LT.

  12. The International Polar Year Data and Information Service--Building a network of sharing, trust, and meaning

    NASA Astrophysics Data System (ADS)

    Parsons, M. A.

    2007-12-01

    Arctic science is inherently interdisciplinary and there is a national and international imperative to understand the Arctic region as a system. This emphasis on interdisciplinary research requires scientists to extend their professional networks across disciplines. Researchers need to access, understand, and assess data and information outside their field where they may not have the relevant disciplinary expertise, including knowledge of core assumptions and metaphors. Correspondingly data providers need to make their data understandable and usable by new users with different knowledge bases. While there are many technical barriers to cross- disciplinary data sharing, the fundamental issues lie in the human interactions and networks of scientific interaction. The International Polar Year 2007-2008 (IPY) is an intensive burst of coordinated scientific activity involving tens of thousands of investigators from 63 countries. It also seeks to create a sustained legacy of international and interdisciplinary cooperation, notably through enhanced polar observing systems. As such, IPY provides an opportunity for focused investigation of how interdisciplinary scientific and data sharing networks are created, extended, and modified. The International Polar Year Data and Information Service (IPYDIS), an international federation of data centers, archives, and networks working to ensure proper stewardship of IPY and related data, is one example of a formal network developing out of IPY. The IPYDIS has a semi-formal structure of governance, but IPY is limited in time. Methods for formal support and extension of the IPYDIS through other international collaborations will be discussed. Ultimately, however, the network is sustained through mechanisms that increase data sharing (technically and socially), establish and reinforce trust (between investigators and of data sources, and codify meanings both formally (ontologies, vocabularies, etc.) and informally (e.g., concepts of

  13. Applying Social Network Analysis to Understand the Knowledge Sharing Behaviour of Practitioners in a Clinical Online Discussion Forum

    PubMed Central

    Abidi, Syed Sibte Raza

    2012-01-01

    Background Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment—an online discussion forum—for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. Objective The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. Methods Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. Results The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is

  14. The role of infection prevention conferences to build and maintain knowledge-sharing networks: a longitudinal evaluation.

    PubMed

    Wiemken, Timothy L; Kelley, Robert R; Pacholski, Emily B; Carrico, Kelly W; Peyrani, Paula; Carrico, Ruth M; Ramirez, Julio A

    2014-02-01

    Well-connected knowledge-sharing networks (KSNs) of infection preventionists are vital to the profession. Face-to-face networking during conferences is considered critical to build and maintain KSNs; however, this has never been formally studied. We used a pre-post survey design to evaluate the effect of a regional infection prevention meeting on the KSNs. We found that the meeting did not alter the KSNs of infection preventionists. Current meeting structures should be re-evaluated with a goal to improve KSNs.

  15. E-Learning in a Large Organization: A Study of the Critical Role of Information Sharing

    ERIC Educational Resources Information Center

    Netteland, Grete; Wasson, Barbara; Morch, Anders I

    2007-01-01

    Purpose: The purpose of this paper is to provide new insights into the implementation of large-scale learning projects; thereby better understanding the difficulties, frustrations, and obstacles encountered when implementing enterprise-wide e-learning as a tool for training and organization transformation in a complex organization.…

  16. Shared end-to-content backup path protection in k-node (edge) content connected elastic optical datacenter networks.

    PubMed

    Li, Xin; Huang, Shanguo; Yin, Shan; Guo, Bingli; Zhao, Yongli; Zhang, Jie; Zhang, Min; Gu, Wanyi

    2016-05-01

    To quantitatively measure content connectivity and provide protection for different kinds of content, the concept of k-node (edge) content connectivity is proposed recently. Based on k-node (edge) content connectivity, k-node (edge) content connected elastic optical datacenter network (KC-EODN) is proposed to design disaster-resilient and spectrum-efficient optical datacenter networks. In KC-EODN, k independent end-to-content paths are established for each request. However, it will consume too much resource to assign dedicated spectrum for each end-to-content path. Spectrum sharing among multiple end-to-content paths of different requests can greatly improve resource efficiency. In this paper, a novel perfect matching based sharing principle among multiple end-to-content paths of different requests is proposed. Based on the new proposed sharing principle, we present the shared end-to-content backup path protection (SEBPP) scheme for KC-EODN. Integer linear program (ILP) model and heuristic algorithms are designed for SEBPP scheme with the objective of minimizing the total of working and backup spectrum resources. Numerical results show that the proposed SEBPP scheme can greatly reduce spectrum consumption while ensuring the survivability against natural disaster and multi-failures. PMID:27137559

  17. Shared end-to-content backup path protection in k-node (edge) content connected elastic optical datacenter networks.

    PubMed

    Li, Xin; Huang, Shanguo; Yin, Shan; Guo, Bingli; Zhao, Yongli; Zhang, Jie; Zhang, Min; Gu, Wanyi

    2016-05-01

    To quantitatively measure content connectivity and provide protection for different kinds of content, the concept of k-node (edge) content connectivity is proposed recently. Based on k-node (edge) content connectivity, k-node (edge) content connected elastic optical datacenter network (KC-EODN) is proposed to design disaster-resilient and spectrum-efficient optical datacenter networks. In KC-EODN, k independent end-to-content paths are established for each request. However, it will consume too much resource to assign dedicated spectrum for each end-to-content path. Spectrum sharing among multiple end-to-content paths of different requests can greatly improve resource efficiency. In this paper, a novel perfect matching based sharing principle among multiple end-to-content paths of different requests is proposed. Based on the new proposed sharing principle, we present the shared end-to-content backup path protection (SEBPP) scheme for KC-EODN. Integer linear program (ILP) model and heuristic algorithms are designed for SEBPP scheme with the objective of minimizing the total of working and backup spectrum resources. Numerical results show that the proposed SEBPP scheme can greatly reduce spectrum consumption while ensuring the survivability against natural disaster and multi-failures.

  18. Managing Information Sharing within an Organizational Setting: A Social Network Perspective

    ERIC Educational Resources Information Center

    Hatala, John-Paul; Lutta, Joseph George

    2009-01-01

    Information sharing is critical to an organization's competitiveness and requires a free flow of information among members if the organization is to remain competitive. A review of the literature on organizational structure and information sharing was conducted to examine the research in this area. A case example illustrates how a social network…

  19. Design of shared instruments to utilize simulated gravities generated by a large-gradient, high-field superconducting magnet

    NASA Astrophysics Data System (ADS)

    Wang, Y.; Yin, D. C.; Liu, Y. M.; Shi, J. Z.; Lu, H. M.; Shi, Z. H.; Qian, A. R.; Shang, P.

    2011-03-01

    A high-field superconducting magnet can provide both high-magnetic fields and large-field gradients, which can be used as a special environment for research or practical applications in materials processing, life science studies, physical and chemical reactions, etc. To make full use of a superconducting magnet, shared instruments (the operating platform, sample holders, temperature controller, and observation system) must be prepared as prerequisites. This paper introduces the design of a set of sample holders and a temperature controller in detail with an emphasis on validating the performance of the force and temperature sensors in the high-magnetic field.

  20. Switch: a planning tool for power systems with large shares of intermittent renewable energy.

    PubMed

    Fripp, Matthias

    2012-06-01

    Wind and solar power are highly variable, so it is it unclear how large a role they can play in future power systems. This work introduces a new open-source electricity planning model--Switch--that identifies the least-cost strategy for using renewable and conventional generators and transmission in a large power system over a multidecade period. Switch includes an unprecedented amount of spatial and temporal detail, making it possible to address a new type of question about the optimal design and operation of power systems with large amounts of renewable power. A case study of California for 2012-2027 finds that there is no maximum possible penetration of wind and solar power--these resources could potentially be used to reduce emissions 90% or more below 1990 levels without reducing reliability or severely raising the cost of electricity. This work also finds that policies that encourage customers to shift electricity demand to times when renewable power is most abundant (e.g., well-timed charging of electric vehicles) could make it possible to achieve radical emission reductions at moderate costs.

  1. Multilevel method for modeling large-scale networks.

    SciTech Connect

    Safro, I. M.

    2012-02-24

    Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

  2. Path-protection routing and wavelength assignment in WDM mesh networks under shared-risk-group constraints

    NASA Astrophysics Data System (ADS)

    Zang, Hui; Ou, Canhui; Mukherjee, Biswanath

    2001-10-01

    This study investigates the problem of fault management in a wavelength-division multiplexing (WDM)-based optical mesh network in which failures occur due to fiber cuts. In reality, bundles of fibers often get cut at the same time due to construction or destructive natural events, such as earthquakes. Fibers laid down in the same duct have a significant probability to fail at the same time. If two fibers reside in the same cable (bundle of fibers) or the same duct, we say that these two fibers are in the same Shared Risk Group (SRG). When path protection is employed, we require the primary path and the backup path to be SRG-disjoint, so that the network is survivable under single-SRG failures. Moreover, if two primary paths go through any common SRG, their backup paths cannot share wavelengths on common links. This study addresses the routing and wavelength-assignment problem in a network with path protection under SRG constraints. Off-line algorithms for static traffic is developed to combat single-SRG failures. The objective is to minimize total number of wavelengths used on all the links in the network. Both Integer Linear Programs (ILPs) and heuristic algorithms are presented and their performances are compared through numerical examples.

  3. Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes

    NASA Astrophysics Data System (ADS)

    Yang, Hui; Tang, Ming; Gross, Thilo

    2015-08-01

    One of the famous results of network science states that networks with heterogeneous connectivity are more susceptible to epidemic spreading than their more homogeneous counterparts. In particular, in networks of identical nodes it has been shown that network heterogeneity, i.e. a broad degree distribution, can lower the epidemic threshold at which epidemics can invade the system. Network heterogeneity can thus allow diseases with lower transmission probabilities to persist and spread. However, it has been pointed out that networks in which the properties of nodes are intrinsically heterogeneous can be very resilient to disease spreading. Heterogeneity in structure can enhance or diminish the resilience of networks with heterogeneous nodes, depending on the correlations between the topological and intrinsic properties. Here, we consider a plausible scenario where people have intrinsic differences in susceptibility and adapt their social network structure to the presence of the disease. We show that the resilience of networks with heterogeneous connectivity can surpass those of networks with homogeneous connectivity. For epidemiology, this implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks.

  4. Triple functional shared channel in WDM PON by orthogonal modulation and network coding

    NASA Astrophysics Data System (ADS)

    Lu, Yang; Wei, Yizhen; Hu, Miao; Zhou, Xuefang; Qian, Zhengfeng; Li, Qiliang

    2015-02-01

    A triple functional shared channel in WDM PON is proposed. The channel can be applied for broadcasting, duplex inter-ONU-communication or dynamical bandwidth allocation, increasing the flexibility and the resource utilization of the scheme. The three applications could be achieved by the same hardware, with different software operations. One example scheme is demonstrated. The test results show error free operation is achieved after for the downstream transmission, upstream transmission and the proposed three applications in shared channel after 25 km transmission.

  5. FluxSuite: a New Scientific Tool for Advanced Network Management and Cross-Sharing of Next-Generation Flux Stations

    NASA Astrophysics Data System (ADS)

    Burba, G. G.; Johnson, D.; Velgersdyk, M.; Beaty, K.; Forgione, A.; Begashaw, I.; Allyn, D.

    2015-12-01

    Significant increases in data generation and computing power in recent years have greatly improved spatial and temporal flux data coverage on multiple scales, from a single station to continental flux networks. At the same time, operating budgets for flux teams and stations infrastructure are getting ever more difficult to acquire and sustain. With more stations and networks, larger data flows from each station, and smaller operating budgets, modern tools are needed to effectively and efficiently handle the entire process. This would help maximize time dedicated to answering research questions, and minimize time and expenses spent on data processing, quality control and station management. Cross-sharing the stations with external institutions may also help leverage available funding, increase scientific collaboration, and promote data analyses and publications. FluxSuite, a new advanced tool combining hardware, software and web-service, was developed to address these specific demands. It automates key stages of flux workflow, minimizes day-to-day site management, and modernizes the handling of data flows: Each next-generation station measures all parameters needed for flux computations Field microcomputer calculates final fully-corrected flux rates in real time, including computation-intensive Fourier transforms, spectra, co-spectra, multiple rotations, stationarity, footprint, etc. Final fluxes, radiation, weather and soil data are merged into a single quality-controlled file Multiple flux stations are linked into an automated time-synchronized network Flux network manager, or PI, can see all stations in real time, including fluxes, supporting data, automated reports, and email alerts PI can assign rights, allow or restrict access to stations and data: selected stations can be shared via rights-managed access internally or with external institutions Researchers without stations could form "virtual networks" for specific projects by collaborating with PIs from

  6. From social network (centralized vs. decentralized) to collective decision-making (unshared vs. shared consensus).

    PubMed

    Sueur, Cédric; Deneubourg, Jean-Louis; Petit, Odile

    2012-01-01

    Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones--star network vs. equal network--led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies.

  7. From Social Network (Centralized vs. Decentralized) to Collective Decision-Making (Unshared vs. Shared Consensus)

    PubMed Central

    Sueur, Cédric; Deneubourg, Jean-Louis; Petit, Odile

    2012-01-01

    Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones – star network vs. equal network - led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies. PMID:22393416

  8. Overview of the gene regulation network and the bacteria biotope tasks in BioNLP'13 shared task

    PubMed Central

    2015-01-01

    Background We present the two Bacteria Track tasks of BioNLP 2013 Shared Task (ST): Gene Regulation Network (GRN) and Bacteria Biotope (BB). These tasks were previously introduced in the 2011 BioNLP-ST Bacteria Track as Bacteria Gene Interaction (BI) and Bacteria Biotope (BB). The Bacteria Track was motivated by a need to develop specific BioNLP tools for fine-grained event extraction in bacteria biology. The 2013 tasks expand on the 2011 version by better addressing the biological knowledge modeling needs. New evaluation metrics were designed for the new goals. Moving beyond a list of gene interactions, the goal of the GRN task is to build a gene regulation network from the extracted gene interactions. BB'13 is dedicated to the extraction of bacteria biotopes, i.e. bacterial environmental information, as was BB'11. BB'13 extends the typology of BB'11 to a large diversity of biotopes, as defined by the OntoBiotope ontology. The detection of entities and events is tackled by distinct subtasks in order to measure the progress achieved by the participant systems since 2011. Results This paper details the corpus preparations and the evaluation metrics, as well as summarizing and discussing the participant results. Five groups participated in each of the two tasks. The high diversity of the participant methods reflects the dynamism of the BioNLP research community. The highest scores for the GRN and BB'13 tasks are similar to those obtained by the participants in 2011, despite of the increase in difficulty. The high density of events in short text segments (multi-event extraction) was a difficult issue for the participating systems for both tasks. The analysis of the BB'13 results also shows that co-reference resolution and entity boundary detection remain major hindrances. Conclusion The evaluation results suggest new research directions for the improvement and development of Information Extraction for molecular and environmental biology. The Bacteria Track tasks

  9. Distribution of entanglement in large-scale quantum networks.

    PubMed

    Perseguers, S; Lapeyre, G J; Cavalcanti, D; Lewenstein, M; Acín, A

    2013-09-01

    The concentration and distribution of quantum entanglement is an essential ingredient in emerging quantum information technologies. Much theoretical and experimental effort has been expended in understanding how to distribute entanglement in one-dimensional networks. However, as experimental techniques in quantum communication develop, protocols for multi-dimensional systems become essential. Here, we focus on recent theoretical developments in protocols for distributing entanglement in regular and complex networks, with particular attention to percolation theory and network-based error correction.

  10. Distributive Computer Networking: Making It Work on a Regional Basis: Effective sharing through a network requires new management and resource distribution techniques.

    PubMed

    Cornew, R W; Morse, P M

    1975-08-15

    -indicate that such networks are best structured in a hierarchical form. This suggests that national networking should be based in part on the more than 30 existing state and regional networks (15). With the groundwork now laid, we expect to see links among existing regional networks to complement development efforts now occurring at the national level. With Greenberger and others, we believe that one or more networking organizations devoted to the management issues discussed in this article will be required to facilitate resource sharing on a national scale. Because of their experience with these problems and their ability to provide service in many areas of the country through existing facilities, regional networks have a major role to play. PMID:17798292

  11. Affect-specific activation of shared networks for perception and execution of facial expressions.

    PubMed

    Kircher, Tilo; Pohl, Anna; Krach, Sören; Thimm, Markus; Schulte-Rüther, Martin; Anders, Silke; Mathiak, Klaus

    2013-04-01

    Previous studies have shown overlapping neural activations for observation and execution or imitation of emotional facial expressions. These shared representations have been assumed to provide indirect evidence for a human mirror neuron system, which is suggested to be a prerequisite of action comprehension. We aimed at clarifying whether shared representations in and beyond human mirror areas are specifically activated by affective facial expressions or whether they are activated by facial expressions independent of the emotional meaning. During neuroimaging, participants observed and executed happy and non-emotional facial expressions. Shared representations were revealed for happy facial expressions in the pars opercularis, the precentral gyrus, in the superior temporal gyrus/medial temporal gyrus (MTG), in the pre-supplementary motor area and in the right amygdala. All areas showed less pronounced activation in the non-emotional condition. When directly compared, significant stronger neural responses emerged for happy facial expressions in the pre-supplementary motor area and in the MTG than for non-emotional stimuli. We assume that activation of shared representations depends on the affect and (social) relevance of the facial expression. The pre-supplementary motor area is a core-shared representation-structure supporting observation and execution of affective contagious facial expressions and might have a modulatory role during the preparation of executing happy facial expressions.

  12. Explicit integration with GPU acceleration for large kinetic networks

    DOE PAGES

    Brock, Benjamin; Belt, Andrew; Billings, Jay Jay; Guidry, Mike W.

    2015-09-15

    In this study, we demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. In addition, this orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies thatmore » important coupled, multiphysics problems in various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.« less

  13. Explicit integration with GPU acceleration for large kinetic networks

    SciTech Connect

    Brock, Benjamin; Belt, Andrew; Billings, Jay Jay; Guidry, Mike W.

    2015-09-15

    In this study, we demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. In addition, this orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems in various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.

  14. A Method of Social Collaboration and Knowledge Sharing Acceleration for e-Learning System: The Distance Learning Network Scenario

    NASA Astrophysics Data System (ADS)

    Różewski, Przemysław

    Nowadays, e-learning systems take the form of the Distance Learning Network (DLN) due to widespread use and accessibility of the Internet and networked e-learning services. The focal point of the DLN performance is efficiency of knowledge processing in asynchronous learning mode and facilitating cooperation between students. In addition, the DLN articulates attention to social aspects of the learning process as well. In this paper, a method for the DLN development is proposed. The main research objectives for the proposed method are the processes of acceleration of social collaboration and knowledge sharing in the DLN. The method introduces knowledge-disposed agents (who represent students in educational scenarios) that form a network of individuals aimed to increase their competence. For every agent the competence expansion process is formulated. Based on that outcome the process of dynamic network formation performed on the social and knowledge levels. The method utilizes formal apparatuses of competence set and network game theories combined with an agent system-based approach.

  15. Abnormalities in large scale functional networks in unmedicated patients with schizophrenia and effects of risperidone

    PubMed Central

    Kraguljac, Nina Vanessa; White, David Matthew; Hadley, Jennifer Ann; Visscher, Kristina; Knight, David; ver Hoef, Lawrence; Falola, Blessing; Lahti, Adrienne Carol

    2015-01-01

    Objective To describe abnormalities in large scale functional networks in unmedicated patients with schizophrenia and to examine effects of risperidone on networks. Material and methods 34 unmedicated patients with schizophrenia and 34 matched healthy controls were enrolled in this longitudinal study. We collected resting state functional MRI data with a 3T scanner at baseline and six weeks after they were started on risperidone. In addition, a group of 19 healthy controls were scanned twice six weeks apart. Four large scale networks, the dorsal attention network, executive control network, salience network, and default mode network were identified with seed based functional connectivity analyses. Group differences in connectivity, as well as changes in connectivity over time, were assessed on the group's participant level functional connectivity maps. Results In unmedicated patients with schizophrenia we found resting state connectivity to be increased in the dorsal attention network, executive control network, and salience network relative to control participants, but not the default mode network. Dysconnectivity was attenuated after six weeks of treatment only in the dorsal attention network. Baseline connectivity in this network was also related to clinical response at six weeks of treatment with risperidone. Conclusions Our results demonstrate abnormalities in large scale functional networks in patients with schizophrenia that are modulated by risperidone only to a certain extent, underscoring the dire need for development of novel antipsychotic medications that have the ability to alleviate symptoms through attenuation of dysconnectivity. PMID:26793436

  16. Clustering in large networks does not promote upstream reciprocity.

    PubMed

    Masuda, Naoki

    2011-01-01

    Upstream reciprocity (also called generalized reciprocity) is a putative mechanism for cooperation in social dilemma situations with which players help others when they are helped by somebody else. It is a type of indirect reciprocity. Although upstream reciprocity is often observed in experiments, most theories suggest that it is operative only when players form short cycles such as triangles, implying a small population size, or when it is combined with other mechanisms that promote cooperation on their own. An expectation is that real social networks, which are known to be full of triangles and other short cycles, may accommodate upstream reciprocity. In this study, I extend the upstream reciprocity game proposed for a directed cycle by Boyd and Richerson to the case of general networks. The model is not evolutionary and concerns the conditions under which the unanimity of cooperative players is a Nash equilibrium. I show that an abundance of triangles or other short cycles in a network does little to promote upstream reciprocity. Cooperation is less likely for a larger population size even if triangles are abundant in the network. In addition, in contrast to the results for evolutionary social dilemma games on networks, scale-free networks lead to less cooperation than networks with a homogeneous degree distribution.

  17. Towards Networked Knowledge: The Learning Registry, an Infrastructure for Sharing Online Learning Resources

    ERIC Educational Resources Information Center

    Lee, Ashley; Hobson, Joe; Bienkowski, Marie; Midgley, Steve; Currier, Sarah; Campbell, Lorna M.; Novoselova, Tatiana

    2012-01-01

    In this article, the authors describe an open-source, open-data digital infrastructure for sharing information about open educational resources (OERs) across disparate systems and platforms. The Learning Registry, which began as a project funded by the U.S. Departments of Education and Defense, currently has an active international community…

  18. The Shared Bibliographic Input Network (SBIN): A Summary of the Experiment.

    ERIC Educational Resources Information Center

    Cotter, Gladys A.

    As part of its mission to provide centralized services for the acquisition, storage, retrieval, and dissemination of scientific and technical information (STI) to support Department of Defense (DoD) research, development, and engineering studies programs, the Defense Technical Information Center (DTIC) sponsors the Shared Bibliographic Input…

  19. Large area controlled assembly of transparent conductive networks

    DOEpatents

    Ivanov, Ilia N.; Simpson, John T.

    2015-09-29

    A method of preparing a network comprises disposing a solution comprising particulate materials in a solvent onto a superhydrophobic surface comprising a plurality of superhydrophobic features and interfacial areas between the superhydrophobic features. The plurality of superhydrophobic features has a water contact angle of at least about 150.degree.. The method of preparing the network also comprises removing the solvent from the solution of the particulate materials, and forming a network of the particulate materials in the interfacial areas, the particulate materials receding to the interfacial areas as the solvent is removed.

  20. Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

    NASA Technical Reports Server (NTRS)

    Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

    2001-01-01

    Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

  1. P2P Watch: Personal Health Information Detection in Peer-to-Peer File-Sharing Networks

    PubMed Central

    El Emam, Khaled; Arbuckle, Luk; Neri, Emilio; Rose, Sean; Jonker, Elizabeth

    2012-01-01

    Background Users of peer-to-peer (P2P) file-sharing networks risk the inadvertent disclosure of personal health information (PHI). In addition to potentially causing harm to the affected individuals, this can heighten the risk of data breaches for health information custodians. Automated PHI detection tools that crawl the P2P networks can identify PHI and alert custodians. While there has been previous work on the detection of personal information in electronic health records, there has been a dearth of research on the automated detection of PHI in heterogeneous user files. Objective To build a system that accurately detects PHI in files sent through P2P file-sharing networks. The system, which we call P2P Watch, uses a pipeline of text processing techniques to automatically detect PHI in files exchanged through P2P networks. P2P Watch processes unstructured texts regardless of the file format, document type, and content. Methods We developed P2P Watch to extract and analyze PHI in text files exchanged on P2P networks. We labeled texts as PHI if they contained identifiable information about a person (eg, name and date of birth) and specifics of the person’s health (eg, diagnosis, prescriptions, and medical procedures). We evaluated the system’s performance through its efficiency and effectiveness on 3924 files gathered from three P2P networks. Results P2P Watch successfully processed 3924 P2P files of unknown content. A manual examination of 1578 randomly selected files marked by the system as non-PHI confirmed that these files indeed did not contain PHI, making the false-negative detection rate equal to zero. Of 57 files marked by the system as PHI, all contained both personally identifiable information and health information: 11 files were PHI disclosures, and 46 files contained organizational materials such as unfilled insurance forms, job applications by medical professionals, and essays. Conclusions PHI can be successfully detected in free-form textual

  2. Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

    PubMed

    Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

    2014-03-15

    The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. PMID:24333753

  3. Flash flood prediction in large dams using neural networks

    NASA Astrophysics Data System (ADS)

    Múnera Estrada, J. C.; García Bartual, R.

    2009-04-01

    A flow forecasting methodology is presented as a support tool for flood management in large dams. The practical and efficient use of hydrological real-time measurements is necessary to operate early warning systems for flood disasters prevention, either in natural catchments or in those regulated with reservoirs. In this latter case, the optimal dam operation during flood scenarios should reduce the downstream risks, and at the same time achieve a compromise between different goals: structural security, minimize predictions uncertainty and water resources system management objectives. Downstream constraints depend basically on the geomorphology of the valley, the critical flow thresholds for flooding, the land use and vulnerability associated with human settlements and their economic activities. A dam operation during a flood event thus requires appropriate strategies depending on the flood magnitude and the initial freeboard at the reservoir. The most important difficulty arises from the inherently stochastic character of peak rainfall intensities, their strong spatial and temporal variability, and the highly nonlinear response of semiarid catchments resulting from initial soil moisture condition and the dominant flow mechanisms. The practical integration of a flow prediction model in a real-time system should include combined techniques of pre-processing, data verification and completion, assimilation of information and implementation of real time filters depending on the system characteristics. This work explores the behaviour of real-time flood forecast algorithms based on artificial neural networks (ANN) techniques, in the River Meca catchment (Huelva, Spain), regulated by El Sancho dam. The dam is equipped with three Taintor gates of 12x6 meters. The hydrological data network includes five high-resolution automatic pluviometers (dt=10 min) and three high precision water level sensors in the reservoir. A cross correlation analysis between precipitation data

  4. An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks.

    PubMed

    He, Qinbin; Xia, Zhile; Lin, Bin

    2016-11-01

    Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which improved the predecessor-based approach. Furthermore, the proposed approach combined with the identification of constant nodes and simplified Boolean networks to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks. If the average degree of the network is not too large, the algorithm can get all attractors of a Boolean network with dozens or even hundreds of nodes.

  5. Analysis of a summary network of co-infection in humans reveals that parasites interact most via shared resources.

    PubMed

    Griffiths, Emily C; Pedersen, Amy B; Fenton, Andy; Petchey, Owen L

    2014-05-01

    Simultaneous infection by multiple parasite species (viruses, bacteria, helminths, protozoa or fungi) is commonplace. Most reports show co-infected humans to have worse health than those with single infections. However, we have little understanding of how co-infecting parasites interact within human hosts. We used data from over 300 published studies to construct a network that offers the first broad indications of how groups of co-infecting parasites tend to interact. The network had three levels comprising parasites, the resources they consume and the immune responses they elicit, connected by potential, observed and experimentally proved links. Pairs of parasite species had most potential to interact indirectly through shared resources, rather than through immune responses or other parasites. In addition, the network comprised 10 tightly knit groups, eight of which were associated with particular body parts, and seven of which were dominated by parasite-resource links. Reported co-infection in humans is therefore structured by physical location within the body, with bottom-up, resource-mediated processes most often influencing how, where and which co-infecting parasites interact. The many indirect interactions show how treating an infection could affect other infections in co-infected patients, but the compartmentalized structure of the network will limit how far these indirect effects are likely to spread.

  6. myExperiment: a repository and social network for the sharing of bioinformatics workflows

    PubMed Central

    Goble, Carole A.; Bhagat, Jiten; Aleksejevs, Sergejs; Cruickshank, Don; Michaelides, Danius; Newman, David; Borkum, Mark; Bechhofer, Sean; Roos, Marco; Li, Peter; De Roure, David

    2010-01-01

    myExperiment (http://www.myexperiment.org) is an online research environment that supports the social sharing of bioinformatics workflows. These workflows are procedures consisting of a series of computational tasks using web services, which may be performed on data from its retrieval, integration and analysis, to the visualization of the results. As a public repository of workflows, myExperiment allows anybody to discover those that are relevant to their research, which can then be reused and repurposed to their specific requirements. Conversely, developers can submit their workflows to myExperiment and enable them to be shared in a secure manner. Since its release in 2007, myExperiment currently has over 3500 registered users and contains more than 1000 workflows. The social aspect to the sharing of these workflows is facilitated by registered users forming virtual communities bound together by a common interest or research project. Contributors of workflows can build their reputation within these communities by receiving feedback and credit from individuals who reuse their work. Further documentation about myExperiment including its REST web service is available from http://wiki.myexperiment.org. Feedback and requests for support can be sent to bugs@myexperiment.org. PMID:20501605

  7. Efficient GPU Accelerationfor Integrating Large Thermonuclear Networks in Astrophysics

    NASA Astrophysics Data System (ADS)

    Guidry, Mike

    2016-02-01

    We demonstrate the systematic implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. We take as representative test cases Type Ia supernova explosions with extremely stiff thermonuclear reaction networks having 150-365 isotopic species and 1600-4400 reactions, assumed coupled to hydrodynamics using operator splitting. In such examples we demonstrate the capability to integrate independent thermonuclear networks from ~250-500 hydro zones (assumed to be deployed on CPU cores) in parallel on a single GPU in the same wall clock time that standard implicit methods can integrate the network for a single zone. This two or more orders of magnitude increase in efficiency for solving systems of realistic thermonuclear networks coupled to fluid dynamics implies that important coupled, multiphysics problems in various scientific and technical disciplines that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible. As examples of such applications I will discuss our ongoing deployment of these new methods for Type Ia supernova explosions in astrophysics and for simulation of the complex atmospheric chemistry entering into weather and climate problems.

  8. Metabolomics integrated elementary flux mode analysis in large metabolic networks.

    PubMed

    Gerstl, Matthias P; Ruckerbauer, David E; Mattanovich, Diethard; Jungreuthmayer, Christian; Zanghellini, Jürgen

    2015-03-10

    Elementary flux modes (EFMs) are non-decomposable steady-state pathways in metabolic networks. They characterize phenotypes, quantify robustness or identify engineering targets. An EFM analysis (EFMA) is currently restricted to medium-scale models, as the number of EFMs explodes with the network's size. However, many topologically feasible EFMs are biologically irrelevant. We present thermodynamic EFMA (tEFMA), which calculates only the small(er) subset of thermodynamically feasible EFMs. We integrate network embedded thermodynamics into EFMA and show that we can use the metabolome to identify and remove thermodynamically infeasible EFMs during an EFMA without losing biologically relevant EFMs. Calculating only the thermodynamically feasible EFMs strongly reduces memory consumption and program runtime, allowing the analysis of larger networks. We apply tEFMA to study the central carbon metabolism of E. coli and find that up to 80% of its EFMs are thermodynamically infeasible. Moreover, we identify glutamate dehydrogenase as a bottleneck, when E. coli is grown on glucose and explain its inactivity as a consequence of network embedded thermodynamics. We implemented tEFMA as a Java package which is available for download at https://github.com/mpgerstl/tEFMA.

  9. MODEL-BASED CLUSTERING OF LARGE NETWORKS1

    PubMed Central

    Vu, Duy Q.; Hunter, David R.; Schweinberger, Michael

    2015-01-01

    We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Relative to other recent model-based clustering work for networks, we introduce a more flexible modeling framework, improve the variational-approximation estimation algorithm, discuss and implement standard error estimation via a parametric bootstrap approach, and apply these methods to much larger data sets than those seen elsewhere in the literature. The more flexible framework is achieved through introducing novel parameterizations of the model, giving varying degrees of parsimony, using exponential family models whose structure may be exploited in various theoretical and algorithmic ways. The algorithms are based on variational generalized EM algorithms, where the E-steps are augmented by a minorization-maximization (MM) idea. The bootstrapped standard error estimates are based on an efficient Monte Carlo network simulation idea. Last, we demonstrate the usefulness of the model-based clustering framework by applying it to a discrete-valued network with more than 131,000 nodes and 17 billion edge variables. PMID:26605002

  10. The Process of Creating a Cross-University Network for Formative and Shared Assessment in Higher Education in Spain and Its Potential Applications

    ERIC Educational Resources Information Center

    Lopez-Pastor, Victor M.; Castejon, Javier; Sicilia-Camacho, Alvaro; Navarro-Adelantado, Vicente; Webb, Graham

    2011-01-01

    During the academic year 2005-2006 a Cross University Network for Formative and Shared Assessment in Higher Education was formed among 48 academics from 16 different universities within Spain and representing a range of academic areas. The Network was formed in response to the determination of a group of individuals who were dissatisfied with, or…

  11. Large-Scale High School Reform through School Improvement Networks: Exploring Possibilities for "Developmental Evaluation"

    ERIC Educational Resources Information Center

    Peurach, Donald J.; Lenhoff, Sarah Winchell; Glazer, Joshua L.

    2016-01-01

    Recognizing school improvement networks as a leading strategy for large-scale high school reform, this analysis examines developmental evaluation as an approach to examining school improvement networks as "learning systems" able to produce, use, and refine practical knowledge in large numbers of schools. Through a case study of one…

  12. mEducator: A Best Practice Network for Repurposing and Sharing Medical Educational Multi-type Content

    NASA Astrophysics Data System (ADS)

    Bamidis, Panagiotis D.; Kaldoudi, Eleni; Pattichis, Costas

    Although there is an abundance of medical educational content available in individual EU academic institutions, this is not widely available or easy to discover and retrieve, due to lack of standardized content sharing mechanisms. The mEducator EU project will face this lack by implementing and experimenting between two different sharing mechanisms, namely, one based one mashup technologies, and one based on semantic web services. In addition, the mEducator best practice network will critically evaluate existing standards and reference models in the field of e-learning in order to enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, repurposed and re-used across European higher academic institutions. Educational content included in mEducator covers and represents the whole range of medical educational content, from traditional instructional teaching to active learning and experiential teaching/studying approaches. It spans the whole range of types, from text to exam sheets, algorithms, teaching files, computer programs (simulators or games) and interactive objects (like virtual patients and electronically traced anatomies), while it covers a variety of topics. In this paper, apart from introducing the relevant project concepts and strategies, emphasis is also placed on the notion of (dynamic) user-generated content, its advantages and peculiarities, as well as, gaps in current research and technology practice upon its embedding into existing standards.

  13. Remote facility sharing with ATM networks [PC based ATM Link Delay Simulator (LDS)]. Final report

    SciTech Connect

    Kung, H. T.

    2001-06-01

    The ATM Link Delay Simulator (LDS) adds propagation delay to the ATM link on which it is installed, to allow control of link propagation delay in network protocol experiments simulating an adjustable piece of optical fiber. Our LDS simulates a delay of between 1.5 and 500 milliseconds and is built with commodity PC hardware, only the ATM network interface card is not generally available. Our implementation is special in that it preserves the exact spacing of ATM data cells a feature that requires sustained high performance. Our implementation shows that applications demanding sustained high performance are possible on commodity PC hardware. This illustrates the promise that PC hardware has for adaptability to demanding specialized testing of high speed network.

  14. The online Managed Knowledge Network that shares knowledge for eHealth in NHS Scotland.

    PubMed

    Dallest, Kathy; Strachan, Heather; Flett, Gillian

    2009-01-01

    The Managed Knowledge Network (MKN) for Nurses, Midwives and the Allied Health Professions (NMAHPs) in NHS Scotland was launched in November 2007. The online portal supports the NMAHP network to manage its knowledge and information sources that facilitate engagement with the national eHealth programme and realisation of benefits that eHealth offers to improve healthcare and service delivery. It is an integrated change management and knowledge management initiative. Web2 technologies support the social networking side of knowledge management and learning, allowing people to contact each other and collaborate. MKN resources are managed within the e-Library also giving access to over 5,000 online journals and over 500 bibliographic databases.

  15. Wide-area ATM networking for large-scale MPPs

    SciTech Connect

    Papadopoulos, P.M.; Geist, G.A. II

    1997-04-01

    This paper presents early experiences with using high-speed ATM interfaces to connect multiple Intel Paragons on both local and wide area networks. The testbed includes the 1024 and 512 node Paragons running the OSF operating system at Oak Ridge National Laboratory and the 1840 node Paragon running the Puma operating system at Sandia National Laboratories. The experimental OC-12 (622 Mbits/sec) interfaces are built by GigaNet and provide a proprietary API for sending AAL-5 encapsulated packets. PVM is used as the massaging infrastructure and significant modifications have been made to use the GigaNet API, operate in the Puma environment, and attain acceptable performance over local networks. These modifications are described along with a discussion of roadblocks to networking MPPs with high-performance interfaces. Our early prototype utilizes approximately 25 percent of an OC-12 circuit and 80 percent of an OC-3 circuit in send plus acknowledgment ping-pong tests.

  16. Large-Scale Functional Brain Network Reorganization During Taoist Meditation.

    PubMed

    Jao, Tun; Li, Chia-Wei; Vértes, Petra E; Wu, Changwei Wesley; Achard, Sophie; Hsieh, Chao-Hsien; Liou, Chien-Hui; Chen, Jyh-Horng; Bullmore, Edward T

    2016-02-01

    Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness.

  17. A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents

    PubMed Central

    He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

    2013-01-01

    It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology. PMID:24191145

  18. A topology visualization early warning distribution algorithm for large-scale network security incidents.

    PubMed

    He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

    2013-01-01

    It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

  19. A topology visualization early warning distribution algorithm for large-scale network security incidents.

    PubMed

    He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

    2013-01-01

    It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology. PMID:24191145

  20. Reconfigurable middleware architectures for large scale sensor networks

    SciTech Connect

    Brennan, Sean M.

    2010-03-01

    Wireless sensor networks, in an e ffort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task recon figuration and high-level object recomposition.

  1. Logistical aspects of large telemedicine networks. 2: Measurement of network activity.

    PubMed

    Wootton, Richard; Smith, Anthony C; Gormley, Sinead; Patterson, Jessica

    2002-01-01

    We carried out a retrospective review of the videoconference activity records in a university-run hospital telemedicine studio. Usage records describing videoconferencing activity in the telemedicine studio were compared with the billing records provided by the telecommunications company. During a seven-month period there were 211 entries in the studio log: 108 calls made from the studio and 103 calls made from a far-end location. We found that 103 calls from a total of 195 calls reported by the telecommunications company were recorded in the usage log. The remaining 92 calls were not recorded, probably for one of several reasons, including: failed calls--a large number of unrecorded calls (57%) lasted for less than 2 min (median 1.6 min); origin of videoconference calls--calls may have been recorded incorrectly in the usage diary (i.e. as being initiated from the far end, when actually initiated from the studio); and human error. Our study showed that manual recording of videoconference activity may not accurately reflect the actual activity taking place. Those responsible for recording and analysing videoconference activity, particularly in large telemedicine networks, should do so with care.

  2. Increasing Family Support Capacity in Child Care Centers through Regional Networking and Information Sharing.

    ERIC Educational Resources Information Center

    Rucker, Karen L.

    Society as a whole has become more transient, creating a weakened network for families in need of assistance. The child care center has the ability to create a support system for families via referrals to outside agencies, support groups, and organizations. This practicum project assessed a strategy to address the difficulty a child care agency…

  3. LibRef-L: Sharing Reference Expertise over the Academic Networks.

    ERIC Educational Resources Information Center

    Robinson, Kara; Kovacs, Diane

    1993-01-01

    Describes LibRef-L, an electronic mail-based conference that was developed to provide a forum for the discussion of library reference service issues. Networks and electronic mail are described; a profile of subscribers is given; and how to subscribe to LibRef-L is explained. (two references) (LRW)

  4. Collaborative Learning in Networked Environments: Interaction through Shared Workspaces and Communication Tools

    ERIC Educational Resources Information Center

    Hakkinen, Paivi

    2003-01-01

    Today, a variety of web-based learning environments have been developed for educational purposes, especially in higher education and continuing education courses. At the same time many studies have reported how networked interaction in many learning projects results in superficial and experience-based discussion, and does not reach the level of…

  5. Sparsely Connected, Hebbian Networks with Strikingly Large Storage Capacities.

    PubMed

    BOOS, WILLIAM; VOGEL, DAVID D.

    1997-06-01

    Conspicuous problems confront the use of fully-connected networks (F-nets) in the construction of realistic partial models of biological memory. These problems include the high synaptic densities of F-nets, and the low information storage capacities of F-nets with simple, biologically realistic learning rules. Most auto-associative networks constructed with low connectivities have employed random projections of path length 1. Projective networks (P-nets) are nonrandom, multilayer networks which achieve extremely low connectivities by linking all neurons in the same layer through paths of length 2. In this paper we derive a lower bound on the storage capacities of a class of simple, two-layer P-nets with binary Hebbian synapses. Given a 1% tolerance for spurious neurons, we find that the P-net with 1000 synapses per neuron (2 x 10(6) neurons) will store more than 1.5 x 10(6) training vectors with 20 active neurons per vector (0.25 bits per synapse). Copyright 1997 Elsevier Science Ltd.

  6. Sparsely Connected, Hebbian Networks with Strikingly Large Storage Capacities.

    PubMed

    BOOS, WILLIAM; VOGEL, DAVID D.

    1997-06-01

    Conspicuous problems confront the use of fully-connected networks (F-nets) in the construction of realistic partial models of biological memory. These problems include the high synaptic densities of F-nets, and the low information storage capacities of F-nets with simple, biologically realistic learning rules. Most auto-associative networks constructed with low connectivities have employed random projections of path length 1. Projective networks (P-nets) are nonrandom, multilayer networks which achieve extremely low connectivities by linking all neurons in the same layer through paths of length 2. In this paper we derive a lower bound on the storage capacities of a class of simple, two-layer P-nets with binary Hebbian synapses. Given a 1% tolerance for spurious neurons, we find that the P-net with 1000 synapses per neuron (2 x 10(6) neurons) will store more than 1.5 x 10(6) training vectors with 20 active neurons per vector (0.25 bits per synapse). Copyright 1997 Elsevier Science Ltd. PMID:12662862

  7. Shared vision, collective impact, and persistent challenges: the first decade of Georgia's oncology research network.

    PubMed

    Paris, Nancy M; Burke, James J; Schnell, Frederick M

    2013-11-01

    Ten years ago, Georgia was lauded for dedicating a portion of tobacco settlement funds to the Georgia Cancer Coalition (GCC). The plan championed by then-Governor Roy E. Barnes was designed to make Georgia a leader in prevention, treatment, and research. This plan called for the expansion of clinical trials to ensure Georgians had access to the highest quality care based on the most current treatments and discoveries. As a result, oncologists in the state were engaged in a planning process that resulted in a shared vision to improve the quality of cancer care through research and the formation of a new organization: the Georgia Center for Oncology Research and Education.

  8. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

    PubMed

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process. PMID:26989410

  9. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

    PubMed

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

  10. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm

    PubMed Central

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K.

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process. PMID:26989410

  11. Visual analysis of large heterogeneous social networks by semantic and structural abstraction.

    PubMed

    Shen, Zeqian; Ma, Kwan-Liu; Eliassi-Rad, Tina

    2006-01-01

    Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies.

  12. Analysis of Online Social Networks to Understand Information Sharing Behaviors Through Social Cognitive Theory

    SciTech Connect

    Yoon, Hong-Jun; Tourassi, Georgia

    2014-01-01

    Analyzing the contents of online social networks is an effective process for monitoring and understanding peoples behaviors. Since the nature of conversation and information propagation is similar to traditional conversation and learning, one of the popular socio-cognitive methods, social cognitive theory was applied to online social networks to. Two major news topics about colon cancer were chosen to monitor traffic of Twitter messages. The activity of leaders on the issue (i.e., news companies or people will prior Twitter activity on topics related to colon cancer) was monitored. In addition, the activity of followers , people who never discussed the topics before, but replied to the discussions was also monitored. Topics that produce tangible benefits such as positive outcomes from appropriate preventive actions received dramatically more attention and online social media traffic. Such characteristics can be explained with social cognitive theory and thus present opportunities for effective health campaigns.

  13. Individuals from different-looking animal species may group together to confuse shared predators: simulations with artificial neural networks.

    PubMed

    Tosh, Colin R; Jackson, Andrew L; Ruxton, Graeme D

    2007-03-22

    Individuals of many quite distantly related animal species find each other attractive and stay together for long periods in groups. We present a mechanism for mixed-species grouping in which individuals from different-looking prey species come together because the appearance of the mixed-species group is visually confusing to shared predators. Using an artificial neural network model of retinotopic mapping in predators, we train networks on random projections of single- and mixed-species prey groups and then test the ability of networks to reconstruct individual prey items from mixed-species groups in a retinotopic map. Over the majority of parameter space, cryptic prey items benefit from association with conspicuous prey because this particular visual combination worsens predator targeting of cryptic individuals. However, this benefit is not mutual as conspicuous prey tends to be targeted most poorly when in same-species groups. Many real mixed-species groups show the asymmetry in willingness to initiate and maintain the relationship predicted by our study. The agreement of model predictions with published empirical work, the efficacy of our modelling approach in previous studies, and the taxonomic ubiquity of retinotopic maps indicate that we may have uncovered an important, generic selective agent in the evolution of mixed-species grouping. PMID:17251090

  14. Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

    SciTech Connect

    Chua, Xin-Yi; Buckingham, Lawrence; Hogan, James M.; Novichkov, Pavel

    2015-06-01

    The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond those of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.

  15. Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

    DOE PAGES

    Chua, Xin-Yi; Buckingham, Lawrence; Hogan, James M.; Novichkov, Pavel

    2015-06-01

    The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond thosemore » of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.« less

  16. Query and Visualization of extremely large network datasets over the web using Quadtree based KML Regional Network Links

    SciTech Connect

    Dadi, Upendra; Liu, Cheng; Vatsavai, Raju

    2009-01-01

    Geographic data sets are often very large in size. Interactive visualization of such data at all scales is not easy because of the limited resolution of the monitors and inability of visualization applications to handle the volume of data. This is especially true for large vector datasets. The end user s experience is frequently unsatisfactory when exploring such data over the web using a naive application. Network bandwidth is another contributing factor to the low performance. In this paper, a Quadtree based technique to visualize extremely large spatial network datasets over the web is described. It involves using custom developed algorithms leveraging a PostGIS database as the data source and Google Earth as the visualization client. This methodology supports both point and range queries along with non-spatial queries. This methodology is demonstrated using a network dataset consisting of several million links. The methodology is based on using some of the powerful features of KML (Keyhole Markup Language). Keyhole Markup Language (KML) is an Open Geospatial Consortium (OGC) standard for displaying geospatial data on Earth browsers. One of the features of KML is the notion of Network Links. Using network links, a wide range of geospatial data sources such as geodatabases, static files and geospatial data services can be simultaneously accessed and visualized seamlessly. Using the network links combined with Level of Detail principle, view based rendering and intelligent server and client-side caching, scalability in visualizing extremely large spatial datasets can be achieved.

  17. Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability.

    PubMed

    Langs, Georg; Wang, Danhong; Golland, Polina; Mueller, Sophia; Pan, Ruiqi; Sabuncu, Mert R; Sun, Wei; Li, Kuncheng; Liu, Hesheng

    2016-10-01

    The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units. The functional connectivity patterns observed in resting-state data were mapped in the embedding space and the maps were aligned across individuals. A clustering algorithm was performed on the aligned embedding maps and the resulting clusters were transformed back to the unique anatomical space of each individual. This novel approach identified functional systems that were reproducible within subjects, but were distributed across different anatomical locations in different subjects. Using this approach for intersubject alignment improved the predictability of individual differences in language laterality when compared with anatomical alignment alone. Our results further revealed that the strength of association between function and macroanatomy varied across the cortex, which was strong in unimodal sensorimotor networks, but weak in association networks.

  18. Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability.

    PubMed

    Langs, Georg; Wang, Danhong; Golland, Polina; Mueller, Sophia; Pan, Ruiqi; Sabuncu, Mert R; Sun, Wei; Li, Kuncheng; Liu, Hesheng

    2016-10-01

    The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units. The functional connectivity patterns observed in resting-state data were mapped in the embedding space and the maps were aligned across individuals. A clustering algorithm was performed on the aligned embedding maps and the resulting clusters were transformed back to the unique anatomical space of each individual. This novel approach identified functional systems that were reproducible within subjects, but were distributed across different anatomical locations in different subjects. Using this approach for intersubject alignment improved the predictability of individual differences in language laterality when compared with anatomical alignment alone. Our results further revealed that the strength of association between function and macroanatomy varied across the cortex, which was strong in unimodal sensorimotor networks, but weak in association networks. PMID:26334050

  19. Using cellular network diagrams to interpret large-scale datasets: past progress and future challenges

    NASA Astrophysics Data System (ADS)

    Karp, Peter D.; Latendresse, Mario; Paley, Suzanne

    2011-03-01

    Cellular networks are graphs of molecular interactions within the cell. Thanks to the confluence of genome sequencing and bioinformatics, scientists are now able to reconstruct cellular network models for more than 1,000 organisms. A variety of bioinformatics tools have been developed to support the visualization and navigation of cellular network data. Another important application is the use of cellular network diagrams to visualize and interpret large-scale datasets, such as gene-expression data. We present the Cellular Overview, a network visualization tool developed at SRI International (SRI) to support visualization, navigation, and interpretation of large-scale datasets on metabolic networks. Different variations of the diagram have been generated algorithmically for more than 1,000 organisms. We discuss the graphical design of the diagram and its interactive capabilities.

  20. Direction of the Organ Procurement and Transplantation Network and United Network for Organ Sharing regarding the oversight of live donor transplantation and solicitation for organs.

    PubMed

    Delmonico, F L; Graham, W K

    2006-01-01

    The Organ Procurement and Transplantation Network (OPTN) operated by United Network for Organ Sharing (UNOS) has taken recent steps to address public solicitation for organ donors and its oversight of live donor transplantation. This report provides the direction of the OPTN regarding deceased donor solicitation. The OPTN has authority under federal law to equitably allocate deceased donor organs within a single national network based upon medical criteria, not upon one's social or economic ability to utilize resources not available to all on the waiting list. The OPTN makes a distinction between solicitations for a live donor organ versus solicitations for directed donation of deceased organs. As to live donor solicitation, the OPTN cannot regulate or restrict ways relationships are developed in our society, nor does it seek to do so. OPTN members have a responsibility of helping protect potential recipients from hazards that can arise from public appeals for live donor organs. Oversight and support of the OPTN for live donor transplantation is now detailed by improving the reporting of live donor follow-up, by providing a mechanism for facilitating anonymous live kidney donation, and by providing information for potential live kidney donors via the UNOS Transplant Living website. PMID:16433754

  1. Integrating large-scale functional genomics data to dissect metabolic networks for hydrogen production

    SciTech Connect

    Harwood, Caroline S

    2012-12-17

    The goal of this project is to identify gene networks that are critical for efficient biohydrogen production by leveraging variation in gene content and gene expression in independently isolated Rhodopseudomonas palustris strains. Coexpression methods were applied to large data sets that we have collected to define probabilistic causal gene networks. To our knowledge this a first systems level approach that takes advantage of strain-to strain variability to computationally define networks critical for a particular bacterial phenotypic trait.

  2. Resonant spatiotemporal learning in large random recurrent networks.

    PubMed

    Daucé, Emmanuel; Quoy, Mathias; Doyon, Bernard

    2002-09-01

    Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present

  3. On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia

    NASA Astrophysics Data System (ADS)

    Rendón de la Torre, Stephanie; Kalda, Jaan; Kitt, Robert; Engelbrecht, Jüri

    2016-09-01

    This paper presents the first topological analysis of the economic structure of an entire country based on payments data obtained from Swedbank. This data set is exclusive in its kind because around 80% of Estonia's bank transactions are done through Swedbank, hence, the economic structure of the country can be reconstructed. Scale-free networks are commonly observed in a wide array of different contexts such as nature and society. In this paper, the nodes are comprised by customers of the bank (legal entities) and the links are established by payments between these nodes. We study the scaling-free and structural properties of this network. We also describe its topology, components and behaviors. We show that this network shares typical structural characteristics known in other complex networks: degree distributions follow a power law, low clustering coefficient and low average shortest path length. We identify the key nodes of the network and perform simulations of resiliency against random and targeted attacks of the nodes with two different approaches. With this, we find that by identifying and studying the links between the nodes is possible to perform vulnerability analysis of the Estonian economy with respect to economic shocks.

  4. Continuum Modeling and Control of Large Nonuniform Wireless Networks via Nonlinear Partial Differential Equations

    DOE PAGES

    Zhang, Yang; Chong, Edwin K. P.; Hannig, Jan; Estep, Donald

    2013-01-01

    We inmore » troduce a continuum modeling method to approximate a class of large wireless networks by nonlinear partial differential equations (PDEs). This method is based on the convergence of a sequence of underlying Markov chains of the network indexed by N , the number of nodes in the network. As N goes to infinity, the sequence converges to a continuum limit, which is the solution of a certain nonlinear PDE. We first describe PDE models for networks with uniformly located nodes and then generalize to networks with nonuniformly located, and possibly mobile, nodes. Based on the PDE models, we develop a method to control the transmissions in nonuniform networks so that the continuum limit is invariant under perturbations in node locations. This enables the networks to maintain stable global characteristics in the presence of varying node locations.« less

  5. A Logically Centralized Approach for Control and Management of Large Computer Networks

    ERIC Educational Resources Information Center

    Iqbal, Hammad A.

    2012-01-01

    Management of large enterprise and Internet service provider networks is a complex, error-prone, and costly challenge. It is widely accepted that the key contributors to this complexity are the bundling of control and data forwarding in traditional routers and the use of fully distributed protocols for network control. To address these…

  6. Consolidating newborn screening efforts in the Asia Pacific region : Networking and shared education.

    PubMed

    Padilla, Carmencita David; Therrell, Bradford L

    2012-01-01

    Many of the countries in the Asia Pacific Region, particularly those with depressed and developing economies, are just initiating newborn screening programs for selected metabolic and other congenital disorders. The cultural, geographic, language, and economic differences that exist throughout the region add to the challenges of developing sustainable newborn screening systems. There are currently more developing programs than developed programs within the region. Newborn screening activities in the Asia Pacific Region are particularly important since births there account for approximately half of the world's births. To date, there have been two workshops to facilitate formation of the Asia Pacific Newborn Screening Collaboratives. The 1st Workshop on Consolidating Newborn Screening Efforts in the Asia Pacific Region occurred in Cebu, Philippines, on March 30-April 1, 2008, as a satellite meeting to the 7th Asia Pacific Conference on Human Genetics. The second workshop was held on June 4-5, 2010, in Manila, Philippines. Workshop participants included key policy-makers, service providers, researchers, and consumer advocates from 11 countries with 50% or less newborn screening coverage. Expert lectures included experiences in the United States and the Netherlands, international quality assurance activities and ongoing and potential research activities. Additional meeting support was provided by the U.S. National Institutes of Health, the Centers for Disease Control and Prevention, the U.S. National Newborn Screening and Genetics Resource Center, the International Society for Neonatal Screening, and the March of Dimes. As part of both meeting activities, participants shared individual experiences in program implementation with formal updates of screening information for each country. This report reviews the activities and country reports from two Workshops on Consolidating Newborn Screening Efforts in the Asia Pacific Region with emphasis on the second workshop. It

  7. Consolidating newborn screening efforts in the Asia Pacific region : Networking and shared education.

    PubMed

    Padilla, Carmencita David; Therrell, Bradford L

    2012-01-01

    Many of the countries in the Asia Pacific Region, particularly those with depressed and developing economies, are just initiating newborn screening programs for selected metabolic and other congenital disorders. The cultural, geographic, language, and economic differences that exist throughout the region add to the challenges of developing sustainable newborn screening systems. There are currently more developing programs than developed programs within the region. Newborn screening activities in the Asia Pacific Region are particularly important since births there account for approximately half of the world's births. To date, there have been two workshops to facilitate formation of the Asia Pacific Newborn Screening Collaboratives. The 1st Workshop on Consolidating Newborn Screening Efforts in the Asia Pacific Region occurred in Cebu, Philippines, on March 30-April 1, 2008, as a satellite meeting to the 7th Asia Pacific Conference on Human Genetics. The second workshop was held on June 4-5, 2010, in Manila, Philippines. Workshop participants included key policy-makers, service providers, researchers, and consumer advocates from 11 countries with 50% or less newborn screening coverage. Expert lectures included experiences in the United States and the Netherlands, international quality assurance activities and ongoing and potential research activities. Additional meeting support was provided by the U.S. National Institutes of Health, the Centers for Disease Control and Prevention, the U.S. National Newborn Screening and Genetics Resource Center, the International Society for Neonatal Screening, and the March of Dimes. As part of both meeting activities, participants shared individual experiences in program implementation with formal updates of screening information for each country. This report reviews the activities and country reports from two Workshops on Consolidating Newborn Screening Efforts in the Asia Pacific Region with emphasis on the second workshop. It

  8. School Leadership in Networked Schools: Deciphering the Impact of Large Technical Systems on Education.

    ERIC Educational Resources Information Center

    Davidson, Judith; Olson, Matthew

    2003-01-01

    Examines electronic networks leadership in education from perspective of the social nature of large technical systems. Focuses on the concepts of translation and social communication spaces. (Contains 31 references.)(PKP)

  9. Next-generation sequencing reveals large connected networks of intra-host HCV variants

    PubMed Central

    2014-01-01

    Background Next-generation sequencing (NGS) allows for sampling numerous viral variants from infected patients. This provides a novel opportunity to represent and study the mutational landscape of Hepatitis C Virus (HCV) within a single host. Results Intra-host variants of the HCV E1/E2 region were extensively sampled from 58 chronically infected patients. After NGS error correction, the average number of reads and variants obtained from each sample were 3202 and 464, respectively. The distance between each pair of variants was calculated and networks were created for each patient, where each node is a variant and two nodes are connected by a link if the nucleotide distance between them is 1. The work focused on large components having > 5% of all reads, which in average account for 93.7% of all reads found in a patient. The distance between any two variants calculated over the component correlated strongly with nucleotide distances (r = 0.9499; p = 0.0001), a better correlation than the one obtained with Neighbour-Joining trees (r = 0.7624; p = 0.0001). In each patient, components were well separated, with the average distance between (6.53%) being 10 times greater than within each component (0.68%). The ratio of nonsynonymous to synonymous changes was calculated and some patients (6.9%) showed a mixture of networks under strong negative and positive selection. All components were robust to in silico stochastic sampling; even after randomly removing 85% of all reads, the largest connected component in the new subsample still involved 82.4% of remaining nodes. In vitro sampling showed that 93.02% of components present in the original sample were also found in experimental replicas, with 81.6% of reads found in both. When syringe-sharing transmission events were simulated, 91.2% of all simulated transmission events seeded all components present in the source. Conclusions Most intra-host variants are organized into distinct single-mutation components that are: well

  10. Elucidating the genotype-phenotype relationships and network perturbations of human shared and specific disease genes from an evolutionary perspective.

    PubMed

    Begum, Tina; Ghosh, Tapash Chandra

    2014-10-05

    To date, numerous studies have been attempted to determine the extent of variation in evolutionary rates between human disease and nondisease (ND) genes. In our present study, we have considered human autosomal monogenic (Mendelian) disease genes, which were classified into two groups according to the number of phenotypic defects, that is, specific disease (SPD) gene (one gene: one defect) and shared disease (SHD) gene (one gene: multiple defects). Here, we have compared the evolutionary rates of these two groups of genes, that is, SPD genes and SHD genes with respect to ND genes. We observed that the average evolutionary rates are slow in SHD group, intermediate in SPD group, and fast in ND group. Group-to-group evolutionary rate differences remain statistically significant regardless of their gene expression levels and number of defects. We demonstrated that disease genes are under strong selective constraint if they emerge through edgetic perturbation or drug-induced perturbation of the interactome network, show tissue-restricted expression, and are involved in transmembrane transport. Among all the factors, our regression analyses interestingly suggest the independent effects of 1) drug-induced perturbation and 2) the interaction term of expression breadth and transmembrane transport on protein evolutionary rates. We reasoned that the drug-induced network disruption is a combination of several edgetic perturbations and, thus, has more severe effect on gene phenotypes.

  11. Shared Pathways Among Autism Candidate Genes Determined by Co-expression Network Analysis of the Developing Human Brain Transcriptome.

    PubMed

    Mahfouz, Ahmed; Ziats, Mark N; Rennert, Owen M; Lelieveldt, Boudewijn P F; Reinders, Marcel J T

    2015-12-01

    Autism spectrum disorder (ASD) is a neurodevelopmental syndrome known to have a significant but complex genetic etiology. Hundreds of diverse genes have been implicated in ASD; yet understanding how many genes, each with disparate function, can all be linked to a single clinical phenotype remains unclear. We hypothesized that understanding functional relationships between autism candidate genes during normal human brain development may provide convergent mechanistic insight into the genetic heterogeneity of ASD. We analyzed the co-expression relationships of 455 genes previously implicated in autism using the BrainSpan human transcriptome database, across 16 anatomical brain regions spanning prenatal life through adulthood. We discovered modules of ASD candidate genes with biologically relevant temporal co-expression dynamics, which were enriched for functional ontologies related to synaptogenesis, apoptosis, and GABA-ergic neurons. Furthermore, we also constructed co-expression networks from the entire transcriptome and found that ASD candidate genes were enriched in modules related to mitochondrial function, protein translation, and ubiquitination. Hub genes central to these ASD-enriched modules were further identified, and their functions supported these ontological findings. Overall, our multi-dimensional co-expression analysis of ASD candidate genes in the normal developing human brain suggests the heterogeneous set of ASD candidates share transcriptional networks related to synapse formation and elimination, protein turnover, and mitochondrial function.

  12. On Window Control Algorithm over Wireless Cellular Networks with Large Delay Variation

    NASA Astrophysics Data System (ADS)

    Lee, Ho-Jin; Byun, Hee-Jung; Lim, Jong-Tae

    In addition to high bit error rates, large and sudden variations in delay often occur in wireless cellular networks. The delay can be several times the typical round-trip time, which can cause the spurious timeout. In this letter, we propose a new window control algorithm to improve TCP performance in wireless cellular networks with large delay variation and high bit error rates. Simulation results illustrate that our proposal improves the performance of TCP in terms of fairness and link utilization.

  13. Control of large wind turbine generators connected to utility networks

    NASA Technical Reports Server (NTRS)

    Hinrichsen, E. N.

    1983-01-01

    This is an investigation of the control requirements for variable pitch wind turbine generators connected to electric power systems. The requirements include operation in very small as well as very large power systems. Control systems are developed for wind turbines with synchronous, induction, and doubly fed generators. Simulation results are presented. It is shown how wind turbines and power system controls can be integrated. A clear distinction is made between fast control of turbine torque, which is a peculiarity of wind turbines, and slow control of electric power, which is a traditional power system requirement.

  14. Large-scale lattice-Boltzmann simulations over lambda networks

    NASA Astrophysics Data System (ADS)

    Saksena, R.; Coveney, P. V.; Pinning, R.; Booth, S.

    Amphiphilic molecules are of immense industrial importance, mainly due to their tendency to align at interfaces in a solution of immiscible species, e.g., oil and water, thereby reducing surface tension. Depending on the concentration of amphiphiles in the solution, they may assemble into a variety of morphologies, such as lamellae, micelles, sponge and cubic bicontinuous structures exhibiting non-trivial rheological properties. The main objective of this work is to study the rheological properties of very large, defect-containing gyroidal systems (of up to 10243 lattice sites) using the lattice-Boltzmann method. Memory requirements for the simulation of such large lattices exceed that available to us on most supercomputers and so we use MPICH-G2/MPIg to investigate geographically distributed domain decomposition simulations across HPCx in the UK and TeraGrid in the US. Use of MPICH-G2/MPIg requires the port-forwarder to work with the grid middleware on HPCx. Data from the simulations is streamed to a high performance visualisation resource at UCL (London) for rendering and visualisation. Lighting the Blue Touchpaper for UK e-Science - Closing Conference of ESLEA Project March 26-28 2007 The George Hotel, Edinburgh, UK

  15. Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.

    PubMed

    Lytton, William W; Seidenstein, Alexandra H; Dura-Bernal, Salvador; McDougal, Robert A; Schürmann, Felix; Hines, Michael L

    2016-10-01

    Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment. PMID:27557104

  16. Multirelational organization of large-scale social networks in an online world.

    PubMed

    Szell, Michael; Lambiotte, Renaud; Thurner, Stefan

    2010-08-01

    The capacity to collect fingerprints of individuals in online media has revolutionized the way researchers explore human society. Social systems can be seen as a nonlinear superposition of a multitude of complex social networks, where nodes represent individuals and links capture a variety of different social relations. Much emphasis has been put on the network topology of social interactions, however, the multidimensional nature of these interactions has largely been ignored, mostly because of lack of data. Here, for the first time, we analyze a complete, multirelational, large social network of a society consisting of the 300,000 odd players of a massive multiplayer online game. We extract networks of six different types of one-to-one interactions between the players. Three of them carry a positive connotation (friendship, communication, trade), three a negative (enmity, armed aggression, punishment). We first analyze these types of networks as separate entities and find that negative interactions differ from positive interactions by their lower reciprocity, weaker clustering, and fatter-tail degree distribution. We then explore how the interdependence of different network types determines the organization of the social system. In particular, we study correlations and overlap between different types of links and demonstrate the tendency of individuals to play different roles in different networks. As a demonstration of the power of the approach, we present the first empirical large-scale verification of the long-standing structural balance theory, by focusing on the specific multiplex network of friendship and enmity relations. PMID:20643965

  17. Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.

    PubMed

    Lytton, William W; Seidenstein, Alexandra H; Dura-Bernal, Salvador; McDougal, Robert A; Schürmann, Felix; Hines, Michael L

    2016-10-01

    Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.

  18. Message spreading in networks with stickiness and persistence: Large clustering does not always facilitate large-scale diffusion

    NASA Astrophysics Data System (ADS)

    Cui, Pengbi; Tang, Ming; Wu, Zhi-Xi

    2014-09-01

    Recent empirical studies have confirmed the key roles of complex contagion mechanisms such as memory, social reinforcement, and decay effects in information diffusion and behavior spreading. Inspired by this fact, we here propose a new agent-based model to capture the whole picture of the joint action of the three mechanisms in information spreading, by quantifying the complex contagion mechanisms as stickiness and persistence, and carry out extensive simulations of the model on various networks. By numerical simulations as well as theoretical analysis, we find that the stickiness of the message determines the critical dynamics of message diffusion on tree-like networks, whereas the persistence plays a decisive role on dense regular lattices. In either network, the greater persistence can effectively make the message more invasive. Of particular interest is that our research results renew our previous knowledge that messages can spread broader in networks with large clustering, which turns out to be only true when they can inform a non-zero fraction of the population in the limit of large system size.

  19. Message spreading in networks with stickiness and persistence: Large clustering does not always facilitate large-scale diffusion

    PubMed Central

    Cui, Pengbi; Tang, Ming; Wu, Zhi-Xi

    2014-01-01

    Recent empirical studies have confirmed the key roles of complex contagion mechanisms such as memory, social reinforcement, and decay effects in information diffusion and behavior spreading. Inspired by this fact, we here propose a new agent–based model to capture the whole picture of the joint action of the three mechanisms in information spreading, by quantifying the complex contagion mechanisms as stickiness and persistence, and carry out extensive simulations of the model on various networks. By numerical simulations as well as theoretical analysis, we find that the stickiness of the message determines the critical dynamics of message diffusion on tree-like networks, whereas the persistence plays a decisive role on dense regular lattices. In either network, the greater persistence can effectively make the message more invasive. Of particular interest is that our research results renew our previous knowledge that messages can spread broader in networks with large clustering, which turns out to be only true when they can inform a non-zero fraction of the population in the limit of large system size. PMID:25200277

  20. Networking for large-scale science: infrastructure, provisioning, transport and application mapping

    NASA Astrophysics Data System (ADS)

    Rao, Nageswara S.; Carter, Steven M.; Wu, Qishi; Wing, William R.; Zhu, Mengxia; Mezzacappa, Anthony; Veeraraghavan, Malathi; Blondin, John M.

    2005-01-01

    Large-scale science computations and experiments require unprecedented network capabilities in the form of large bandwidth and dynamically stable connections to support data transfers, interactive visualizations, and monitoring and steering operations. A number of component technologies dealing with the infrastructure, provisioning, transport and application mappings must be developed and/or optimized to achieve these capabilities. We present a brief account of the following technologies that contribute toward achieving these network capabilities: (a) DOE UltraScienceNet and NSF CHEETAH network testbeds that provide on-demand and scheduled dedicated network connections; (b) experimental results on transport protocols that achieve close to 100% utilization on dedicated 1Gbps wide-area channels; (c) a scheme for optimally mapping a visualization pipeline onto a network to minimize the end-to-end delays; and (d) interconnect configuration and protocols that provides multiple Gbps flows from Cray X1 to external hosts.

  1. Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy.

    PubMed

    Bonilha, Leonardo; Tabesh, Ali; Dabbs, Kevin; Hsu, David A; Stafstrom, Carl E; Hermann, Bruce P; Lin, Jack J

    2014-08-01

    Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared with controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment.

  2. Dynamic competition between large-scale functional networks differentiates fear conditioning and extinction in humans.

    PubMed

    Marstaller, Lars; Burianová, Hana; Reutens, David C

    2016-07-01

    The high evolutionary value of learning when to respond to threats or when to inhibit previously learned associations after changing threat contingencies is reflected in dedicated networks in the animal and human brain. Recent evidence further suggests that adaptive learning may be dependent on the dynamic interaction of meta-stable functional brain networks. However, it is still unclear which functional brain networks compete with each other to facilitate associative learning and how changes in threat contingencies affect this competition. The aim of this study was to assess the dynamic competition between large-scale networks related to associative learning in the human brain by combining a repeated differential conditioning and extinction paradigm with independent component analysis of functional magnetic resonance imaging data. The results (i) identify three task-related networks involved in initial and sustained conditioning as well as extinction, and demonstrate that (ii) the two main networks that underlie sustained conditioning and extinction are anti-correlated with each other and (iii) the dynamic competition between these two networks is modulated in response to changes in associative contingencies. These findings provide novel evidence for the view that dynamic competition between large-scale functional networks differentiates fear conditioning from extinction learning in the healthy brain and suggest that dysfunctional network dynamics might contribute to learning-related neuropsychiatric disorders.

  3. Dynamic competition between large-scale functional networks differentiates fear conditioning and extinction in humans.

    PubMed

    Marstaller, Lars; Burianová, Hana; Reutens, David C

    2016-07-01

    The high evolutionary value of learning when to respond to threats or when to inhibit previously learned associations after changing threat contingencies is reflected in dedicated networks in the animal and human brain. Recent evidence further suggests that adaptive learning may be dependent on the dynamic interaction of meta-stable functional brain networks. However, it is still unclear which functional brain networks compete with each other to facilitate associative learning and how changes in threat contingencies affect this competition. The aim of this study was to assess the dynamic competition between large-scale networks related to associative learning in the human brain by combining a repeated differential conditioning and extinction paradigm with independent component analysis of functional magnetic resonance imaging data. The results (i) identify three task-related networks involved in initial and sustained conditioning as well as extinction, and demonstrate that (ii) the two main networks that underlie sustained conditioning and extinction are anti-correlated with each other and (iii) the dynamic competition between these two networks is modulated in response to changes in associative contingencies. These findings provide novel evidence for the view that dynamic competition between large-scale functional networks differentiates fear conditioning from extinction learning in the healthy brain and suggest that dysfunctional network dynamics might contribute to learning-related neuropsychiatric disorders. PMID:27079532

  4. Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems

    PubMed Central

    Rosvall, Martin; Bergstrom, Carl T.

    2011-01-01

    To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network — the optimal number of levels and modular partition at each level — with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks. PMID:21494658

  5. Locating Acoustic Events Based on Large-Scale Sensor Networks

    PubMed Central

    Kim, Yungeun; Ahn, Junho; Cha, Hojung

    2009-01-01

    Research on acoustic source localization is actively being conducted to enhance accuracy and coverage. However, the performance is inherently limited due to the use of expensive sensor nodes and inefficient communication methods. This paper proposes an acoustic source localization algorithm for a large area that uses low-cost sensor nodes. The proposed mechanism efficiently handles multiple acoustic sources by removing false-positive errors that arise from the different propagation ranges of radio and sound. Extensive outdoor experiments with real hardware validated that the proposed mechanism could localize four acoustic sources within a 3 m error in a 60 m by 60 m area, where conventional systems could hardly achieve similar performance. PMID:22303155

  6. Northwestern University schizophrenia data sharing for SchizConnect: A longitudinal dataset for large-scale integration.

    PubMed

    Kogan, Alex; Alpert, Kathryn; Ambite, Jose Luis; Marcus, Daniel S; Wang, Lei

    2016-01-01

    In this paper, we describe an instance of the Northwestern University Schizophrenia Data and Software Tool (NUSDAST), a schizophrenia-related dataset hosted at XNAT Central, and the SchizConnect data portal used for accessing and sharing the dataset. NUSDAST was built and extended upon existing, standard schemas available for data sharing on XNAT Central (http://central.xnat.org/). With the creation of SchizConnect, we were able to link NUSDAST to other neuroimaging data sources and create a powerful, federated neuroimaging resource.

  7. Inadvertent Exposure to Pornography on the Internet: Implications of Peer-to-Peer File-Sharing Networks for Child Development and Families

    ERIC Educational Resources Information Center

    Greenfield, P.M.

    2004-01-01

    This essay comprises testimony to the Congressional Committee on Government Reform. The Committee's concern was the possibility of exposure to pornography when children and teens participate in peer-to-peer file-sharing networks, which are extremely popular in these age groups. A review of the relevant literature led to three major conclusions:…

  8. A Comparison of Users' Personal Information Sharing Awareness, Habits, and Practices in Social Networking Sites and E-Learning Systems

    ERIC Educational Resources Information Center

    Ball, Albert L.

    2012-01-01

    Although reports of identity theft continue to be widely published, users continue to post an increasing amount of personal information online, especially within social networking sites (SNS) and e-learning systems (ELS). Research has suggested that many users lack awareness of the threats that risky online personal information sharing poses to…

  9. Optimizing Cluster Heads for Energy Efficiency in Large-Scale Heterogeneous Wireless Sensor Networks

    DOE PAGES

    Gu, Yi; Wu, Qishi; Rao, Nageswara S. V.

    2010-01-01

    Many complex sensor network applications require deploying a large number of inexpensive and small sensors in a vast geographical region to achieve quality through quantity. Hierarchical clustering is generally considered as an efficient and scalable way to facilitate the management and operation of such large-scale networks and minimize the total energy consumption for prolonged lifetime. Judicious selection of cluster heads for data integration and communication is critical to the success of applications based on hierarchical sensor networks organized as layered clusters. We investigate the problem of selecting sensor nodes in a predeployed sensor network to be the cluster headsmore » to minimize the total energy needed for data gathering. We rigorously derive an analytical formula to optimize the number of cluster heads in sensor networks under uniform node distribution, and propose a Distance-based Crowdedness Clustering algorithm to determine the cluster heads in sensor networks under general node distribution. The results from an extensive set of experiments on a large number of simulated sensor networks illustrate the performance superiority of the proposed solution over the clustering schemes based on k -means algorithm.« less

  10. Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks

    PubMed Central

    Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

    2015-01-01

    Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators’ careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system

  11. Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

    PubMed

    Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

    2015-01-01

    Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system

  12. Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

    PubMed

    Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

    2015-01-01

    Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system

  13. Limitations and tradeoffs in synchronization of large-scale networks with uncertain links

    NASA Astrophysics Data System (ADS)

    Diwadkar, Amit; Vaidya, Umesh

    2016-04-01

    The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies.

  14. Limitations and tradeoffs in synchronization of large-scale networks with uncertain links

    PubMed Central

    Diwadkar, Amit; Vaidya, Umesh

    2016-01-01

    The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies. PMID:27067994

  15. Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks

    PubMed Central

    Wang, Shuihua; Chen, Mengmeng; Li, Yang; Zhang, Yudong; Han, Liangxiu; Wu, Jane; Du, Sidan

    2015-01-01

    Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer's disease, Parkinson's diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines. PMID:26692046

  16. Under Utilization of Pancreas Transplants in Cystic Fibrosis Recipients in the United Network Organ Sharing (UNOS) Data 1987-2014.

    PubMed

    Usatin, D J; Perito, E R; Posselt, A M; Rosenthal, P

    2016-05-01

    Despite a high prevalence of pancreatic endocrine and exocrine insufficiency in cystic fibrosis (CF), pancreas transplantation is rarely reported. United Network for Organ Sharing (UNOS) data were used to examine utilization of pancreas transplant and posttransplant outcomes in CF patients. Between 1987-2014, CF patients (N = 4600) underwent 17 liver-pancreas, three lung-pancreas, one liver-lung pancreas, four kidney-pancreas, and three pancreas-only transplants. Of the 303 CF patients who received liver transplantation, 20% had CF-related diabetes (CFRD) before transplantation, and nine of those received a liver-pancreas transplant. Of 4241 CF patients who underwent lung transplantation, 33% had CFRD before transplantation, and three of those received a pancreas transplant. Of 49 CF patients who received a liver-lung transplant, 57% had CFRD before transplantation and one received a pancreas transplant. Posttransplantation diabetes developed in 7% of CF pancreas transplant recipients versus 24% of CF liver and 29% of CF lung recipients. UNOS has no data on pancreas exocrine insufficiency. Two-year posttransplantation survival was 88% after liver-pancreas transplant, 33% after lung-pancreas transplant, and 100% after pancreas-kidney and pancreas-only transplants. Diabetes is common pretransplantation and posttransplantation in CF solid organ transplant recipients, but pancreas transplantation remains rare. Further consideration of pancreas transplant in CF patients undergoing other solid organ transplant may be warranted.

  17. Detecting and mitigating abnormal events in large scale networks: budget constrained placement on smart grids

    SciTech Connect

    Santhi, Nandakishore; Pan, Feng

    2010-10-19

    Several scenarios exist in the modern interconnected world which call for an efficient network interdiction algorithm. Applications are varied, including various monitoring and load shedding applications on large smart energy grids, computer network security, preventing the spread of Internet worms and malware, policing international smuggling networks, and controlling the spread of diseases. In this paper we consider some natural network optimization questions related to the budget constrained interdiction problem over general graphs, specifically focusing on the sensor/switch placement problem for large-scale energy grids. Many of these questions turn out to be computationally hard to tackle. We present a particular form of the interdiction question which is practically relevant and which we show as computationally tractable. A polynomial-time algorithm will be presented for solving this problem.

  18. Management and services for large-scale virtual 3D urban model data based on network

    NASA Astrophysics Data System (ADS)

    He, Zhengwei; Chen, Jing; Wu, Huayi

    2008-10-01

    The buildings in modern city are complex and diverse, and the quantity is huge. These bring very big challenge for constructing 3D GIS under network circumstance and eventually realizing the Digital Earth. After analyzed the characteristic of network service about massive 3D urban building model data, this paper focuses on the organization and management of spatial data and the network services strategy, proposes a progressive network transmission schema based on the spatial resolution and the component elements of 3D building model data. Next, this paper put forward multistage-link three-dimensional spatial data organization model and encoding method of spatial index based on fully level quadtree structure. Then, a virtual earth platform, called GeoGlobe, was developed using above theory. Experimental results show that above 3D spatial data management model and service theory can availably provide network services for large-scale 3D urban model data. The application results and user experience good .

  19. Patterns of interactions of a large fish-parasite network in a tropical floodplain.

    PubMed

    Lima, Dilermando P; Giacomini, Henrique C; Takemoto, Ricardo M; Agostinho, Angelo A; Bini, Luis M

    2012-07-01

    1. Describing and explaining the structure of species interaction networks is of paramount importance for community ecology. Yet much has to be learned about the mechanisms responsible for major patterns, such as nestedness and modularity in different kinds of systems, of which large and diverse networks are a still underrepresented and scarcely studied fraction. 2. We assembled information on fishes and their parasites living in a large floodplain of key ecological importance for freshwater ecosystems in the Paraná River basin in South America. The resulting fish-parasite network containing 72 and 324 species of fishes and parasites, respectively, was analysed to investigate the patterns of nestedness and modularity as related to fish and parasite features. 3. Nestedness was found in the entire network and among endoparasites, multiple-host life cycle parasites and native hosts, but not in networks of ectoparasites, single-host life cycle parasites and non-native fishes. All networks were significantly modular. Taxonomy was the major host's attribute influencing both nestedness and modularity: more closely related host species tended to be associated with more nested parasite compositions and had greater chance of belonging to the same network module. Nevertheless, host abundance had a positive relationship with nestedness when only native host species pairs of the same network module were considered for analysis. 4. These results highlight the importance of evolutionary history of hosts in linking patterns of nestedness and formation of modules in the network. They also show that functional attributes of parasites (i.e. parasitism mode and life cycle) and origin of host populations (i.e. natives versus non-natives) are crucial to define the relative contribution of these two network properties and their dependence on other ecological factors (e.g. host abundance), with potential implications for community dynamics and stability.

  20. Major technological innovations introduced in the large antennas of the Deep Space Network

    NASA Technical Reports Server (NTRS)

    Imbriale, W. A.

    2002-01-01

    The NASA Deep Space Network (DSN) is the largest and most sensitive scientific, telecommunications and radio navigation network in the world. Its principal responsibilities are to provide communications, tracking, and science services to most of the world's spacecraft that travel beyond low Earth orbit. The network consists of three Deep Space Communications Complexes. Each of the three complexes consists of multiple large antennas equipped with ultra sensitive receiving systems. A centralized Signal Processing Center (SPC) remotely controls the antennas, generates and transmits spacecraft commands, and receives and processes the spacecraft telemetry.

  1. The Presence of a Large Chiari Network in a Patient with Atrial Fibrillation and Stroke

    PubMed Central

    Schwimmer-Okike, Nneka; Niebuhr, Johannes; Schramek, Grit Gesine Ruth; Frantz, Stefan

    2016-01-01

    The Chiari network is an embryological remnant found in the right atrium, mostly without any significant pathophysiological consequences. However, several cardiac associations are reported in the literature including supraventricular tachyarrhythmias. We present a case of a 96-year-old body donor with a stroke episode and intermittent atrial fibrillations. The dissection of the heart revealed the presence of an immense Chiari network with a large central thrombus. The role of a Chiari network in the pathogenesis of stroke and pulmonary embolism is discussed. PMID:27547469

  2. Par@Graph - a parallel toolbox for the construction and analysis of large complex climate networks

    NASA Astrophysics Data System (ADS)

    Ihshaish, H.; Tantet, A.; Dijkzeul, J. C. M.; Dijkstra, H. A.

    2015-10-01

    In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least 106) and edges (up to at least 1012). The key innovation is an efficient set of parallel software tools designed to leverage the inherited hybrid parallelism in distributed-memory clusters of multi-core machines. The performance of the toolbox is illustrated through networks derived from sea surface height (SSH) data of a global high-resolution ocean model. Less than 8 min are needed on 90 Intel Xeon E5-4650 processors to reconstruct a climate network including the preprocessing and the correlation of 3 × 105 SSH time series, resulting in a weighted graph with the same number of vertices and about 3.2 × 108 edges. In less than 14 min on 30 processors, the resulted graph's degree centrality, strength, connected components, eigenvector centrality, entropy and clustering coefficient metrics were obtained. These results indicate that a complete cycle to construct and analyze a large-scale climate network is available under 22 min Par@Graph therefore facilitates the application of climate network analysis on high-resolution observations and model results, by enabling fast network reconstruct from the calculation of statistical similarities between climate time series. It also enables network analysis at unprecedented scales on a variety of different sizes of input data sets.

  3. Multilevel hierarchical kernel spectral clustering for real-life large scale complex networks.

    PubMed

    Mall, Raghvendra; Langone, Rocco; Suykens, Johan A K

    2014-01-01

    Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks. PMID:24949877

  4. Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks

    PubMed Central

    Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

    2014-01-01

    Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks. PMID:24949877

  5. Moving Large Data Sets Over High-Performance Long Distance Networks

    SciTech Connect

    Hodson, Stephen W; Poole, Stephen W; Ruwart, Thomas; Settlemyer, Bradley W

    2011-04-01

    In this project we look at the performance characteristics of three tools used to move large data sets over dedicated long distance networking infrastructure. Although performance studies of wide area networks have been a frequent topic of interest, performance analyses have tended to focus on network latency characteristics and peak throughput using network traffic generators. In this study we instead perform an end-to-end long distance networking analysis that includes reading large data sets from a source file system and committing large data sets to a destination file system. An evaluation of end-to-end data movement is also an evaluation of the system configurations employed and the tools used to move the data. For this paper, we have built several storage platforms and connected them with a high performance long distance network configuration. We use these systems to analyze the capabilities of three data movement tools: BBcp, GridFTP, and XDD. Our studies demonstrate that existing data movement tools do not provide efficient performance levels or exercise the storage devices in their highest performance modes. We describe the device information required to achieve high levels of I/O performance and discuss how this data is applicable in use cases beyond data movement performance.

  6. Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers.

    PubMed

    Lee, Sun-Mi; Abbott, Patricia A

    2003-01-01

    The growth of nursing databases necessitates new approaches to data analyses. These databases, which are known to be massive and multidimensional, easily exceed the capabilities of both human cognition and traditional analytical approaches. One innovative approach, knowledge discovery in large databases (KDD), allows investigators to analyze very large data sets more comprehensively in an automatic or a semi-automatic manner. Among KDD techniques, Bayesian networks, a state-of-the art representation of probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks allow investigators to combine domain knowledge with statistical data, enabling nurse researchers to incorporate clinical and theoretical knowledge into the process of knowledge discovery in large datasets. This tailored discussion presents the basic concepts of Bayesian networks and their use as knowledge discovery tools for nurse researchers.

  7. Dispersion in Rectangular Networks: Effective Diffusivity and Large-Deviation Rate Function

    NASA Astrophysics Data System (ADS)

    Tzella, Alexandra; Vanneste, Jacques

    2016-09-01

    The dispersion of a diffusive scalar in a fluid flowing through a network has many applications including to biological flows, porous media, water supply, and urban pollution. Motivated by this, we develop a large-deviation theory that predicts the evolution of the concentration of a scalar released in a rectangular network in the limit of large time t ≫1 . This theory provides an approximation for the concentration that remains valid for large distances from the center of mass, specifically for distances up to O (t ) and thus much beyond the O (t1 /2) range where a standard Gaussian approximation holds. A byproduct of the approach is a closed-form expression for the effective diffusivity tensor that governs this Gaussian approximation. Monte Carlo simulations of Brownian particles confirm the large-deviation results and demonstrate their effectiveness in describing the scalar distribution when t is only moderately large.

  8. Dispersion in Rectangular Networks: Effective Diffusivity and Large-Deviation Rate Function.

    PubMed

    Tzella, Alexandra; Vanneste, Jacques

    2016-09-01

    The dispersion of a diffusive scalar in a fluid flowing through a network has many applications including to biological flows, porous media, water supply, and urban pollution. Motivated by this, we develop a large-deviation theory that predicts the evolution of the concentration of a scalar released in a rectangular network in the limit of large time t≫1. This theory provides an approximation for the concentration that remains valid for large distances from the center of mass, specifically for distances up to O(t) and thus much beyond the O(t^{1/2}) range where a standard Gaussian approximation holds. A byproduct of the approach is a closed-form expression for the effective diffusivity tensor that governs this Gaussian approximation. Monte Carlo simulations of Brownian particles confirm the large-deviation results and demonstrate their effectiveness in describing the scalar distribution when t is only moderately large. PMID:27661692

  9. Dispersion in Rectangular Networks: Effective Diffusivity and Large-Deviation Rate Function.

    PubMed

    Tzella, Alexandra; Vanneste, Jacques

    2016-09-01

    The dispersion of a diffusive scalar in a fluid flowing through a network has many applications including to biological flows, porous media, water supply, and urban pollution. Motivated by this, we develop a large-deviation theory that predicts the evolution of the concentration of a scalar released in a rectangular network in the limit of large time t≫1. This theory provides an approximation for the concentration that remains valid for large distances from the center of mass, specifically for distances up to O(t) and thus much beyond the O(t^{1/2}) range where a standard Gaussian approximation holds. A byproduct of the approach is a closed-form expression for the effective diffusivity tensor that governs this Gaussian approximation. Monte Carlo simulations of Brownian particles confirm the large-deviation results and demonstrate their effectiveness in describing the scalar distribution when t is only moderately large.

  10. Effects of Knowledge Sharing and Social Presence on the Intention to Continuously Use Social Networking Sites: The Case of Twitter in Korea

    NASA Astrophysics Data System (ADS)

    Park, Bong-Won; Lee, Kun Chang

    Recent surge of social networking websites in the world supports a widely accepted assumption that people aspires to be recognized online by sharing information with others, perceive enjoyment and keeps to use their social networking site continuously. Different from traditional social networking sites (SNSs) like Cyworld and Facebook, Twitter is famous for its short message and ease of sharing knowledge with others in a prompt manner. Therefore, Twitter is preferred most by many people who seem innovative generically. In this sense, Twitter accumulates its fame as the most influential SNS media among users. However, there is no study to investigate why people holds continuous intention to use the Twitter from the perspective of knowledge-sharing and social presence. To resolve this research issue, this paper adopts six constructs such as personal innovativeness, knowledge-sharing intention, perceived ease of use, perceived enjoyment, social presence, and intention to continuously use. Empirical results with 105 valid questionnaires revealed that the proposed research model is statistically significant, and people's intention to use the Twitter continuously is influenced by social presence, perceived enjoyment, and perceived ease of use.

  11. A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks.

    PubMed

    Xu, Yang; Hu, Xuemei; Hu, Haixiao; Liu, Ming

    2016-01-01

    In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors' local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption. PMID:27537891

  12. A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks

    PubMed Central

    Xu, Yang; Hu, Xuemei; Hu, Haixiao; Liu, Ming

    2016-01-01

    In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors’ local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption. PMID:27537891

  13. A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks.

    PubMed

    Xu, Yang; Hu, Xuemei; Hu, Haixiao; Liu, Ming

    2016-08-16

    In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors' local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption.

  14. Analysis of a large-scale weighted network of one-to-one human communication

    NASA Astrophysics Data System (ADS)

    Onnela, Jukka-Pekka; Saramäki, Jari; Hyvönen, Jörkki; Szabó, Gábor; Argollo de Menezes, M.; Kaski, Kimmo; Barabási, Albert-László; Kertész, János

    2007-06-01

    We construct a connected network of 3.9 million nodes from mobile phone call records, which can be regarded as a proxy for the underlying human communication network at the societal level. We assign two weights on each edge to reflect the strength of social interaction, which are the aggregate call duration and the cumulative number of calls placed between the individuals over a period of 18 weeks. We present a detailed analysis of this weighted network by examining its degree, strength, and weight distributions, as well as its topological assortativity and weighted assortativity, clustering and weighted clustering, together with correlations between these quantities. We give an account of motif intensity and coherence distributions and compare them to a randomized reference system. We also use the concept of link overlap to measure the number of common neighbours any two adjacent nodes have, which serves as a useful local measure for identifying the interconnectedness of communities. We report a positive correlation between the overlap and weight of a link, thus providing strong quantitative evidence for the weak ties hypothesis, a central concept in social network analysis. The percolation properties of the network are found to depend on the type and order of removed links, and they can help understand how the local structure of the network manifests itself at the global level. We hope that our results will contribute to modelling weighted large-scale social networks, and believe that the systematic approach followed here can be adopted to study other weighted networks.

  15. Role of Delays in Shaping Spatiotemporal Dynamics of Neuronal Activity in Large Networks

    SciTech Connect

    Roxin, Alex; Brunel, Nicolas; Hansel, David

    2005-06-17

    We study the effect of delays on the dynamics of large networks of neurons. We show that delays give rise to a wealth of bifurcations and to a rich phase diagram, which includes oscillatory bumps, traveling waves, lurching waves, standing waves arising via a period-doubling bifurcation, aperiodic regimes, and regimes of multistability. We study the existence and the stability of the various dynamical patterns analytically and numerically in a simplified rate model as a function of the interaction parameters. The results derived in that framework allow us to understand the origin of the diversity of dynamical states observed in large networks of spiking neurons.

  16. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

    PubMed

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework. PMID:27303272

  17. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity

    PubMed Central

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework. PMID:27303272

  18. Shared and nonshared neural networks of cognitive and affective theory-of-mind: a neuroimaging study using cartoon picture stories.

    PubMed

    Schlaffke, Lara; Lissek, Silke; Lenz, Melanie; Juckel, Georg; Schultz, Thomas; Tegenthoff, Martin; Schmidt-Wilcke, Tobias; Brüne, Martin

    2015-01-01

    Theory of mind (ToM) refers to the ability to represent one's own and others' cognitive and affective mental states. Recent imaging studies have aimed to disentangle the neural networks involved in cognitive as opposed to affective ToM, based on clinical observations that the two can functionally dissociate. Due to large differences in stimulus material and task complexity findings are, however, inconclusive. Here, we investigated the neural correlates of cognitive and affective ToM in psychologically healthy male participants (n = 39) using functional brain imaging, whereby the same set of stimuli was presented for all conditions (affective, cognitive and control), but associated with different questions prompting either a cognitive or affective ToM inference. Direct contrasts of cognitive versus affective ToM showed that cognitive ToM recruited the precuneus and cuneus, as well as regions in the temporal lobes bilaterally. Affective ToM, in contrast, involved a neural network comprising prefrontal cortical structures, as well as smaller regions in the posterior cingulate cortex and the basal ganglia. Notably, these results were complemented by a multivariate pattern analysis (leave one study subject out), yielding a classifier with an accuracy rate of more than 85% in distinguishing between the two ToM-conditions. The regions contributing most to successful classification corresponded to those found in the univariate analyses. The study contributes to the differentiation of neural patterns involved in the representation of cognitive and affective mental states of others.

  19. A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

    NASA Technical Reports Server (NTRS)

    Bhaduri, Kanishka; Srivastava, Ashok N.

    2009-01-01

    This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.

  20. A Technique for Moving Large Data Sets over High-Performance Long Distance Networks

    SciTech Connect

    Settlemyer, Bradley W; Dobson, Jonathan D; Hodson, Stephen W; Kuehn, Jeffery A; Poole, Stephen W; Ruwart, Thomas

    2011-01-01

    In this paper we look at the performance characteristics of three tools used to move large data sets over dedicated long distance networking infrastructure. Although performance studies of wide area networks have been a frequent topic of interest, performance analyses have tended to focus on network latency characteristics and peak throughput using network traffic generators. In this study we instead perform an end-to-end long distance networking analysis that includes reading large data sets from a source file system and committing the data to a remote destination file system. An evaluation of end-to-end data movement is also an evaluation of the system configurations employed and the tools used to move the data. For this paper, we have built several storage platforms and connected them with a high performance long distance network configuration. We use these systems to analyze the capabilities of three data movement tools: BBcp, GridFTP, and XDD. Our studies demonstrate that existing data movement tools do not provide efficient performance levels or exercise the storage devices in their highest performance modes.

  1. A Large-Scale Remote Wireless Data Acquisition Network for Environmental Sensors

    NASA Astrophysics Data System (ADS)

    Brown, R. F.; Natvig, D. O.

    2013-12-01

    Over the past nine years we have constructed a large-scale wireless telemetry network that connects remote environmental research experiments and wildlife monitoring webcams to the Internet. This network, which connects back to the University of New Mexico Sevilleta Field Station, is distributed across several thousand square kilometers in central New Mexico, providing real-time automated data acquisition from nearly fifty dataloggers and thousands of sensors located at meteorological stations, global change experiments, and eddy covariance flux towers. This is one of the largest remote environmental wireless data acquisition networks in the world. While the majority of sites connected to this network are within the boundaries of the Sevilleta National Wildlife Refuge, the network includes several sites outside the Refuge, with the most distant link being nearly one hundred kilometers from the Sevilleta Field Station. An ancillary network in the Valles Caldera National Preserve in northern New Mexico exists to provide remote connectivity to additional environmental research experiments. Hundreds of person hours and thousands of vehicle miles are saved each year by eliminating regular visits to download data at these remote sites. Additionally, this network allows for prompt detection of equipment and power failures, reducing data loss. The use of Wi-Fi devices has permitted tremendous flexibility in the overall network design while keeping costs low. Moreover, such devices have allowed wireless links averaging more than ten kilometers and in several instances, exceeding thirty kilometers. Here, we describe the basic elements of this remote wireless data acquisition network, including network design, equipment choices, power options, and datalogger interfaces.

  2. Beyond Music Sharing: An Evaluation of Peer-to-Peer Data Dissemination Techniques in Large Scientific Collaborations

    SciTech Connect

    Ripeanu, Matei; Al-Kiswany, Samer; Iamnitchi, Adriana; Vazhkudai, Sudharshan S

    2009-03-01

    The avalanche of data from scientific instruments and the ensuing interest from geographically distributed users to analyze and interpret it accentuates the need for efficient data dissemination. A suitable data distribution scheme will find the delicate balance between conflicting requirements of minimizing transfer times, minimizing the impact on the network, and uniformly distributing load among participants. We identify several data distribution techniques, some successfully employed by today's peer-to-peer networks: staging, data partitioning, orthogonal bandwidth exploitation, and combinations of the above. We use simulations to explore the performance of these techniques in contexts similar to those used by today's data-centric scientific collaborations and derive several recommendations for efficient data dissemination. Our experimental results show that the peer-to-peer solutions that offer load balancing and good fault tolerance properties and have embedded participation incentives lead to unjustified costs in today's scientific data collaborations deployed on over-provisioned network cores. However, as user communities grow and these deployments scale, peer-to-peer data delivery mechanisms will likely outperform other techniques.

  3. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

    PubMed

    Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

    2015-01-01

    Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

  4. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

    PubMed Central

    Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

    2015-01-01

    Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation. PMID:26681933

  5. Modeling and simulation of complex network attributes on coordinating large multiagent system.

    PubMed

    Xu, Yang; Li, Xiang; Liu, Ming

    2014-01-01

    With the expansion of distributed multiagent systems, traditional coordination strategy becomes a severe bottleneck when the system scales up to hundreds of agents. The key challenge is that in typical large multiagent systems, sparsely distributed agents can only communicate directly with very few others and the network is typically modeled as an adaptive complex network. In this paper, we present simulation testbed CoordSim built to model the coordination of network centric multiagent systems. Based on the token-based strategy, the coordination can be built as a communication decision problem that agents make decisions to target communications and pass them over to the capable agents who will potentially benefit the team most. We have theoretically analyzed that the characters of complex network make a significant difference with both random and intelligent coordination strategies, which may contribute to future multiagent algorithm design. PMID:24955399

  6. On a digital wireless impact-monitoring network for large-scale composite structures

    NASA Astrophysics Data System (ADS)

    Yuan, Shenfang; Mei, Hanfei; Qiu, Lei; Ren, Yuanqiang

    2014-08-01

    Impact, which may occur during manufacture, service or maintenance, is one of the major concerns to be monitored throughout the lifetime of aircraft composite structures. Aiming at monitoring impacts online while minimizing the weight added to the aircraft to meet the strict limitations of aerospace engineering, this paper puts forward a new digital wireless network based on miniaturized wireless digital impact-monitoring nodes developed for large-scale composite structures. In addition to investigations on the design methods of the network architecture, time synchronization and implementation method, a conflict resolution method based on the feature parameters of digital sequences is first presented to address impact localization conflicts when several nodes are arranged close together. To verify the feasibility and stability of the wireless network, experiments are performed on a complex aircraft composite wing box and an unmanned aerial vehicle (UAV) composite wing. Experimental results show the successful design of the presented network.

  7. Modeling and Simulation of Complex Network Attributes on Coordinating Large Multiagent System

    PubMed Central

    Li, Xiang; Liu, Ming

    2014-01-01

    With the expansion of distributed multiagent systems, traditional coordination strategy becomes a severe bottleneck when the system scales up to hundreds of agents. The key challenge is that in typical large multiagent systems, sparsely distributed agents can only communicate directly with very few others and the network is typically modeled as an adaptive complex network. In this paper, we present simulation testbed CoordSim built to model the coordination of network centric multiagent systems. Based on the token-based strategy, the coordination can be built as a communication decision problem that agents make decisions to target communications and pass them over to the capable agents who will potentially benefit the team most. We have theoretically analyzed that the characters of complex network make a significant difference with both random and intelligent coordination strategies, which may contribute to future multiagent algorithm design. PMID:24955399

  8. Coordinated Information Generation and Mental Flexibility: Large-Scale Network Disruption in Children with Autism

    PubMed Central

    Mišić, Bratislav; Doesburg, Sam M.; Fatima, Zainab; Vidal, Julie; Vakorin, Vasily A.; Taylor, Margot J.; McIntosh, Anthony R.

    2015-01-01

    Autism spectrum disorder (ASD) includes deficits in social cognition, communication, and executive function. Recent neuroimaging studies suggest that ASD disrupts the structural and functional organization of brain networks and, presumably, how they generate information. Here, we relate deficits in an aspect of cognitive control to network-level disturbances in information processing. We recorded magnetoencephalography while children with ASD and typically developing controls performed a set-shifting task designed to test mental flexibility. We used multiscale entropy (MSE) to estimate the rate at which information was generated in a set of sources distributed across the brain. Multivariate partial least-squares analysis revealed 2 distributed networks, operating at fast and slow time scales, that respond completely differently to set shifting in ASD compared with control children, indicating disrupted temporal organization within these networks. Moreover, when typically developing children engaged these networks, they achieved faster reaction times. When children with ASD engaged these networks, there was no improvement in performance, suggesting that the networks were ineffective in children with ASD. Our data demonstrate that the coordination and temporal organization of large-scale neural assemblies during the performance of cognitive control tasks is disrupted in children with ASD, contributing to executive function deficits in this group. PMID:24770713

  9. Large scale gene regulatory network inference with a multi-level strategy.

    PubMed

    Wu, Jun; Zhao, Xiaodong; Lin, Zongli; Shao, Zhifeng

    2016-02-01

    Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology. Inspired by the Dialogue for Reverse Engineering Assessments and Methods (DREAM) projects, many excellent gene regulatory network inference algorithms have been proposed. However, it is still a challenging problem to infer a gene regulatory network from gene expression data on a large scale. In this paper, we propose a gene regulatory network inference method based on a multi-level strategy (GENIMS), which can give results that are more accurate and robust than the state-of-the-art methods. The proposed method mainly consists of three levels, which are an original feature selection step based on guided regularized random forest, normalization of individual feature selection and the final refinement step according to the topological property of the gene regulatory network. To prove the accuracy and robustness of our method, we compare our method with the state-of-the-art methods on the DREAM4 and DREAM5 benchmark networks and the results indicate that the proposed method can significantly improve the performance of gene regulatory network inference. Additionally, we also discuss the influence of the selection of different parameters in our method. PMID:26687446

  10. Analysis and visualization of large complex attack graphs for networks security

    NASA Astrophysics Data System (ADS)

    Chen, Hongda; Chen, Genshe; Blasch, Erik; Kruger, Martin; Sityar, Irma

    2007-04-01

    In this paper, we have proposed a comprehensive and innovative approach for analysis and visualization of large complex multi-step cyber attack graphs. As an automated tool for cyber attack detection, prediction, and visualization, the newly proposed method transforms large quantities of network security data into real-time actionable intelligence, which can be used to (1) provide guidance on network hardening to prevent attacks, (2) perform real-time attack event correlation during active attacks, and (3) formulate post-attack responses. We show that it is possible to visualize the complex graphs, including all possible network attack paths while still keeping complexity manageable. The proposed analysis and visualization tool provides an efficient and effective solution for predicting potential attacks upon observed intrusion evidence, as well as interactive multi-resolution views such that an analyst can first obtain high-level overviews quickly, and then drill down to specific details.

  11. An Efficient Steady-State Analysis Method for Large Boolean Networks with High Maximum Node Connectivity

    PubMed Central

    Hong, Changki; Hwang, Jeewon; Cho, Kwang-Hyun; Shin, Insik

    2015-01-01

    Boolean networks have been widely used to model biological processes lacking detailed kinetic information. Despite their simplicity, Boolean network dynamics can still capture some important features of biological systems such as stable cell phenotypes represented by steady states. For small models, steady states can be determined through exhaustive enumeration of all state transitions. As the number of nodes increases, however, the state space grows exponentially thus making it difficult to find steady states. Over the last several decades, many studies have addressed how to handle such a state space explosion. Recently, increasing attention has been paid to a satisfiability solving algorithm due to its potential scalability to handle large networks. Meanwhile, there still lies a problem in the case of large models with high maximum node connectivity where the satisfiability solving algorithm is known to be computationally intractable. To address the problem, this paper presents a new partitioning-based method that breaks down a given network into smaller subnetworks. Steady states of each subnetworks are identified by independently applying the satisfiability solving algorithm. Then, they are combined to construct the steady states of the overall network. To efficiently apply the satisfiability solving algorithm to each subnetwork, it is crucial to find the best partition of the network. In this paper, we propose a method that divides each subnetwork to be smallest in size and lowest in maximum node connectivity. This minimizes the total cost of finding all steady states in entire subnetworks. The proposed algorithm is compared with others for steady states identification through a number of simulations on both published small models and randomly generated large models with differing maximum node connectivities. The simulation results show that our method can scale up to several hundreds of nodes even for Boolean networks with high maximum node connectivity. The

  12. An Efficient Steady-State Analysis Method for Large Boolean Networks with High Maximum Node Connectivity.

    PubMed

    Hong, Changki; Hwang, Jeewon; Cho, Kwang-Hyun; Shin, Insik

    2015-01-01

    Boolean networks have been widely used to model biological processes lacking detailed kinetic information. Despite their simplicity, Boolean network dynamics can still capture some important features of biological systems such as stable cell phenotypes represented by steady states. For small models, steady states can be determined through exhaustive enumeration of all state transitions. As the number of nodes increases, however, the state space grows exponentially thus making it difficult to find steady states. Over the last several decades, many studies have addressed how to handle such a state space explosion. Recently, increasing attention has been paid to a satisfiability solving algorithm due to its potential scalability to handle large networks. Meanwhile, there still lies a problem in the case of large models with high maximum node connectivity where the satisfiability solving algorithm is known to be computationally intractable. To address the problem, this paper presents a new partitioning-based method that breaks down a given network into smaller subnetworks. Steady states of each subnetworks are identified by independently applying the satisfiability solving algorithm. Then, they are combined to construct the steady states of the overall network. To efficiently apply the satisfiability solving algorithm to each subnetwork, it is crucial to find the best partition of the network. In this paper, we propose a method that divides each subnetwork to be smallest in size and lowest in maximum node connectivity. This minimizes the total cost of finding all steady states in entire subnetworks. The proposed algorithm is compared with others for steady states identification through a number of simulations on both published small models and randomly generated large models with differing maximum node connectivities. The simulation results show that our method can scale up to several hundreds of nodes even for Boolean networks with high maximum node connectivity. The

  13. Cholinergic and serotonergic modulations differentially affect large-scale functional networks in the mouse brain.

    PubMed

    Shah, Disha; Blockx, Ines; Keliris, Georgios A; Kara, Firat; Jonckers, Elisabeth; Verhoye, Marleen; Van der Linden, Annemie

    2016-07-01

    Resting-state functional MRI (rsfMRI) is a widely implemented technique used to investigate large-scale topology in the human brain during health and disease. Studies in mice provide additional advantages, including the possibility to flexibly modulate the brain by pharmacological or genetic manipulations in combination with high-throughput functional connectivity (FC) investigations. Pharmacological modulations that target specific neurotransmitter systems, partly mimicking the effect of pathological events, could allow discriminating the effect of specific systems on functional network disruptions. The current study investigated the effect of cholinergic and serotonergic antagonists on large-scale brain networks in mice. The cholinergic system is involved in cognitive functions and is impaired in, e.g., Alzheimer's disease, while the serotonergic system is involved in emotional and introspective functions and is impaired in, e.g., Alzheimer's disease, depression and autism. Specific interest goes to the default-mode-network (DMN), which is studied extensively in humans and is affected in many neurological disorders. The results show that both cholinergic and serotonergic antagonists impaired the mouse DMN-like network similarly, except that cholinergic modulation additionally affected the retrosplenial cortex. This suggests that both neurotransmitter systems are involved in maintaining integrity of FC within the DMN-like network in mice. Cholinergic and serotonergic modulations also affected other functional networks, however, serotonergic modulation impaired the frontal and thalamus networks more extensively. In conclusion, this study demonstrates the utility of pharmacological rsfMRI in animal models to provide insights into the role of specific neurotransmitter systems on functional networks in neurological disorders. PMID:26195064

  14. Transient Analysis Generator /TAG/ simulates behavior of large class of electrical networks

    NASA Technical Reports Server (NTRS)

    Thomas, W. J.

    1967-01-01

    Transient Analysis Generator program simulates both transient and dc steady-state behavior of a large class of electrical networks. It generates a special analysis program for each circuit described in an easily understood and manipulated programming language. A generator or preprocessor and a simulation system make up the TAG system.

  15. The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth

    ERIC Educational Resources Information Center

    Steyvers, Mark; Tenenbaum, Joshua B.

    2005-01-01

    We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of…

  16. The Use of Online Social Networks by Polish Former Erasmus Students: A Large-Scale Survey

    ERIC Educational Resources Information Center

    Bryla, Pawel

    2014-01-01

    There is an increasing role of online social networks in the life of young Poles. We conducted a large-scale survey among Polish former Erasmus students. We have received 2450 completed questionnaires from alumni of 115 higher education institutions all over Poland. 85.4% of our respondents reported they kept in touch with their former Erasmus…

  17. A data management and publication workflow for a large-scale, heterogeneous sensor network.

    PubMed

    Jones, Amber Spackman; Horsburgh, Jeffery S; Reeder, Stephanie L; Ramírez, Maurier; Caraballo, Juan

    2015-06-01

    It is common for hydrology researchers to collect data using in situ sensors at high frequencies, for extended durations, and with spatial distributions that produce data volumes requiring infrastructure for data storage, management, and sharing. The availability and utility of these data in addressing scientific questions related to water availability, water quality, and natural disasters relies on effective cyberinfrastructure that facilitates transformation of raw sensor data into usable data products. It also depends on the ability of researchers to share and access the data in useable formats. In this paper, we describe a data management and publication workflow and software tools for research groups and sites conducting long-term monitoring using in situ sensors. Functionality includes the ability to track monitoring equipment inventory and events related to field maintenance. Linking this information to the observational data is imperative in ensuring the quality of sensor-based data products. We present these tools in the context of a case study for the innovative Urban Transitions and Aridregion Hydrosustainability (iUTAH) sensor network. The iUTAH monitoring network includes sensors at aquatic and terrestrial sites for continuous monitoring of common meteorological variables, snow accumulation and melt, soil moisture, surface water flow, and surface water quality. We present the overall workflow we have developed for effectively transferring data from field monitoring sites to ultimate end-users and describe the software tools we have deployed for storing, managing, and sharing the sensor data. These tools are all open source and available for others to use. PMID:25968554

  18. Sensorpedia: Information Sharing Across Autonomous Sensor Systems

    SciTech Connect

    Gorman, Bryan L; Resseguie, David R; Tomkins-Tinch, Christopher H

    2009-01-01

    The concept of adapting social media technologies is introduced as a means of achieving information sharing across autonomous sensor systems. Historical examples of interoperability as an underlying principle in loosely-coupled systems is compared and contrasted with corresponding tightly-coupled, integrated systems. Examples of ad hoc information sharing solutions based on Web 2.0 social networks, mashups, blogs, wikis, and data tags are presented and discussed. The underlying technologies of these solutions are isolated and defined, and Sensorpedia is presented as a formalized application for implementing sensor information sharing across large-scale enterprises with incompatible autonomous sensor systems.

  19. Weighted and directed interactions in evolving large-scale epileptic brain networks

    NASA Astrophysics Data System (ADS)

    Dickten, Henning; Porz, Stephan; Elger, Christian E.; Lehnertz, Klaus

    2016-10-01

    Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only – in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

  20. Weighted and directed interactions in evolving large-scale epileptic brain networks

    PubMed Central

    Dickten, Henning; Porz, Stephan; Elger, Christian E.; Lehnertz, Klaus

    2016-01-01

    Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only – in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control. PMID:27708381

  1. Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

    SciTech Connect

    Rossi, R; Gallagher, B; Neville, J; Henderson, K

    2011-11-11

    Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

  2. Large scale silver nanowires network fabricated by MeV hydrogen (H+) ion beam irradiation

    NASA Astrophysics Data System (ADS)

    Honey, S.; Naseem, S.; Ishaq, A.; Maaza, M.; Bhatti, M. T.; Wan, D.

    2016-04-01

    A random two-dimensional large scale nano-network of silver nanowires (Ag-NWs) is fabricated by MeV hydrogen (H+) ion beam irradiation. Ag-NWs are irradiated under H+ ion beam at different ion fluences at room temperature. The Ag-NW network is fabricated by H+ ion beam-induced welding of Ag-NWs at intersecting positions. H+ ion beam induced welding is confirmed by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Moreover, the structure of Ag NWs remains stable under H+ ion beam, and networks are optically transparent. Morphology also remains stable under H+ ion beam irradiation. No slicings or cuttings of Ag-NWs are observed under MeV H+ ion beam irradiation. The results exhibit that the formation of Ag-NW network proceeds through three steps: ion beam induced thermal spikes lead to the local heating of Ag-NWs, the formation of simple junctions on small scale, and the formation of a large scale network. This observation is useful for using Ag-NWs based devices in upper space where protons are abandoned in an energy range from MeV to GeV. This high-quality Ag-NW network can also be used as a transparent electrode for optoelectronics devices. Project supported by the National Research Foundation of South Africa (NRF), the French Centre National pour la Recherche Scientifique, iThemba-LABS, the UNESCO-UNISA Africa Chair in Nanosciences & Nanotechnology, the Third World Academy of Science (TWAS), Organization of Women in Science for the Developing World (OWSDW), the Abdus Salam ICTP via the Nanosciences African Network (NANOAFNET), and the Higher Education Commission (HEC) of Pakistan.

  3. ShakeNet: a portable wireless sensor network for instrumenting large civil structures

    USGS Publications Warehouse

    Kohler, Monica D.; Hao, Shuai; Mishra, Nilesh; Govindan, Ramesh; Nigbor, Robert

    2015-01-01

    We report our findings from a U.S. Geological Survey (USGS) National Earthquake Hazards Reduction Program-funded project to develop and test a wireless, portable, strong-motion network of up to 40 triaxial accelerometers for structural health monitoring. The overall goal of the project was to record ambient vibrations for several days from USGS-instrumented structures. Structural health monitoring has important applications in fields like civil engineering and the study of earthquakes. The emergence of wireless sensor networks provides a promising means to such applications. However, while most wireless sensor networks are still in the experimentation stage, very few take into consideration the realistic earthquake engineering application requirements. To collect comprehensive data for structural health monitoring for civil engineers, high-resolution vibration sensors and sufficient sampling rates should be adopted, which makes it challenging for current wireless sensor network technology in the following ways: processing capabilities, storage limit, and communication bandwidth. The wireless sensor network has to meet expectations set by wired sensor devices prevalent in the structural health monitoring community. For this project, we built and tested an application-realistic, commercially based, portable, wireless sensor network called ShakeNet for instrumentation of large civil structures, especially for buildings, bridges, or dams after earthquakes. Two to three people can deploy ShakeNet sensors within hours after an earthquake to measure the structural response of the building or bridge during aftershocks. ShakeNet involved the development of a new sensing platform (ShakeBox) running a software suite for networking, data collection, and monitoring. Deployments reported here on a tall building and a large dam were real-world tests of ShakeNet operation, and helped to refine both hardware and software. 

  4. Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy.

    PubMed

    de Campos, Brunno Machado; Coan, Ana Carolina; Lin Yasuda, Clarissa; Casseb, Raphael Fernandes; Cendes, Fernando

    2016-09-01

    Mesial temporal lobe epilepsy (MTLE) with hippocampus sclerosis (HS) is associated with functional and structural alterations extending beyond the temporal regions and abnormal pattern of brain resting state networks (RSNs) connectivity. We hypothesized that the interaction of large-scale RSNs is differently affected in patients with right- and left-MTLE with HS compared to controls. We aimed to determine and characterize these alterations through the analysis of 12 RSNs, functionally parceled in 70 regions of interest (ROIs), from resting-state functional-MRIs of 99 subjects (52 controls, 26 right- and 21 left-MTLE patients with HS). Image preprocessing and statistical analysis were performed using UF(2) C-toolbox, which provided ROI-wise results for intranetwork and internetwork connectivity. Intranetwork abnormalities were observed in the dorsal default mode network (DMN) in both groups of patients and in the posterior salience network in right-MTLE. Both groups showed abnormal correlation between the dorsal-DMN and the posterior salience, as well as between the dorsal-DMN and the executive-control network. Patients with left-MTLE also showed reduced correlation between the dorsal-DMN and visuospatial network and increased correlation between bilateral thalamus and the posterior salience network. The ipsilateral hippocampus stood out as a central area of abnormalities. Alterations on left-MTLE expressed a low cluster coefficient, whereas the altered connections on right-MTLE showed low cluster coefficient in the DMN but high in the posterior salience regions. Both right- and left-MTLE patients with HS have widespread abnormal interactions of large-scale brain networks; however, all parameters evaluated indicate that left-MTLE has a more intricate bihemispheric dysfunction compared to right-MTLE. Hum Brain Mapp 37:3137-3152, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  5. ShakeNet: a portable wireless sensor network for instrumenting large civil structures

    USGS Publications Warehouse

    Kohler, Monica D.; Hao, Shuai; Mishra, Nilesh; Govindan, Ramesh; Nigbor, Robert

    2015-08-03

    We report our findings from a U.S. Geological Survey (USGS) National Earthquake Hazards Reduction Program-funded project to develop and test a wireless, portable, strong-motion network of up to 40 triaxial accelerometers for structural health monitoring. The overall goal of the project was to record ambient vibrations for several days from USGS-instrumented structures. Structural health monitoring has important applications in fields like civil engineering and the study of earthquakes. The emergence of wireless sensor networks provides a promising means to such applications. However, while most wireless sensor networks are still in the experimentation stage, very few take into consideration the realistic earthquake engineering application requirements. To collect comprehensive data for structural health monitoring for civil engineers, high-resolution vibration sensors and sufficient sampling rates should be adopted, which makes it challenging for current wireless sensor network technology in the following ways: processing capabilities, storage limit, and communication bandwidth. The wireless sensor network has to meet expectations set by wired sensor devices prevalent in the structural health monitoring community. For this project, we built and tested an application-realistic, commercially based, portable, wireless sensor network called ShakeNet for instrumentation of large civil structures, especially for buildings, bridges, or dams after earthquakes. Two to three people can deploy ShakeNet sensors within hours after an earthquake to measure the structural response of the building or bridge during aftershocks. ShakeNet involved the development of a new sensing platform (ShakeBox) running a software suite for networking, data collection, and monitoring. Deployments reported here on a tall building and a large dam were real-world tests of ShakeNet operation, and helped to refine both hardware and software. 

  6. A survey on routing protocols for large-scale wireless sensor networks.

    PubMed

    Li, Changle; Zhang, Hanxiao; Hao, Binbin; Li, Jiandong

    2011-01-01

    With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. "Large-scale" means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and

  7. A Survey on Routing Protocols for Large-Scale Wireless Sensor Networks

    PubMed Central

    Li, Changle; Zhang, Hanxiao; Hao, Binbin; Li, Jiandong

    2011-01-01

    With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. “Large-scale” means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner

  8. The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments

    PubMed Central

    Leider, Jonathon P.; Castrucci, Brian C.; Harris, Jenine K.; Hearne, Shelley

    2015-01-01

    Background: The relationship between policy networks and policy development among local health departments (LHDs) is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. Methods: This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer) in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. Results: All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD). Connectedness was highest among local health officials (density = .55), and slightly lower for chief science officers (d = .33) and chiefs of policy (d = .29). After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic) and tenure were the most significant predictors of formation of network ties. Conclusion: Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff. PMID:26258784

  9. SMETANA: Accurate and Scalable Algorithm for Probabilistic Alignment of Large-Scale Biological Networks

    PubMed Central

    Sahraeian, Sayed Mohammad Ebrahim; Yoon, Byung-Jun

    2013-01-01

    In this paper we introduce an efficient algorithm for alignment of multiple large-scale biological networks. In this scheme, we first compute a probabilistic similarity measure between nodes that belong to different networks using a semi-Markov random walk model. The estimated probabilities are further enhanced by incorporating the local and the cross-species network similarity information through the use of two different types of probabilistic consistency transformations. The transformed alignment probabilities are used to predict the alignment of multiple networks based on a greedy approach. We demonstrate that the proposed algorithm, called SMETANA, outperforms many state-of-the-art network alignment techniques, in terms of computational efficiency, alignment accuracy, and scalability. Our experiments show that SMETANA can easily align tens of genome-scale networks with thousands of nodes on a personal computer without any difficulty. The source code of SMETANA is available upon request. The source code of SMETANA can be downloaded from http://www.ece.tamu.edu/~bjyoon/SMETANA/. PMID:23874484

  10. High Fidelity Simulations of Large-Scale Wireless Networks (Plus-Up)

    SciTech Connect

    Onunkwo, Uzoma

    2015-11-01

    Sandia has built a strong reputation in scalable network simulation and emulation for cyber security studies to protect our nation’s critical information infrastructures. Georgia Tech has preeminent reputation in academia for excellence in scalable discrete event simulations, with strong emphasis on simulating cyber networks. Many of the experts in this field, such as Dr. Richard Fujimoto, Dr. George Riley, and Dr. Chris Carothers, have strong affiliations with Georgia Tech. The collaborative relationship that we intend to immediately pursue is in high fidelity simulations of practical large-scale wireless networks using ns-3 simulator via Dr. George Riley. This project will have mutual benefits in bolstering both institutions’ expertise and reputation in the field of scalable simulation for cyber-security studies. This project promises to address high fidelity simulations of large-scale wireless networks. This proposed collaboration is directly in line with Georgia Tech’s goals for developing and expanding the Communications Systems Center, the Georgia Tech Broadband Institute, and Georgia Tech Information Security Center along with its yearly Emerging Cyber Threats Report. At Sandia, this work benefits the defense systems and assessment area with promise for large-scale assessment of cyber security needs and vulnerabilities of our nation’s critical cyber infrastructures exposed to wireless communications.

  11. SHARE and Share Alike

    ERIC Educational Resources Information Center

    Baird, Jeffrey Marshall

    2006-01-01

    This article describes a reading comprehension program adopted at J. E. Cosgriff Memorial Catholic School in Salt Lake City, Utah. The program is called SHARE: Students Helping Achieve Reading Excellence, and involves seventh and eighth grade students teaching first and second graders reading comprehension strategies learned in middle school…

  12. Networks of silicon nanowires: A large-scale atomistic electronic structure analysis

    SciTech Connect

    Keleş, Ümit; Bulutay, Ceyhun; Liedke, Bartosz; Heinig, Karl-Heinz

    2013-11-11

    Networks of silicon nanowires possess intriguing electronic properties surpassing the predictions based on quantum confinement of individual nanowires. Employing large-scale atomistic pseudopotential computations, as yet unexplored branched nanostructures are investigated in the subsystem level as well as in full assembly. The end product is a simple but versatile expression for the bandgap and band edge alignments of multiply-crossing Si nanowires for various diameters, number of crossings, and wire orientations. Further progress along this line can potentially topple the bottom-up approach for Si nanowire networks to a top-down design by starting with functionality and leading to an enabling structure.

  13. 76 FR 63811 - Structural Reforms To Improve the Security of Classified Networks and the Responsible Sharing and...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-10-13

    ... (classified information) on computer networks, it is hereby ordered as follows: Section 1. Policy. Our Nation... information on computer networks that shall be consistent with appropriate protections for privacy and civil... standards will address all agencies that operate or access classified computer networks, all users...

  14. Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks

    PubMed Central

    Yamanaka, Ryota; Kitano, Hiroaki

    2013-01-01

    Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks. PMID:24278007

  15. 'You should at least ask'. The expectations, hopes and fears of rare disease patients on large-scale data and biomaterial sharing for genomics research.

    PubMed

    McCormack, Pauline; Kole, Anna; Gainotti, Sabina; Mascalzoni, Deborah; Molster, Caron; Lochmüller, Hanns; Woods, Simon

    2016-10-01

    Within the myriad articles about participants' opinions of genomics research, the views of a distinct group - people with a rare disease (RD) - are unknown. It is important to understand if their opinions differ from the general public by dint of having a rare disease and vulnerabilities inherent in this. Here we document RD patients' attitudes to participation in genomics research, particularly around large-scale, international data and biosample sharing. This work is unique in exploring the views of people with a range of rare disorders from many different countries. The authors work within an international, multidisciplinary consortium, RD-Connect, which has developed an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for RD research. Focus groups were conducted with 52 RD patients from 16 countries. Using a scenario-based approach, participants were encouraged to raise topics relevant to their own experiences, rather than these being determined by the researcher. Issues include wide data sharing, and consent for new uses of historic samples and for children. Focus group members are positively disposed towards research and towards allowing data and biosamples to be shared internationally. Expressions of trust and attitudes to risk are often affected by the nature of the RD which they have experience of, as well as regulatory and cultural practices in their home country. Participants are concerned about data security and misuse. There is an acute recognition of the vulnerability inherent in having a RD and the possibility that open knowledge of this could lead to discrimination.

  16. Scalable Triadic Analysis of Large-Scale Graphs: Multi-Core vs. Multi-Processor vs. Multi-Threaded Shared Memory Architectures

    SciTech Connect

    Chin, George; Marquez, Andres; Choudhury, Sutanay; Feo, John T.

    2012-09-01

    Triadic analysis encompasses a useful set of graph mining methods that is centered on the concept of a triad, which is a subgraph of three nodes and the configuration of directed edges across the nodes. Such methods are often applied in the social sciences as well as many other diverse fields. Triadic methods commonly operate on a triad census that counts the number of triads of every possible edge configuration in a graph. Like other graph algorithms, triadic census algorithms do not scale well when graphs reach tens of millions to billions of nodes. To enable the triadic analysis of large-scale graphs, we developed and optimized a triad census algorithm to efficiently execute on shared memory architectures. We will retrace the development and evolution of a parallel triad census algorithm. Over the course of several versions, we continually adapted the code’s data structures and program logic to expose more opportunities to exploit parallelism on shared memory that would translate into improved computational performance. We will recall the critical steps and modifications that occurred during code development and optimization. Furthermore, we will compare the performances of triad census algorithm versions on three specific systems: Cray XMT, HP Superdome, and AMD multi-core NUMA machine. These three systems have shared memory architectures but with markedly different hardware capabilities to manage parallelism.

  17. 'You should at least ask'. The expectations, hopes and fears of rare disease patients on large-scale data and biomaterial sharing for genomics research.

    PubMed

    McCormack, Pauline; Kole, Anna; Gainotti, Sabina; Mascalzoni, Deborah; Molster, Caron; Lochmüller, Hanns; Woods, Simon

    2016-10-01

    Within the myriad articles about participants' opinions of genomics research, the views of a distinct group - people with a rare disease (RD) - are unknown. It is important to understand if their opinions differ from the general public by dint of having a rare disease and vulnerabilities inherent in this. Here we document RD patients' attitudes to participation in genomics research, particularly around large-scale, international data and biosample sharing. This work is unique in exploring the views of people with a range of rare disorders from many different countries. The authors work within an international, multidisciplinary consortium, RD-Connect, which has developed an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for RD research. Focus groups were conducted with 52 RD patients from 16 countries. Using a scenario-based approach, participants were encouraged to raise topics relevant to their own experiences, rather than these being determined by the researcher. Issues include wide data sharing, and consent for new uses of historic samples and for children. Focus group members are positively disposed towards research and towards allowing data and biosamples to be shared internationally. Expressions of trust and attitudes to risk are often affected by the nature of the RD which they have experience of, as well as regulatory and cultural practices in their home country. Participants are concerned about data security and misuse. There is an acute recognition of the vulnerability inherent in having a RD and the possibility that open knowledge of this could lead to discrimination. PMID:27049302

  18. School Improvement Networks as a Strategy for Large-Scale Education Reform: The Role of Educational Environments

    ERIC Educational Resources Information Center

    Glazer, Joshua L.; Peurach, Donald J.

    2013-01-01

    The development and scale-up of school improvement networks is among the most important educational innovations of the last decade, and current federal, state, and district efforts attempt to use school improvement networks as a mechanism for supporting large-scale change. The potential of improvement networks, however, rests on the extent to…

  19. Detection of arbitrarily large dynamic ground motions with a dense high-rate GPS network

    NASA Astrophysics Data System (ADS)

    Bock, Yehuda; Prawirodirdjo, Linette; Melbourne, Timothy I.

    2004-03-01

    We describe the detection of teleseismic surface waves from the 3 November 2002 Mw 7.9 Denali fault earthquake in Alaska with a dense network of 1 Hz GPS stations in southern California, about 3900 km from the event. Relative horizontal displacements with amplitudes in excess of 15 mm and duration of 700 seconds agree with integrated velocities recorded by nearby broadband seismometers with an rms difference of 2-3 mm. The displacements are derived from independent 1 Hz instantaneous positions demonstrating that a GPS network can provide direct measurements of arbitrarily large dynamic and static ground horizontal displacements at periods longer than 1 s and amplitudes above 2 mm, with an inherent precision (signal to noise) that improves indefinitely with amplitude without clipping and in real time. High-rate, real-time GPS networks can enhance earthquake detection and seismic risk mitigation and support other applications such as intelligent transportation and civil infrastructure monitoring.

  20. Load reduction test method of similarity theory and BP neural networks of large cranes

    NASA Astrophysics Data System (ADS)

    Yang, Ruigang; Duan, Zhibin; Lu, Yi; Wang, Lei; Xu, Gening

    2016-01-01

    Static load tests are an important means of supervising and detecting a crane's lift capacity. Due to space restrictions, however, there are difficulties and potential danger when testing large bridge cranes. To solve the loading problems of large-tonnage cranes during testing, an equivalency test is proposed based on the similarity theory and BP neural networks. The maximum stress and displacement of a large bridge crane is tested in small loads, combined with the training neural network of a similar structure crane through stress and displacement data which is collected by a physics simulation progressively loaded to a static load test load within the material scope of work. The maximum stress and displacement of a crane under a static load test load can be predicted through the relationship of stress, displacement, and load. By measuring the stress and displacement of small tonnage weights, the stress and displacement of large loads can be predicted, such as the maximum load capacity, which is 1.25 times the rated capacity. Experimental study shows that the load reduction test method can reflect the lift capacity of large bridge cranes. The load shedding predictive analysis for Sanxia 1200 t bridge crane test data indicates that when the load is 1.25 times the rated lifting capacity, the predicted displacement and actual displacement error is zero. The method solves the problem that lifting capacities are difficult to obtain and testing accidents are easily possible when 1.25 times related weight loads are tested for large tonnage cranes.

  1. Spread of Methicillin-Resistant Staphylococcus aureus in a Large Tertiary NICU: Network Analysis

    PubMed Central

    Geva, Alon; Wright, Sharon B.; Baldini, Linda M.; Smallcomb, Jane A.; Safran, Charles

    2011-01-01

    OBJECTIVE: Methicillin-resistant Staphylococcus aureus (MRSA) colonization in NICUs increases the risk of nosocomial infection. Network analysis provides tools to examine the interactions among patients and staff members that put patients at risk of colonization. METHODS: Data from MRSA surveillance cultures were combined with patient room locations, nursing assignments, and sibship information to create patient- and unit-based networks. Multivariate models were constructed to quantify the risk of incident MRSA colonization as a function of exposure to MRSA-colonized infants in these networks. RESULTS: A MRSA-negative infant in the NICU simultaneously with a MRSA-positive infant had higher odds of becoming colonized when the colonized infant was a sibling, compared with an unrelated patient (odds ratio: 8.8 [95% confidence interval [CI]: 5.3–14.8]). Although knowing that a patient was MRSA-positive and was placed on contact precautions reduced the overall odds of another patient becoming colonized by 35% (95% CI: 20%–47%), having a nurse in common with that patient still increased the odds of colonization by 43% (95% CI: 14%–80%). Normalized group degree centrality, a unitwide network measure of connectedness between colonized and uncolonized patients, was a significant predictor of incident MRSA cases (odds ratio: 18.1 [95% CI: 3.6–90.0]). CONCLUSIONS: Despite current infection-control strategies, patients remain at significant risk of MRSA colonization from MRSA-positive siblings and from other patients with whom they share nursing care. Strategies that minimize the frequency of staff members caring for both colonized and uncolonized infants may be beneficial in reducing the spread of MRSA colonization. PMID:22007011

  2. Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware.

    PubMed

    Knight, James C; Tully, Philip J; Kaplan, Bernhard A; Lansner, Anders; Furber, Steve B

    2016-01-01

    SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models. PMID:27092061

  3. Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware.

    PubMed

    Knight, James C; Tully, Philip J; Kaplan, Bernhard A; Lansner, Anders; Furber, Steve B

    2016-01-01

    SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

  4. Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware

    PubMed Central

    Knight, James C.; Tully, Philip J.; Kaplan, Bernhard A.; Lansner, Anders; Furber, Steve B.

    2016-01-01

    SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models. PMID:27092061

  5. Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

    PubMed

    Pesce, Lorenzo L; Lee, Hyong C; Hereld, Mark; Visser, Sid; Stevens, Rick L; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers. PMID:24416069

  6. Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

    PubMed

    Pesce, Lorenzo L; Lee, Hyong C; Hereld, Mark; Visser, Sid; Stevens, Rick L; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

  7. Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks

    PubMed Central

    Li, Xing; Chen, Dexin; Li, Chunyan; Wang, Liangmin

    2015-01-01

    With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people’s lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs). Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA) in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs), this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme. PMID:26151208

  8. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

    PubMed

    Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

    2016-09-19

    Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

  9. Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity

    PubMed Central

    Wang, Lubin; Zhai, Tianye; Zou, Feng; Ye, Enmao; Jin, Xiao; Li, Wuju; Qi, Jianlin; Yang, Zheng

    2015-01-01

    Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI). Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI) study during rested wakefulness (RW) and after 36 h of total sleep deprivation (TSD). Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM) task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN) and default mode network (DMN). Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation. PMID:26218521

  10. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories

    PubMed Central

    Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

    2016-01-01

    Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp. PMID:27657141

  11. Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

    DOE PAGES

    Pesce, Lorenzo L.; Lee, Hyong C.; Hereld, Mark; Visser, Sid; Stevens, Rick L.; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determinedmore » the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.« less

  12. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

    PubMed

    Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

    2016-01-01

    Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp. PMID:27657141

  13. Exact computation and large angular momentum asymptotics of 3nj symbols: Semiclassical disentangling of spin networks.

    PubMed

    Anderson, Roger W; Aquilanti, Vincenzo; da Silva Ferreira, Cristiane

    2008-10-28

    Spin networks, namely, the 3nj symbols of quantum angular momentum theory and their generalizations to groups other than SU(2) and to quantum groups, permeate many areas of pure and applied science. The issues of their computation and characterization for large values of their entries are a challenge for diverse fields, such as spectroscopy and quantum chemistry, molecular and condensed matter physics, quantum computing, and the geometry of space time. Here we record progress both in their efficient calculation and in the study of the large j asymptotics. For the 9j symbol, a prototypical entangled network, we present and extensively check numerically formulas that illustrate the passage to the semiclassical limit, manifesting both the occurrence of disentangling and the discrete-continuum transition.

  14. Exact computation and large angular momentum asymptotics of 3nj symbols: Semiclassical disentangling of spin networks

    SciTech Connect

    Anderson, Roger W.; Aquilanti, Vincenzo; Silva Ferreira, Cristiane da

    2008-10-28

    Spin networks, namely, the 3nj symbols of quantum angular momentum theory and their generalizations to groups other than SU(2) and to quantum groups, permeate many areas of pure and applied science. The issues of their computation and characterization for large values of their entries are a challenge for diverse fields, such as spectroscopy and quantum chemistry, molecular and condensed matter physics, quantum computing, and the geometry of space time. Here we record progress both in their efficient calculation and in the study of the large j asymptotics. For the 9j symbol, a prototypical entangled network, we present and extensively check numerically formulas that illustrate the passage to the semiclassical limit, manifesting both the occurrence of disentangling and the discrete-continuum transition.

  15. Spontaneous Neuronal Activity in Developing Neocortical Networks: From Single Cells to Large-Scale Interactions

    PubMed Central

    Luhmann, Heiko J.; Sinning, Anne; Yang, Jenq-Wei; Reyes-Puerta, Vicente; Stüttgen, Maik C.; Kirischuk, Sergei; Kilb, Werner

    2016-01-01

    Neuronal activity has been shown to be essential for the proper formation of neuronal circuits, affecting developmental processes like neurogenesis, migration, programmed cell death, cellular differentiation, formation of local and long-range axonal connections, synaptic plasticity or myelination. Accordingly, neocortical areas reveal distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, when immature neurons start to develop voltage-dependent channels, spontaneous activity is highly synchronized within small neuronal networks and governed by electrical synaptic transmission. Subsequently, spontaneous activity patterns become more complex, involve larger networks and propagate over several neocortical areas. The developmental shift from local to large-scale network activity is accompanied by a gradual shift from electrical to chemical synaptic transmission with an initial excitatory action of chloride-gated channels activated by GABA, glycine and taurine. Transient neuronal populations in the subplate (SP) support temporary circuits that play an important role in tuning early neocortical activity and the formation of mature neuronal networks. Thus, early spontaneous activity patterns control the formation of developing networks in sensory cortices, and disturbances of these activity patterns may lead to long-lasting neuronal deficits. PMID:27252626

  16. Spontaneous Neuronal Activity in Developing Neocortical Networks: From Single Cells to Large-Scale Interactions.

    PubMed

    Luhmann, Heiko J; Sinning, Anne; Yang, Jenq-Wei; Reyes-Puerta, Vicente; Stüttgen, Maik C; Kirischuk, Sergei; Kilb, Werner

    2016-01-01

    Neuronal activity has been shown to be essential for the proper formation of neuronal circuits, affecting developmental processes like neurogenesis, migration, programmed cell death, cellular differentiation, formation of local and long-range axonal connections, synaptic plasticity or myelination. Accordingly, neocortical areas reveal distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, when immature neurons start to develop voltage-dependent channels, spontaneous activity is highly synchronized within small neuronal networks and governed by electrical synaptic transmission. Subsequently, spontaneous activity patterns become more complex, involve larger networks and propagate over several neocortical areas. The developmental shift from local to large-scale network activity is accompanied by a gradual shift from electrical to chemical synaptic transmission with an initial excitatory action of chloride-gated channels activated by GABA, glycine and taurine. Transient neuronal populations in the subplate (SP) support temporary circuits that play an important role in tuning early neocortical activity and the formation of mature neuronal networks. Thus, early spontaneous activity patterns control the formation of developing networks in sensory cortices, and disturbances of these activity patterns may lead to long-lasting neuronal deficits.

  17. Spontaneous Neuronal Activity in Developing Neocortical Networks: From Single Cells to Large-Scale Interactions.

    PubMed

    Luhmann, Heiko J; Sinning, Anne; Yang, Jenq-Wei; Reyes-Puerta, Vicente; Stüttgen, Maik C; Kirischuk, Sergei; Kilb, Werner

    2016-01-01

    Neuronal activity has been shown to be essential for the proper formation of neuronal circuits, affecting developmental processes like neurogenesis, migration, programmed cell death, cellular differentiation, formation of local and long-range axonal connections, synaptic plasticity or myelination. Accordingly, neocortical areas reveal distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, when immature neurons start to develop voltage-dependent channels, spontaneous activity is highly synchronized within small neuronal networks and governed by electrical synaptic transmission. Subsequently, spontaneous activity patterns become more complex, involve larger networks and propagate over several neocortical areas. The developmental shift from local to large-scale network activity is accompanied by a gradual shift from electrical to chemical synaptic transmission with an initial excitatory action of chloride-gated channels activated by GABA, glycine and taurine. Transient neuronal populations in the subplate (SP) support temporary circuits that play an important role in tuning early neocortical activity and the formation of mature neuronal networks. Thus, early spontaneous activity patterns control the formation of developing networks in sensory cortices, and disturbances of these activity patterns may lead to long-lasting neuronal deficits. PMID:27252626

  18. A Very Large Area Network (VLAN) knowledge-base applied to space communication problems

    NASA Technical Reports Server (NTRS)

    Zander, Carol S.

    1988-01-01

    This paper first describes a hierarchical model for very large area networks (VLAN). Space communication problems whose solution could profit by the model are discussed and then an enhanced version of this model incorporating the knowledge needed for the missile detection-destruction problem is presented. A satellite network or VLAN is a network which includes at least one satellite. Due to the complexity, a compromise between fully centralized and fully distributed network management has been adopted. Network nodes are assigned to a physically localized group, called a partition. Partitions consist of groups of cell nodes with one cell node acting as the organizer or master, called the Group Master (GM). Coordinating the group masters is a Partition Master (PM). Knowledge is also distributed hierarchically existing in at least two nodes. Each satellite node has a back-up earth node. Knowledge must be distributed in such a way so as to minimize information loss when a node fails. Thus the model is hierarchical both physically and informationally.

  19. A Very Large Area Network (VLAN) knowledge-base applied to space communication problems

    NASA Astrophysics Data System (ADS)

    Zander, Carol S.

    1988-10-01

    This paper first describes a hierarchical model for very large area networks (VLAN). Space communication problems whose solution could profit by the model are discussed and then an enhanced version of this model incorporating the knowledge needed for the missile detection-destruction problem is presented. A satellite network or VLAN is a network which includes at least one satellite. Due to the complexity, a compromise between fully centralized and fully distributed network management has been adopted. Network nodes are assigned to a physically localized group, called a partition. Partitions consist of groups of cell nodes with one cell node acting as the organizer or master, called the Group Master (GM). Coordinating the group masters is a Partition Master (PM). Knowledge is also distributed hierarchically existing in at least two nodes. Each satellite node has a back-up earth node. Knowledge must be distributed in such a way so as to minimize information loss when a node fails. Thus the model is hierarchical both physically and informationally.

  20. GPCR Network: a large-scale collaboration on GPCR structure and function

    PubMed Central

    Stevens, Raymond C.; Cherezov, Vadim; Katritch, Vsevolod; Abagyan, Ruben; Kuhn, Peter; Rosen, Hugh; Wüthrich, Kurt

    2013-01-01

    Preface Collaboration is a cornerstone of many successful scientific research endeavors, which distributes risks and rewards to encourage progress in challenging areas. A striking illustration is the large-scale project that seeks to achieve a robust fundamental understanding of structure and function in the human G protein-coupled receptor superfamily. The GPCR Network was created to achieve this goal based on an active outreach program addressing an interdisciplinary community of scientists interested in GPCR structure, chemistry and biology. PMID:23237917

  1. Memory Transmission in Small Groups and Large Networks: An Agent-Based Model.

    PubMed

    Luhmann, Christian C; Rajaram, Suparna

    2015-12-01

    The spread of social influence in large social networks has long been an interest of social scientists. In the domain of memory, collaborative memory experiments have illuminated cognitive mechanisms that allow information to be transmitted between interacting individuals, but these experiments have focused on small-scale social contexts. In the current study, we took a computational approach, circumventing the practical constraints of laboratory paradigms and providing novel results at scales unreachable by laboratory methodologies. Our model embodied theoretical knowledge derived from small-group experiments and replicated foundational results regarding collaborative inhibition and memory convergence in small groups. Ultimately, we investigated large-scale, realistic social networks and found that agents are influenced by the agents with which they interact, but we also found that agents are influenced by nonneighbors (i.e., the neighbors of their neighbors). The similarity between these results and the reports of behavioral transmission in large networks offers a major theoretical insight by linking behavioral transmission to the spread of information.

  2. Asymptotic Analysis of Large Cooperative Relay Networks Using Random Matrix Theory

    NASA Astrophysics Data System (ADS)

    Li, Husheng; Han, Z.; Poor, H.

    2008-12-01

    Cooperative transmission is an emerging communication technology that takes advantage of the broadcast nature of wireless channels. In cooperative transmission, the use of relays can create a virtual antenna array so that multiple-input/multiple-output (MIMO) techniques can be employed. Most existing work in this area has focused on the situation in which there are a small number of sources and relays and a destination. In this paper, cooperative relay networks with large numbers of nodes are analyzed, and in particular the asymptotic performance improvement of cooperative transmission over direction transmission and relay transmission is analyzed using random matrix theory. The key idea is to investigate the eigenvalue distributions related to channel capacity and to analyze the moments of this distribution in large wireless networks. A performance upper bound is derived, the performance in the low signal-to-noise-ratio regime is analyzed, and two approximations are obtained for high and low relay-to-destination link qualities, respectively. Finally, simulations are provided to validate the accuracy of the analytical results. The analysis in this paper provides important tools for the understanding and the design of large cooperative wireless networks.

  3. Performance of Thorup's Shortest Path Algorithm for Large-Scale Network Simulation

    NASA Astrophysics Data System (ADS)

    Sakumoto, Yusuke; Ohsaki, Hiroyuki; Imase, Makoto

    In this paper, we investigate the performance of Thorup's algorithm by comparing it to Dijkstra's algorithm for large-scale network simulations. One of the challenges toward the realization of large-scale network simulations is the efficient execution to find shortest paths in a graph with N vertices and M edges. The time complexity for solving a single-source shortest path (SSSP) problem with Dijkstra's algorithm with a binary heap (DIJKSTRA-BH) is O((M+N)log N). An sophisticated algorithm called Thorup's algorithm has been proposed. The original version of Thorup's algorithm (THORUP-FR) has the time complexity of O(M+N). A simplified version of Thorup's algorithm (THORUP-KL) has the time complexity of O(Mα(N)+N) where α(N) is the functional inverse of the Ackerman function. In this paper, we compare the performances (i.e., execution time and memory consumption) of THORUP-KL and DIJKSTRA-BH since it is known that THORUP-FR is at least ten times slower than Dijkstra's algorithm with a Fibonaccii heap. We find that (1) THORUP-KL is almost always faster than DIJKSTRA-BH for large-scale network simulations, and (2) the performances of THORUP-KL and DIJKSTRA-BH deviate from their time complexities due to the presence of the memory cache in the microprocessor.

  4. Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

    PubMed Central

    Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

    2015-01-01

    Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation. PMID:25780910

  5. Real-time target tracking simulations in large disparate sensor networks

    NASA Astrophysics Data System (ADS)

    Lin, Hong; Rushing, John; Graves, Sara; Criswell, Evans; Tanner, Steve

    2007-04-01

    Real-time target tracking in large disparate sensor networks has been simulated with a parallelized search based data fusion algorithm using a simulated annealing approach. The networks are composed of large numbers of low fidelity binary and bearing-only sensors, and small numbers of high fidelity position sensors over a large region. The primitive sensors provide limited information, not sufficient to locate targets; the position sensors can report both range and direction of the targets. Target positions are determined through fusing information from all types of sensors. A score function, which takes into account the fidelity of sensors of different types, is defined and used as the evaluation function for the optimization search. The fusion algorithm is parallelized using spatial decomposition so that the fusion process can finish before the arrival of the next set of sensor data. A series of target tracking simulations are performed on a Linux cluster with communication between nodes facilitated by the Message Passing Interface (MPI). The probability of detection (POD), false alarm rate (FAR), and average deviation (AVD) are used to evaluate the network performance. The input target information used for all the simulations is a set of target track data created from a theater level air combat simulation.

  6. Large patch convolutional neural networks for the scene classification of high spatial resolution imagery

    NASA Astrophysics Data System (ADS)

    Zhong, Yanfei; Fei, Feng; Zhang, Liangpei

    2016-04-01

    The increase of the spatial resolution of remote-sensing sensors helps to capture the abundant details related to the semantics of surface objects. However, it is difficult for the popular object-oriented classification approaches to acquire higher level semantics from the high spatial resolution remote-sensing (HSR-RS) images, which is often referred to as the "semantic gap." Instead of designing sophisticated operators, convolutional neural networks (CNNs), a typical deep learning method, can automatically discover intrinsic feature descriptors from a large number of input images to bridge the semantic gap. Due to the small data volume of the available HSR-RS scene datasets, which is far away from that of the natural scene datasets, there have been few reports of CNN approaches for HSR-RS image scene classifications. We propose a practical CNN architecture for HSR-RS scene classification, named the large patch convolutional neural network (LPCNN). The large patch sampling is used to generate hundreds of possible scene patches for the feature learning, and a global average pooling layer is used to replace the fully connected network as the classifier, which can greatly reduce the total parameters. The experiments confirm that the proposed LPCNN can learn effective local features to form an effective representation for different land-use scenes, and can achieve a performance that is comparable to the state-of-the-art on public HSR-RS scene datasets.

  7. Large-scale transportation network congestion evolution prediction using deep learning theory.

    PubMed

    Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

    2015-01-01

    Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation. PMID:25780910

  8. Large-scale transportation network congestion evolution prediction using deep learning theory.

    PubMed

    Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

    2015-01-01

    Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  9. A robust and high-performance queue management controller for large round trip time networks

    NASA Astrophysics Data System (ADS)

    Khoshnevisan, Ladan; Salmasi, Farzad R.

    2016-05-01

    Congestion management for transmission control protocol is of utmost importance to prevent packet loss within a network. This necessitates strategies for active queue management. The most applied active queue management strategies have their inherent disadvantages which lead to suboptimal performance and even instability in the case of large round trip time and/or external disturbance. This paper presents an internal model control robust queue management scheme with two degrees of freedom in order to restrict the undesired effects of large and small round trip time and parameter variations in the queue management. Conventional approaches such as proportional integral and random early detection procedures lead to unstable behaviour due to large delay. Moreover, internal model control-Smith scheme suffers from large oscillations due to the large round trip time. On the other hand, other schemes such as internal model control-proportional integral and derivative show excessive sluggish performance for small round trip time values. To overcome these shortcomings, we introduce a system entailing two individual controllers for queue management and disturbance rejection, simultaneously. Simulation results based on Matlab/Simulink and also Network Simulator 2 (NS2) demonstrate the effectiveness of the procedure and verify the analytical approach.

  10. International Conference of Directors of National Libraries on Resource Sharing in Asia and Oceanic [Proceedings] (Canberra, Australia, May 14-18, 1979). Development of Resource Sharing Networks. Networks Study No. 11.

    ERIC Educational Resources Information Center

    National Library of Australia, Canberra.

    The proceedings of this 1979 conference on library cooperation begin with proposals for the promotion of resource sharing among the national libraries of Asia and Oceania, the text of a policy statement on the role of national and international systems as approved at a 1976 meeting of directors of national libraries held in Lausanne, and a summary…

  11. Emotional speech synchronizes brains across listeners and engages large-scale dynamic brain networks.

    PubMed

    Nummenmaa, Lauri; Saarimäki, Heini; Glerean, Enrico; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Hari, Riitta; Sams, Mikko

    2014-11-15

    Speech provides a powerful means for sharing emotions. Here we implement novel intersubject phase synchronization and whole-brain dynamic connectivity measures to show that networks of brain areas become synchronized across participants who are listening to emotional episodes in spoken narratives. Twenty participants' hemodynamic brain activity was measured with functional magnetic resonance imaging (fMRI) while they listened to 45-s narratives describing unpleasant, neutral, and pleasant events spoken in neutral voice. After scanning, participants listened to the narratives again and rated continuously their feelings of pleasantness-unpleasantness (valence) and of arousal-calmness. Instantaneous intersubject phase synchronization (ISPS) measures were computed to derive both multi-subject voxel-wise similarity measures of hemodynamic activity and inter-area functional dynamic connectivity (seed-based phase synchronization, SBPS). Valence and arousal time series were subsequently used to predict the ISPS and SBPS time series. High arousal was associated with increased ISPS in the auditory cortices and in Broca's area, and negative valence was associated with enhanced ISPS in the thalamus, anterior cingulate, lateral prefrontal, and orbitofrontal cortices. Negative valence affected functional connectivity of fronto-parietal, limbic (insula, cingulum) and fronto-opercular circuitries, and positive arousal affected the connectivity of the striatum, amygdala, thalamus, cerebellum, and dorsal frontal cortex. Positive valence and negative arousal had markedly smaller effects. We propose that high arousal synchronizes the listeners' sound-processing and speech-comprehension networks, whereas negative valence synchronizes circuitries supporting emotional and self-referential processing.

  12. The Plate Boundary Observatory Cascadia Network: Development and Installation of a Large Scale Real-time GPS Network

    NASA Astrophysics Data System (ADS)

    Austin, K. E.; Blume, F.; Berglund, H. T.; Feaux, K.; Gallaher, W. W.; Hodgkinson, K. M.; Mattioli, G. S.; Mencin, D.

    2014-12-01

    The EarthScope Plate Boundary Observatory (PBO), through a NSF-ARRA supplement, has enhanced the geophysical infrastructure in in the Pacific Northwest by upgrading a total of 282 Plate Boundary Observatory GPS stations to allow the collection and distribution of high-rate (1 Hz), low-latency (<1 s) data streams (RT-GPS). These upgraded stations supplemented the original 100 RT-GPS stations in the PBO GPS network. The addition of the new RT-GPS sites in Cascadia should spur new volcano and earthquake research opportunities in an area of great scientific interest and high geophysical hazard. Streaming RT-GPS data will enable researchers to detect and investigate strong ground motion during large geophysical events, including a possible plate-interface earthquake, which has implications for earthquake hazard mitigation. A Mw 6.9 earthquake occurred on March 10, 2014, off the coast of northern California. As a response, UNAVCO downloaded high-rate GPS data from Plate Boundary Observatory stations within 500 km of the epicenter of the event, providing a good test of network performance.In addition to the 282 stations upgraded to real-time, 22 new meteorological instruments were added to existing PBO stations. Extensive testing of BGAN satellite communications systems has been conducted to support the Cascadia RT-GPS upgrades and the installation of three BGAN satellite fail over systems along the Cascadia margin will allow for the continuation of data flow in the event of a loss of primary communications during in a large geophysical event or other interruptions in commercial cellular networks. In summary, with these additional upgrades in the Cascadia region, the PBO RT-GPS network will increase to 420 stations. Upgrades to the UNAVCO data infrastructure included evaluation and purchase of the Trimble Pivot Platform, servers, and additional hardware for archiving the high rate data, as well as testing and implementation of GLONASS and Trimble RTX positioning on the

  13. Latency-Efficient Communication in Wireless Mesh Networks under Consideration of Large Interference Range

    NASA Astrophysics Data System (ADS)

    Xin, Qin; Yao, Xiaolan; Engelstad, Paal E.

    2010-09-01

    Wireless Mesh Networking is an emerging communication paradigm to enable resilient, cost-efficient and reliable services for the future-generation wireless networks. We study here the minimum-latency communication primitive of gossiping (all-to-all communication) in multi-hop ad-hoc Wireless Mesh Networks (WMNs). Each mesh node in the WMN is initially given a message and the objective is to design a minimum-latency schedule such that each mesh node distributes its message to all other mesh nodes. Minimum-latency gossiping problem is well known to be NP-hard even for the scenario in which the topology of the WMN is known to all mesh nodes in advance. In this paper, we propose a new latency-efficient approximation scheme that can accomplish gossiping task in polynomial time units in any ad-hoc WMN under consideration of Large Interference Range (LIR), e.g., the interference range is much larger than the transmission range. To the best of our knowledge, it is first time to investigate such a scenario in ad-hoc WMNs under LIR, our algorithm allows the labels (e.g., identifiers) of the mesh nodes to be polynomially large in terms of the size of the WMN, which is the first time that the scenario of large labels has been considered in ad-hoc WMNs under LIR. Furthermore, our gossiping scheme can be considered as a framework which can be easily implied to the scenario under consideration of mobility-related issues since we assume that the mesh nodes have no knowledge on the network topology even for its neighboring mesh nodes.

  14. User-Friendly Data-Sharing Practices for Fostering Collaboration within a Research Network: Roles of a Vanguard Center for a Community-Based Study

    PubMed Central

    Lee, Jae Eun; Sung, Jung Hye; Barnett, M. Edwina; Norris, Keith

    2015-01-01

    Although various attempts have been made to build collaborative cultures for data sharing, their effectiveness is still questionable. The Jackson Heart Study (JHS) Vanguard Center (JHSVC) at the NIH-funded Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN) Data Coordinating Center (DCC) may be a new concept in that the data are being shared with a research network where a plethora of scientists/researchers are working together to achieve their common goal. This study describes the current practices to share the JHS data through the mechanism of JHSVC. The JHS is the largest single-site cohort study to prospectively investigate the determinants of cardiovascular disease among African-Americans. It has adopted a formal screened access method through a formalized JHSVC mechanism, in which only a qualified scientist(s) can access the data. The role of the DCC was to help RTRN researchers explore hypothesis-driven ideas to enhance the output and impact of JHS data through customized services, such as feasibility tests, data querying, manuscript proposal development and data analyses for publication. DCC has implemented these various programs to facilitate data utility. A total of 300 investigators attended workshops and/or received training booklets. DCC provided two online and five onsite workshops and developed/distributed more than 250 copies of the booklet to help potential data users understand the structure of and access to the data. Information on data use was also provided through the RTRN website. The DCC efforts led to the production of five active manuscript proposals, seven completed publications, 11 presentations and four NIH grant proposals. These outcomes resulted from activities during the first four years; over the last couple of years, there were few new requests. Our study suggested that DCC-customized services enhanced the accessibility of JHS data and their utility by RTRN researchers and helped to achieve the

  15. User-Friendly Data-Sharing Practices for Fostering Collaboration within a Research Network: Roles of a Vanguard Center for a Community-Based Study.

    PubMed

    Lee, Jae Eun; Sung, Jung Hye; Barnett, M Edwina; Norris, Keith

    2015-12-22

    Although various attempts have been made to build collaborative cultures for data sharing, their effectiveness is still questionable. The Jackson Heart Study (JHS) Vanguard Center (JHSVC) at the NIH-funded Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN) Data Coordinating Center (DCC) may be a new concept in that the data are being shared with a research network where a plethora of scientists/researchers are working together to achieve their common goal. This study describes the current practices to share the JHS data through the mechanism of JHSVC. The JHS is the largest single-site cohort study to prospectively investigate the determinants of cardiovascular disease among African-Americans. It has adopted a formal screened access method through a formalized JHSVC mechanism, in which only a qualified scientist(s) can access the data. The role of the DCC was to help RTRN researchers explore hypothesis-driven ideas to enhance the output and impact of JHS data through customized services, such as feasibility tests, data querying, manuscript proposal development and data analyses for publication. DCC has implemented these various programs to facilitate data utility. A total of 300 investigators attended workshops and/or received training booklets. DCC provided two online and five onsite workshops and developed/distributed more than 250 copies of the booklet to help potential data users understand the structure of and access to the data. Information on data use was also provided through the RTRN website. The DCC efforts led to the production of five active manuscript proposals, seven completed publications, 11 presentations and four NIH grant proposals. These outcomes resulted from activities during the first four years; over the last couple of years, there were few new requests. Our study suggested that DCC-customized services enhanced the accessibility of JHS data and their utility by RTRN researchers and helped to achieve the

  16. The topology of large Open Connectome networks for the human brain

    PubMed Central

    Gastner, Michael T.; Ódor, Géza

    2016-01-01

    The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space. PMID:27270602

  17. A large scale code resolution service network in the Internet of Things.

    PubMed

    Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan

    2012-11-07

    In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS.

  18. A Large Scale Code Resolution Service Network in the Internet of Things

    PubMed Central

    Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan

    2012-01-01

    In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT's advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS. PMID:23202207

  19. Collaboration Networks from a Large CV Database: Dynamics, Topology and Bonus Impact

    PubMed Central

    Araújo, Eduardo B.; Moreira, André A.; Furtado, Vasco; Pequeno, Tarcisio H. C.; Andrade, Jr, José S.

    2014-01-01

    Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions, and funding agencies. Here we propose the use of a large and multidisciplinary database of scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. Detailed information about publications and researchers is available in this database. Individual curricula are submitted by the researchers themselves so that coauthorship is unambiguous. Researchers can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors, regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity. We also show that the collaboration network under investigation displays a typical assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike. PMID:24603470

  20. FASIMU: flexible software for flux-balance computation series in large metabolic networks

    PubMed Central

    2011-01-01

    Background Flux-balance analysis based on linear optimization is widely used to compute metabolic fluxes in large metabolic networks and gains increasingly importance in network curation and structural analysis. Thus, a computational tool flexible enough to realize a wide variety of FBA algorithms and able to handle batch series of flux-balance optimizations is of great benefit. Results We present FASIMU, a command line oriented software for the computation of flux distributions using a variety of the most common FBA algorithms, including the first available implementation of (i) weighted flux minimization, (ii) fitness maximization for partially inhibited enzymes, and (iii) of the concentration-based thermodynamic feasibility constraint. It allows batch computation with varying objectives and constraints suited for network pruning, leak analysis, flux-variability analysis, and systematic probing of metabolic objectives for network curation. Input and output supports SBML. FASIMU can work with free (lp_solve and GLPK) or commercial solvers (CPLEX, LINDO). A new plugin (faBiNA) for BiNA allows to conveniently visualize calculated flux distributions. The platform-independent program is an open-source project, freely available under GNU public license at http://www.bioinformatics.org/fasimu including manual, tutorial, and plugins. Conclusions We present a flux-balance optimization program whose main merits are the implementation of thermodynamics as a constraint, batch series of computations, free availability of sources, choice on various external solvers, and the flexibility on metabolic objectives and constraints. PMID:21255455

  1. A large scale code resolution service network in the Internet of Things.

    PubMed

    Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan

    2012-01-01

    In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS. PMID:23202207

  2. Regional contraction of brain surface area involves three large-scale networks in schizophrenia.

    PubMed

    Palaniyappan, Lena; Mallikarjun, Pavan; Joseph, Verghese; White, Thomas P; Liddle, Peter F

    2011-07-01

    In schizophrenia, morphological changes in the cerebral cortex have been primarily investigated using volumetric or cortical thickness measurements. In healthy subjects, as the brain size increases, the surface area expands disproportionately when compared to the scaling of cortical thickness. In this structural MRI study, we investigated the changes in brain surface area in schizophrenia by constructing relative areal contraction/expansion maps showing group differences in surface area using Freesurfer software in 57 patients and 41 controls. We observed relative areal contraction affecting Default Mode Network, Central Executive Network and Salience Network, in addition to other regions in schizophrenia. We confirmed the surface area reduction across these three large-scale brain networks by undertaking further region-of-interest analysis of surface area. We also observed a significant hemispheric asymmetry in the surface area changes, with the left hemisphere showing a greater reduction in the areal contraction maps. Our findings suggest that a fundamental disturbance in cortical expansion is likely in individuals who develop schizophrenia. PMID:21497489

  3. The topology of large Open Connectome networks for the human brain

    NASA Astrophysics Data System (ADS)

    Gastner, Michael T.; Ódor, Géza

    2016-06-01

    The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension D and the small-world coefficient σ of these networks. While σ suggests a small-world topology, we found that D < 4 showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.

  4. Large-scale modeling of condition-specific gene regulatory networks by information integration and inference

    PubMed Central

    Ellwanger, Daniel Christian; Leonhardt, Jörn Florian; Mewes, Hans-Werner

    2014-01-01

    Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η2 (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.

  5. Large-scale modeling of condition-specific gene regulatory networks by information integration and inference.

    PubMed

    Ellwanger, Daniel Christian; Leonhardt, Jörn Florian; Mewes, Hans-Werner

    2014-12-01

    Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η(2) (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.

  6. Algorithmic approaches for computing elementary modes in large biochemical reaction networks.

    PubMed

    Klamt, S; Gagneur, J; von Kamp, A

    2005-12-01

    The concept of elementary (flux) modes provides a rigorous description of pathways in metabolic networks and proved to be valuable in a number of applications. However, the computation of elementary modes is a hard computational task that gave rise to several variants of algorithms during the last years. This work brings substantial progresses to this issue. The authors start with a brief review of results obtained from previous work regarding (a) a unified framework for elementary-mode computation, (b) network compression and redundancy removal and (c) the binary approach by which elementary modes are determined as binary patterns reducing the memory demand drastically without loss of speed. Then the authors will address herein further issues. First, a new way to perform the elementarity tests required during the computation of elementary modes which empirically improves significantly the computation time in large networks is proposed. Second, a method to compute only those elementary modes where certain reactions are involved is derived. Relying on this method, a promising approach for computing EMs in a completely distributed manner by decomposing the full problem in arbitrarity many sub-tasks is presented. The new methods have been implemented in the freely available software tools FluxAnalyzer and Metatool and benchmark tests in realistic networks emphasise the potential of our proposed algorithms.

  7. The Development of the Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS): A Large-Scale Data Sharing Initiative

    PubMed Central

    Lutomski, Jennifer E.; Baars, Maria A. E.; Schalk, Bianca W. M.; Boter, Han; Buurman, Bianca M.; den Elzen, Wendy P. J.; Jansen, Aaltje P. D.; Kempen, Gertrudis I. J. M.; Steunenberg, Bas; Steyerberg, Ewout W.; Olde Rikkert, Marcel G. M.; Melis, René J. F.

    2013-01-01

    Introduction In 2008, the Ministry of Health, Welfare and Sport commissioned the National Care for the Elderly Programme. While numerous research projects in older persons’ health care were to be conducted under this national agenda, the Programme further advocated the development of The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS) which would be integrated into all funded research protocols. In this context, we describe TOPICS data sharing initiative (www.topics-mds.eu). Materials and Methods A working group drafted TOPICS-MDS prototype, which was subsequently approved by a multidisciplinary panel. Using instruments validated for older populations, information was collected on demographics, morbidity, quality of life, functional limitations, mental health, social functioning and health service utilisation. For informal caregivers, information was collected on demographics, hours of informal care and quality of life (including subjective care-related burden). Results Between 2010 and 2013, a total of 41 research projects contributed data to TOPICS-MDS, resulting in preliminary data available for 32,310 older persons and 3,940 informal caregivers. The majority of studies sampled were from primary care settings and inclusion criteria differed across studies. Discussion TOPICS-MDS is a public data repository which contains essential data to better understand health challenges experienced by older persons and informal caregivers. Such findings are relevant for countries where increasing health-related expenditure has necessitated the evaluation of contemporary health care delivery. Although open sharing of data can be difficult to achieve in practice, proactively addressing issues of data protection, conflicting data analysis requests and funding limitations during TOPICS-MDS developmental phase has fostered a data sharing culture. To date, TOPICS-MDS has been successfully incorporated into 41 research projects, thus supporting the

  8. Personal Information Sharing Behaviors of College Students via the Internet and Online Social Networks: A Case Study

    ERIC Educational Resources Information Center

    Flinn, Michael Bradley

    2009-01-01

    With privacy concerns growing on a daily basis, it is important to understand how college students guard their personally identifiable information. Despite the students' perceived readiness and several studies on the topic, it is not fully understood what personally identifiable information college students are sharing via online social networks…

  9. Large and stable emission current from synthesized carbon nanotube/fiber network

    SciTech Connect

    Di, Yunsong; Xiao, Mei; Zhang, Xiaobing Wang, Qilong; Li, Chen; Lei, Wei; Cui, Yunkang

    2014-02-14

    In order to obtain a large and stable electron field emission current, the carbon nanotubes have been synthesized on carbon fibers by cold wall chemical vapor deposition method. In the hierarchical nanostructures, carbon fibers are entangled together to form a conductive network, it could provide excellent electron transmission and adhesion property between electrode and emitters, dispersed clusters of carbon nanotubes with smaller diameters have been synthesized on the top of carbon fibers as field emitters, this kind of emitter distribution could alleviate electrostatic shielding effect and protect emitters from being wholly destroyed. Field emission properties of this kind of carbon nanotube/fiber network have been tested, up to 30 mA emission current at an applied electric field of 6.4 V/μm was emitted from as-prepared hierarchical nanostructures. Small current degradation at large emission current output by DC power operation indicated that carbon nanotube/fiber network could be a promising candidate for field emission electron source.

  10. Direction of information flow in large-scale resting-state networks is frequency-dependent.

    PubMed

    Hillebrand, Arjan; Tewarie, Prejaas; van Dellen, Edwin; Yu, Meichen; Carbo, Ellen W S; Douw, Linda; Gouw, Alida A; van Straaten, Elisabeth C W; Stam, Cornelis J

    2016-04-01

    Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow. PMID:27001844

  11. Large-Scale Brain Networks of the Human Left Temporal Pole: A Functional Connectivity MRI Study

    PubMed Central

    Pascual, Belen; Masdeu, Joseph C.; Hollenbeck, Mark; Makris, Nikos; Insausti, Ricardo; Ding, Song-Lin; Dickerson, Bradford C.

    2015-01-01

    The most rostral portion of the human temporal cortex, the temporal pole (TP), has been described as “enigmatic” because its functional neuroanatomy remains unclear. Comparative anatomy studies are only partially helpful, because the human TP is larger and cytoarchitectonically more complex than in nonhuman primates. Considered by Brodmann as a single area (BA 38), the human TP has been recently parceled into an array of cytoarchitectonic subfields. In order to clarify the functional connectivity of subregions of the TP, we undertook a study of 172 healthy adults using resting-state functional connectivity MRI. Remarkably, a hierarchical cluster analysis performed to group the seeds into distinct subsystems according to their large-scale functional connectivity grouped 87.5% of the seeds according to the recently described cytoarchitectonic subregions of the TP. Based on large-scale functional connectivity, there appear to be 4 major subregions of the TP: 1) dorsal, with predominant connectivity to auditory/somatosensory and language networks; 2) ventromedial, predominantly connected to visual networks; 3) medial, connected to paralimbic structures; and 4) anterolateral, connected to the default-semantic network. The functional connectivity of the human TP, far more complex than its known anatomic connectivity in monkey, is concordant with its hypothesized role as a cortical convergence zone. PMID:24068551

  12. Neuron-synapse IC chip-set for large-scale chaotic neural networks.

    PubMed

    Horio, Y; Aihara, K; Yamamoto, O

    2003-01-01

    We propose a neuron-synapse integrated circuit (IC) chip-set for large-scale chaotic neural networks. We use switched-capacitor (SC) circuit techniques to implement a three-internal-state transiently-chaotic neural network model. The SC chaotic neuron chip faithfully reproduces complex chaotic dynamics in real numbers through continuous state variables of the analog circuitry. We can digitally control most of the model parameters by means of programmable capacitive arrays embedded in the SC chaotic neuron chip. Since the output of the neuron is transfered into a digital pulse according to the all-or-nothing property of an axon, we design a synapse chip with digital circuits. We propose a memory-based synapse circuit architecture to achieve a rapid calculation of a vast number of weighted summations. Both of the SC neuron and the digital synapse circuits have been fabricated as IC forms. We have tested these IC chips extensively, and confirmed the functions and performance of the chip-set. The proposed neuron-synapse IC chip-set makes it possible to construct a scalable and reconfigurable large-scale chaotic neural network with 10000 neurons and 10000/sup 2/ synaptic connections. PMID:18244585

  13. Direction of information flow in large-scale resting-state networks is frequency-dependent

    PubMed Central

    Hillebrand, Arjan; Tewarie, Prejaas; van Dellen, Edwin; Yu, Meichen; Carbo, Ellen W. S.; Douw, Linda; Gouw, Alida A.; van Straaten, Elisabeth C. W.; Stam, Cornelis J.

    2016-01-01

    Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow. PMID:27001844

  14. On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

    SciTech Connect

    Thanh, Vo Hong; Zunino, Roberto; Priami, Corrado

    2015-06-28

    Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.

  15. Effective Boolean dynamics analysis to identify functionally important genes in large-scale signaling networks.

    PubMed

    Trinh, Hung-Cuong; Kwon, Yung-Keun

    2015-11-01

    Efficiently identifying functionally important genes in order to understand the minimal requirements of normal cellular development is challenging. To this end, a variety of structural measures have been proposed and their effectiveness has been investigated in recent literature; however, few studies have shown the effectiveness of dynamics-based measures. This led us to investigate a dynamic measure to identify functionally important genes, and the effectiveness of which was verified through application on two large-scale human signaling networks. We specifically consider Boolean sensitivity-based dynamics against an update-rule perturbation (BSU) as a dynamic measure. Through investigations on two large-scale human signaling networks, we found that genes with relatively high BSU values show slower evolutionary rate and higher proportions of essential genes and drug targets than other genes. Gene-ontology analysis showed clear differences between the former and latter groups of genes. Furthermore, we compare the identification accuracies of essential genes and drug targets via BSU and five well-known structural measures. Although BSU did not always show the best performance, it effectively identified the putative set of genes, which is significantly different from the results obtained via the structural measures. Most interestingly, BSU showed the highest synergy effect in identifying the functionally important genes in conjunction with other measures. Our results imply that Boolean-sensitive dynamics can be used as a measure to effectively identify functionally important genes in signaling networks.

  16. Network connectivity paradigm for the large data produced by weather radar systems

    NASA Astrophysics Data System (ADS)

    Guenzi, Diego; Bechini, Renzo; Boraso, Rodolfo; Cremonini, Roberto; Fratianni, Simona

    2014-05-01

    The traffic over Internet is constantly increasing; this is due in particular to social networks activities but also to the enormous exchange of data caused especially by the so-called "Internet of Things". With this term we refer to every device that has the capability of exchanging information with other devices on the web. In geoscience (and, in particular, in meteorology and climatology) there is a constantly increasing number of sensors that are used to obtain data from different sources (like weather radars, digital rain gauges, etc.). This information-gathering activity, frequently, must be followed by a complex data analysis phase, especially when we have large data sets that can be very difficult to analyze (very long historical series of large data sets, for example), like the so called big data. These activities are particularly intensive in resource consumption and they lead to new computational models (like cloud computing) and new methods for storing data (like object store, linked open data, NOSQL or NewSQL). The weather radar systems can be seen as one of the sensors mentioned above: it transmit a large amount of raw data over the network (up to 40 megabytes every five minutes), with 24h/24h continuity and in any weather condition. Weather radar are often located in peaks and in wild areas where connectivity is poor. For this reason radar measurements are sometimes processed partially on site and reduced in size to adapt them to the limited bandwidth currently available by data transmission systems. With the aim to preserve the maximum flow of information, an innovative network connectivity paradigm for the large data produced by weather radar system is here presented. The study is focused on the Monte Settepani operational weather radar system, located over a wild peak summit in north-western Italy.

  17. Comparative Transcriptional Profiling of 3 Murine Models of SLE Nephritis Reveals Both Unique and Shared Regulatory Networks

    PubMed Central

    Zhang, Weijia; Kretzler, Matthias; Davidson, Anne

    2013-01-01

    Objective To define shared and unique features of SLE nephritis in mouse models of proliferative and glomerulosclerotic renal disease. Methods Perfused kidneys from NZB/W F1, NZW/BXSB and NZM2410 mice were harvested before and after nephritis onset. Affymetrix based gene expression profiles of kidney RNA were analyzed using Genomatix Pathway Systems and Ingenuity Pathway Analysis software. Gene expression patterns were confirmed using real-time PCR. Results 955, 1168 and 755 genes were regulated in the kidneys of nephritic NZB/W F1, NZM2410 and NZW/BXSB mice respectively. 263 genes were regulated concordantly in all three strains reflecting immune cell infiltration, endothelial cell activation, complement activation, cytokine signaling, tissue remodeling and hypoxia. STAT3 was the top associated transcription factor, having a binding site in the gene promoter of 60/263 regulated genes. The two strains with proliferative nephritis shared a macrophage/DC infiltration and activation signature. NZB/W and NZM2410 mice shared a mitochondrial dysfunction signature. Dominant T cell and plasma cell signatures in NZB/W mice reflected lymphoid aggregates; this was the only strain with regulatory T cell infiltrates. NZW/BXSB mice manifested tubular regeneration and NZM2410 mice had the most metabolic stress and manifested loss of nephrin, indicating podocyte loss. Conclusions These findings identify shared inflammatory mechanisms of SLE nephritis that can be therapeutically targeted. Nevertheless, the heterogeneity of effector mechanisms suggests that individualized therapy might need to be based on biopsy findings. Some common mechanisms are shared with non-immune–mediated renal diseases, suggesting that strategies to prevent tissue hypoxia and remodeling may be useful in SLE nephritis. PMID:24167575

  18. Decision support system to divide a large network into suitable District Metered Areas.

    PubMed

    Gomes, Ricardo; Marques, Alfeu Sá; Sousa, Joaquim

    2012-01-01

    This paper presents a new approach to divide large Water Distribution Networks (WDN) into suitable District Metered Areas (DMAs). It uses a hydraulic simulator and two operational models to identify the optimal number of DMAs, their entry points and boundary valves, and the network reinforcement/replacement needs throughout the project plan. The first model divides the WDN into suitable DMAs based on graph theory concepts and some user-defined criteria. The second model uses a simulated annealing algorithm to identify the optimal number and location of entry points and boundary valves, and the pipes reinforcement/replacement, necessary to meet the velocity and pressure requirements. The objective function is the difference between the economic benefits in terms of water loss reduction (arising from the average pressure reduction) and the cost of implementing the DMAs. To illustrate the proposed methodology, the results from a hypothetical case study are presented and discussed.

  19. Large File Transfers from Space Using Multiple Ground Terminals and Delay-Tolerant Networking

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.; Paulsen, Phillip; Stewart, Dave; Eddy, Wesley; McKim, James; Taylor, John; Lynch, Scott; Heberle, Jay; Northam, James; Jackson, Chris; Wood, Lloyd

    2010-01-01

    We use Delay-Tolerant Networking (DTN) to break control loops between space-ground communication links and ground-ground communication links to increase overall file delivery efficiency, as well as to enable large files to be proactively fragmented and received across multiple ground stations. DTN proactive fragmentation and reactive fragmentation were demonstrated from the UK-DMC satellite using two independent ground stations. The files were reassembled at a bundle agent, located at Glenn Research Center in Cleveland Ohio. The first space-based demonstration of this occurred on September 30 and October 1, 2009. This paper details those experiments. Communication, delay-tolerant networking, DTN, satellite, Internet, protocols, bundle, IP, TCP.

  20. A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs

    PubMed Central

    Fukuoka, Yutaka; Takei, Daiki; Ogawa, Hisamichi

    2013-01-01

    The present study proposed a two-step drug repositioning method based on a protein-protein interaction (PPI) network of two diseases and the similarity of the drugs prescribed for one of the two. In the proposed method, first, lists of disease related genes were obtained from a meta-database called Genotator. Then genes shared by a pair of diseases were sought. At the first step of the method, if a drug having its target(s) in the PPI network, the drug was deemed a repositioning candidate. Because targets of many drugs are still unknown, the similarities between the prescribed drugs for a specific disease were used to infer repositioning candidates at the second step. As a first attempt, we applied the proposed method to four different types of diseases: hypertension, diabetes mellitus, Crohn disease, and autism. Some repositioning candidates were found both at the first and second steps. PMID:23390352

  1. Large-scale topology and the default mode network in the mouse connectome.

    PubMed

    Stafford, James M; Jarrett, Benjamin R; Miranda-Dominguez, Oscar; Mills, Brian D; Cain, Nicholas; Mihalas, Stefan; Lahvis, Garet P; Lattal, K Matthew; Mitchell, Suzanne H; David, Stephen V; Fryer, John D; Nigg, Joel T; Fair, Damien A

    2014-12-30

    Noninvasive functional imaging holds great promise for serving as a translational bridge between human and animal models of various neurological and psychiatric disorders. However, despite a depth of knowledge of the cellular and molecular underpinnings of atypical processes in mouse models, little is known about the large-scale functional architecture measured by functional brain imaging, limiting translation to human conditions. Here, we provide a robust processing pipeline to generate high-resolution, whole-brain resting-state functional connectivity MRI (rs-fcMRI) images in the mouse. Using a mesoscale structural connectome (i.e., an anterograde tracer mapping of axonal projections across the mouse CNS), we show that rs-fcMRI in the mouse has strong structural underpinnings, validating our procedures. We next directly show that large-scale network properties previously identified in primates are present in rodents, although they differ in several ways. Last, we examine the existence of the so-called default mode network (DMN)--a distributed functional brain system identified in primates as being highly important for social cognition and overall brain function and atypically functionally connected across a multitude of disorders. We show the presence of a potential DMN in the mouse brain both structurally and functionally. Together, these studies confirm the presence of basic network properties and functional networks of high translational importance in structural and functional systems in the mouse brain. This work clears the way for an important bridge measurement between human and rodent models, enabling us to make stronger conclusions about how regionally specific cellular and molecular manipulations in mice relate back to humans.

  2. Large-scale topology and the default mode network in the mouse connectome.

    PubMed

    Stafford, James M; Jarrett, Benjamin R; Miranda-Dominguez, Oscar; Mills, Brian D; Cain, Nicholas; Mihalas, Stefan; Lahvis, Garet P; Lattal, K Matthew; Mitchell, Suzanne H; David, Stephen V; Fryer, John D; Nigg, Joel T; Fair, Damien A

    2014-12-30

    Noninvasive functional imaging holds great promise for serving as a translational bridge between human and animal models of various neurological and psychiatric disorders. However, despite a depth of knowledge of the cellular and molecular underpinnings of atypical processes in mouse models, little is known about the large-scale functional architecture measured by functional brain imaging, limiting translation to human conditions. Here, we provide a robust processing pipeline to generate high-resolution, whole-brain resting-state functional connectivity MRI (rs-fcMRI) images in the mouse. Using a mesoscale structural connectome (i.e., an anterograde tracer mapping of axonal projections across the mouse CNS), we show that rs-fcMRI in the mouse has strong structural underpinnings, validating our procedures. We next directly show that large-scale network properties previously identified in primates are present in rodents, although they differ in several ways. Last, we examine the existence of the so-called default mode network (DMN)--a distributed functional brain system identified in primates as being highly important for social cognition and overall brain function and atypically functionally connected across a multitude of disorders. We show the presence of a potential DMN in the mouse brain both structurally and functionally. Together, these studies confirm the presence of basic network properties and functional networks of high translational importance in structural and functional systems in the mouse brain. This work clears the way for an important bridge measurement between human and rodent models, enabling us to make stronger conclusions about how regionally specific cellular and molecular manipulations in mice relate back to humans. PMID:25512496

  3. The Mekong Fish Network: expanding the capacity of the people and institutions of the Mekong River Basin to share information and conduct standardized fisheries monitoring

    USGS Publications Warehouse

    Patricio, Harmony C.; Ainsley, Shaara M.; Andersen, Matthew E.; Beeman, John W.; Hewitt, David A.

    2012-01-01

    The Mekong River is one of the most biologically diverse rivers in the world, and it supports the most productive freshwater fisheries in the world. Millions of people in the Lower Mekong River Basin (LMB) countries of the Union of Myanmar (Burma), Lao People’s Democratic Republic, the Kingdom of Thailand, the Kingdom of Cambodia, and the Socialist Republic of Vietnam rely on the fisheries of the basin to provide a source of protein. The Mekong Fish Network Workshop was convened in Phnom Penh, Cambodia, in February 2012 to discuss the potential for coordinating fisheries monitoring among nations and the utility of establishing standard methods for short- and long-term monitoring and data sharing throughout the LMB. The concept for this network developed out of a frequently cited need for fisheries researchers in the LMB to share their knowledge with other scientists and decisionmakers. A fish monitoring network could be a valuable forum for researchers to exchange ideas, store data, or access general information regarding fisheries studies in the LMB region. At the workshop, representatives from governments, nongovernmental organizations, and universities, as well as participating foreign technical experts, cited a great need for more international cooperation and technical support among them. Given the limited staff and resources of many institutions in the LMB, the success of the proposed network would depend on whether it could offer tools that would provide benefits to network participants. A potential tool discussed at the workshop was a user-friendly, Web-accessible portal and database that could help streamline data entry and storage at the institutional level, as well as facilitate communication and data sharing among institutions. The workshop provided a consensus to establish pilot standardized data collection and database efforts that will be further reviewed by the workshop participants. Overall, workshop participants agreed that this is the type of

  4. Experimental demonstration of large capacity WSDM optical access network with multicore fibers and advanced modulation formats.

    PubMed

    Li, Borui; Feng, Zhenhua; Tang, Ming; Xu, Zhilin; Fu, Songnian; Wu, Qiong; Deng, Lei; Tong, Weijun; Liu, Shuang; Shum, Perry Ping

    2015-05-01

    Towards the next generation optical access network supporting large capacity data transmission to enormous number of users covering a wider area, we proposed a hybrid wavelength-space division multiplexing (WSDM) optical access network architecture utilizing multicore fibers with advanced modulation formats. As a proof of concept, we experimentally demonstrated a WSDM optical access network with duplex transmission using our developed and fabricated multicore (7-core) fibers with 58.7km distance. As a cost-effective modulation scheme for access network, the optical OFDM-QPSK signal has been intensity modulated on the downstream transmission in the optical line terminal (OLT) and it was directly detected in the optical network unit (ONU) after MCF transmission. 10 wavelengths with 25GHz channel spacing from an optical comb generator are employed and each wavelength is loaded with 5Gb/s OFDM-QPSK signal. After amplification, power splitting, and fan-in multiplexer, 10-wavelength downstream signal was injected into six outer layer cores simultaneously and the aggregation downstream capacity reaches 300 Gb/s. -16 dBm sensitivity has been achieved for 3.8 × 10-3 bit error ratio (BER) with 7% Forward Error Correction (FEC) limit for all wavelengths in every core. Upstream signal from ONU side has also been generated and the bidirectional transmission in the same core causes negligible performance degradation to the downstream signal. As a universal platform for wired/wireless data access, our proposed architecture provides additional dimension for high speed mobile signal transmission and we hence demonstrated an upstream delivery of 20Gb/s per wavelength with QPSK modulation formats using the inner core of MCF emulating a mobile backhaul service. The IQ modulated data was coherently detected in the OLT side. -19 dBm sensitivity has been achieved under the FEC limit and more than 18 dB power budget is guaranteed.

  5. Fast decision tree-based method to index large DNA-protein sequence databases using hybrid distributed-shared memory programming model.

    PubMed

    Jaber, Khalid Mohammad; Abdullah, Rosni; Rashid, Nur'Aini Abdul

    2014-01-01

    In recent times, the size of biological databases has increased significantly, with the continuous growth in the number of users and rate of queries; such that some databases have reached the terabyte size. There is therefore, the increasing need to access databases at the fastest rates possible. In this paper, the decision tree indexing model (PDTIM) was parallelised, using a hybrid of distributed and shared memory on resident database; with horizontal and vertical growth through Message Passing Interface (MPI) and POSIX Thread (PThread), to accelerate the index building time. The PDTIM was implemented using 1, 2, 4 and 5 processors on 1, 2, 3 and 4 threads respectively. The results show that the hybrid technique improved the speedup, compared to a sequential version. It could be concluded from results that the proposed PDTIM is appropriate for large data sets, in terms of index building time.

  6. Bifurcation and control in a neural network with small and large delays.

    PubMed

    Xiao, Min; Zheng, Wei Xing; Cao, Jinde

    2013-08-01

    This paper investigates a neural network modeled by a scalar delay differential equation. The focus is placed upon the Hopf bifurcation generated by varying the interaction parameter. A general expression for the periodic solutions arising from the Hopf bifurcation is obtained, and the direction of the bifurcation is also determined. Then, our results are tested in the two limits of small and large delays. For small delays, it is shown that a Hopf bifurcation to sinusoidal oscillations emerges as long as the interaction parameter is large enough (bifurcation from infinity) (Rosenblat & Davis, 1979). For large delays, it is pointed out that the oscillation progressively changes from sine to square-wave (Chow, Hale, & Huang, 1992; Hale & Huang, 1994). Moreover, a time delayed feedback control algorithm is introduced to generate the Hopf bifurcation at a desired bifurcation point for our neural network model. It is shown that the linear gain regulates the onset of the bifurcation, while the nonlinear gains govern the direction and the stability of the periodic solutions generated from the Hopf bifurcation.

  7. Suspended sediment transport trough a large fluvial-tidal channel network

    USGS Publications Warehouse

    Wright, Scott A.; Morgan, Tara

    2015-01-01

    The confluence of the Sacramento and San Joaquin Rivers, CA, forms a large network of interconnected channels, referred to as the Sacramento-San Joaquin Delta (the Delta). The Delta comprises the transition zone from the fluvial influences of the upstream rivers and tidal influences of San Francisco Bay downstream. Formerly an extensive tidal marsh, the hydrodynamics and geomorphology of Delta have been substantially modified by humans to support agriculture, navigation, and water supply. These modifications, including construction of new channels, diking and draining of tidal wetlands, dredging of navigation channels, and the operation of large pumping facilities for distribution of freshwater from the Delta to other parts of the state, have had a dramatic impact on the physical and ecological processes within the Delta. To better understand the current physical processes, and their linkages to ecological processes, the USGS maintains an extensive network of flow, sediment, and water quality gages in the Delta. Flow gaging is accomplished through use of the index-velocity method, and sediment monitoring uses turbidity as a surrogate for suspended-sediment concentration. Herein, we present analyses of the transport and dispersal of suspended sediment through the complex network of channels in the Delta. The primary source of sediment to the Delta is the Sacramento River, which delivers pulses of sediment primarily during winter and spring runoff events. Upon reaching the Delta, the sediment pulses move through the fluvial-tidal transition while also encountering numerous channel junctions as the Sacramento River branches into several distributary channels. The monitoring network allows us to track these pulses through the network and document the dominant transport pathways for suspended sediment. Further, the flow gaging allows for an assessment of the relative effects of advection (the fluvial signal) and dispersion (from the tides) on the sediment pulses as they

  8. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

    PubMed

    Xiao, Xiangyun; Zhang, Wei; Zou, Xiufen

    2015-01-01

    The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

  9. Cryogenic Design of the Deep Space Network Large Array Low-Noise Amplifier System

    NASA Astrophysics Data System (ADS)

    Britcliffe, M. J.; Hanson, T. R.; Franco, M. M.

    2004-05-01

    This article describes the cryogenic design and performance of a prototype low-noise amplifier (LNA) system for the Deep Space Network (DSN) Large Array task. The system is used to cool a dual-frequency feed system equipped with high-electron mobility transistor (HEMT) low-noise amplifiers and the associated support electronics. The LNA/feed system operates at a temperature under 18 K. The system is designed to be manufactured at minimum cost. The design considerations, including the cryocooler to be used, vacuum system, microwave interconnects, mechanical components, and radiation shielding, are discussed.

  10. Environmental networks for large-scale monitoring of Earth and atmosphere

    NASA Astrophysics Data System (ADS)

    Maurodimou, Olga; Kolios, Stavros; Konstantaras, Antonios; Georgoulas, George; Stylios, Chrysostomos

    2013-04-01

    Installation and operation of instrument/sensor networks are proven fundamental in the monitoring of the physical environment from local to global scale. The advances in electronics, wireless communications and informatics has led to the development of a huge number of networks at different spatial scales that measure, collect and store a wide range of environmental parameters. These networks have been gradually evolved into integrated information systems that provide real time monitoring, forecasts and different products from the initial collected datasets. Instrument/sensor networks have nowadays become important solutions for environmental monitoring, comprising a basic component of fully automated systems developing worldwide that contribute in the efforts for a sustainable Earth's environment (e.g. Hart et al., 2006, Othman et al., 2012). They are also used as a source of data for models parameterization and as verification tools for accuracy assessment techniques of the satellite imagery. Environmental networks can be incorporated into decision support systems (e.g Rizzi et al., 2012) providing informational background along with data from satellites for decision making, manage problems, suggest solutions and best practices for a sustainable management of the environment. This is a comparative study aiming to examine and highlight the significant role of existing instrument/sensor networks for large-scale monitoring of environmental issues, especially atmospheric and marine environment as well as weather and climate. We provide characteristic examples of integrated systems based on large scale instrument/sensor networks along with other sources of data (like satellite datasets) as informational background to measure, identify, monitor, analyze and forecast a vast series of atmospheric parameters (like CO2, O3, particle matter and solar irradiance), weather, climate and their impacts (e.g., cloud systems, lightnings, rainfall, air and surface temperature

  11. Macroscopic properties of isotropic and anisotropic fracture networks from the percolation threshold to very large densities

    NASA Astrophysics Data System (ADS)

    Adler, P. M.; Thovert, J.; Mourzenko, V.

    2011-12-01

    The main purpose of this review paper is to summarize some recent studies of fracture networks. Progress has been made possible thanks to a very versatile numerical technique based on a three-dimensional discrete description of the fracture networks. Any network geometry, any boundary condition, and any distribution of the fractures can be addressed. The first step is to mesh the fracture network as it is by triangles of a controlled size. The second step consists in the discretization of the conservation equations by the finite volume technique. Two important properties were systematically studied, namely the percolation threshold rho_c and the macroscopic permeability K_n of the fracture network. Dimensionless quantities are denoted by a prime. The numerical results are interpreted in a systematic way with the concept of excluded volume which enables us to define a dimensionless fracture density rho' equal in the average to the average number of intersections per fracture. 1. Isotropic networks of identical fractures The dimensionless percolation threshold rho'_c of such networks was systematically studied for fractures of various shapes. rho'_c was shown to be almost independent of the shape except when one has very elongated rectangles. A formula is proposed for rho'_c. The permeability of these networks was calculated for a wide range of fracture densities and shapes. K'_n(rho') is almost independent of the fracture shape; an empirical formula is proposed for any value of rho' between rho'_c and infinity. For large rho', K_n is well approximated by the Snow formula initially derived for infinite fractures. 2. Anisotropic networks of identical fractures The fracture orientations are supposed to follow a Fisher distribution characterized by the parameter kappa; when kappa=0, the fractures are isotropic; when kappa=infinity, the fractures are perpendicular to a given direction. rho'_c does not depend significantly on kappa and the general formula proposed in 1

  12. Towards stable kinetics of large metabolic networks: Nonequilibrium potential function approach.

    PubMed

    Chen, Yong-Cong; Yuan, Ruo-Shi; Ao, Ping; Xu, Min-Juan; Zhu, Xiao-Mei

    2016-06-01

    While the biochemistry of metabolism in many organisms is well studied, details of the metabolic dynamics are not fully explored yet. Acquiring adequate in vivo kinetic parameters experimentally has always been an obstacle. Unless the parameters of a vast number of enzyme-catalyzed reactions happened to fall into very special ranges, a kinetic model for a large metabolic network would fail to reach a steady state. In this work we show that a stable metabolic network can be systematically established via a biologically motivated regulatory process. The regulation is constructed in terms of a potential landscape description of stochastic and nongradient systems. The constructed process draws enzymatic parameters towards stable metabolism by reducing the change in the Lyapunov function tied to the stochastic fluctuations. Biologically it can be viewed as interplay between the flux balance and the spread of workloads on the network. Our approach allows further constraints such as thermodynamics and optimal efficiency. We choose the central metabolism of Methylobacterium extorquens AM1 as a case study to demonstrate the effectiveness of the approach. Growth efficiency on carbon conversion rate versus cell viability and futile cycles is investigated in depth. PMID:27415300

  13. Hybrid Multiphoton Volumetric Functional Imaging of Large Scale Bioengineered Neuronal Networks

    PubMed Central

    Paluch, Shir; Dvorkin, Roman; Brosh, Inbar; Shoham, Shy

    2014-01-01

    Planar neural networks and interfaces serve as versatile in vitro models of central nervous system physiology, but adaptations of related methods to three dimensions (3D) have met with limited success. Here, we demonstrate for the first time volumetric functional imaging in a bio-engineered neural tissue growing in a transparent hydrogel with cortical cellular and synaptic densities, by introducing complementary new developments in nonlinear microscopy and neural tissue engineering. Our system uses a novel hybrid multiphoton microscope design combining a 3D scanning-line temporal-focusing subsystem and a conventional laser-scanning multiphoton microscope to provide functional and structural volumetric imaging capabilities: dense microscopic 3D sampling at tens of volumes/sec of structures with mm-scale dimensions containing a network of over 1000 developing cells with complex spontaneous activity patterns. These developments open new opportunities for large-scale neuronal interfacing and for applications of 3D engineered networks ranging from basic neuroscience to the screening of neuroactive substances. PMID:24898000

  14. Understanding structural-functional relationships in the human brain: a large-scale network perspective.

    PubMed

    Wang, Zhijiang; Dai, Zhengjia; Gong, Gaolang; Zhou, Changsong; He, Yong

    2015-06-01

    Relating the brain's structural connectivity (SC) to its functional connectivity (FC) is a fundamental goal in neuroscience because it is capable of aiding our understanding of how the relatively fixed SC architecture underlies human cognition and diverse behaviors. With the aid of current noninvasive imaging technologies (e.g., structural MRI, diffusion MRI, and functional MRI) and graph theory methods, researchers have modeled the human brain as a complex network of interacting neuronal elements and characterized the underlying structural and functional connectivity patterns that support diverse cognitive functions. Specifically, research has demonstrated a tight SC-FC coupling, not only in interregional connectivity strength but also in network topologic organizations, such as community, rich-club, and motifs. Moreover, this SC-FC coupling exhibits significant changes in normal development and neuropsychiatric disorders, such as schizophrenia and epilepsy. This review summarizes recent progress regarding the SC-FC relationship of the human brain and emphasizes the important role of large-scale brain networks in the understanding of structural-functional associations. Future research directions related to this topic are also proposed.

  15. Laser Speckle Flowmetry Method for Measuring Spatial and Temporal Hemodynamic Alterations Throughout Large Microvascular Networks

    PubMed Central

    Meisner, Joshua K.; Sumer, Suna; Murrell, Kelsey P.; Higgins, Timothy J.; Price, Richard J.

    2012-01-01

    Objectives 1) Develop and validate laser speckle flowmetry (LSF) as a quantitative tool for individual microvessel hemodynamics in large networks. 2) Use LSF to determine if structural differences in the dorsal skinfold microcirculation (DSFWC) of C57BL/6 and BALB/c mice impart differential network hemodynamic responses to occlusion. Methods We compared LSF velocity measurements to known/measured velocities in vitro using capillary tube tissue phantoms and in vivo using mouse DSFWCs and cremaster muscles. Hemodynamic changes induced by feed arteriole occlusion were measured using LSF in DSFWCs implanted on C57BL/6 and BALB/c. Results In vitro, we found that the normalized speckle intensity (NSI) versus velocity linear relationship (R2≥0.97) did not vary with diameter or hematocrit and can be shifted to meet an expected operating range. In vivo, DSFWC and cremaster muscle preparations (R2=0.92 and 0.95, respectively) demonstrated similar linear relationships between NSI and centerline velocity. Stratification of arterioles into predicted collateral pathways revealed significant differences between C57BL/6 and BALB/c strains in response to feed arteriole occlusion. Conclusions These data demonstrate the applicability of LSF to intravital microscopy microcirculation preparations for determining both relative and absolute hemodynamics on a network-wide scale while maintaining the resolution of individual microvessels. PMID:22591575

  16. Towards stable kinetics of large metabolic networks: Nonequilibrium potential function approach

    NASA Astrophysics Data System (ADS)

    Chen, Yong-Cong; Yuan, Ruo-Shi; Ao, Ping; Xu, Min-Juan; Zhu, Xiao-Mei

    2016-06-01

    While the biochemistry of metabolism in many organisms is well studied, details of the metabolic dynamics are not fully explored yet. Acquiring adequate in vivo kinetic parameters experimentally has always been an obstacle. Unless the parameters of a vast number of enzyme-catalyzed reactions happened to fall into very special ranges, a kinetic model for a large metabolic network would fail to reach a steady state. In this work we show that a stable metabolic network can be systematically established via a biologically motivated regulatory process. The regulation is constructed in terms of a potential landscape description of stochastic and nongradient systems. The constructed process draws enzymatic parameters towards stable metabolism by reducing the change in the Lyapunov function tied to the stochastic fluctuations. Biologically it can be viewed as interplay between the flux balance and the spread of workloads on the network. Our approach allows further constraints such as thermodynamics and optimal efficiency. We choose the central metabolism of Methylobacterium extorquens AM1 as a case study to demonstrate the effectiveness of the approach. Growth efficiency on carbon conversion rate versus cell viability and futile cycles is investigated in depth.

  17. A Triple Network Connectivity Study of Large-Scale Brain Systems in Cognitively Normal APOE4 Carriers

    PubMed Central

    Wu, Xia; Li, Qing; Yu, Xinyu; Chen, Kewei; Fleisher, Adam S.; Guo, Xiaojuan; Zhang, Jiacai; Reiman, Eric M.; Yao, Li; Li, Rui

    2016-01-01

    The triple network model, consisting of the central executive network (CEN), salience network (SN) and default mode network (DMN), has been recently employed to understand dysfunction in core networks across various disorders. Here we used the triple network model to investigate the large-scale brain networks in cognitively normal apolipoprotein e4 (APOE4) carriers who are at risk of Alzheimer’s disease (AD). To explore the functional connectivity for each of the three networks and the effective connectivity among them, we evaluated 17 cognitively normal individuals with a family history of AD and at least one copy of the APOE4 allele and compared the findings to those of 12 individuals who did not carry the APOE4 gene or have a family history of AD, using independent component analysis (ICA) and Bayesian network (BN) approach. Our findings indicated altered within-network connectivity that suggests future cognitive decline risk, and preserved between-network connectivity that may support their current preserved cognition in the cognitively normal APOE4 allele carriers. The study provides novel sights into our understanding of the risk factors for AD and their influence on the triple network model of major psychopathology. PMID:27733827

  18. Restoring large-scale brain networks in PTSD and related disorders: a proposal for neuroscientifically-informed treatment interventions

    PubMed Central

    Lanius, Ruth A.; Frewen, Paul A.; Tursich, Mischa; Jetly, Rakesh; McKinnon, Margaret C.

    2015-01-01

    Background Three intrinsic connectivity networks in the brain, namely the central executive, salience, and default mode networks, have been identified as crucial to the understanding of higher cognitive functioning, and the functioning of these networks has been suggested to be impaired in psychopathology, including posttraumatic stress disorder (PTSD). Objective 1) To describe three main large-scale networks of the human brain; 2) to discuss the functioning of these neural networks in PTSD and related symptoms; and 3) to offer hypotheses for neuroscientifically-informed interventions based on treating the abnormalities observed in these neural networks in PTSD and related disorders. Method Literature relevant to this commentary was reviewed. Results Increasing evidence for altered functioning of the central executive, salience, and default mode networks in PTSD has been demonstrated. We suggest that each network is associated with specific clinical symptoms observed in PTSD, including cognitive dysfunction (central executive network), increased and decreased arousal/interoception (salience network), and an altered sense of self (default mode network). Specific testable neuroscientifically-informed treatments aimed to restore each of these neural networks and related clinical dysfunction are proposed. Conclusions Neuroscientifically-informed treatment interventions will be essential to future research agendas aimed at targeting specific PTSD and related symptoms. PMID:25854674

  19. Object-Oriented NeuroSys: Parallel Programs for Simulating Large Networks of Biologically Accurate Neurons

    SciTech Connect

    Pacheco, P; Miller, P; Kim, J; Leese, T; Zabiyaka, Y

    2003-05-07

    Object-oriented NeuroSys (ooNeuroSys) is a collection of programs for simulating very large networks of biologically accurate neurons on distributed memory parallel computers. It includes two principle programs: ooNeuroSys, a parallel program for solving the large systems of ordinary differential equations arising from the interconnected neurons, and Neurondiz, a parallel program for visualizing the results of ooNeuroSys. Both programs are designed to be run on clusters and use the MPI library to obtain parallelism. ooNeuroSys also includes an easy-to-use Python interface. This interface allows neuroscientists to quickly develop and test complex neuron models. Both ooNeuroSys and Neurondiz have a design that allows for both high performance and relative ease of maintenance.

  20. Examining Food Risk in the Large using a Complex, Networked System-of-sytems Approach

    SciTech Connect

    Ambrosiano, John; Newkirk, Ryan; Mc Donald, Mark P

    2010-12-03

    The food production infrastructure is a highly complex system of systems. Characterizing the risks of intentional contamination in multi-ingredient manufactured foods is extremely challenging because the risks depend on the vulnerabilities of food processing facilities and on the intricacies of the supply-distribution networks that link them. A pure engineering approach to modeling the system is impractical because of the overall system complexity and paucity of data. A methodology is needed to assess food contamination risk 'in the large', based on current, high-level information about manufacturing facilities, corrunodities and markets, that will indicate which food categories are most at risk of intentional contamination and warrant deeper analysis. The approach begins by decomposing the system for producing a multi-ingredient food into instances of two subsystem archetypes: (1) the relevant manufacturing and processing facilities, and (2) the networked corrunodity flows that link them to each other and consumers. Ingredient manufacturing subsystems are modeled as generic systems dynamics models with distributions of key parameters that span the configurations of real facilities. Networks representing the distribution systems are synthesized from general information about food corrunodities. This is done in a series of steps. First, probability networks representing the aggregated flows of food from manufacturers to wholesalers, retailers, other manufacturers, and direct consumers are inferred from high-level approximate information. This is followed by disaggregation of the general flows into flows connecting 'large' and 'small' categories of manufacturers, wholesalers, retailers, and consumers. Optimization methods are then used to determine the most likely network flows consistent with given data. Vulnerability can be assessed for a potential contamination point using a modified CARVER + Shock model. Once the facility and corrunodity flow models are

  1. Unscheduled load flow effect due to large variation in the distributed generation in a subtransmission network

    NASA Astrophysics Data System (ADS)

    Islam, Mujahidul

    A sustainable energy delivery infrastructure implies the safe and reliable accommodation of large scale penetration of renewable sources in the power grid. In this dissertation it is assumed there will be no significant change in the power transmission and distribution structure currently in place; except in the operating strategy and regulatory policy. That is to say, with the same old structure, the path towards unveiling a high penetration of switching power converters in the power system will be challenging. Some of the dimensions of this challenge are power quality degradation, frequent false trips due to power system imbalance, and losses due to a large neutral current. The ultimate result is the reduced life of many power distribution components - transformers, switches and sophisticated loads. Numerous ancillary services are being developed and offered by the utility operators to mitigate these problems. These services will likely raise the system's operational cost, not only from the utility operators' end, but also reflected on the Independent System Operators and by the Regional Transmission Operators (RTO) due to an unforeseen backlash of frequent variation in the load-side generation or distributed generation. The North American transmission grid is an interconnected system similar to a large electrical circuit. This circuit was not planned but designed over 100 years. The natural laws of physics govern the power flow among loads and generators except where control mechanisms are installed. The control mechanism has not matured enough to withstand the high penetration of variable generators at uncontrolled distribution ends. Unlike a radial distribution system, mesh or loop networks can alleviate complex channels for real and reactive power flow. Significant variation in real power injection and absorption on the distribution side can emerge as a bias signal on the routing reactive power in some physical links or channels that are not distinguishable

  2. A Case Study of Israeli Higher-Education Institutes Sharing Scholarly Information with the Community via Social Networks

    ERIC Educational Resources Information Center

    Forkosh-Baruch, Alona; Hershkovitz, Arnon

    2012-01-01

    The purpose of this study is to empirically examine cases in which Social Networking Sites (SNS) are being utilized for scholarly purposes by higher-education institutes in Israel. The research addresses questions regarding content patterns, activity patterns, and interactivity within Facebook and Twitter accounts of these institutes. Research…

  3. Situated Learning through Social Networking Communities: The Development of Joint Enterprise, Mutual Engagement, and a Shared Repertoire

    ERIC Educational Resources Information Center

    Mills, Nicole

    2011-01-01

    Scholars praise social networking tools for their ability to engage and motivate iGeneration students in meaningful communicative practice, content exchange, and collaboration (Greenhow, Robelia, & Hughes, 2009; Ziegler, 2007). To gain further insight about the nature of student participation, knowledge acquisition, and relationship development…

  4. An efective fractal-tree closure model for simulating blood flow in large arterial networks

    PubMed Central

    Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em.

    2014-01-01

    The aim of the present work is to address the closure problem for hemodynamic simulations by developing a exible and effective model that accurately distributes flow in the downstream vasculature and can stably provide a physiological pressure out flow boundary condition. To achieve this goal, we model blood flow in the sub-pixel vasculature by using a non-linear 1D model in self-similar networks of compliant arteries that mimic the structure and hierarchy of vessels in the meso-vascular regime (radii 500 μm – 10 μm). We introduce a variable vessel length-to-radius ratio for small arteries and arterioles, while also addressing non-Newtonian blood rheology and arterial wall viscoelasticity effects in small arteries and arterioles. This methodology aims to overcome substantial cut-off radius sensitivities, typically arising in structured tree and linearized impedance models. The proposed model is not sensitive to out flow boundary conditions applied at the end points of the fractal network, and thus does not require calibration of resistance/capacitance parameters typically required for out flow conditions. The proposed model convergences to a periodic state in two cardiac cycles even when started from zero-flow initial conditions. The resulting fractal-trees typically consist of thousands to millions of arteries, posing the need for efficient parallel algorithms. To this end, we have scaled up a Discontinuous Galerkin solver that utilizes the MPI/OpenMP hybrid programming paradigm to thousands of computer cores, and can simulate blood flow in networks of millions of arterial segments at the rate of one cycle per 5 minutes. The proposed model has been extensively tested on a large and complex cranial network with 50 parent, patient-specific arteries and 21 outlets to which fractal trees where attached, resulting to a network of up to 4,392,484 vessels in total, and a detailed network of the arm with 276 parent arteries and 103 outlets (a total of 702,188 vessels

  5. An effective fractal-tree closure model for simulating blood flow in large arterial networks.

    PubMed

    Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em

    2015-06-01

    The aim of the present work is to address the closure problem for hemodynamic simulations by developing a flexible and effective model that accurately distributes flow in the downstream vasculature and can stably provide a physiological pressure outflow boundary condition. To achieve this goal, we model blood flow in the sub-pixel vasculature by using a non-linear 1D model in self-similar networks of compliant arteries that mimic the structure and hierarchy of vessels in the meso-vascular regime (radii [Formula: see text]). We introduce a variable vessel length-to-radius ratio for small arteries and arterioles, while also addressing non-Newtonian blood rheology and arterial wall viscoelasticity effects in small arteries and arterioles. This methodology aims to overcome substantial cut-off radius sensitivities, typically arising in structured tree and linearized impedance models. The proposed model is not sensitive to outflow boundary conditions applied at the end points of the fractal network, and thus does not require calibration of resistance/capacitance parameters typically required for outflow conditions. The proposed model convergences to a periodic state in two cardiac cycles even when started from zero-flow initial conditions. The resulting fractal-trees typically consist of thousands to millions of arteries, posing the need for efficient parallel algorithms. To this end, we have scaled up a Discontinuous Galerkin solver that utilizes the MPI/OpenMP hybrid programming paradigm to thousands of computer cores, and can simulate blood flow in networks of millions of arterial segments at the rate of one cycle per 5 min. The proposed model has been extensively tested on a large and complex cranial network with 50 parent, patient-specific arteries and 21 outlets to which fractal trees where attached, resulting to a network of up to 4,392,484 vessels in total, and a detailed network of the arm with 276 parent arteries and 103 outlets (a total of 702,188 vessels

  6. Networks of Food Sharing Reveal the Functional Significance of Multilevel Sociality in Two Hunter-Gatherer Groups.

    PubMed

    Dyble, Mark; Thompson, James; Smith, Daniel; Salali, Gul Deniz; Chaudhary, Nikhil; Page, Abigail E; Vinicuis, Lucio; Mace, Ruth; Migliano, Andrea Bamberg

    2016-08-01

    Like many other mammalian and primate societies [1-4], humans are said to live in multilevel social groups, with individuals situated in a series of hierarchically structured sub-groups [5, 6]. Although this multilevel social organization has been described among contemporary hunter-gatherers [5], questions remain as to the benefits that individuals derive from living in such groups. Here, we show that food sharing among two populations of contemporary hunter-gatherers-the Palanan Agta (Philippines) and Mbendjele BaYaka (Republic of Congo)-reveals similar multilevel social structures, with individuals situated in households, within sharing clusters of 3-4 households, within the wider residential camps, which vary in size. We suggest that these groupings serve to facilitate inter-sexual provisioning, kin provisioning, and risk reduction reciprocity, three levels of cooperation argued to be fundamental in human societies [7, 8]. Humans have a suite of derived life history characteristics including a long childhood and short inter-birth intervals that make offspring energetically demanding [9] and have moved to a dietary niche that often involves the exploitation of difficult to acquire foods with highly variable return rates [10-12]. This means that human foragers face both day-to-day and more long-term energetic deficits that conspire to make humans energetically interdependent. We suggest that a multilevel social organization allows individuals access to both the food sharing partners required to buffer themselves against energetic shortfalls and the cooperative partners required for skill-based tasks such as cooperative foraging.

  7. Networks of Food Sharing Reveal the Functional Significance of Multilevel Sociality in Two Hunter-Gatherer Groups.

    PubMed

    Dyble, Mark; Thompson, James; Smith, Daniel; Salali, Gul Deniz; Chaudhary, Nikhil; Page, Abigail E; Vinicuis, Lucio; Mace, Ruth; Migliano, Andrea Bamberg

    2016-08-01

    Like many other mammalian and primate societies [1-4], humans are said to live in multilevel social groups, with individuals situated in a series of hierarchically structured sub-groups [5, 6]. Although this multilevel social organization has been described among contemporary hunter-gatherers [5], questions remain as to the benefits that individuals derive from living in such groups. Here, we show that food sharing among two populations of contemporary hunter-gatherers-the Palanan Agta (Philippines) and Mbendjele BaYaka (Republic of Congo)-reveals similar multilevel social structures, with individuals situated in households, within sharing clusters of 3-4 households, within the wider residential camps, which vary in size. We suggest that these groupings serve to facilitate inter-sexual provisioning, kin provisioning, and risk reduction reciprocity, three levels of cooperation argued to be fundamental in human societies [7, 8]. Humans have a suite of derived life history characteristics including a long childhood and short inter-birth intervals that make offspring energetically demanding [9] and have moved to a dietary niche that often involves the exploitation of difficult to acquire foods with highly variable return rates [10-12]. This means that human foragers face both day-to-day and more long-term energetic deficits that conspire to make humans energetically interdependent. We suggest that a multilevel social organization allows individuals access to both the food sharing partners required to buffer themselves against energetic shortfalls and the cooperative partners required for skill-based tasks such as cooperative foraging. PMID:27451900

  8. Toward perpetual wireless networks: Opportunistic large arrays with transmission thresholds and energy harvesting

    NASA Astrophysics Data System (ADS)

    Kailas, Aravind

    Solving the key issue of sustainability of battery-powered sensors continues to attract significant research attention. The prevailing theme of this research is to address this concern using energy-efficient protocols based on a form of simple cooperative transmission (CT) called the opportunistic large arrays (OLAs), and intelligent exploitation of energy harvesting and hybrid energy storage systems (HESSs). The two key contributions of this research, namely, OLA with transmission threshold (OLA-T) and alternating OLA-T (A-OLA-T), offer an signal-to-noise ratio (SNR) advantage (i.e., benefits of diversity and array (power) gains) in a multi-path fading environment, thereby reducing transmit powers or extending range. Because these protocols do not address nodes individually, the network overhead remains constant for high density networks or nodes with mobility. During broadcasting across energy-constrained networks, while OLA-T saves energy by limiting node participation within a single broadcast, A-OLA-T optimizes over multiple broadcasts and drains the nodes in an equitable fashion. A major bottleneck for network sustainability is the ability of a rechargeable battery (RB) to store energy, which is limited by the number of charge-discharge cycles. Energy harvesting using a HESS that comprises a RB and a supercapacitor (SC) will minimize the RB usage, thereby preserving the charge-discharge cycles. Studying the HESS is important, rather than the SC-alone because while an SC with harvested energy may be sufficient for routine monitoring, if there is an alert, the RB could be used as necessary to support the heavier reporting requirements. Therefore, another key contribution of this research is the design and analysis of a novel routing metric called communications using HESS (CHESS), which extends the RB-life by relaying exclusively with SC energy.

  9. Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

    PubMed

    Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

    2016-01-01

    Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities. PMID:26512872

  10. Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

    PubMed

    Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

    2016-01-01

    Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities.

  11. A Secure Multicast Framework in Large and High-Mobility Network Groups

    NASA Astrophysics Data System (ADS)

    Lee, Jung-San; Chang, Chin-Chen

    With the widespread use of Internet applications such as Teleconference, Pay-TV, Collaborate tasks, and Message services, how to construct and distribute the group session key to all group members securely is becoming and more important. Instead of adopting the point-to-point packet delivery, these emerging applications are based upon the mechanism of multicast communication, which allows the group member to communicate with multi-party efficiently. There are two main issues in the mechanism of multicast communication: Key Distribution and Scalability. The first issue is how to distribute the group session key to all group members securely. The second one is how to maintain the high performance in large network groups. Group members in conventional multicast systems have to keep numerous secret keys in databases, which makes it very inconvenient for them. Furthermore, in case that a member joins or leaves the communication group, many involved participants have to change their own secret keys to preserve the forward secrecy and the backward secrecy. We consequently propose a novel version for providing secure multicast communication in large network groups. Our proposed framework not only preserves the forward secrecy and the backward secrecy but also possesses better performance than existing alternatives. Specifically, simulation results demonstrate that our scheme is suitable for high-mobility environments.

  12. Oscillations in large-scale cortical networks: map-based model.

    PubMed

    Rulkov, N F; Timofeev, I; Bazhenov, M

    2004-01-01

    We develop a new computationally efficient approach for the analysis of complex large-scale neurobiological networks. Its key element is the use of a new phenomenological model of a neuron capable of replicating important spike pattern characteristics and designed in the form of a system of difference equations (a map). We developed a set of map-based models that replicate spiking activity of cortical fast spiking, regular spiking and intrinsically bursting neurons. Interconnected with synaptic currents these model neurons demonstrated responses very similar to those found with Hodgkin-Huxley models and in experiments. We illustrate the efficacy of this approach in simulations of one- and two-dimensional cortical network models consisting of regular spiking neurons and fast spiking interneurons to model sleep and activated states of the thalamocortical system. Our study suggests that map-based models can be widely used for large-scale simulations and that such models are especially useful for tasks where the modeling of specific firing patterns of different cell classes is important. PMID:15306740

  13. Macroscopic properties of fracture networks from the percolation threshold to very large densities

    NASA Astrophysics Data System (ADS)

    Adler, P.; Thovert, J.-F.; Mourzenko, V. V.

    2012-04-01

    Progress has been made possible thanks to a very versatile numerical technique based on a three-dimensional discrete description of the fracture networks. Any network geometry, any boundary condition, and any distribution of the fractures can be addressed. The first step is to mesh the fracture network as it is by triangles of a controlled size. The second step consists in the discretization of the conservation equations by the finite volume technique. Two important properties were systematically studied, namely the percolation threshold rhoc and the macroscopic permeability Kn of the fracture network. Dimensionless quantities are denoted by a prime. The numerical results are interpreted in a systematic way with the concept of excluded volume which enables us to define a dimensionless fracture density rho' equal in the average to the average number of intersections per fracture. 1. Isotropic networks of identical fractures The dimensionless percolation threshold rho'c of such networks was systematically studied for fractures of various shapes. rho'c was shown to be almost independent of the shape except when one has very elongated rectangles. A formula is proposed for rho'_c. The permeability of these networks was calculated for a wide range of fracture densities and shapes. K'_n(rho') is almost independent of the fracture shape; an empirical formula is proposed for any value of rho' between rho'c and infinity. For large rho', Kn is well approximated by the Snow formula initially derived for infinite fractures. 2. Anisotropic networks of identical fractures The fracture orientations are supposed to follow a Fisher distribution characterized by the parameter kappa; when kappa=0, the fractures are isotropic; when kappa=infinity, the fractures are perpendicular to a given direction. rho'c does not depend significantly on kappa and the general formula proposed in 1 can be used as a first approximation. A considerable simplification occurs for permeability. The

  14. Predicting top-L missing links with node and link clustering information in large-scale networks

    NASA Astrophysics Data System (ADS)

    Wu, Zhihao; Lin, Youfang; Wan, Huaiyu; Jamil, Waleed

    2016-08-01

    Networks are mathematical structures that are universally used to describe a large variety of complex systems, such as social, biological, and technological systems. The prediction of missing links in incomplete complex networks aims to estimate the likelihood of the existence of a link between a pair of nodes. Various topological features of networks have been applied to develop link prediction methods. However, the exploration of features of links is still limited. In this paper, we demonstrate the power of node and link clustering information in predicting top -L missing links. In the existing literature, link prediction algorithms have only been tested on small-scale and middle-scale networks. The network scale factor has not attracted the same level of attention. In our experiments, we test the proposed method on three groups of networks. For small-scale networks, since the structures are not very complex, advanced methods cannot perform significantly better than classical methods. For middle-scale networks, the proposed index, combining both node and link clustering information, starts to demonstrate its advantages. In many networks, combining both node and link clustering information can improve the link prediction accuracy a great deal. Large-scale networks with more than 100 000 links have rarely been tested previously. Our experiments on three large-scale networks show that local clustering information based methods outperform other methods, and link clustering information can further improve the accuracy of node clustering information based methods, in particular for networks with a broad distribution of the link clustering coefficient.

  15. When private actors matter: Information-sharing network and surveillance of Highly Pathogenic Avian Influenza in Vietnam.

    PubMed

    Delabouglise, A; Dao, T H; Truong, D B; Nguyen, T T; Nguyen, N T X; Duboz, R; Fournié, G; Antoine-Moussiaux, N; Grosbois, V; Vu, D T; Le, T H; Nguyen, V K; Salem, G; Peyre, M

    2015-07-01

    The effectiveness of animal health surveillance systems depends on their capacity to gather sanitary information from the animal production sector. In order to assess this capacity we analyzed the flow of sanitary information regarding Highly Pathogenic Avian Influenza (HPAI) suspicions in poultry in Vietnam. Participatory methods were applied to assess the type of actors and likelihood of information sharing between actors in case of HPAI suspicion in poultry. While the reporting of HPAI suspicions is mandatory, private actors had more access to information than public actors. Actors of the upstream sector (medicine and feed sellers) played a key role in the diffusion of information. The central role of these actors and the influence of the information flow on the adoption by poultry production stakeholders of behaviors limiting (e.g. prevention measures) or promoting disease transmission (e.g. increased animal movements) should be accounted for in the design of surveillance and control programs. PMID:25847263

  16. When private actors matter: Information-sharing network and surveillance of Highly Pathogenic Avian Influenza in Vietnam.

    PubMed

    Delabouglise, A; Dao, T H; Truong, D B; Nguyen, T T; Nguyen, N T X; Duboz, R; Fournié, G; Antoine-Moussiaux, N; Grosbois, V; Vu, D T; Le, T H; Nguyen, V K; Salem, G; Peyre, M

    2015-07-01

    The effectiveness of animal health surveillance systems depends on their capacity to gather sanitary information from the animal production sector. In order to assess this capacity we analyzed the flow of sanitary information regarding Highly Pathogenic Avian Influenza (HPAI) suspicions in poultry in Vietnam. Participatory methods were applied to assess the type of actors and likelihood of information sharing between actors in case of HPAI suspicion in poultry. While the reporting of HPAI suspicions is mandatory, private actors had more access to information than public actors. Actors of the upstream sector (medicine and feed sellers) played a key role in the diffusion of information. The central role of these actors and the influence of the information flow on the adoption by poultry production stakeholders of behaviors limiting (e.g. prevention measures) or promoting disease transmission (e.g. increased animal movements) should be accounted for in the design of surveillance and control programs.

  17. Large-Scale, Three–Dimensional, Free–Standing, and Mesoporous Metal Oxide Networks for High–Performance Photocatalysis

    PubMed Central

    Bai, Hua; Li, Xinshi; Hu, Chao; Zhang, Xuan; Li, Junfang; Yan, Yan; Xi, Guangcheng

    2013-01-01

    Mesoporous nanostructures represent a unique class of photocatalysts with many applications, including splitting of water, degradation of organic contaminants, and reduction of carbon dioxide. In this work, we report a general Lewis acid catalytic template route for the high–yield producing single– and multi–component large–scale three–dimensional (3D) mesoporous metal oxide networks. The large-scale 3D mesoporous metal oxide networks possess large macroscopic scale (millimeter–sized) and mesoporous nanostructure with huge pore volume and large surface exposure area. This method also can be used for the synthesis of large–scale 3D macro/mesoporous hierarchical porous materials and noble metal nanoparticles loaded 3D mesoporous networks. Photocatalytic degradation of Azo dyes demonstrated that the large–scale 3D mesoporous metal oxide networks enable high photocatalytic activity. The present synthetic method can serve as the new design concept for functional 3D mesoporous nanomaterials. PMID:23857595

  18. Dynamic contact network between ribosomal subunits enables rapid large-scale rotation during spontaneous translocation

    PubMed Central

    Bock, Lars V.; Blau, Christian; Vaiana, Andrea C.; Grubmüller, Helmut

    2015-01-01

    During ribosomal translation, the two ribosomal subunits remain associated through intersubunit bridges, despite rapid large-scale intersubunit rotation. The absence of large barriers hindering rotation is a prerequisite for rapid rotation. Here, we investigate how such a flat free-energy landscape is achieved, in particular considering the large shifts the bridges undergo at the periphery. The dynamics and energetics of the intersubunit contact network are studied using molecular dynamics simulations of the prokaryotic ribosome in intermediate states of spontaneous translocation. Based on observed occupancies of intersubunit contacts, residues were grouped into clusters. In addition to the central contact clusters, peripheral clusters were found to maintain strong steady interactions by changing contacts in the course of rotation. The peripheral B1 bridges are stabilized by a changing contact pattern of charged residues that adapts to the rotational state. In contrast, steady strong interactions of the B4 bridge are ensured by the flexible helix H34 following the movement of protein S15. The tRNAs which span the subunits contribute to the intersubunit binding enthalpy to an almost constant degree, despite their different positions in the ribosome. These mechanisms keep the intersubunit interaction strong and steady during rotation, thereby preventing dissociation and enabling rapid rotation. PMID:26109353

  19. Dynamic contact network between ribosomal subunits enables rapid large-scale rotation during spontaneous translocation.

    PubMed

    Bock, Lars V; Blau, Christian; Vaiana, Andrea C; Grubmüller, Helmut

    2015-08-18

    During ribosomal translation, the two ribosomal subunits remain associated through intersubunit bridges, despite rapid large-scale intersubunit rotation. The absence of large barriers hindering rotation is a prerequisite for rapid rotation. Here, we investigate how such a flat free-energy landscape is achieved, in particular considering the large shifts the bridges undergo at the periphery. The dynamics and energetics of the intersubunit contact network are studied using molecular dynamics simulations of the prokaryotic ribosome in intermediate states of spontaneous translocation. Based on observed occupancies of intersubunit contacts, residues were grouped into clusters. In addition to the central contact clusters, peripheral clusters were found to maintain strong steady interactions by changing contacts in the course of rotation. The peripheral B1 bridges are stabilized by a changing contact pattern of charged residues that adapts to the rotational state. In contrast, steady strong interactions of the B4 bridge are ensured by the flexible helix H34 following the movement of protein S15. The tRNAs which span the subunits contribute to the intersubunit binding enthalpy to an almost constant degree, despite their different positions in the ribosome. These mechanisms keep the intersubunit interaction strong and steady during rotation, thereby preventing dissociation and enabling rapid rotation.

  20. Dynamic contact network between ribosomal subunits enables rapid large-scale rotation during spontaneous translocation.

    PubMed

    Bock, Lars V; Blau, Christian; Vaiana, Andrea C; Grubmüller, Helmut

    2015-08-18

    During ribosomal translation, the two ribosomal subunits remain associated through intersubunit bridges, despite rapid large-scale intersubunit rotation. The absence of large barriers hindering rotation is a prerequisite for rapid rotation. Here, we investigate how such a flat free-energy landscape is achieved, in particular considering the large shifts the bridges undergo at the periphery. The dynamics and energetics of the intersubunit contact network are studied using molecular dynamics simulations of the prokaryotic ribosome in intermediate states of spontaneous translocation. Based on observed occupancies of intersubunit contacts, residues were grouped into clusters. In addition to the central contact clusters, peripheral clusters were found to maintain strong steady interactions by changing contacts in the course of rotation. The peripheral B1 bridges are stabilized by a changing contact pattern of charged residues that adapts to the rotational state. In contrast, steady strong interactions of the B4 bridge are ensured by the flexible helix H34 following the movement of protein S15. The tRNAs which span the subunits contribute to the intersubunit binding enthalpy to an almost constant degree, despite their different positions in the ribosome. These mechanisms keep the intersubunit interaction strong and steady during rotation, thereby preventing dissociation and enabling rapid rotation. PMID:26109353