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. Distributed shared memory for roaming large volumes.

    PubMed

    Castanié, Laurent; Mion, Christophe; Cavin, Xavier; Lévy, Bruno

    2006-01-01

    We present a cluster-based volume rendering system for roaming very large volumes. This system allows to move a gigabyte-sized probe inside a total volume of several tens or hundreds of gigabytes in real-time. While the size of the probe is limited by the total amount of texture memory on the cluster, the size of the total data set has no theoretical limit. The cluster is used as a distributed graphics processing unit that both aggregates graphics power and graphics memory. A hardware-accelerated volume renderer runs in parallel on the cluster nodes and the final image compositing is implemented using a pipelined sort-last rendering algorithm. Meanwhile, volume bricking and volume paging allow efficient data caching. On each rendering node, a distributed hierarchical cache system implements a global software-based distributed shared memory on the cluster. In case of a cache miss, this system first checks page residency on the other cluster nodes instead of directly accessing local disks. Using two Gigabit Ethernet network interfaces per node, we accelerate data fetching by a factor of 4 compared to directly accessing local disks. The system also implements asynchronous disk access and texture loading, which makes it possible to overlap data loading, volume slicing and rendering for optimal volume roaming. PMID:17080865

  8. 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…

  9. 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.

  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. 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.

  12. Creating, documenting and sharing network models.

    PubMed

    Crook, Sharon M; Bednar, James A; Berger, Sandra; Cannon, Robert; Davison, Andrew P; Djurfeldt, Mikael; Eppler, Jochen; Kriener, Birgit; Furber, Steve; Graham, Bruce; Plesser, Hans E; Schwabe, Lars; Smith, Leslie; Steuber, Volker; van Albada, Sacha

    2012-01-01

    As computational neuroscience matures, many simulation environments are available that are useful for neuronal network modeling. However, methods for successfully documenting models for publication and for exchanging models and model components among these projects are still under development. Here we briefly review existing software and applications for network model creation, documentation and exchange. Then we discuss a few of the larger issues facing the field of computational neuroscience regarding network modeling and suggest solutions to some of these problems, concentrating in particular on standardized network model terminology, notation, and descriptions and explicit documentation of model scaling. We hope this will enable and encourage computational neuroscientists to share their models more systematically in the future. PMID:22994683

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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... 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....

  18. 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... 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 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....

  20. 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....

  1. 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.

  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. 47 CFR 27.1310 - Network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 47 Telecommunication 2 2011-10-01 2011-10-01 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...

  4. 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.…

  5. 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…

  6. 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

  7. Detecting communities in large networks

    NASA Astrophysics Data System (ADS)

    Capocci, A.; Servedio, V. D. P.; Caldarelli, G.; Colaiori, F.

    2005-07-01

    We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

  8. 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).

  9. 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...

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

    PubMed

    Nolin, David A

    2012-07-01

    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

  12. 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.

  13. 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…

  14. 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.

  15. 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

  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. Efficient generation of large random networks

    NASA Astrophysics Data System (ADS)

    Batagelj, Vladimir; Brandes, Ulrik

    2005-03-01

    Random networks are frequently generated, for example, to investigate the effects of model parameters on network properties or to test the performance of algorithms. Recent interest in the statistics of large-scale networks sparked a growing demand for network generators that can generate large numbers of large networks quickly. We here present simple and efficient algorithms to randomly generate networks according to the most commonly used models. Their running time and space requirement is linear in the size of the network generated, and they are easily implemented.

  18. esyN: Network Building, Sharing and Publishing

    PubMed Central

    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

  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. Efficient capacity sharing for path protection in meshed optical networks

    NASA Astrophysics Data System (ADS)

    Dziong, Zbigniew; Nagarajan, Ramesh

    2004-03-01

    Efficient resource management in meshed optical networks is critical for supporting differentiated services, such as network restoration, in a cost-competitive manner. We propose and study several alternatives for dynamic and distributed selection of primary and protection paths. The focus is on algorithms where the bandwidth can be shared efficiently among protection paths, although other alternatives are considered as well. The routing algorithm and link cost function are based on a Markov decision-process framework. In particular, we use this framework to justify the link cost structure for primary and shared bandwidth. We also propose and study several options for describing the link state, which in turn determines the link cost, with the objective of minimizing the amount of data to be advertised without sacrificing performance. The proposed solutions can be implemented without changing the existing set of protocols. The numerical results show performance and cost savings for the different algorithm options.

  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. Graph theoretic modeling of large-scale semantic networks.

    PubMed

    Bales, Michael E; Johnson, Stephen B

    2006-08-01

    During the past several years, social network analysis methods have been used to model many complex real-world phenomena, including social networks, transportation networks, and the Internet. Graph theoretic methods, based on an elegant representation of entities and relationships, have been used in computational biology to study biological networks; however they have not yet been adopted widely by the greater informatics community. The graphs produced are generally large, sparse, and complex, and share common global topological properties. In this review of research (1998-2005) on large-scale semantic networks, we used a tailored search strategy to identify articles involving both a graph theoretic perspective and semantic information. Thirty-one relevant articles were retrieved. The majority (28, 90.3%) involved an investigation of a real-world network. These included corpora, thesauri, dictionaries, large computer programs, biological neuronal networks, word association networks, and files on the Internet. Twenty-two of the 28 (78.6%) involved a graph comprised of words or phrases. Fifteen of the 28 (53.6%) mentioned evidence of small-world characteristics in the network investigated. Eleven (39.3%) reported a scale-free topology, which tends to have a similar appearance when examined at varying scales. The results of this review indicate that networks generated from natural language have topological properties common to other natural phenomena. It has not yet been determined whether artificial human-curated terminology systems in biomedicine share these properties. Large network analysis methods have potential application in a variety of areas of informatics, such as in development of controlled vocabularies and for characterizing a given domain. PMID:16442849

  3. 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

  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. 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.

  7. 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. PMID:20802788

  8. 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

  9. 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.

  10. Integrative Biology Identifies Shared Transcriptional Networks in CKD

    PubMed Central

    Martini, Sebastian; Nair, Viji; Keller, Benjamin J.; Eichinger, Felix; Hawkins, Jennifer J.; Randolph, Ann; Böger, Carsten A.; Gadegbeku, Crystal A.; Fox, Caroline S.; Cohen, Clemens D.

    2014-01-01

    A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR-associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases. PMID:24925724

  11. Image transmission in tactical radio frequency shared network propagation environments

    NASA Astrophysics Data System (ADS)

    White, Kent H.; Wagner, Kerry A.; O'Hanian, Scott

    1997-06-01

    The need to transmit images across tactical radio frequency (rf) links has been identified in army digitization applications. For example, military doctrine requires that tactical functions like identification of battlefield entities as potential targets and battle damage assessment be performed by the soldier. Currently, a key input to these processes is imagery. Therefore, the quality and timeliness of the image directly impact tactical performance. The military is investigating the employment of remote sensors and advanced communications systems to meet this requirement as part of its digitization effort. Army communications systems exist that partially meet this requirement. However, many existing solutions employ these legacy systems in the context of a point-to-point communications architecture. Solutions to the problem of transmitting images across a rf network have not been fully explored. The term network implies that the rf transmission media is common to and shared by multiple subscribers. It is a suite of capabilities that collectively manage media access and information transfer for its subscribers thus providing substantial improvements in effectiveness, efficiency, and robustness. This paper discusses the challenges of transmitting images using one army legacy communications system in a tactical rf network, presents a conceptual framework for attacking the problem, and discusses one solution.

  12. Optimal media sharing policies in peer-to-peer networks

    NASA Astrophysics Data System (ADS)

    Sood, Ritesh; van der Schaar, Mihaela

    2004-11-01

    Multimedia content distribution through a distributed system, a peer-to-peer (P2P) network for instance, is attractive since it harnesses the resources available with the numerous peers in the network. Another advantage of such a system is that the potentially available resources scale in proportion to the demand as more and more peers join the system. Recent studies have concentrated mainly on such aspects of these distributed networks as querying, indexing, etc. These studies however take for granted the voluntary contribution of resources by peers in the system. Empirical evidence however points to the contrary, i.e. in existing P2P systems, a substantial fraction of peers do not contribute resources to the system, while benefiting from the services it provides at the expense of the contributing peers. In this paper we analyze a P2P system in a game-theoretic setting in which games involving content exchange are played repeatedly. The model takes into account the manner in which a peer adapts his contribution to the system depending on the benefit he has derived from the system so far and expects to derive in the long run. The model enables us to formulate an optimization problem that yields optimal content sharing strategies that a peer should adopt in order to maximize his net benefit by participating in the system.

  13. 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

  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. 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...

  17. 47 CFR 27.1315 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 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...

  18. [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. PMID:17039943

  19. Partly shared spinal cord networks for locomotion and scratching.

    PubMed

    Berkowitz, Ari; Hao, Zhao-Zhe

    2011-12-01

    Animals produce a variety of behaviors using a limited number of muscles and motor neurons. Rhythmic behaviors are often generated in basic form by networks of neurons within the central nervous system, or central pattern generators (CPGs). It is known from several invertebrates that different rhythmic behaviors involving the same muscles and motor neurons can be generated by a single CPG, multiple separate CPGs, or partly overlapping CPGs. Much less is known about how vertebrates generate multiple, rhythmic behaviors involving the same muscles. The spinal cord of limbed vertebrates contains CPGs for locomotion and multiple forms of scratching. We investigated the extent of sharing of CPGs for hind limb locomotion and for scratching. We used the spinal cord of adult red-eared turtles. Animals were immobilized to remove movement-related sensory feedback and were spinally transected to remove input from the brain. We took two approaches. First, we monitored individual spinal cord interneurons (i.e., neurons that are in between sensory neurons and motor neurons) during generation of each kind of rhythmic output of motor neurons (i.e., each motor pattern). Many spinal cord interneurons were rhythmically activated during the motor patterns for forward swimming and all three forms of scratching. Some of these scratch/swim interneurons had physiological and morphological properties consistent with their playing a role in the generation of motor patterns for all of these rhythmic behaviors. Other spinal cord interneurons, however, were rhythmically activated during scratching motor patterns but inhibited during swimming motor patterns. Thus, locomotion and scratching may be generated by partly shared spinal cord CPGs. Second, we delivered swim-evoking and scratch-evoking stimuli simultaneously and monitored the resulting motor patterns. Simultaneous stimulation could cause interactions of scratch inputs with subthreshold swim inputs to produce normal swimming, acceleration

  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. Implementation of the morphological shared-weight neural network (MSNN) for target recognition on the Parallel Algebraic Logic (PAL) computer

    NASA Astrophysics Data System (ADS)

    Li, Hongzheng; Shi, Hongchi; Gader, Paul D.; Keller, James M.

    1998-09-01

    The morphological shared-weight neural network (MSNN) is an effective approach to automatic target recognition. Implementation of the network in parallel is critical for real-time target recognition systems. Although there is significant parallelism inherent in the MSNN, it is a challenge to implement it on an SIMD parallel computer consisting of a large array of simple processing elements. This paper discusses issues related to detection accuracy and throughput in implementing the MSNN on the Parallel Algebraic Logic computer.

  2. 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…

  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. 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…

  5. Predictive Control of Large Complex Networks

    NASA Astrophysics Data System (ADS)

    Haber, Aleksandar; Motter, Adilson E.

    Networks of coupled dynamical subsystems are increasingly used to represent complex natural and engineered systems. While recent technological developments give us improved means to actively control the dynamics of individual subsystems in various domains, network control remains a challenging problem due to difficulties imposed by intrinsic nonlinearities, control constraints, and the large-scale nature of the systems. In this talk, we will present a model predictive control approach that is effective while accounting for these realistic properties of complex networks. Our method can systematically identify control interventions that steer the trajectory to a desired state, even in the presence of strong nonlinearities and constraints. Numerical tests show that the method is applicable to a variety of networks, ranging from power grids to chemical reaction systems.

  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. 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 ...

  9. Time geography for ad-hoc shared-ride trip planning in mobile geosensor networks

    NASA Astrophysics Data System (ADS)

    Raubal, Martin; Winter, Stephan; Teβmann, Sven; Gaisbauer, Christian

    Ad-hoc shared-ride trip planning in an urban environment is a complex task within a non-deterministic transportation network. Mobile geosensor networks provide the technical environment for realizing ad-hoc shared-ride trip planning: Network nodes are autonomous agents that interact locally by ad-hoc short-range communication and arrange for shared rides. In a mobile geosensor network, communication costs are critical because of constraints regarding bandwidth, available energy, and memory. This paper introduces spatio-temporal concepts from time geography, which can be employed during the planning process to significantly reduce communication costs. We will integrate network-based algorithms and different wayfinding strategies to assist both shared-ride clients and hosts in finding optimal travel assignments. Multi-agent geosimulation in a real street network is used to demonstrate the applicability of the approach and quantitatively confirm the theoretically foreseen reduction in communication costs.

  10. 77 FR 58415 - Large Scale Networking (LSN); Joint Engineering Team (JET)

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-09-20

    ... From the Federal Register Online via the Government Publishing Office NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN); Joint Engineering Team (JET) AGENCY: The Networking and Information..._Engineering_Team_ (JET). SUMMARY: The JET, established in 1997, provides for information sharing among...

  11. 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.

  12. 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.

  13. Shared and Distinct Intrinsic Functional Network Centrality in Autism and Attention-Deficit/Hyperactivity Disorder

    PubMed Central

    Di Martino, Adriana; Zuo, Xi-Nian; Kelly, Clare; Grzadzinski, Rebecca; Mennes, Maarten; Schvarcz, Ariel; Rodman, Jennifer; Lord, Catherine; Castellanos, F. Xavier; Milham, Michael P.

    2015-01-01

    Background Individuals with autism spectrum disorders (ASD) often exhibit symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD). Across both disorders, observations of distributed functional abnormalities suggest aberrant large-scale brain network connectivity. Yet, common and distinct network correlates of ASD and ADHD remain unidentified. Here, we aimed to examine patterns of dysconnection in school-age children with ASD, ADHD and typically developing children (TDC) who completed a resting state fMRI (R-fMRI) scan. Methods We measured voxel-wise network centrality, functional connectivity metrics indexing local (degree centrality; DC) and global (eigenvector centrality; EC) functional relationships across the entire brain connectome, in R-fMRI data from 56 children with ASD, 45 children with ADHD and 50 TDC. A one-way ANCOVA, with group as fixed factor (whole-brain corrected), was followed by post-hoc pair-wise comparisons. Results Cortical and subcortical areas exhibited centrality abnormalities; some common to both ADHD and ASD, such as in precuneus. Others were disorder-specific and included ADHD-related increases in DC in right striatum/pallidum, in contrast with ASD-related increases in bilateral temporolimbic areas. Secondary analyses differentiating children with ASD into those with or without ADHD-like comorbidity (ASD+ and ASD−, respectively) revealed that the ASD+ group shared ADHD-specific abnormalities in basal ganglia. By contrast, centrality increases in temporolimbic areas characterized children with ASD regardless of ADHD-like comorbidity. At the cluster level eignevector centrality group patterns were similar to DC. Conclusions ADHD and ASD are neurodevelopmental disorders with distinct and overlapping clinical presentations. This work provides evidence for both shared and distinct underlying mechanisms at the large-scale network level. PMID:23541632

  14. 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.

  15. 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.

  16. Network issues for large mass storage requirements

    NASA Technical Reports Server (NTRS)

    Perdue, James

    1992-01-01

    File Servers and Supercomputing environments need high performance networks to balance the I/O requirements seen in today's demanding computing scenarios. UltraNet is one solution which permits both high aggregate transfer rates and high task-to-task transfer rates as demonstrated in actual tests. UltraNet provides this capability as both a Server-to-Server and Server-to-Client access network giving the supercomputing center the following advantages highest performance Transport Level connections (to 40 MBytes/sec effective rates); matches the throughput of the emerging high performance disk technologies, such as RAID, parallel head transfer devices and software striping; supports standard network and file system applications using SOCKET's based application program interface such as FTP, rcp, rdump, etc.; supports access to the Network File System (NFS) and LARGE aggregate bandwidth for large NFS usage; provides access to a distributed, hierarchical data server capability using DISCOS UniTree product; supports file server solutions available from multiple vendors, including Cray, Convex, Alliant, FPS, IBM, and others.

  17. 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.

  18. 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…

  19. Attitudes towards Social Networking and Sharing Behaviors among Consumers of Direct-to-Consumer Personal Genomics

    PubMed Central

    Lee, Sandra Soo-Jin; Vernez, Simone L.; Ormond, K.E.; Granovetter, Mark

    2013-01-01

    Little is known about how consumers of direct-to-consumer personal genetic services share personal genetic risk information. In an age of ubiquitous online networking and rapid development of social networking tools, understanding how consumers share personal genetic risk assessments is critical in the development of appropriate and effective policies. This exploratory study investigates how consumers share personal genetic information and attitudes towards social networking behaviors. Methods: Adult participants aged 23 to 72 years old who purchased direct-to-consumer genetic testing from a personal genomics company were administered a web-based survey regarding their sharing activities and social networking behaviors related to their personal genetic test results. Results: 80 participants completed the survey; of those, 45% shared results on Facebook and 50.9% reported meeting or reconnecting with more than 10 other individuals through the sharing of their personal genetic information. For help interpreting test results, 70.4% turned to Internet websites and online sources, compared to 22.7% who consulted their healthcare providers. Amongst participants, 51.8% reported that they believe the privacy of their personal genetic information would be breached in the future. Conclusion: Consumers actively utilize online social networking tools to help them share and interpret their personal genetic information. These findings suggest a need for careful consideration of policy recommendations in light of the current ambiguity of regulation and oversight of consumer initiated sharing activities. PMID:25562728

  20. 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

  1. 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…

  2. Matchmaking in Learning Networks: Bringing Learners Together for Knowledge Sharing

    ERIC Educational Resources Information Center

    Kester, Liesbeth; van Rosmalen, Peter; Sloep, Peter; Brouns, Francis; Kone, Malik; Koper, Rob

    2007-01-01

    In this article we describe a system that matches learners with complementary content expertise in reaction to a learner-request for knowledge sharing. It works through the formation of "ad hoc," transient communities, that exist for a limited period of time and stimulate learners socially to interact. The matchmaking system consists of a request…

  3. 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

  4. Large-Scale Organization of Glycosylation Networks

    NASA Astrophysics Data System (ADS)

    Kim, Pan-Jun; Lee, Dong-Yup; Jeong, Hawoong

    2009-03-01

    Glycosylation is a highly complex process to produce a diverse repertoire of cellular glycans that are frequently attached to proteins and lipids. Glycans participate in fundamental biological processes including molecular trafficking and clearance, cell proliferation and apoptosis, developmental biology, immune response, and pathogenesis. N-linked glycans found on proteins are formed by sequential attachments of monosaccharides with the help of a relatively small number of enzymes. Many of these enzymes can accept multiple N-linked glycans as substrates, thus generating a large number of glycan intermediates and their intermingled pathways. Motivated by the quantitative methods developed in complex network research, we investigate the large-scale organization of such N-glycosylation pathways in a mammalian cell. The uncovered results give the experimentally-testable predictions for glycosylation process, and can be applied to the engineering of therapeutic glycoproteins.

  5. 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...

  6. 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…

  7. 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…

  8. 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…

  9. 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.

  10. A Method of Discovery of Shared Topic Networks among People from WWW Bookmarks and Its Evaluations

    NASA Astrophysics Data System (ADS)

    Hamasaki, Masahiro; Takeda, Hideaki; Matsuzuka, Takeshi; Taniguchi, Yuichiro; Kono, Yasuyuki; Kidode, Masatsugu

    In this paper, we propose shared topic networks as a model of human network to organize Internet Information, and developed a system called kMedia that can generate shared topic networks by using WWW bookmark files. We also evaluate the system with experiments to know how shared topics network can help uesrs especially to know each other. A shared topic network is formed by linking topics of participants, and used to know interests of others and to exchange information with others. kMedia can generate shared topics networks by using structures of WWW bookmarks, i.e., folders of bookmarks are regarded as topics of their owners. Relations among topics of different users are estimated by aggregating similarity among pages in these topics. The experiments were performed to clarify two points; one is whether topics is a better way to exchange information among people and the other is how we can measure human relationship. The first point is examined that topic recommendation is more acceptable than page recommendation. For the second point, we propose category resemblance as measurement of human relationship. Since we compare results between cases with subjects belonging to the same community and cases without communities, we noticed similarity of topics structure is affective. The category resemblance is to estimate this similarity of topic structure and it is proved that it is better than any other parameters with respect to measurement for human relationship.

  11. 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…

  12. 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…

  13. 47 CFR 90.1410 - Network sharing agreement.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... specifications and equipment are consistent with reasonable network control requirements established in the NSA... that includes a seamlessly integrated satellite solution pursuant to the terms, conditions, and... this chapter. (c) The definition of “emergency” for purposes of emergency priority access. (d)...

  14. 47 CFR 27.1310 - Network sharing agreement.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... specifications and equipment are consistent with reasonable network control requirements established in the NSA... that includes a seamlessly integrated satellite solution pursuant to the terms, conditions, and... comply with § 27.1305. (c) The definition of “emergency” for purposes of emergency priority access....

  15. 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

  16. 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.

  17. Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive1

    PubMed Central

    Kalpathy-Cramer, Jayashree; Freymann, John Blake; Kirby, Justin Stephen; Kinahan, Paul Eugene; Prior, Fred William

    2014-01-01

    The Quantitative Imaging Network (QIN), supported by the National Cancer Institute, is designed to promote research and development of quantitative imaging methods and candidate biomarkers for the measurement of tumor response in clinical trial settings. An integral aspect of the QIN mission is to facilitate collaborative activities that seek to develop best practices for the analysis of cancer imaging data. The QIN working groups and teams are developing new algorithms for image analysis and novel biomarkers for the assessment of response to therapy. To validate these algorithms and biomarkers and translate them into clinical practice, algorithms need to be compared and evaluated on large and diverse data sets. Analysis competitions, or “challenges,” are being conducted within the QIN as a means to accomplish this goal. The QIN has demonstrated, through its leveraging of The Cancer Imaging Archive (TCIA), that data sharing of clinical images across multiple sites is feasible and that it can enable and support these challenges. In addition to Digital Imaging and Communications in Medicine (DICOM) imaging data, many TCIA collections provide linked clinical, pathology, and “ground truth” data generated by readers that could be used for further challenges. The TCIA-QIN partnership is a successful model that provides resources for multisite sharing of clinical imaging data and the implementation of challenges to support algorithm and biomarker validation. PMID:24772218

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. Connection Availability Analysis of Shared Backup Path-Protected Mesh Networks

    NASA Astrophysics Data System (ADS)

    Zhou, Ling; Held, Marcel; Sennhauser, Urs

    2007-05-01

    Dual-span failures dominate the system unavailability in a mesh-restorable network with full restorability to single-span failures. Traditional availability analysis based on reliability block diagrams is not suitable for survivable networks with shared spare capacity. Therefore, a new concept is proposed to facilitate the calculations of connection availability. A U.S. network consisting of 19 nodes and 28 spans yielding 171 bidirectional connections is investigated. We find that networks with shared backup path protection can have average connection unavailabilities of the same order of magnitude as those with dedicated automatic protection switching, however, with a much better capacity efficiency. The proposed method can exactly calculate the unavailability of a specific connection with known restoration details or the average connection performance without any restoration details by presuming the dual-span failures to be the only failure mode and an arbitrary allocation rule of spare capacity.

  3. 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.

  4. Gene-sharing networks reveal organizing principles of transcriptomes in Arabidopsis and other multicellular organisms.

    PubMed

    Li, Song; Pandey, Sona; Gookin, Timothy E; Zhao, Zhixin; Wilson, Liza; Assmann, Sarah M

    2012-04-01

    Understanding tissue-related gene expression patterns can provide important insights into gene, tissue, and organ function. Transcriptome analyses often have focused on housekeeping or tissue-specific genes or on gene coexpression. However, by analyzing thousands of single-gene expression distributions in multiple tissues of Arabidopsis thaliana, rice (Oryza sativa), human (Homo sapiens), and mouse (Mus musculus), we found that these organisms primarily operate by gene sharing, a phenomenon where, in each organism, most genes exhibit a high expression level in a few key tissues. We designed an analytical pipeline to characterize this phenomenon and then derived Arabidopsis and human gene-sharing networks, in which tissues are connected solely based on the extent of shared preferentially expressed genes. The results show that tissues or cell types from the same organ system tend to group together to form network modules. Tissues that are in consecutive developmental stages or have common physiological functions are connected in these networks, revealing the importance of shared preferentially expressed genes in conferring specialized functions of each tissue type. The networks provide predictive power for each tissue type regarding gene functions of both known and heretofore unknown genes, as shown by the identification of four new genes with functions in guard cell and abscisic acid response. We provide a Web interface that enables, based on the extent of gene sharing, both prediction of tissue-related functions for any Arabidopsis gene of interest and predictions concerning the relatedness of tissues. Common gene-sharing patterns observed in the four model organisms suggest that gene sharing evolved as a fundamental organizing principle of gene expression in diverse multicellular eukaryotes. PMID:22517316

  5. 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 ...

  6. 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

  7. 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.

  8. 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…

  9. 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…

  10. 47 CFR 90.1415 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 47 Telecommunication 5 2012-10-01 2012-10-01 false Establishment, execution, and application of... COMMISSION (CONTINUED) SAFETY AND SPECIAL RADIO SERVICES PRIVATE LAND MOBILE RADIO SERVICES 700 MHz Public/Private Partnership § 90.1415 Establishment, execution, and application of the network sharing...

  11. 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…

  12. Resource Sharing or Cost Shifting?--The Unequal Burden of Cooperative Cataloging and ILL in Network.

    ERIC Educational Resources Information Center

    Lowry, Charles B.

    1990-01-01

    Examines the dilemmas of participation in resource sharing library networks and proposes solutions based on shifting inducements toward contribution by remunerating the contributing library for the unit cost of original cataloging. A "contribution pricing" model recently announced by OCLC is also evaluated as a possible solution. (21 references)…

  13. 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.

  14. 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

  15. 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.

  16. Efficient File Sharing by Multicast - P2P Protocol Using Network Coding and Rank Based Peer Selection

    NASA Technical Reports Server (NTRS)

    Stoenescu, Tudor M.; Woo, Simon S.

    2009-01-01

    In this work, we consider information dissemination and sharing in a distributed peer-to-peer (P2P highly dynamic communication network. In particular, we explore a network coding technique for transmission and a rank based peer selection method for network formation. The combined approach has been shown to improve information sharing and delivery to all users when considering the challenges imposed by the space network environments.

  17. 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. PMID:24612360

  18. 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

  19. Categorical Structure among Shared Features in Networks of Early-learned Nouns

    PubMed Central

    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 overlap of words normatively acquired by children prior to 2 ½ years of age and perceptual and conceptual (functional) features acquired from adult feature generation norms. The resulting networks have small-world structure, indicative of a high degree of feature overlap in local clusters. However, perceptual features—due to their abundance and redundancy—generate networks more robust to feature omissions, while conceptual features are more discriminating and, per feature, offer more categorical information than perceptual features. Using a network specific cluster identification algorithm (the clique percolation method) we also show that shared features among these early learned nouns create higher-order groupings common to adult taxonomic designations. Again, perceptual and conceptual features play distinct roles among different categories, typically with perceptual features being more inclusive and conceptual features being more exclusive of category memberships. The results offer new and testable hypotheses about the role of shared features in human category knowledge. PMID:19576579

  20. Categorical structure among shared features in networks of early-learned nouns.

    PubMed

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

    2009-09-01

    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 overlap of words normatively acquired by children prior to 2(1/2) years of age and perceptual and conceptual (functional) features acquired from adult feature generation norms. The resulting networks have small-world structure, indicative of a high degree of feature overlap in local clusters. However, perceptual features--due to their abundance and redundancy--generate networks more robust to feature omissions, while conceptual features are more discriminating and, per feature, offer more categorical information than perceptual features. Using a network specific cluster identification algorithm (the clique percolation method) we also show that shared features among these early-learned nouns create higher-order groupings common to adult taxonomic designations. Again, perceptual and conceptual features play distinct roles among different categories, typically with perceptual features being more inclusive and conceptual features being more exclusive of category memberships. The results offer new and testable hypotheses about the role of shared features in human category knowledge. PMID:19576579

  1. Beyond Networking: How Large-Scale Change Really Happens

    ERIC Educational Resources Information Center

    Wheatley, Margaret; Frieze, Deborah

    2007-01-01

    Despite the claims of a popular slogan, the world does not change one person at a time. It changes as networks of relationships form among people who share a common cause and vision of what is possible. This is good news for those who want to change public education. Making connections however is not the whole story. This article describes how a…

  2. 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

    2014-01-01

    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. PMID:24875444

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

    PubMed Central

    Pierson, Emma; Koller, Daphne; Battle, Alexis; Mostafavi, Sara

    2015-01-01

    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. PMID:25970446

  4. 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

  5. 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.

  6. 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

  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. 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.

  9. 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

  10. 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…

  11. Evolutionary constraints permeate large metabolic networks

    PubMed Central

    Wagner, Andreas

    2009-01-01

    Background Metabolic networks show great evolutionary plasticity, because they can differ substantially even among closely related prokaryotes. Any one metabolic network can also effectively compensate for the blockage of individual reactions by rerouting metabolic flux through other pathways. These observations, together with the continual discovery of new microbial metabolic pathways and enzymes, raise the possibility that metabolic networks are only weakly constrained in changing their complement of enzymatic reactions. Results To ask whether this is the case, I characterized pairwise and higher-order associations in the co-occurrence of genes encoding metabolic enzymes in more than 200 completely sequenced representatives of prokaryotic genera. The majority of reactions show constrained evolution. Specifically, genes encoding most reactions tend to co-occur with genes encoding other reaction(s). Constrained reaction pairs occur in small sets whose number is substantially greater than expected by chance alone. Most such sets are associated with single biochemical pathways. The respective genes are not always tightly linked, which renders horizontal co-transfer of constrained reaction sets an unlikely sole cause for these patterns of association. Conclusion Even a limited number of available genomes suffices to show that metabolic network evolution is highly constrained by reaction combinations that are favored by natural selection. With increasing numbers of completely sequenced genomes, an evolutionary constraint-based approach may enable a detailed characterization of co-evolving metabolic modules. PMID:19747381

  12. Honeycomb: Visual Analysis of Large Scale Social Networks

    NASA Astrophysics Data System (ADS)

    van Ham, Frank; Schulz, Hans-Jörg; Dimicco, Joan M.

    The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.

  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. PMID:26595315

  15. 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

  16. A new method optimizing the subgraph centrality of large networks

    NASA Astrophysics Data System (ADS)

    Yan, Xin; Li, Chunlin; Zhang, Ling; Hu, Yaogai

    2016-02-01

    Since many realistic networks such as wireless sensor/ad hoc networks usually do not agree very well with the basic network models such as small-word and scale-free models, we often need to obtain some expected structural features such as a small average path length and a regular degree distribution while optimizing the connectivity of these networks. Although a minor addition of links for optimizing network connectivity is not likely to change the structural properties of a network, it is necessary to investigate the impact of link addition on network properties as the number of the added links increases. However, to the best of our knowledge, the study of that problem has not been found so far. Furthermore, two closely related questions to that problem, i.e., how to measure and how to improve network connectivity, have not been studied carefully enough yet. To address the three problems above, the authors derive a better measure of network connectivity for large networks and a new strategy that can increase/decrease network connectivity the most, and propose a spectral density algorithm optimizing the connectivity of large networks, which is able to indicate the impact on the structural properties of a network while increasing/decreasing its connectivity, providing us a guided optimization of network connectivity. In other words, our algorithm can optimize not only the connectivity of a large network but also its structural features. Meanwhile, our new findings about spectral density are also concluded in this paper. In addition, we may also apply this algorithm to solve all eigenvalues of an N × N matrix, with a low complexity of O(N2) at most.

  17. 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

  18. Experimental analysis of large belief networks for medical diagnosis.

    PubMed

    Pradhan, M; Provan, G; Henrion, M

    1994-01-01

    We present an experimental analysis of two parameters that are important in knowledge engineering for large belief networks. We conducted the experiments on a network derived from the Internist-1 medical knowledge base. In this network, a generalization of the noisy-OR gate is used to model causal independence for the multivalued variables, and leak probabilities are used to represent the nonspecified causes of intermediate states and findings. We study two network parameters, (1) the parameter governing the assignment of probability values to the network, and (2) the parameter denoting whether the network nodes represent variables with two or more than two values. The experimental results demonstrate that the binary simplification computes diagnoses with similar accuracy to the full multivalued network. We discuss the implications of these parameters, as well other network parameters, for knowledge engineering for medical applications. PMID:7950030

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

    PubMed

    Fang, Gengfa; Orgun, Mehmet A; Shankaran, Rajan; 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

  20. 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

  1. 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.

  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. 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. PMID:24795540

  5. Assessment on Knowledge Network Sharing Capability of Industrial Cluster Based on Dempster-Shafer Theory of Evidence

    PubMed Central

    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. PMID:24795540

  6. 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.

  7. Shared “Core” Areas between the Pain and Other Task-Related Networks

    PubMed Central

    Cauda, Franco; Torta, Diana M-E.; Sacco, Katiuscia; Geda, Elisabetta; D’Agata, Federico; Costa, Tommaso; Duca, Sergio; Geminiani, Giuliano; Amanzio, Martina

    2012-01-01

    The idea of a ‘pain matrix’ specifically devoted to the processing of nociceptive inputs has been challenged. Alternative views now propose that the activity of the primary and secondary somatosensory cortices (SI, SII), the insula and cingulate cortex may be related to a basic defensive system through which significant potentially dangerous events for the body's integrity are detected. By reviewing the role of the SI, SII, the cingulate and the insular cortices in the perception of nociceptive and tactile stimuli, in attentional, emotional and reward tasks, and in interoception and memory, we found that all these task-related networks overlap in the dorsal anterior cingulate cortex, the anterior insula and the dorsal medial thalamus. A thorough analysis revealed that the ‘pain-related’ network shares important functional similarities with both somatomotor-somatosensory networks and emotional-interoceptive ones. We suggest that these shared areas constitute the central part of an adaptive control system involved in the processing and integration of salient information coming both from external and internal sources. These areas are activated in almost all fMRI tasks and have been indicated to play a pivotal role in switching between externally directed and internally directed brain networks. PMID:22900003

  8. 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

  9. 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

  10. Towards online multiresolution community detection in large-scale networks.

    PubMed

    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

  11. Revealing shared and distinct gene network organization in Arabidopsis immune responses by integrative analysis.

    PubMed

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

    2015-03-01

    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

  12. 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

  13. 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.

  14. Emergence of large cliques in random scale-free networks

    NASA Astrophysics Data System (ADS)

    Bianconi, Ginestra; Marsili, Matteo

    2006-05-01

    In a network cliques are fully connected subgraphs that reveal which are the tight communities present in it. Cliques of size c > 3 are present in random Erdös and Renyi graphs only in the limit of diverging average connectivity. Starting from the finding that real scale-free graphs have large cliques, we study the clique number in uncorrelated scale-free networks finding both upper and lower bounds. Interestingly, we find that in scale-free networks large cliques appear also when the average degree is finite, i.e. even for networks with power law degree distribution exponents γin(2,3). Moreover, as long as γ < 3, scale-free networks have a maximal clique which diverges with the system size.

  15. Optimal access to large databases via networks

    SciTech Connect

    Munro, J.K.; Fellows, R.L.; Phifer, D. Carrick, M.R.; Tarlton, N.

    1997-10-01

    A CRADA with Stephens Engineering was undertaken in order to transfer knowledge and experience about access to information in large text databases, with results of queries and searches provided using the multimedia capabilities of the World Wide Web. Data access is optimized by the use of intelligent agents. Technology Logic Diagram documents published for the DOE facilities in Oak Ridge (K-25, X-10, Y-12) were chosen for this effort because of the large number of technologies identified, described, evaluated, and ranked for possible use in the environmental remediation of these facilities. Fast, convenient access to this information is difficult because of the volume and complexity of the data. WAIS software used to provide full-text, field-based search capability can also be used, through the development of an appropriate hierarchy of menus, to provide tabular summaries of technologies satisfying a wide range of criteria. The menu hierarchy can also be used to regenerate dynamically many of the tables that appeared in the original hardcopy publications, all from a single text database of the technology descriptions. Use of the Web environment permits linking many of the Technology Logic Diagram references to on-line versions of these publications, particularly the DOE Orders and related directives providing the legal requirements that were the basis for undertaking the Technology Logic Diagram studies in the first place.

  16. 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

  17. 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).

  18. 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

  19. 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

  20. 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.

  1. Exhaustive identification of steady state cycles in large stoichiometric networks

    PubMed Central

    Wright, Jeremiah; Wagner, Andreas

    2008-01-01

    Background Identifying cyclic pathways in chemical reaction networks is important, because such cycles may indicate in silico violation of energy conservation, or the existence of feedback in vivo. Unfortunately, our ability to identify cycles in stoichiometric networks, such as signal transduction and genome-scale metabolic networks, has been hampered by the computational complexity of the methods currently used. Results We describe a new algorithm for the identification of cycles in stoichiometric networks, and we compare its performance to two others by exhaustively identifying the cycles contained in the genome-scale metabolic networks of H. pylori, M. barkeri, E. coli, and S. cerevisiae. Our algorithm can substantially decrease both the execution time and maximum memory usage in comparison to the two previous algorithms. Conclusion The algorithm we describe improves our ability to study large, real-world, biochemical reaction networks, although additional methodological improvements are desirable. PMID:18616835

  2. 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

  3. Comparative Study of Message Passing and Shared Memory Parallel Programming Models in Neural Network Training

    SciTech Connect

    Vitela, J.; Gordillo, J.; Cortina, L; Hanebutte, U.

    1999-12-14

    It is presented a comparative performance study of a coarse grained parallel neural network training code, implemented in both OpenMP and MPI, standards for shared memory and message passing parallel programming environments, respectively. In addition, these versions of the parallel training code are compared to an implementation utilizing SHMEM the native SGI/CRAY environment for shared memory programming. The multiprocessor platform used is a SGI/Cray Origin 2000 with up to 32 processors. It is shown that in this study, the native CRAY environment outperforms MPI for the entire range of processors used, while OpenMP shows better performance than the other two environments when using more than 19 processors. In this study, the efficiency is always greater than 60% regardless of the parallel programming environment used as well as of the number of processors.

  4. The Deep Space Network Large Array

    NASA Astrophysics Data System (ADS)

    Gatti, M. S.

    2004-05-01

    In recent years it has become evident that, if future science needs are to be met, the capacity of the telecommunications link between planetary spacecraft and the Earth must be increased by orders of magnitude. Both the number of spacecraft and higher data rates demand the increased capacity. Technologies to support the increased capacity include even larger antennas, optical receiving systems, or arrays of antennas. This article describes a large array of small antennas that would be implemented for a fraction of the cost of an equivalent 70-m aperture. Adding additional antennas can increase the sensitivity many fold over current capabilities. The array will consist of 400 parabolic reflector antennas, each of which will be 12 m in diameter. Each antenna will operate simultaneously at both X-band (8 to 8.8 GHz) and Ka-band (31 to 38 GHz) and will be configured with radio frequency (RF) electronics, including the feeds, low-noise amplifiers, and frequency converters, as well as the appropriate servo controls and drives. The array also includes the signal transmission and signal processing to enable the system to track from between 1 and 16 different signals. A significant feature of this system is that it will be done for relatively very low cost compared to the current antenna paradigms. This is made possible by the use of low-cost antenna reflector technology, the extensive use of monolithic microwave integrated circuits (MMICs), and, finally, by using commercially available equipment to the maximum extent possible. Cost can be further reduced by the acceptance of lower antenna element reliability. High system availability will be maintained by a design paradigm that provides for a marginal set of excess antenna elements for any particular tracking period. Thus, the same total system availability is achieved for lower element availability. The "plug-and-play" aspects of the assemblies will enhance maintainability and operability. The project plans include a

  5. Detection and classification of MSTAR objects via morphological shared-weight neural networks

    NASA Astrophysics Data System (ADS)

    Theera-Umpon, Nipon; Khabou, Mohamed A.; Gader, Paul D.; Keller, James M.; Shi, Hongchi; Li, Hongzheng

    1998-09-01

    In this paper we describe the application of morphological shared-weight neural networks to the problems of classification and detection of vehicles in synthetic aperture radar (SAR). Classification experiments were carried out with SAR images of T72 tanks and armored personnel carriers. A correct classification rate of more than 98% was achieved on a testing data set. Detection experiments were carried out with T72 tanks embedded in SAR images of clutter scenes. A near perfect detection rate and a low false alarm rate were achieved. The data used in the experiments was the standard training and testing MSTAR data set collected by Sandia National Laboratory.

  6. Control and management of large and dynamic networks

    SciTech Connect

    Tsai, W.T.

    1986-01-01

    Dynamic networks are computer networks whose links and nodes could fail and recover frequently. The nodes may also be mobile. Large dynamic networks are dynamic networks of large size and they are clustered as hierarchical networks. The objective of this research task is to design a mechanism to support nodes to communicate with each other. To communicate with each other, the nodes would first have to know the names of other nodes, then their addresses, and finally the routes. Thus, the network should provide two mechanisms to supports the communications: binding of names of nodes with their addresses, and binding of address of nodes to the routes dynamically. The first problem, i.e., bind the names of nodes with their addresses, is usually called the naming or addressing problem. The second problem, i.e., binding the addresses with the corresponding routes, is conventionally called the routing problem. This dissertation discusses techniques to handle both problems. The author first discusses techniques for the routing problem. As the network is assumed to be very large and dynamic, he concentrates on developing hierarchical routing algorithms. A new adaptive hierarchical routing algorithm is proposed. The algorithm is based on the new Arpanet algorithm. The algorithm is good for both updating and initialization the routing tables.

  7. Genomic analysis of regulatory network dynamics reveals large topological changes

    NASA Astrophysics Data System (ADS)

    Luscombe, Nicholas M.; Madan Babu, M.; Yu, Haiyuan; Snyder, Michael; Teichmann, Sarah A.; Gerstein, Mark

    2004-09-01

    Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here-particularly the large-scale topological changes and hub transience-will apply to other biological networks, including complex sub-systems in higher eukaryotes.

  8. 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

  9. Functional interactions between large-scale networks during memory search.

    PubMed

    Kragel, James E; Polyn, Sean M

    2015-03-01

    Neuroimaging studies have identified two major large-scale brain networks, the default mode network (DMN) and the dorsal attention network (DAN), which are engaged for internally and externally directed cognitive tasks respectively, and which show anticorrelated activity during cognitively demanding tests and at rest. We identified these brain networks using independent component analysis (ICA) of functional magnetic resonance imaging data, and examined their interactions during the free-recall task, a self-initiated memory search task in which retrieval is performed in the absence of external cues. Despite the internally directed nature of the task, the DAN showed transient engagement in the seconds leading up to successful retrieval. ICA revealed a fractionation of the DMN into 3 components. A posteromedial network increased engagement during memory search, while the two others showed suppressed activity during memory search. Cooperative interactions between this posteromedial network, a right-lateralized frontoparietal control network, and a medial prefrontal network were maintained during memory search. The DAN demonstrated heterogeneous task-dependent shifts in functional coupling with various subnetworks within the DMN. This functional reorganization suggests a broader role of the DAN in the absence of externally directed cognition, and highlights the contribution of the posteromedial network to episodic retrieval. PMID:24084128

  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. 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

  12. 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

  13. The Climate-G testbed: towards a large scale data sharing environment for climate change

    NASA Astrophysics Data System (ADS)

    Aloisio, G.; Fiore, S.; Denvil, S.; Petitdidier, M.; Fox, P.; Schwichtenberg, H.; Blower, J.; Barbera, R.

    2009-04-01

    The Climate-G testbed provides an experimental large scale data environment for climate change addressing challenging data and metadata management issues. The main scope of Climate-G is to allow scientists to carry out geographical and cross-institutional climate data discovery, access, visualization and sharing. Climate-G is a multidisciplinary collaboration involving both climate and computer scientists and it currently involves several partners such as: Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC), Institut Pierre-Simon Laplace (IPSL), Fraunhofer Institut für Algorithmen und Wissenschaftliches Rechnen (SCAI), National Center for Atmospheric Research (NCAR), University of Reading, University of Catania and University of Salento. To perform distributed metadata search and discovery, we adopted a CMCC metadata solution (which provides a high level of scalability, transparency, fault tolerance and autonomy) leveraging both on P2P and grid technologies (GRelC Data Access and Integration Service). Moreover, data are available through OPeNDAP/THREDDS services, Live Access Server as well as the OGC compliant Web Map Service and they can be downloaded, visualized, accessed into the proposed environment through the Climate-G Data Distribution Centre (DDC), the web gateway to the Climate-G digital library. The DDC is a data-grid portal allowing users to easily, securely and transparently perform search/discovery, metadata management, data access, data visualization, etc. Godiva2 (integrated into the DDC) displays 2D maps (and animations) and also exports maps for display on the Google Earth virtual globe. Presently, Climate-G publishes (through the DDC) about 2TB of data related to the ENSEMBLES project (also including distributed replicas of data) as well as to the IPCC AR4. The main results of the proposed work are: wide data access/sharing environment for climate change; P2P/grid metadata approach; production-level Climate-G DDC; high quality tools for

  14. Predicting Positive and Negative Relationships in Large Social Networks

    PubMed Central

    Wang, Guan-Nan; Gao, Hui; Chen, Lian; Mensah, Dennis N. A.; Fu, Yan

    2015-01-01

    In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods. PMID:26075404

  15. 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

  16. 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

  17. 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

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

    PubMed

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

    2016-03-01

    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

  19. Folksonomical P2P File Sharing Networks Using Vectorized KANSEI Information as Search Tags

    NASA Astrophysics Data System (ADS)

    Ohnishi, Kei; Yoshida, Kaori; Oie, Yuji

    We present the concept of folksonomical peer-to-peer (P2P) file sharing networks that allow participants (peers) to freely assign structured search tags to files. These networks are similar to folksonomies in the present Web from the point of view that users assign search tags to information distributed over a network. As a concrete example, we consider an unstructured P2P network using vectorized Kansei (human sensitivity) information as structured search tags for file search. Vectorized Kansei information as search tags indicates what participants feel about their files and is assigned by the participant to each of their files. A search query also has the same form of search tags and indicates what participants want to feel about files that they will eventually obtain. A method that enables file search using vectorized Kansei information is the Kansei query-forwarding method, which probabilistically propagates a search query to peers that are likely to hold more files having search tags that are similar to the query. The similarity between the search query and the search tags is measured in terms of their dot product. The simulation experiments examine if the Kansei query-forwarding method can provide equal search performance for all peers in a network in which only the Kansei information and the tendency with respect to file collection are different among all of the peers. The simulation results show that the Kansei query forwarding method and a random-walk-based query forwarding method, for comparison, work effectively in different situations and are complementary. Furthermore, the Kansei query forwarding method is shown, through simulations, to be superior to or equal to the random-walk based one in terms of search speed.

  20. Semantic overlay network for large-scale spatial information indexing

    NASA Astrophysics Data System (ADS)

    Zou, Zhiqiang; Wang, Yue; Cao, Kai; Qu, Tianshan; Wang, Zhongmin

    2013-08-01

    The increased demand for online services of spatial information poses new challenges to the combined filed of Computer Science and Geographic Information Science. Amongst others, these include fast indexing of spatial data in distributed networks. In this paper we propose a novel semantic overlay network for large-scale multi-dimensional spatial information indexing, called SON_LSII, which has a hybrid structure integrating a semantic quad-tree and Chord ring. The SON_LSII is a small world overlay network that achieves a very competitive trade-off between indexing efficiency and maintenance overhead. To create SON_LSII, we use an effective semantic clustering strategy that considers two aspects, i.e., the semantic of spatial information that peer holds in overlay network and physical network performances. Based on SON_LSII, a mapping method is used to reduce the multi-dimensional features into a single dimension and an efficient indexing algorithm is presented to support complex range queries of the spatial information with a massive number of concurrent users. The results from extensive experiments demonstrate that SON_LSII is superior to existing overlay networks in various respects, including scalability, maintenance, rate of indexing hits, indexing logical hops, and adaptability. Thus, the proposed SON_LSII can be used for large-scale spatial information indexing.

  1. Multi-dimension and Comprehensive Assessment on the Utilizing and Sharing of Regional Large-Scale Scientific Equipment

    PubMed Central

    Li, Chen; Yongbo, Lv; Chi, Chen

    2015-01-01

    Based on the data from 30 provincial regions in China, an assessment and empirical analysis was carried out on the utilizing and sharing of the large-scale scientific equipment with a comprehensive assessment model established on the three dimensions, namely, equipment, utilization and sharing. The assessment results were interpreted in light of relevant policies. The results showed that on the whole, the overall development level in the provincial regions in eastern and central China is higher than that in western China. This is mostly because of the large gap among the different provincial regions with respect to the equipped level. But in terms of utilizing and sharing, some of the Western provincial regions, such as Ningxia, perform well, which is worthy of our attention. Policy adjustment targeting at the differentiation, elevation of the capacity of the equipment management personnel, perfection of the sharing and cooperation platform, and the promotion of the establishment of open sharing funds, are all important measures to promote the utilization and sharing of the large-scale scientific equipment and to narrow the gap among different regions. PMID:25937850

  2. Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis

    PubMed Central

    2015-01-01

    Background Modern methods for mining biomolecular interactions from literature typically make predictions based solely on the immediate textual context, in effect a single sentence. No prior work has been published on extending this context to the information automatically gathered from the whole biomedical literature. Thus, our motivation for this study is to explore whether mutually supporting evidence, aggregated across several documents can be utilized to improve the performance of the state-of-the-art event extraction systems. In this paper, we describe our participation in the latest BioNLP Shared Task using the large-scale text mining resource EVEX. We participated in the Genia Event Extraction (GE) and Gene Regulation Network (GRN) tasks with two separate systems. In the GE task, we implemented a re-ranking approach to improve the precision of an existing event extraction system, incorporating features from the EVEX resource. In the GRN task, our system relied solely on the EVEX resource and utilized a rule-based conversion algorithm between the EVEX and GRN formats. Results In the GE task, our re-ranking approach led to a modest performance increase and resulted in the first rank of the official Shared Task results with 50.97% F-score. Additionally, in this paper we explore and evaluate the usage of distributed vector representations for this challenge. In the GRN task, we ranked fifth in the official results with a strict/relaxed SER score of 0.92/0.81 respectively. To try and improve upon these results, we have implemented a novel machine learning based conversion system and benchmarked its performance against the original rule-based system. Conclusions For the GRN task, we were able to produce a gene regulatory network from the EVEX data, warranting the use of such generic large-scale text mining data in network biology settings. A detailed performance and error analysis provides more insight into the relatively low recall rates. In the GE task we

  3. Complex networks with large numbers of labelable attractors

    NASA Astrophysics Data System (ADS)

    Mi, Yuanyuan; Zhang, Lisheng; Huang, Xiaodong; Qian, Yu; Hu, Gang; Liao, Xuhong

    2011-09-01

    Information storage in many functional subsystems of the brain is regarded by theoretical neuroscientists to be related to attractors of neural networks. The number of attractors is large and each attractor can be temporarily represented or suppressed easily by corresponding external stimulus. In this letter, we discover that complex networks consisting of excitable nodes have similar fascinating properties of coexistence of large numbers of oscillatory attractors, most of which can be labeled with a few nodes. According to a simple labeling rule, different attractors can be identified and the number of labelable attractors can be predicted from the analysis of network topology. With the cues of the labeling association, these attractors can be conveniently retrieved or suppressed on purpose.

  4. 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...

  5. 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…

  6. 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

  7. Conserved Noncoding Sequences Highlight Shared Components of Regulatory Networks in Dicotyledonous Plants[W

    PubMed Central

    Baxter, Laura; Jironkin, Aleksey; Hickman, Richard; Moore, Jay; Barrington, Christopher; Krusche, Peter; Dyer, Nigel P.; Buchanan-Wollaston, Vicky; Tiskin, Alexander; Beynon, Jim; Denby, Katherine; Ott, Sascha

    2012-01-01

    Conserved noncoding sequences (CNSs) in DNA are reliable pointers to regulatory elements controlling gene expression. Using a comparative genomics approach with four dicotyledonous plant species (Arabidopsis thaliana, papaya [Carica papaya], poplar [Populus trichocarpa], and grape [Vitis vinifera]), we detected hundreds of CNSs upstream of Arabidopsis genes. Distinct positioning, length, and enrichment for transcription factor binding sites suggest these CNSs play a functional role in transcriptional regulation. The enrichment of transcription factors within the set of genes associated with CNS is consistent with the hypothesis that together they form part of a conserved transcriptional network whose function is to regulate other transcription factors and control development. We identified a set of promoters where regulatory mechanisms are likely to be shared between the model organism Arabidopsis and other dicots, providing areas of focus for further research. PMID:23110901

  8. Development of large-scale functional brain networks in children.

    PubMed

    Supekar, Kaustubh; Musen, Mark; Menon, Vinod

    2009-07-01

    The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y) and 22 young-adults (ages 19-22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism. PMID:19621066

  9. 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

  10. Do friends share similar body image and eating problems? The role of social networks and peer influences in early adolescence.

    PubMed

    Hutchinson, Delyse M; Rapee, Ronald M

    2007-07-01

    This study examined the role of friendship networks and peer influences in body image concern, dietary restraint, extreme weight loss behaviours (EWLBs) and binge eating in a large community sample of young adolescent females. Based on girls' self-reported friendship groups, social network analysis was used to identify 173 friendship cliques. Results indicated that clique members shared similar scores on measures of dieting, EWLB and binge eating, but not body image concern. Average clique scores for dieting, EWLB and binge eating, were also correlated significantly with clique averages on measures of perceived peer influence, body mass index and psychological variables. Multiple regression analyses indicated that perceived peer influences in weight-related attitudes and behaviours were predictive of individual girls' level of body image concern, dieting, EWLB use and binge eating. Notably, an individual girl's dieting and EWLB use could be predicted from her friends' respective dieting and EWLB scores. Findings highlight the significance of the peer environment in body image and eating problems during early adolescence. PMID:17258173

  11. Hybrid centralized pre-computing/local distributed optimization of shared disjoint-backup path approach to GMPLS optical mesh network intelligent restoration

    NASA Astrophysics Data System (ADS)

    Gong, Qian; Xu, Rong; Lin, Jintong

    2004-04-01

    Wavelength Division Multiplexed (WDM) networks that route optical connections using intelligent optical cross-connects (OXCs) is firmly established as the core constituent of next generation networks. Rapid failure recovery is fundamental to building reliable transport networks. Mesh restoration promises cost effective failure recovery compared with legacy ring networks, and is now seeing large-scale deployment. Many carriers are migrating away from SONET ring restoration for their core transport networks and replacing it with mesh restoration through "intelligent" O-E-O cross-connects (XC). The mesh restoration is typically provided via two fiber-disjoint paths: a service path and a restoration path. this scheme can restore any single link failure or node failure. And by used shared mesh restoration, although every service route is assigned a restoration route, no dedicated capacity needs to be reserved for the restoration route, resulting in capacity savings. The restoration approach we propose is Centralized Pre-computing, Local Distributed Optimization, and Shared Disjoint-backup Path. This approach combines the merits of centralized and distributed solutions. It avoids the scalability issues of centralized solutions by using a distributed control plane for disjoint service path computation and restoration path provisioning. Moreover, if the service routes of two demands are disjoint, no single failure will affect both demands simultaneously. This means that the restoration routes of these two demands can share link capacities, because these two routes will not be activated at the same time. So we can say, this restoration capacity sharing approach achieves low restoration capacity and fast restoration speed, while requiring few control plane changes.

  12. Large-scale functional connectivity networks in the rodent brain.

    PubMed

    Gozzi, Alessandro; Schwarz, Adam J

    2016-02-15

    Resting-state functional Magnetic Resonance Imaging (rsfMRI) of the human brain has revealed multiple large-scale neural networks within a hierarchical and complex structure of coordinated functional activity. These distributed neuroanatomical systems provide a sensitive window on brain function and its disruption in a variety of neuropathological conditions. The study of macroscale intrinsic connectivity networks in preclinical species, where genetic and environmental conditions can be controlled and manipulated with high specificity, offers the opportunity to elucidate the biological determinants of these alterations. While rsfMRI methods are now widely used in human connectivity research, these approaches have only relatively recently been back-translated into laboratory animals. Here we review recent progress in the study of functional connectivity in rodent species, emphasising the ability of this approach to resolve large-scale brain networks that recapitulate neuroanatomical features of known functional systems in the human brain. These include, but are not limited to, a distributed set of regions identified in rats and mice that may represent a putative evolutionary precursor of the human default mode network (DMN). The impact and control of potential experimental and methodological confounds are also critically discussed. Finally, we highlight the enormous potential and some initial application of connectivity mapping in transgenic models as a tool to investigate the neuropathological underpinnings of the large-scale connectional alterations associated with human neuropsychiatric and neurological conditions. We conclude by discussing the translational potential of these methods in basic and applied neuroscience. PMID:26706448

  13. 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.

  14. Large-scale brain networks and psychopathology: a unifying triple network model.

    PubMed

    Menon, Vinod

    2011-10-01

    The science of large-scale brain networks offers a powerful paradigm for investigating cognitive and affective dysfunction in psychiatric and neurological disorders. This review examines recent conceptual and methodological developments which are contributing to a paradigm shift in the study of psychopathology. I summarize methods for characterizing aberrant brain networks and demonstrate how network analysis provides novel insights into dysfunctional brain architecture. Deficits in access, engagement and disengagement of large-scale neurocognitive networks are shown to play a prominent role in several disorders including schizophrenia, depression, anxiety, dementia and autism. Synthesizing recent research, I propose a triple network model of aberrant saliency mapping and cognitive dysfunction in psychopathology, emphasizing the surprising parallels that are beginning to emerge across psychiatric and neurological disorders. PMID:21908230

  15. 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

  16. Multiscale analysis of spreading in a large communication network

    NASA Astrophysics Data System (ADS)

    Kivelä, Mikko; Pan, Raj Kumar; Kaski, Kimmo; Kertész, János; Saramäki, Jari; Karsai, Márton

    2012-03-01

    In temporal networks, both the topology of the underlying network and the timings of interaction events can be crucial in determining how a dynamic process mediated by the network unfolds. We have explored the limiting case of the speed of spreading in the SI model, set up such that an event between an infectious and a susceptible individual always transmits the infection. The speed of this process sets an upper bound for the speed of any dynamic process that is mediated through the interaction events of the network. With the help of temporal networks derived from large-scale time-stamped data on mobile phone calls, we extend earlier results that indicate the slowing-down effects of burstiness and temporal inhomogeneities. In such networks, links are not permanently active, but dynamic processes are mediated by recurrent events taking place on the links at specific points in time. We perform a multiscale analysis and pinpoint the importance of the timings of event sequences on individual links, their correlations with neighboring sequences, and the temporal pathways taken by the network-scale spreading process. This is achieved by studying empirically and analytically different characteristic relay times of links, relevant to the respective scales, and a set of temporal reference models that allow for removing selected time-domain correlations one by one. Our analysis shows that for the spreading velocity, time-domain inhomogeneities are as important as the network topology, which indicates the need to take time-domain information into account when studying spreading dynamics. In particular, results for the different characteristic relay times underline the importance of the burstiness of individual links.

  17. Mycorrhizal Networks: Common Goods of Plants Shared under Unequal Terms of Trade1[W][OA

    PubMed Central

    Walder, Florian; Niemann, Helge; Natarajan, Mathimaran; Lehmann, Moritz F.; Boller, Thomas; Wiemken, Andres

    2012-01-01

    Plants commonly live in a symbiotic association with arbuscular mycorrhizal fungi (AMF). They invest photosynthetic products to feed their fungal partners, which, in return, provide mineral nutrients foraged in the soil by their intricate hyphal networks. Intriguingly, AMF can link neighboring plants, forming common mycorrhizal networks (CMNs). What are the terms of trade in such CMNs between plants and their shared fungal partners? To address this question, we set up microcosms containing a pair of test plants, interlinked by a CMN of Glomus intraradices or Glomus mosseae. The plants were flax (Linum usitatissimum; a C3 plant) and sorghum (Sorghum bicolor; a C4 plant), which display distinctly different 13C/12C isotope compositions. This allowed us to differentially assess the carbon investment of the two plants into the CMN through stable isotope tracing. In parallel, we determined the plants’ “return of investment” (i.e. the acquisition of nutrients via CMN) using 15N and 33P as tracers. Depending on the AMF species, we found a strong asymmetry in the terms of trade: flax invested little carbon but gained up to 94% of the nitrogen and phosphorus provided by the CMN, which highly facilitated growth, whereas the neighboring sorghum invested massive amounts of carbon with little return but was barely affected in growth. Overall biomass production in the mixed culture surpassed the mean of the two monocultures. Thus, CMNs may contribute to interplant facilitation and the productivity boosts often found with intercropping compared with conventional monocropping. PMID:22517410

  18. Shared signaling networks active in B cells isolated from genetically distinct mouse models of lupus

    PubMed Central

    Wu, Tianfu; Qin, Xiangmei; Kurepa, Zoran; Kumar, Kirthi Raman; Liu, Kui; Kanta, Hasna; Zhou, Xin J.; Satterthwaite, Anne B.; Davis, Laurie S.; Mohan, Chandra

    2007-01-01

    Though B cells play key roles in lupus pathogenesis, the molecular circuitry and its dysregulation in these cells as disease evolves remain poorly understood. To address this, a comprehensive scan of multiple signaling axes using multiplexed Western blotting was undertaken in several different murine lupus strains. PI3K/AKT/mTOR (mTOR, mammalian target of rapamycin), MEK1/Erk1/2, p38, NF-κB, multiple Bcl-2 family members, and cell-cycle molecules were observed to be hyperexpressed in lupus B cells in an age-dependent and lupus susceptibility gene–dose–dependent manner. Therapeutic targeting of the AKT/mTOR axis using a rapamycin (sirolimus) derivative ameliorated the serological, cellular, and pathological phenotypes associated with lupus. Surprisingly, the targeting of this axis was associated with the crippling of several other signaling axes. These studies reveal that lupus pathogenesis is contingent upon the activation of an elaborate network of signaling cascades that is shared among genetically distinct mouse models and raise hope that targeting pivotal nodes in these networks may offer therapeutic benefit. PMID:17641780

  19. 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.

  20. 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.

  1. An improved unified network protocol framework for large-scale wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Ding, Jin; Sivalingam, Krishna M.

    2004-08-01

    Rapid technological advances in wireless communication have made it possible for networking sensor devices. Given the low computation and battery power capacities of these sensor nodes, the key design factors of network protocols are self-configuring, energy-efficient, adaptive, and scalable. We presented the multi-hop infrastructure network architecture (MINA) for a wireless sensor network consisting of a few hundred sensors that communicate data to a base station (BS). We designed a Unified Network Protocol Framework for MINA that encompasses network organization, medium access control (MAC) and routing protocols. In this paper, we improve it by adaptively varying transmission range to maintain network connectivity. It is a derivative-free optimization algorithm. The BS periodically evaluates the objective function, chooses the appropriate transmission range and broadcasts it to the sensor nodes that then update the transmission range. The advantages are: (i) Avoids the disconnectivity; (ii) Maximizes the number of nodes that can be connected to the BS, (iii) Minimizes the energyxdelay metric and (iv) Avoids the "hot-spot" nodes in the network. The performance in terms of delay, throughput, energy consumption and network lifetimes, is studied in detail using discrete-event simulation compared with other protocol. The results show that it is energy efficient in a large scale network.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. Network for the recognition of a large spoken vocabulary

    SciTech Connect

    De Mori, R.; Mong, Y.; Palakal, M.

    1983-01-01

    A new solution for accessing a large lexicon in continuous speech is proposed. The words of a lexicon and their relations with syllables, acoustic and prosodic cues are represented by a network similar to semantic networks used in artificial intelligence for knowledge representation. The network for lexical representation is generated by a graph grammar in which rules are applied either when suitable acoustic cues are detected in the signal or when model driven predictions are made. The piece of network generated by the application of a set of rules contains nodes and links. Nodes are processes capable of executing algorithms. Links are channels through which messages between processes can be exchanged. The type of a link specifies the type of message that can be exchanged. The model exhibits a high degree of parallelism and allows to efficiently perform intersections of large sets of descriptions. Some nodes correspond to word classes which are hypothesized when some sets of sufficient conditions are detected in the data. These conditions are hypotheses about robust features called primary phonetic features. 11 references.

  7. 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

  8. 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

  9. 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.

  10. Gene-Sharing Networks Reveal Organizing Principles of Transcriptomes in Arabidopsis and Other Multicellular Organisms[W

    PubMed Central

    Li, Song; Pandey, Sona; Gookin, Timothy E.; Zhao, Zhixin; Wilson, Liza; Assmann, Sarah M.

    2012-01-01

    Understanding tissue-related gene expression patterns can provide important insights into gene, tissue, and organ function. Transcriptome analyses often have focused on housekeeping or tissue-specific genes or on gene coexpression. However, by analyzing thousands of single-gene expression distributions in multiple tissues of Arabidopsis thaliana, rice (Oryza sativa), human (Homo sapiens), and mouse (Mus musculus), we found that these organisms primarily operate by gene sharing, a phenomenon where, in each organism, most genes exhibit a high expression level in a few key tissues. We designed an analytical pipeline to characterize this phenomenon and then derived Arabidopsis and human gene-sharing networks, in which tissues are connected solely based on the extent of shared preferentially expressed genes. The results show that tissues or cell types from the same organ system tend to group together to form network modules. Tissues that are in consecutive developmental stages or have common physiological functions are connected in these networks, revealing the importance of shared preferentially expressed genes in conferring specialized functions of each tissue type. The networks provide predictive power for each tissue type regarding gene functions of both known and heretofore unknown genes, as shown by the identification of four new genes with functions in guard cell and abscisic acid response. We provide a Web interface that enables, based on the extent of gene sharing, both prediction of tissue-related functions for any Arabidopsis gene of interest and predictions concerning the relatedness of tissues. Common gene-sharing patterns observed in the four model organisms suggest that gene sharing evolved as a fundamental organizing principle of gene expression in diverse multicellular eukaryotes. PMID:22517316

  11. 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

  12. 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.

  13. 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

  14. 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.

  15. Resonant RF network antennas for large-area and large-volume inductively coupled plasma sources

    NASA Astrophysics Data System (ADS)

    Hollenstein, Ch; Guittienne, Ph; Howling, A. A.

    2013-10-01

    Large-area and large-volume radio frequency (RF) plasmas are produced by different arrangements of an elementary electrical mesh consisting of two conductors interconnected by a capacitor at each end. The obtained cylindrical and planar RF networks are resonant and generate very high RF currents. The input impedance of such RF networks shows the behaviour of an RLC parallel resonance equivalent circuit. The real impedance at the resonance frequency is of great advantage for power matching compared with conventional inductive devices. Changes in the RLC equivalent circuit during the observed E-H transition will allow future interpretation of the plasma-antenna coupling. Furthermore, high power transfer efficiencies are found during inductively coupled plasma (ICP) operation. For the planar RF antenna network it is shown that the E-H transition occurs simultaneously over the entire antenna. The underlying physics of these discharges induced by the resonant RF network antenna is found to be identical to that of the conventional ICP devices described in the literature. The resonant RF network antenna is a new versatile plasma source, which can be adapted to applications in industry and research.

  16. Cellular computational networks--a scalable architecture for learning the dynamics of large networked systems.

    PubMed

    Luitel, Bipul; Venayagamoorthy, Ganesh Kumar

    2014-02-01

    Neural networks for implementing large networked systems such as smart electric power grids consist of multiple inputs and outputs. Many outputs lead to a greater number of parameters to be adapted. Each additional variable increases the dimensionality of the problem and hence learning becomes a challenge. Cellular computational networks (CCNs) are a class of sparsely connected dynamic recurrent networks (DRNs). By proper selection of a set of input elements for each output variable in a given application, a DRN can be modified into a CCN which significantly reduces the complexity of the neural network and allows use of simple training methods for independent learning in each cell thus making it scalable. This article demonstrates this concept of developing a CCN using dimensionality reduction in a DRN for scalability and better performance. The concept has been analytically explained and empirically verified through application. PMID:24300549

  17. 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

    ... agents, or any other person. (Presidential Sig.) THE WHITE HOUSE, October 7, 2011. [FR Doc. 2011-26729... (classified information) on computer networks, it is hereby ordered as follows: Section 1. Policy. Our Nation... world but also requires sophisticated and vigilant means to ensure it is shared securely....

  18. 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. PMID:23215527

  19. 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.

  20. Identifying Large-Scale Brain Networks in Fragile X Syndrome

    PubMed Central

    Hall, Scott S.; Jiang, Heidi; Reiss, Allan L.; Greicius, Michael D.

    2014-01-01

    IMPORTANCE Fragile X syndrome (FXS) is an X-linked neurogenetic disorder characterized by a cognitive and behavioral phenotype resembling features of autism spectrum disorder. Until now, research has focused largely on identifying regional differences in brain structure and function between individuals with FXS and various control groups. Very little is known about the large-scale brain networks that may underlie the cognitive and behavioral symptoms of FXS. OBJECTIVE To identify large-scale, resting-state networks in FXS that differ from control individuals matched on age, IQ, and severity of behavioral and cognitive symptoms. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional, in vivo neuroimaging study conducted in an academic medical center. Participants (aged 10–23 years) included 17 males and females with FXS and 16 males and females serving as controls. MAIN OUTCOMES AND MEASURES Univariate voxel-based morphometric analyses, fractional amplitude of low-frequency fluctuations (fALFF) analysis, and group-independent component analysis with dual regression. RESULTS Patients with FXS showed decreased functional connectivity in the salience, precuneus, left executive control, language, and visuospatial networks compared with controls. Decreased fALFF in the bilateral insular, precuneus, and anterior cingulate cortices also was found in patients with FXS compared with control participants. Furthermore, fALFF in the left insular cortex was significantly positively correlated with IQ in patients with FXS. Decreased gray matter density, resting-state connectivity, and fALFF converged in the left insular cortex in patients with FXS. CONCLUSIONS AND RELEVANCE Fragile X syndrome results in widespread reductions in functional connectivity across multiple cognitive and affective brain networks. Converging structural and functional abnormalities in the left insular cortex, a region also implicated in individuals diagnosed with autism spectrum disorder, suggests that

  1. Prekindergarten Teachers' Verbal References to Print during Classroom-Based, Large-Group Shared Reading

    ERIC Educational Resources Information Center

    Zucker, Tricia A.; Justice, Laura M.; Piasta, Shayne B.

    2009-01-01

    Purpose: The frequency with which adults reference print when reading with preschool-age children is associated with growth in children's print knowledge (e.g., L.M. Justice & H.K. Ezell, 2000, 2002). This study examined whether prekindergarten (pre-K) teachers naturally reference print during classroom shared reading and if verbal print…

  2. Share with thy neighbors

    NASA Astrophysics Data System (ADS)

    Chandra, Surendar; Yu, Xuwen

    2007-01-01

    Peer to peer (P2P) systems are traditionally designed to scale to a large number of nodes. However, we focus on scenarios where the sharing is effected only among neighbors. Localized sharing is particularly attractive in scenarios where wide area network connectivity is undesirable, expensive or unavailable. On the other hand, local neighbors may not offer the wide variety of objects possible in a much larger system. The goal of this paper is to investigate a P2P system that shares contents with its neighbors. We analyze the sharing behavior of Apple iTunes users in an University setting. iTunes restricts the sharing of audio and video objects to peers within the same LAN sub-network. We show that users are already making a significant amount of content available for local sharing. We show that these systems are not appropriate for applications that require access to a specific object. We argue that mechanisms that allow the user to specify classes of interesting objects are better suited for these systems. Mechanisms such as bloom filters can allow each peer to summarize the contents available in the neighborhood, reducing network search overhead. This research can form the basis for future storage systems that utilize the shared storage available in neighbors and build a probabilistic storage for local consumption.

  3. 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

  4. 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.

  5. 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. PMID:19567788

  6. 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.

  7. An efficient bandwidth sharing strategy through users' cooperation for multirate networks

    NASA Astrophysics Data System (ADS)

    El-Ganainy, Noha O.; El-Khamy, Said E.

    2013-09-01

    This paper adopts an efficient strategy that allocates the network resources to the users according to the transmission rate requirement. Based on cooperation and the use of variable length spreading codes, the terminals requiring high transmission rate are supported by larger bandwidth and power in order to enhance their quality of service. The proposed resource allocation strategy assigns the terminals with low transmission rate to cooperate with those with high transmission rate by dedicating a part of their bandwidth and power to either relay their data or send a part of it. This will result in a slight limitation on the performance of the cooperating terminals (low rate). Two proposed schemes are presented namely the relaying strategy and re-allocation strategy. These strategies are evaluated against the commonly used rate-matching algorithm which constrains the terminals to fairly share the bandwidth regardless of the transmission rate. The proposed receiver is simple and efficient and the utilized spreading codes provide signal separation and limit multiple access interference MAI. In a CDMA-based framework supporting multirate transmissions, a thorough performance assessment in terms of the error probability and the probability of outage is discussed for the considered strategies under different transmission conditions. The proposed strategies enhance the performance of the high rate user compared to the rate-matching algorithm and slightly hold back the low rate user performance. Moreover, the re-allocation strategy enhances the system capability to support higher transmission rate at no additional cost.

  8. 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.

  9. IR wireless cluster synapses of HYDRA very large neural networks

    NASA Astrophysics Data System (ADS)

    Jannson, Tomasz; Forrester, Thomas

    2008-04-01

    RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

  10. 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.

  11. 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

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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

  17. A sendmail. cf scheme for a large network

    SciTech Connect

    Darmohray, T.M.

    1991-08-14

    Like most large networked sites our users depend heavily on the electronic mail system for both internal and off-site communications. Unfortunately the sendmail.cf file, which is used to control the behavior of the sendmail program, is somewhat cryptic and difficult to decipher for the neophyte. So, on one hand you have a highly visible, frequently used utility, and on the other hand a not-so-easily acquired system administration forte. Here is the sendmail topology of our site, what premises we based it on, and the parts of the sendmail.cf files which support the topology.

  18. 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.

  19. 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. PMID:25439765

  20. Wavelength and opto-electro-opto sharing and optimization in wavelength division multiplexing mesh networks with path protection

    NASA Astrophysics Data System (ADS)

    Wang, Kefei; Hamza, Haitham S.; Deogun, Jitender S.

    2006-03-01

    Given a physical network topology and a traffic demand, the problem of designing a survivable network with path protection is to select primary and backup paths based on resource optimization. A common approach to minimize resources is sharing. This problem has been investigated with the goal of optimizing the number of channels (i.e., wavelengths) needed for backup paths by imposing capacity sharing. However, because of the need and the cost for other devices, such as opto-electro-opto (OEO) regenerators, network provisioning should also take into consideration such resources in the optimization process. We address the problem of routing and wavelength assignment (RWA) for survivable networks with the objective of simultaneously optimizing wavelength links and OEOs. An integer linear program solution, a tabu search heuristic, and a genetic algorithm are proposed, and their performance is experimentally evaluated through extensive simulation. Our simulation results confirm an average of 30% reduction in the number of OEOs compared to that required with the well known shared-path protection scheme.

  1. 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.

  2. 77 FR 58416 - Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-09-20

    ... From the Federal Register Online via the Government Publishing Office NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team AGENCY: The... to the Large Scale Networking (LSN) Coordinating Group (CG). Public Comments: The government...

  3. 78 FR 7464 - Large Scale Networking (LSN)-Middleware And Grid Interagency Coordination (MAGIC) Team

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-02-01

    ... From the Federal Register Online via the Government Publishing Office NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN)--Middleware And Grid Interagency Coordination (MAGIC) Team AGENCY: The... Team reports to the Large Scale Networking (LSN) Coordinating Group (CG). Public Comments:...

  4. Effect of diabetes and acute rejection on liver transplant outcomes: An analysis of the organ procurement and transplantation network/united network for organ sharing database.

    PubMed

    Kuo, Hung-Tien; Lum, Erik; Martin, Paul; Bunnapradist, Suphamai

    2016-06-01

    The effects of diabetic status and acute rejection (AR) on liver transplant outcomes are largely unknown. We studied 13,736 liver recipients from the United Network for Organ Sharing/Organ Procurement Transplant Network database who underwent transplantation between 2004 and 2007 with a functioning graft for greater than 1 year. The association of pretransplant diabetes mellitus (PDM), new-onset diabetes after transplant (NODAT), and AR rates on allograft failure, all-cause mortality, and cardiovascular mortality were determined. To determine the differential and joint effects of diabetic status and AR on transplant outcomes, recipients were further stratified into 6 groups: neither (reference, n = 6600); NODAT alone (n = 2054); PDM alone (n = 2414); AR alone (n = 1448); NODAT and AR (n = 707); and PDM and AR (n = 513). An analysis with hepatitis C virus (HCV) serostatus was also performed (HCV recipients, n = 6384; and non-HCV recipient, n = 5934). The median follow-up was 2537 days. The prevalence of PDM was 21.3%. At 1 year after transplant, the rates of NODAT and AR were 25.5% and 19.4%, respectively. Overall, PDM, NODAT, and AR were associated with increased risks for graft failure (PDM, hazard ratio [HR] = 1.31, P < 0.01; NODAT, HR = 1.11, P = 0.02; AR, HR = 1.28, P < 0.01). A multivariate Cox regression analysis of the 6 recipient groups demonstrated that NODAT alone was not significantly associated with any study outcomes. The presence of PDM, AR, NODAT and AR, and PDM and AR were associated with higher overall graft failure risk and mortality risk. The presence of PDM was associated with higher cardiovascular mortality risk. The analyses in both HCV-positive and HCV-negative cohorts showed a similar trend as in the overall cohort. In conclusion, PDM and AR, but not NODAT, is associated with increased mortality and liver allograft failure. Liver Transplantation 22 796-804 2016 AASLD. PMID:26850091

  5. 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

  6. 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

  7. 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…

  8. Relationship Between Large-Scale Functional and Structural Covariance Networks in Idiopathic Generalized Epilepsy

    PubMed Central

    Zhang, Zhiqiang; Mantini, Dante; Xu, Qiang; Wang, Zhengge; Chen, Guanghui; Jiao, Qing; Zang, Yu-Feng

    2013-01-01

    Abstract The human brain can be modeled as a network, whose structure can be revealed by either anatomical or functional connectivity analyses. Little is known, so far, about the topological features of the large-scale interregional functional covariance network (FCN) in the brain. Further, the relationship between the FCN and the structural covariance network (SCN) has not been characterized yet, in the intact as well as in the diseased brain. Here, we studied 59 patients with idiopathic generalized epilepsy characterized by tonic–clonic seizures and 59 healthy controls. We estimated the FCN and the SCN by measuring amplitude of low-frequency fluctuations (ALFF) and gray matter volume (GMV), respectively, and then we conducted graph theoretical analyses. Our ALFF-based FCN and GMV-based results revealed that the normal human brain is characterized by specific topological properties such as small worldness and highly-connected hub regions. The patients had an altered overall topology compared to the controls, suggesting that epilepsy is primarily a disorder of the cerebral network organization. Further, the patients had altered nodal characteristics in the subcortical and medial temporal regions and default-mode regions, for both the FCN and SCN. Importantly, the correspondence between the FCN and SCN was significantly larger in patients than in the controls. These results support the hypothesis that the SCN reflects shared long-term trophic mechanisms within functionally synchronous systems. They can also provide crucial information for understanding the interactions between the whole-brain network organization and pathology in generalized tonic–clonic seizures. PMID:23510272

  9. Peer Support Networks in a Large Introductory Psychology Class.

    ERIC Educational Resources Information Center

    Slotnick, Robert S.; And Others

    Networks have emerged as a major topic of interest in the behavioral sciences, and network concepts have recently been extended by community psychologists to higher education. To examine the effectiveness of peer networks within an introductory psychology class, networks of four students each met weekly in place of a lecture to review material and…

  10. 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. PMID:22506835

  11. 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.

  12. 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

  13. 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

  14. Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets

    PubMed Central

    Heath, Allison P; Greenway, Matthew; Powell, Raymond; Spring, Jonathan; Suarez, Rafael; Hanley, David; Bandlamudi, Chai; McNerney, Megan E; White, Kevin P; Grossman, Robert L

    2014-01-01

    Background As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. Methods Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. Results Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. Conclusions Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics. PMID:24464852

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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…

  1. 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…

  2. Distribution of entanglement in large-scale quantum networks

    NASA Astrophysics Data System (ADS)

    Perseguers, S.; Lapeyre, G. J., Jr.; 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.

  3. Wi-fi walkman: a wireless handheld that shares and recommends music on peer-to-peer networks

    NASA Astrophysics Data System (ADS)

    Wang, Jun; Reinders, Marcel J.; Pouwelse, Johan; Lagendijk, Reginald L.

    2005-03-01

    The Wi-Fi walkman is a mobile multimedia application that we developed to investigate the technological and usability aspects of human-computer interaction with personalized, intelligent and context-aware wearable devices in peer-to-peer wireless environments such as the future home, office, or university campuses. It is a small handheld device with a wireless link that contains music content. Users carry their own walkman around and listen to music. All this music content is distributed in the peer-to-peer network and is shared using ad-hoc networking. The walkman naturally interacts with the users and users" interest with each other in a peer-to-peer environment. Without annoying interactions, it can learn the users" music interest/taste and consequently provide personalized music recommendation according to the current situated context and user"s interest.

  4. Italian Frontotemporal Dementia Network (FTD Group-SINDEM): sharing clinical and diagnostic procedures in Frontotemporal Dementia in Italy.

    PubMed

    Borroni, B; Turrone, R; Galimberti, D; Nacmias, B; Alberici, A; Benussi, A; Caffarra, P; Caltagirone, C; Cappa, S F; Frisoni, G B; Ghidoni, R; Marra, C; Padovani, A; Rainero, I; Scarpini, E; Silani, V; Sorbi, S; Tagliavini, F; Tremolizzo, L; Bruni, A C

    2015-05-01

    In the prospect of improved disease management and future clinical trials in Frontotemporal Dementia, it is desirable to share common diagnostic procedures. To this aim, the Italian FTD Network, under the aegis of the Italian Neurological Society for Dementia, has been established. Currently, 85 Italian Centers involved in dementia care are part of the network. Each Center completed a questionnaire on the local clinical procedures, focused on (1) clinical assessment, (2) use of neuroimaging and genetics; (3) support for patients and caregivers; (4) an opinion about the prevalence of FTD. The analyses of the results documented a comprehensive clinical and instrumental approach to FTD patients and their caregivers in Italy, with about 1,000 newly diagnosed cases per year and 2,500 patients currently followed by the participating Centers. In analogy to other European FTD consortia, future aims will be devoted to collect data on epidemiology of FTD and its subtypes and to provide harmonization of procedures among Centers. PMID:25528460

  5. Director of anesthesiology for liver transplantation: existing practices and recommendations by the United Network for Organ Sharing.

    PubMed

    Mandell, M Susan; Pomfret, Elizabeth A; Steadman, Randall; Hirose, Ryutaro; Reich, David J; Schumann, Roman; Walia, Ann

    2013-04-01

    A new Organ Procurement and Transplantation Network/United Network for Organ Sharing bylaw recommends that all centers appoint a director of liver transplant anesthesia with a uniform set of criteria. We obtained survey data from the Liver Transplant Anesthesia Consortium so that we could compare existing criteria for a director in the United States with the current recommendations. The data set included responses from adult academic liver transplant programs before the new bylaw. The respondent rates were within statistical limits to exclude sampling bias. All centers had a director of liver transplant anesthesia. The criteria varied between institutions, and the data suggest that the availability of resources influenced the choice of criteria. The information suggests that the criteria used in the new bylaw reflect existing practices. The bylaw plays an important role in supporting emerging leadership roles in liver transplant anesthesia and brings greater uniformity to the directorship position. PMID:23447113

  6. 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

  7. 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

  8. Explicit integration with GPU acceleration for large kinetic networks

    DOE PAGESBeta

    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

  9. 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.

  10. Explicit integration with GPU acceleration for large kinetic networks

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

    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. 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.

  11. 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

  12. 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…

  13. 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.

  14. Topology of large-scale engineering problem-solving networks

    NASA Astrophysics Data System (ADS)

    Braha, Dan; Bar-Yam, Yaneer

    2004-01-01

    The last few years have led to a series of discoveries that uncovered statistical properties that are common to a variety of diverse real-world social, information, biological, and technological networks. The goal of the present paper is to investigate the statistical properties of networks of people engaged in distributed problem solving and discuss their significance. We show that problem-solving networks have properties (sparseness, small world, scaling regimes) that are like those displayed by information, biological, and technological networks. More importantly, we demonstrate a previously unreported difference between the distribution of incoming and outgoing links of directed networks. Specifically, the incoming link distributions have sharp cutoffs that are substantially lower than those of the outgoing link distributions (sometimes the outgoing cutoffs are not even present). This asymmetry can be explained by considering the dynamical interactions that take place in distributed problem solving and may be related to differences between each actor’s capacity to process information provided by others and the actor’s capacity to transmit information over the network. We conjecture that the asymmetric link distribution is likely to hold for other human or nonhuman directed networks when nodes represent information processing and using elements.

  15. Validation of Candidate Causal Genes for Abdominal Obesity Which Affect Shared Metabolic Pathways and Networks

    PubMed Central

    Yang, Xia; Deignan, Joshua L.; Qi, Hongxiu; Zhu, Jun; Qian, Su; Zhong, Judy; Torosyan, Gevork; Majid, Sana; Falkard, Brie; Kleinhanz, Robert R.; Karlsson, Jenny; Castellani, Lawrence W.; Mumick, Sheena; Wang, Kai; Xie, Tao; Coon, Michael; Zhang, Chunsheng; Estrada-Smith, Daria; Farber, Charles R.; Wang, Susanna S.; Van Nas, Atila; Ghazalpour, Anatole; Zhang, Bin; MacNeil, Douglas J.; Lamb, John R.; Dipple, Katrina M.; Reitman, Marc L.; Mehrabian, Margarete; Lum, Pek Y.; Schadt, Eric E.; Lusis, Aldons J.

    2010-01-01

    A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes and identification of involved pathways and networks. PMID:19270708

  16. 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.

  17. 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.

  18. 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.

  19. Complementing Security Breach of Authentication by Using Shared Authentication Information in Mobile WiMAX Networks

    NASA Astrophysics Data System (ADS)

    Kim, Youngwook; Lim, Hyoung-Kyu; Bahk, Saewoong

    The signalling protocol vulnerability opens DDoS problem in Mobile WiMAX networks. This letter proposes an authentication method that uses the unrevealed upper 64bits of Cipher-based MAC as a solution. It runs for MSs in idle mode and reduces the calculation complexity by 59% under DDoS attack while incurring 1% overhead under normal condition.

  20. 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)

  1. Stability constraints on large-scale structural brain networks

    PubMed Central

    Gray, Richard T.; Robinson, Peter A.

    2013-01-01

    Stability is an important dynamical property of complex systems and underpins a broad range of coherent self-organized behavior. Based on evidence that some neurological disorders correspond to linear instabilities, we hypothesize that stability constrains the brain's electrical activity and influences its structure and physiology. Using a physiologically-based model of brain electrical activity, we investigated the stability and dispersion solutions of networks of neuronal populations with propagation time delays and dendritic time constants. We find that stability is determined by the spectrum of the network's matrix of connection strengths and is independent of the temporal damping rate of axonal propagation with stability restricting the spectrum to a region in the complex plane. Time delays and dendritic time constants modify the shape of this region but it always contains the unit disk. Instabilities resulting from changes in connection strength initially have frequencies less than a critical frequency. For physiologically plausible parameter values based on the corticothalamic system, this critical frequency is approximately 10 Hz. For excitatory networks and networks with randomly distributed excitatory and inhibitory connections, time delays and non-zero dendritic time constants have no impact on network stability but do effect dispersion frequencies. Random networks with both excitatory and inhibitory connections can have multiple marginally stable modes at low delta frequencies. PMID:23630490

  2. Women Saw Large Decrease In Out-Of-Pocket Spending For Contraceptives After ACA Mandate Removed Cost Sharing.

    PubMed

    Becker, Nora V; Polsky, Daniel

    2015-07-01

    The Affordable Care Act mandates that private health insurance plans cover prescription contraceptives with no consumer cost sharing. The positive financial impact of this new provision on consumers who purchase contraceptives could be substantial, but it has not yet been estimated. Using a large administrative claims data set from a national insurer, we estimated out-of-pocket spending before and after the mandate. We found that mean and median per prescription out-of-pocket expenses have decreased for almost all reversible contraceptive methods on the market. The average percentages of out-of-pocket spending for oral contraceptive pill prescriptions and intrauterine device insertions by women using those methods both dropped by 20 percentage points after implementation of the ACA mandate. We estimated average out-of-pocket savings per contraceptive user to be $248 for the intrauterine device and $255 annually for the oral contraceptive pill. Our results suggest that the mandate has led to large reductions in total out-of-pocket spending on contraceptives and that these price changes are likely to be salient for women with private health insurance. PMID:26153316

  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. 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.

  5. The large-scale organization of the hadron decay network

    NASA Astrophysics Data System (ADS)

    Xu, Xinping; Liu, Feng

    2008-01-01

    The standard model of particle physics predicts a complex structure of decay modes for hadrons, which opens up an avenue for observing the internal forces governing the decay dynamics. In this paper, we present the decay modes of hadrons as a network in which the nodes are particles and directed links are pointing from the mother particles to daughter particles. Using the database of decay modes collected from the Particle Data Group, we try to unveil the topological structure and possible intrinsic nature of hadron decays in the light of recent investigations of complex networks. We study distributions of the numbers of daughter and mother particles, and explore scaling laws that may govern the underlying decay structure of the system. We find that it is a small-world network with symmetrical structure. We also study the influence of constraints arising from conservation laws on the network structure, and our analysis suggests that the constraints of conservations of momentum energy, charge, lepton number and baryon number play important roles in the topology of the decay network. Finally, we classify the hadrons into communities according to their quark component, and uncover the relationship between the particle roles and connection patterns in the communities.

  6. Large-scale feature selection using evolved neural networks

    NASA Astrophysics Data System (ADS)

    Stathakis, Demetris; Topouzelis, Kostas; Karathanassi, Vassilia

    2006-09-01

    In this paper computational intelligence, referring here to the synergy of neural networks and genetic algorithms, is deployed in order to determine a near-optimal neural network for the classification of dark formations in oil spills and look-alikes. Optimality is sought in the framework of a multi-objective problem, i.e. the minimization of input features used and, at the same time, the maximization of overall testing classification accuracy. The proposed method consists of two concurrent actions. The first is the identification of the subset of features that results in the highest classification accuracy on the testing data set i.e. feature selection. The second parallel process is the search for the neural network topology, in terms of number of nodes in the hidden layer, which is able to yield optimal results with respect to the selected subset of features. The results show that the proposed method, i.e. concurrently evolving features and neural network topology, yields superior classification accuracy compared to sequential floating forward selection as well as to using all features together. The accuracy matrix is deployed to show the generalization capacity of the discovered neural network topology on the evolved sub-set of features.

  7. 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

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

    PubMed Central

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

    2015-01-01

    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. PMID:25754258

  9. 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.

  10. 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…

  11. Peer Network Drinking Predicts Increased Alcohol Use From Adolescence to Early Adulthood After Controlling for Genetic and Shared Environmental Selection

    PubMed Central

    Cruz, Jennifer E.; Emery, Robert E.; Turkheimer, Eric

    2013-01-01

    Research consistently links adolescents' and young adults' drinking with their peers' alcohol intake. In interpreting this correlation, 2 essential questions are often overlooked. First, which peers are more important, best friends or broader social networks? Second, do peers cause increased drinking, or do young people select friends whose drinking habits match their own? The present study combines social network analyses with family (twin and sibling) designs to answer these questions via data from the National Longitudinal Study of Adolescent Health. Analysis of peer nomination data from 134 schools (n = 82,629) and 1,846 twin and sibling pairs shows that peer network substance use predicts changes in drinking from adolescence into young adult life even after controlling for genetic and shared environmental selection, as well as best friend substance use. This effect was particularly strong for high-intensity friendships. Although the peer-adolescent drinking correlation is partially explained by selection, the present finding offers powerful evidence that peers also cause increased drinking. PMID:22390657

  12. Locating the Source of Diffusion in Large-Scale Networks

    NASA Astrophysics Data System (ADS)

    Pinto, Pedro C.; Thiran, Patrick; Vetterli, Martin

    2012-08-01

    How can we localize the source of diffusion in a complex network? Because of the tremendous size of many real networks—such as the internet or the human social graph—it is usually unfeasible to observe the state of all nodes in a network. We show that it is fundamentally possible to estimate the location of the source from measurements collected by sparsely placed observers. We present a strategy that is optimal for arbitrary trees, achieving maximum probability of correct localization. We describe efficient implementations with complexity O(Nα), where α=1 for arbitrary trees and α=3 for arbitrary graphs. In the context of several case studies, we determine how localization accuracy is affected by various system parameters, including the structure of the network, the density of observers, and the number of observed cascades.

  13. 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.

  14. Communications performance of an undersea acoustic large-area network

    NASA Astrophysics Data System (ADS)

    Kriewaldt, Hannah A.; Rice, Joseph A.

    2005-04-01

    The U.S. Navy is developing Seaweb acoustic networking capability for integrating undersea systems. Seaweb architectures generally involve a wide-area network of fixed nodes consistent with future distributed autonomous sensors on the seafloor. Mobile nodes including autonomous undersea vehicles (AUVs) and submarines operate in the context of the grid by using the fixed nodes as both navigation reference points and communication access points. In October and November 2004, Theater Anti-Submarine Warfare Exercise (TASWEX04) showcased Seaweb in its first fleet appearance. This paper evaluates the TASWEX04 Seaweb performance in support of networked communications between a submarine and a surface ship. Considerations include physical-layer dependencies on the 9-14 kHz acoustic channel, such as refraction, wind-induced ambient noise, and submarine aspect angle. [Work supported by SSC San Diego.

  15. 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

  16. 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

  17. 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. PMID:26165867

  18. Modeling deuterium chemistry of interstellar space with large chemical networks

    NASA Astrophysics Data System (ADS)

    Albertsson, T.; Semenov, D. A.; Vasyunin, A. I.; Henning, Th.; Herbst, E.

    2015-03-01

    Observations of deuterated species are essential to probing the properties and thermal history of various astrophysical environments, and the ALMA observing facilities will reveal a multitude of new deuterated molecules. To analyze these new vast data we have constructed a new up-to-date network with the largest collection of deuterium chemistry reactions to date. We assess the reliability of the network and probe the role of physical parameters and initial abundances on the chemical evolution of deuterated species. Finally, we perform a sensitivity study to assess the uncertainties in the estimated abundances and D/H ratios.

  19. 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.

  20. 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

  1. Large-Scale Neural Network for Sentence Processing

    ERIC Educational Resources Information Center

    Cooke, Ayanna; Grossman, Murray; DeVita, Christian; Gonzalez-Atavales, Julio; Moore, Peachie; Chen, Willis; Gee, James; Detre, John

    2006-01-01

    Our model of sentence comprehension includes at least grammatical processes important for structure-building, and executive resources such as working memory that support these grammatical processes. We hypothesized that a core network of brain regions supports grammatical processes, and that additional brain regions are activated depending on the…

  2. 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

  3. 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

  4. Effect of staff migration on kidney transplant activity in United Network for Organ Sharing region 1 transplant centers.

    PubMed

    Saidi, Reza F; Khaksari, Sahriar; Ko, Dicken S C

    2014-09-01

    Organ shortage is unquestionably the greatest challenge facing the field of transplantation today. Transplant centers are constantly competing with one another for limited numbers of organs for their recipients. Recruitment of specialized transplant surgical expertise and leadership is thought to enable a center to grow in volume and thus profitability in the increasingly difficult world of health care reimbursement. In this study, the pattern of kidney transplants at 13 different centers in the United Network for Organ Sharing's region 1 is examined: the comparison is between transplant volume before and after changes in the centers' leadership between 2000 and 2011. Each center's kidney transplant volume showed a significant increase after a leadership change that ultimately regressed to the center's baseline. This study is the first to show that behavioral changes in transplant center competition cause transient increases in transplant volume that quickly regress back to mean levels. PMID:25193733

  5. scMRI Reveals Large-Scale Brain Network Abnormalities in Autism

    PubMed Central

    Zielinski, Brandon A.; Anderson, Jeffrey S.; Froehlich, Alyson L.; Prigge, Molly B. D.; Nielsen, Jared A.; Cooperrider, Jason R.; Cariello, Annahir N.; Fletcher, P. Thomas; Alexander, Andrew L.; Lange, Nicholas; Bigler, Erin D.; Lainhart, Janet E.

    2012-01-01

    Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a ‘posteriorization’ of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI. PMID:23185305

  6. Discrete derivative: a data slicing algorithm for exploration of sharing biological networks between rheumatoid arthritis and coronary heart disease

    PubMed Central

    2011-01-01

    Background One important concept in traditional Chinese medicine (TCM) is "treating different diseases with the same therapy". In TCM practice, some patients with Rheumatoid Arthritis (RA) and some other patients with Coronary Heart Disease (CHD) can be treated with similar therapies. This suggests that there might be something commonly existed between RA and CHD, for example, biological networks or biological basis. As the amount of biomedical data in leading databases (i.e., PubMed, SinoMed, etc.) is growing at an exponential rate, it might be possible to get something interesting and meaningful through the techniques developed in data mining. Results Based on the large data sets of Western medicine literature (PubMed) and traditional Chinese medicine literature (SinoMed), by applying data slicing algorithm in text mining, we retrieved some simple and meaningful networks. The Chinese herbs used in treatment of both RA and CHD, might affect the commonly existed networks between RA and CHD. This might support the TCM concept of treating different diseases with the same therapy. Conclusions First, the data mining results might show the positive answer that there are biological basis/networks commonly existed in both RA and CHD. Second, there are basic Chinese herbs used in the treatment of both RA and CHD. Third, these commonly existed networks might be affected by the basic Chinese herbs. Forth, discrete derivative, the data slicing algorithm is feasible in mining out useful data from literature of PubMed and SinoMed. PMID:21696640

  7. Discovering the Influences of Complex Network Effects on Recovering Large Scale Multiagent Systems

    PubMed Central

    Xu, Yang; Liu, Pengfei; Li, Xiang; Ren, Wei

    2014-01-01

    Building efficient distributed coordination algorithms is critical for the large scale multiagent system design, and the communication network has been shown as a key factor to influence system performance even under the same coordination protocol. Although many distributed algorithm designs have been proved to be feasible to build their functions in the large scale multiagent systems as claimed, the performances may not be stable if the multiagent networks were organized with different complex network topologies. For example, if the network was recovered from the broken links or disfunction nodes, the network topology might have been shifted. Therefore, their influences on the overall multiagent system performance are unknown. In this paper, we have made an initial effort to find how a standard network recovery policy, MPLS algorithm, may change the network topology of the multiagent system in terms of network congestion. We have established that when the multiagent system is organized as different network topologies according to different complex network attributes, the network shifts in different ways. Those interesting discoveries are helpful to predict how complex network attributes influence on system performance and in turn are useful for new algorithm designs that make a good use of those attributes. PMID:24982947

  8. Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

    DOE PAGESBeta

    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

  9. 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.

  10. Large-Scale Coronal Heating from "Cool" Activity in the Solar Magnetic Network

    NASA Technical Reports Server (NTRS)

    Falconer, D. A.; Moore, R. L.; Porter, J. G.; Hathaway, D. H.

    1999-01-01

    In Fe XII images from SOHO/EIT, the quiet solar corona shows structure on scales ranging from sub-supergranular (i.e., bright points and coronal network) to multi-supergranular (large-scale corona). In Falconer et al 1998 (Ap.J., 501, 386) we suppressed the large-scale background and found that the network-scale features are predominantly rooted in the magnetic network lanes at the boundaries of the supergranules. Taken together, the coronal network emission and bright point emission are only about 5% of the entire quiet solar coronal Fe XII emission. Here we investigate the relationship between the large-scale corona and the network as seen in three different EIT filters (He II, Fe IX-X, and Fe XII). Using the median-brightness contour, we divide the large-scale Fe XII corona into dim and bright halves, and find that the bright-half/dim half brightness ratio is about 1.5. We also find that the bright half relative to the dim half has 10 times greater total bright point Fe XII emission, 3 times greater Fe XII network emission, 2 times greater Fe IX-X network emission, 1.3 times greater He II network emission, and has 1.5 times more magnetic flux. Also, the cooler network (He II) radiates an order of magnitude more energy than the hotter coronal network (Fe IX-X, and Fe XII). From these results we infer that: 1) The heating of the network and the heating of the large-scale corona each increase roughly linearly with the underlying magnetic flux. 2) The production of network coronal bright points and heating of the coronal network each increase nonlinearly with the magnetic flux. 3) The heating of the large-scale corona is driven by widespread cooler network activity rather than by the exceptional network activity that produces the network coronal bright points and the coronal network. 4) The large-scale corona is heated by a nonthermal process since the driver of its heating is cooler than it is. This work was funded by the Solar Physics Branch of NASA's office of

  11. 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.

  12. 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. PMID:26399424

  13. Evolution of the large Deep Space Network antennas

    NASA Astrophysics Data System (ADS)

    Imbriale, William A.

    1991-12-01

    The evolution of the largest antenna of the US NASA Deep Space Network (DSN) is described. The design, performance analysis, and measurement techniques, beginning with its initial 64-m operation at S-band (2295 MHz) in 1966 and continuing through the present ka-band (32-GHz) operation at 70 m, is described. Although their diameters and mountings differ, these parabolic antennas all employ a Cassegrainian feed system, and each antenna dish surface is constructed of precision-shaped perforated-aluminum panels that are secured to an open steel framework

  14. A shared neural network for emotional expression and perception: an anatomical study in the macaque monkey

    PubMed Central

    Jezzini, Ahmad; Rozzi, Stefano; Borra, Elena; Gallese, Vittorio; Caruana, Fausto; Gerbella, Marzio

    2015-01-01

    Over the past two decades, the insula has been described as the sensory “interoceptive cortex”. As a consequence, human brain imaging studies have focused on its role in the sensory perception of emotions. However, evidence from neurophysiological studies in non-human primates have shown that the insula is also involved in generating emotional and communicative facial expressions. In particular, a recent study demonstrated that electrical stimulation of the mid-ventral sector of the insula evoked affiliative facial expressions. The present study aimed to describe the cortical connections of this “affiliative field”. To this aim, we identified the region with electrical stimulation and injected neural tracers to label incoming and outgoing projections. Our results show that the insular field underlying emotional expression is part of a network involving specific frontal, cingulate, temporal, and parietal areas, as well as the amygdala, the basal ganglia, and thalamus, indicating that this sector of the insula is a site of integration of motor, emotional, sensory and social information. Together with our previous functional studies, this result challenges the classic view of the insula as a multisensory area merely reflecting bodily and internal visceral states. In contrast, it supports an alternative perspective; that the emotional responses classically attributed to the insular cortex are endowed with an enactive component intrinsic to each social and emotional behavior. PMID:26441573

  15. 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.

  16. Building a Large-Scale Computational Model of a Cortical Neuronal Network

    NASA Astrophysics Data System (ADS)

    Zemanová, Lucia; Zhou, Changsong; Kurths, Jürgen

    We introduce the general framework of the large-scale neuronal model used in the 5th Helmholtz Summer School — Complex Brain Networks. The main aim is to build a universal large-scale model of a cortical neuronal network, structured as a network of networks, which is flexible enough to implement different kinds of topology and neuronal models and which exhibits behavior in various dynamical regimes. First, we describe important biological aspects of brain topology and use them in the construction of a large-scale cortical network. Second, the general dynamical model is presented together with explanations of the major dynamical properties of neurons. Finally, we discuss the implementation of the model into parallel code and its possible modifications and improvements.

  17. 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.

  18. MicroRNA and gene networks in human diffuse large B-cell lymphoma.

    PubMed

    Wang, Kunhao; Xu, Zhiwen; Wang, Ning; Xu, Ting; Zhu, Minghui

    2014-11-01

    Molecular biologists have collected considerable data regarding the involvement of genes and microRNAs (miRNAs) in cancer. However the underlying mechanisms of cancer with regard to genes and miRNAs remain unclear. The aim of the present study was to evaluate diffuse large B-cell lymphoma (DLBCL) and construct regulatory networks of genes and miRNAs to gradually reveal the underlying mechanisms of DLBCL development. The first differential expression network that is presented is an experimentally validated network of miRNAs and genes. This network presents known biological regulatory associations among miRNAs and genes in the human body. The second network is a DLBCL differential expression network. Differentially expressed gene and miRNA data regarding DLBCL were collected and, based on the first network and the differentially expressed data, the second network was inferred, which demonstrates the irregular regulatory associations that may lead to the occurrence of DLBCL. The third network is a DLBCL-associated network. This network is comprised of non-differentially expressed genes and miRNAs that contribute to numerous DLBCL processes. The similarities and differences among the three networks were extracted and compared to distinguish key regulatory associations; furthermore, important signaling pathways in DLBCL were identified. The present study partially clarified the pathogenesis of DLBCL and provided an improved understanding of the underlying molecular mechanisms, as well as a potential treatment for DLBCL. PMID:25289101

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

    DOE PAGESBeta

    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

  20. 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

  1. 78 FR 7464 - Large Scale Networking (LSN) ; Joint Engineering Team (JET)

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-02-01

    ... From the Federal Register Online via the Government Publishing Office NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN) ; Joint Engineering Team (JET) AGENCY: The Networking and Information...?title=Joint_Engineering_Team_ (JET)#title. SUMMARY: The JET, established in 1997, provides...

  2. 78 FR 70076 - Large Scale Networking (LSN)-Joint Engineering Team (JET)

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-11-22

    ... From the Federal Register Online via the Government Publishing Office NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN)--Joint Engineering Team (JET) AGENCY: The Networking and Information... the JET Web site at: http://www.nitrd.gov/nitrdgroups/index.php?title=Joint_Engineering_Team_...

  3. 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…

  4. 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)

  5. Extending key sharing: how to generate a key tightly coupled to a network security policy

    NASA Astrophysics Data System (ADS)

    Kazantzidis, Matheos

    2006-04-01

    Current state of the art security policy technologies, besides the small scale limitation and largely manual nature of accompanied management methods, are lacking a) in real-timeliness of policy implementation and b) vulnerabilities and inflexibility stemming from the centralized policy decision making; even if, for example, a policy description or access control database is distributed, the actual decision is often a centralized action and forms a system single point of failure. In this paper we are presenting a new fundamental concept that allows implement a security policy by a systematic and efficient key distribution procedure. Specifically, we extend the polynomial Shamir key splitting. According to this, a global key is split into n parts, any k of which can re-construct the original key. In this paper we present a method that instead of having "any k parts" be able to re-construct the original key, the latter can only be reconstructed if keys are combined as any access control policy describes. This leads into an easily deployable key generation procedure that results a single key per entity that "knows" its role in the specific access control policy from which it was derived. The system is considered efficient as it may be used to avoid expensive PKI operations or pairwise key distributions as well as provides superior security due to its distributed nature, the fact that the key is tightly coupled to the policy, and that policy change may be implemented easier and faster.

  6. 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…

  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. Construction and analyses of human large-scale tissue specific networks.

    PubMed

    Liu, Wei; Wang, Jianying; Wang, Tengjiao; Xie, Hongwei

    2014-01-01

    Construction and analyses of tissue specific networks is crucial to unveil the function and organizational structure of biological systems. As a direct method to detect protein dynamics, human proteome-wide expression data provide an valuable resource to investigate the tissue specificity of proteins and interactions. By integrating protein expression data with large-scale interaction network, we constructed 30 tissue/cell specific networks in human and analyzed their properties and functions. Rather than the tissue specificity of proteins, we mainly focused on the tissue specificity of interactions to distill tissue specific networks. Through comparing our tissue specific networks with those inferred from gene expression data, we found our networks have larger scales and higher reliability. Furthermore, we investigated the similar extent of multiple tissue specific networks, which proved that tissues with similar functions tend to contain more common interactions. Finally, we found that the tissue specific networks differed from the static network in multiple topological properties. The proteins in tissue specific networks are interacting looser and the hubs play more important roles than those in the static network. PMID:25513809

  9. Combing the hairball with BioFabric: a new approach for visualization of large networks

    PubMed Central

    2012-01-01

    Background The analysis of large, complex networks is an important aspect of ongoing biological research. Yet there is a need for entirely new, scalable approaches for network visualization that can provide more insight into the structure and function of these complex networks. Results To address this need, we have developed a software tool named BioFabric, which uses a novel network visualization technique that depicts nodes as one-dimensional horizontal lines arranged in unique rows. This is in distinct contrast to the traditional approach that represents nodes as discrete symbols that behave essentially as zero-dimensional points. BioFabric then depicts each edge in the network using a vertical line assigned to its own unique column, which spans between the source and target rows, i.e. nodes. This method of displaying the network allows a full-scale view to be organized in a rational fashion; interesting network structures, such as sets of nodes with similar connectivity, can be quickly scanned and visually identified in the full network view, even in networks with well over 100,000 edges. This approach means that the network is being represented as a fundamentally linear, sequential entity, where the horizontal scroll bar provides the basic navigation tool for browsing the entire network. Conclusions BioFabric provides a novel and powerful way of looking at any size of network, including very large networks, using horizontal lines to represent nodes and vertical lines to represent edges. It is freely available as an open-source Java application. PMID:23102059

  10. 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.

  11. 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

  12. 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.

  13. 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

  14. Large-scale cortical network properties predict future sound-to-word learning success.

    PubMed

    Sheppard, John Patrick; Wang, Ji-Ping; Wong, Patrick C M

    2012-05-01

    The human brain possesses a remarkable capacity to interpret and recall novel sounds as spoken language. These linguistic abilities arise from complex processing spanning a widely distributed cortical network and are characterized by marked individual variation. Recently, graph theoretical analysis has facilitated the exploration of how such aspects of large-scale brain functional organization may underlie cognitive performance. Brain functional networks are known to possess small-world topologies characterized by efficient global and local information transfer, but whether these properties relate to language learning abilities remains unknown. Here we applied graph theory to construct large-scale cortical functional networks from cerebral hemodynamic (fMRI) responses acquired during an auditory pitch discrimination task and found that such network properties were associated with participants' future success in learning words of an artificial spoken language. Successful learners possessed networks with reduced local efficiency but increased global efficiency relative to less successful learners and had a more cost-efficient network organization. Regionally, successful and less successful learners exhibited differences in these network properties spanning bilateral prefrontal, parietal, and right temporal cortex, overlapping a core network of auditory language areas. These results suggest that efficient cortical network organization is associated with sound-to-word learning abilities among healthy, younger adults. PMID:22360625

  15. 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

  16. 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

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

    PubMed Central

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

    2014-01-01

    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 to 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. PMID:24453089

  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. Identifying disease candidate genes via large-scale gene network analysis.

    PubMed

    Kim, Haseong; Park, Taesung; Gelenbe, Erol

    2014-01-01

    Gene Regulatory Networks (GRN) provide systematic views of complex living systems, offering reliable and large-scale GRNs to identify disease candidate genes. A reverse engineering technique, Bayesian Model Averaging-based Networks (BMAnet), which ensembles all appropriate linear models to tackle uncertainty in model selection that integrates heterogeneous biological data sets is introduced. Using network evaluation metrics, we compare the networks that are thus identified. The metric 'Random walk with restart (Rwr)' is utilised to search for disease genes. In a simulation our method shows better performance than elastic-net and Gaussian graphical models, but topological quantities vary among the three methods. Using real-data, brain tumour gene expression samples consisting of non-tumour, grade III and grade IV are analysed to estimate networks with a total of 4422 genes. Based on these networks, 169 brain tumour-related candidate genes were identified and some were found to relate to 'wound', 'apoptosis', and 'cell death' processes. PMID:25796737

  20. 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

  1. 78 FR 70076 - Large Scale Networking (LSN)-Middleware and Grid Interagency Coordination (MAGIC) Team

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-11-22

    ... From the Federal Register Online via the Government Publishing Office NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN)--Middleware and Grid Interagency Coordination (MAGIC) Team AGENCY: The... and responsibility for middleware, Grid, and cloud projects. The MAGIC Team reports to the Large...

  2. Modeling dynamic functional information flows on large-scale brain networks.

    PubMed

    Lv, Peili; Guo, Lei; Hu, Xintao; Li, Xiang; Jin, Changfeng; Han, Junwei; Li, Lingjiang; Liu, Tianming

    2013-01-01

    Growing evidence from the functional neuroimaging field suggests that human brain functions are realized via dynamic functional interactions on large-scale structural networks. Even in resting state, functional brain networks exhibit remarkable temporal dynamics. However, it has been rarely explored to computationally model such dynamic functional information flows on large-scale brain networks. In this paper, we present a novel computational framework to explore this problem using multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. Basically, recent literature reports including our own studies have demonstrated that the resting state brain networks dynamically undergo a set of distinct brain states. Within each quasi-stable state, functional information flows from one set of structural brain nodes to other sets of nodes, which is analogous to the message package routing on the Internet from the source node to the destination. Therefore, based on the large-scale structural brain networks constructed from DTI data, we employ a dynamic programming strategy to infer functional information transition routines on structural networks, based on which hub routers that most frequently participate in these routines are identified. It is interesting that a majority of those hub routers are located within the default mode network (DMN), revealing a possible mechanism of the critical functional hub roles played by the DMN in resting state. Also, application of this framework on a post trauma stress disorder (PTSD) dataset demonstrated interesting difference in hub router distributions between PTSD patients and healthy controls. PMID:24579202

  3. Optimization of Cluster Heads for Energy Efficiency in Large-Scale Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Gu, Yi; Wu, Qishi

    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 nodes in a pre-deployed sensor network to be the cluster heads 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.

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

    DOE PAGESBeta

    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

  5. Reverse engineering and analysis of large genome-scale gene networks

    PubMed Central

    Aluru, Maneesha; Zola, Jaroslaw; Nettleton, Dan; Aluru, Srinivas

    2013-01-01

    Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web. PMID:23042249

  6. 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

  7. Multiple Mobile Sinks Deployment for Energy Efficiency in Large Scale Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Slama, Ines; Jouaber, Badii; Zeghlache, Djamal

    In this paper, we consider the multiple sinks placement problem in energy constrained large-scale Wireless Sensor Networks (WSN). First, some fundamental design parameters in WSNs are investigated such as nodes deployment, the network architecture, sink velocity and transmission range. Each of these parameters is analysed and discussed according to its influence on the energy consumption in a WSN. Second, a simple and efficient approach for the placement of multiple sinks within large-scale WSNs is proposed. The objective is to determine optimal sinks’ positions that maximize the network lifetime by reducing energy consumption related to data transmissions from sensor nodes to different sinks. Balanced graph partitioning techniques are used to split the entire WSN into connected sub-networks. Smaller sub-networks are created, having similar characteristics and where energy consumption can be optimized independently but in the same way. Therefore, different approaches and mechanisms that enhance the network lifetime in small-size WSN can be deployed inside each sub-network. Performance results show that the proposed technique significantly enhances the network lifetime.

  8. 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.

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

    PubMed

    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

  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. 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.

  13. Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA

    PubMed Central

    2016-01-01

    Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity. PMID:26785116

  14. 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.

  15. Communities and beyond: Mesoscopic analysis of a large social network with complementary methods

    NASA Astrophysics Data System (ADS)

    Tibély, Gergely; Kovanen, Lauri; Karsai, Márton; Kaski, Kimmo; Kertész, János; Saramäki, Jari

    2011-05-01

    Community detection methods have so far been tested mostly on small empirical networks and on synthetic benchmarks. Much less is known about their performance on large real-world networks, which nonetheless are a significant target for application. We analyze the performance of three state-of-the-art community detection methods by using them to identify communities in a large social network constructed from mobile phone call records. We find that all methods detect communities that are meaningful in some respects but fall short in others, and that there often is a hierarchical relationship between communities detected by different methods. Our results suggest that community detection methods could be useful in studying the general mesoscale structure of networks, as opposed to only trying to identify dense structures.

  16. Voltage Control of Distribution Network with a Large Penetration of Photovoltaic Generations using FACTS Devices

    NASA Astrophysics Data System (ADS)

    Kondo, Taro; Baba, Jumpei; Yokoyama, Akihiko

    In recent years, there is a great deal of interest in distributed generations from viewpoints of environmental problem and energy saving measure. Thus, a lot of distributed generators will be connected to the distribution network in the future. However, increase of distributed generators, which convert natural energy into electric energy, is concerned on their adverse effects on distribution network. Therefore, control of distribution networks using Flexible AC Transmission System (FACTS) devices is considered in order to adjust the voltage profile, and as a result more distributed generations can be installed into the networks. In this paper, four types of FACTS devices, Static Synchronous Compensator (STATCOM), Static Synchronous Series Compensator (SSSC), Unified Power Flow Controller (UPFC) and self-commutated Back-To-Back converter (BTB), are analyzed by comparison of required minimum capacity of the inverters in a residential distribution network with a large penetration of photovoltaic generations.

  17. 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. PMID:25131828

  18. Bioinspired Principles for Large-Scale Networked Sensor Systems: An Overview

    PubMed Central

    Jacobsen, Rune Hylsberg; Zhang, Qi; Toftegaard, Thomas Skjødeberg

    2011-01-01

    Biology has often been used as a source of inspiration in computer science and engineering. Bioinspired principles have found their way into network node design and research due to the appealing analogies between biological systems and large networks of small sensors. This paper provides an overview of bioinspired principles and methods such as swarm intelligence, natural time synchronization, artificial immune system and intercellular information exchange applicable for sensor network design. Bioinspired principles and methods are discussed in the context of routing, clustering, time synchronization, optimal node deployment, localization and security and privacy. PMID:22163841

  19. 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

  20. A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks

    PubMed Central

    Yin, Junming; Ho, Qirong; Xing, Eric P.

    2014-01-01

    We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction. PMID:25400487

  1. 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.

  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.

    2015-01-01

    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 O (106)) and of edges (up to at least O (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 construct 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 × 106 edges. In less than 5 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 13 min. Par@Graph therefore facilitates the application of climate network analysis on high-resolution observations and model results, by enabling fast network construction 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. 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.

  4. Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks.

    PubMed

    Thompson, Todd W; Waskom, Michael L; Gabrieli, John D E

    2016-04-01

    Working memory is central to human cognition, and intensive cognitive training has been shown to expand working memory capacity in a given domain. It remains unknown, however, how the neural systems that support working memory are altered through intensive training to enable the expansion of working memory capacity. We used fMRI to measure plasticity in activations associated with complex working memory before and after 20 days of training. Healthy young adults were randomly assigned to train on either a dual n-back working memory task or a demanding visuospatial attention task. Training resulted in substantial and task-specific expansion of dual n-back abilities accompanied by changes in the relationship between working memory load and activation. Training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. Activations in both networks linearly scaled with working memory load before training, but training dissociated the role of the two networks and eliminated this relationship in the executive control network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training. PMID:26741799

  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. 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

  7. 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

  8. Large-scale brain networks in cognition: emerging methods and principles.

    PubMed

    Bressler, Steven L; Menon, Vinod

    2010-06-01

    An understanding of how the human brain produces cognition ultimately depends on knowledge of large-scale brain organization. Although it has long been assumed that cognitive functions are attributable to the isolated operations of single brain areas, we demonstrate that the weight of evidence has now shifted in support of the view that cognition results from the dynamic interactions of distributed brain areas operating in large-scale networks. We review current research on structural and functional brain organization, and argue that the emerging science of large-scale brain networks provides a coherent framework for understanding of cognition. Critically, this framework allows a principled exploration of how cognitive functions emerge from, and are constrained by, core structural and functional networks of the brain. PMID:20493761

  9. Reconstruction of large-scale gene regulatory networks using Bayesian model averaging.

    PubMed

    Kim, Haseong; Gelenbe, Erol

    2012-09-01

    Gene regulatory networks provide the systematic view of molecular interactions in a complex living system. However, constructing large-scale gene regulatory networks is one of the most challenging problems in systems biology. Also large burst sets of biological data require a proper integration technique for reliable gene regulatory network construction. Here we present a new reverse engineering approach based on Bayesian model averaging which attempts to combine all the appropriate models describing interactions among genes. This Bayesian approach with a prior based on the Gibbs distribution provides an efficient means to integrate multiple sources of biological data. In a simulation study with maximum of 2000 genes, our method shows better sensitivity than previous elastic-net and Gaussian graphical models, with a fixed specificity of 0.99. The study also shows that the proposed method outperforms the other standard methods for a DREAM dataset generated by nonlinear stochastic models. In brain tumor data analysis, three large-scale networks consisting of 4422 genes were built using the gene expression of non-tumor, low and high grade tumor mRNA expression samples, along with DNA-protein binding affinity information. We found that genes having a large variation of degree distribution among the three tumor networks are the ones that see most involved in regulatory and developmental processes, which possibly gives a novel insight concerning conventional differentially expressed gene analysis. PMID:22987132

  10. 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

  11. Channel Networks on Large Fans: Refining Analogs for the Ridge-forming Unit, Sinus Meridiani

    NASA Technical Reports Server (NTRS)

    Wilkinson, Justin

    2009-01-01

    Stream channels are generally thought of as forming within confined valley settings, separated by interfluves. Sinuous ridges on Mars and Earth are often interpreted as stream channels inverted by subsequent erosion of valley sides. In the case of the ridge-forming unit (RFU), this interpretation fails to explain the (i) close spacing of the ridges, which are (ii) organized in networks, and which (iii) cover large areas (approximately 175,000 km (exp 2)). Channel networks on terrestrial fans develop unconfined by valley slopes. Large fans (100s km long) are low-angle, fluvial features, documented worldwide, with characteristics that address these aspects of the RFU. Ridge patterns Channels on large fans provide an analog for the sinuous and elongated morphology of RFU ridges, but more especially for other patterns such as subparallel, branching and crossing networks. Branches are related to splays (delta-like distributaries are rare), whose channels can rejoin the main channel. Crossing patterns can be caused by even slight sinuosity splay-related side channels often intersect. An avulsion node distant from the fan apex, gives rise to channels with slightly different, and hence intersecting, orientations. Channels on neighboring fans intersect along the common fan margin. 2. Network density Channels are the dominant feature on large terrestrial fans (lakes and dune fields are minor). Inverted landscapes on subsequently eroded fans thus display indurated channels as networks of significantly close-spaced ridges. 3. Channel networks covering large areas Areas of individual large terrestrial fans can reach >200,000 km 2 (105-6 km 2 with nested fans), providing an analog for the wide area distribution of the RFU.

  12. Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction

    NASA Astrophysics Data System (ADS)

    Šubelj, Lovro; Bajec, Marko

    2011-03-01

    Label propagation has proven to be a fast method for detecting communities in large complex networks. Recent developments have also improved the accuracy of the approach; however, a general algorithm is still an open issue. We present an advanced label propagation algorithm that combines two unique strategies of community formation, namely, defensive preservation and offensive expansion of communities. The two strategies are combined in a hierarchical manner to recursively extract the core of the network and to identify whisker communities. The algorithm was evaluated on two classes of benchmark networks with planted partition and on 23 real-world networks ranging from networks with tens of nodes to networks with several tens of millions of edges. It is shown to be comparable to the current state-of-the-art community detection algorithms and superior to all previous label propagation algorithms, with comparable time complexity. In particular, analysis on real-world networks has proven that the algorithm has almost linear complexity, O(m1.19), and scales even better than the basic label propagation algorithm (m is the number of edges in the network).

  13. 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.

  14. Reconstruction of canine diffuse large B-cell lymphoma gene regulatory network: detection of functional modules and hub genes.

    PubMed

    Zamani-Ahmadmahmudi, M; Najafi, A; Nassiri, S M

    2015-01-01

    Lymphoma is one of the most common malignancies in dogs. Canine lymphoma is similar to human non-Hodgkin's lymphoma (NHL) with shared clinical presentation and histopathological features. This study reports the construction of a comprehensive gene regulatory network (GRN) for canine diffuse large B-cell lymphoma (DLBCL), the most common type of canine lymphoma, and performs analysis for detection of major functional modules and hub genes (the most important genes in a GRN). The canine DLBCL GRN was reconstructed from gene expression data (NCBI GEO dataset: GSE30881) using the STRING and MiMI interaction databases. Reconstructed GRNs were then assessed, using various bioinformatics programmes, in order to analyze network topology and identify major pathways and hub genes. The resultant network from both interaction databases had a logically scale-free pattern. Gene ontology (GO) analysis revealed cell activation, cell cycle phase, immune effector process, immune system development, immune system process, integrin-mediated signalling pathway, intracellular protein kinase cascade, intracellular signal transduction, leucocyte activation and differentiation, lymphocyte activation and differentiation as major GO terms in the biological processes of the networks. Moreover, bioinformatics analysis showed E2F1, E2F4, PTEN, CDKN1A, PCNA, DKC1, MNAT1, NDUFB4, ATP5J, PRKDC, BRCA1, MYCN, RFC4 and POLA1 as the most important hub genes. The phosphatidyl inositol signalling system, P53 signalling pathway, Rac CycD pathway, G1/S checkpoint, chemokine signalling pathway and telomere maintenance were the main signalling pathways in which the protein products of the hub genes are involved. PMID:25678421

  15. 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

  16. 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

  17. Scalability of Abstraction-Network-Based Quality Assurance to Large SNOMED Hierarchies

    PubMed Central

    Ochs, Christopher; Perl, Yehoshua; Geller, James; Halper, Michael; Gu, Huanying; Chen, Yan; Elhanan, Gai

    2013-01-01

    Abstraction networks are compact summarizations of terminologies used to support orientation and terminology quality assurance (TQA). Area taxonomies and partial-area taxonomies are abstraction networks that have been successfully employed in support of TQA of small SNOMED CT hierarchies. However, nearly half of SNOMED CT’s concepts are in the large Procedure and Clinical Finding hierarchies. Abstraction network derivation methodologies applied to those hierarchies resulted in taxonomies that were too large to effectively support TQA. A methodology for deriving sub-taxonomies from large taxonomies is presented, and the resultant smaller abstraction networks are shown to facilitate TQA, allowing for the scaling of our taxonomy-based TQA regimen to large hierarchies. Specifically, sub-taxonomies are derived for the Procedure hierarchy and a review for errors and inconsistencies is performed. Concepts are divided into groups within the sub-taxonomy framework, and it is shown that small groups are statistically more likely to harbor erroneous and inconsistent concepts than large groups. PMID:24551393

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Active Self-Testing Noise Measurement Sensors for Large-Scale Environmental Sensor Networks

    PubMed Central

    Domínguez, Federico; Cuong, Nguyen The; Reinoso, Felipe; Touhafi, Abdellah; Steenhaut, Kris

    2013-01-01

    Large-scale noise pollution sensor networks consist of hundreds of spatially distributed microphones that measure environmental noise. These networks provide historical and real-time environmental data to citizens and decision makers and are therefore a key technology to steer environmental policy. However, the high cost of certified environmental microphone sensors render large-scale environmental networks prohibitively expensive. Several environmental network projects have started using off-the-shelf low-cost microphone sensors to reduce their costs, but these sensors have higher failure rates and produce lower quality data. To offset this disadvantage, we developed a low-cost noise sensor that actively checks its condition and indirectly the integrity of the data it produces. The main design concept is to embed a 13 mm speaker in the noise sensor casing and, by regularly scheduling a frequency sweep, estimate the evolution of the microphone's frequency response over time. This paper presents our noise sensor's hardware and software design together with the results of a test deployment in a large-scale environmental network in Belgium. Our middle-range-value sensor (around €50) effectively detected all experienced malfunctions, in laboratory tests and outdoor deployments, with a few false positives. Future improvements could further lower the cost of our sensor below €10. PMID:24351634

  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 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

  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. Coordinated Information Generation and Mental Flexibility: Large-Scale Network Disruption in Children with Autism.

    PubMed

    Mišić, Bratislav; Doesburg, Sam M; Fatima, Zainab; Vidal, Julie; Vakorin, Vasily A; Taylor, Margot J; McIntosh, Anthony R

    2015-09-01

    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

  8. 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.

  9. Proposal for Environmental Observation System for Large Scale Gas Pipeline Networks Using Unmanned Airship

    NASA Astrophysics Data System (ADS)

    Shiho, Makoto; Horioka, Kazuhiko; Inoue, Gen; Onda, Masahiko; Leighty, William C.; Yokoo, Kuniyoshi; Ono, Shoichi; Ohashi, Kazuhiko; Hirata, Masaru

    2004-03-01

    Construction of a large scale natural gas pipeline network system in the Northeast Asian area has been proposed by several researchers, including Prof. Masaru Hirata, which will extend over tens of thousands of kilometers. To monitor the gas leakage, and to cope with any other hazardous problems, continuous surveillance of the network will be required. For this purpose, an environmental observation system for the large scale pipeline network is proposed. In this system unmanned airships are used as platforms for various environmental diagnostics. The unmanned airship is routed along the pipeline with the aid of GPS. Propulsion power of the air ship is transmitted from the ground bases by microwave; the microwave power stations are located every 100-200km along the pipeline. This paper describes the unmanned airships, environmental diagnostic systems, microwave generation tubes, and microwave powering system.

  10. 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

  11. Large-Scale Brain Network Coupling Predicts Acute Nicotine Abstinence Effects on Craving and Cognitive Function

    PubMed Central

    Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A.

    2014-01-01

    IMPORTANCE Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. OBJECTIVES To test the hypothesis that the strength of coupling among 3 large-scale brain networks–salience, executive control, and default mode–will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. DESIGN, SETTING, AND PARTICIPANTS A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. INTERVENTIONS Twenty-four hours of abstinence vs smoking satiety. MAIN OUTCOMES AND MEASURES Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). RESULTS The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = −0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = −0.66, P = .003; posterior cingulate cortex, r = −0.65, P = .001). CONCLUSIONS AND RELEVANCE Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may

  12. 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

  13. 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

  14. Validating the BERMS in situ soil moisture network with a large scale temporary network

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Calibration and validation of soil moisture satellite products requires data records of large spatial and temporal extent, but obtaining this data can be challenging. These challenges can include remote locations, and expense of equipment. One location with a long record of soil moisture data is th...

  15. 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…

  16. 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…

  17. 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.

  18. Synchronization of the DOE/NASA 100-kilowatt wind turbine generator with a large utility network

    NASA Technical Reports Server (NTRS)

    Gilbert, L. J.

    1977-01-01

    The DOE/NASA 100 kilowatt wind turbine generator system was synchronized with a large utility network. The system equipments and procedures associated with the synchronization process were described. Time history traces of typical synchronizations were presented indicating that power and current transients resulting from the synchronizing procedure are limited to acceptable magnitudes.

  19. 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.

  20. Damage identification for large span structure based on multiscale inputs to artificial neural networks.

    PubMed

    Lu, Wei; Teng, Jun; Cui, Yan

    2014-01-01

    In structural health monitoring system, little research on the damage identification from different types of sensors applied to large span structure has been done in the field. In fact, it is significant to estimate the whole structural safety if the multitype sensors or multiscale measurements are used in application of structural health monitoring and the damage identification for large span structure. A methodology to combine the local and global measurements in noisy environments based on artificial neural network is proposed in this paper. For a real large span structure, the capacity of the methodology is validated, including the decision on damage placement, the discussions on the number of the sensors, and the optimal parameters for artificial neural networks. Furthermore, the noisy environments in different levels are simulated to demonstrate the robustness and effectiveness of the proposed approach. PMID:24977207

  1. Network response to disturbances in large sand-bed braided rivers

    NASA Astrophysics Data System (ADS)

    Schuurman, F.; Kleinhans, M. G.; Middelkoop, H.

    2016-01-01

    The reach-scale effects of human-induced disturbances on the channel network in large braided rivers are a challenge to understand and to predict. In this study, we simulated different types of disturbances in a large braided river to get insight into the propagation of disturbances through a braided channel network. The results showed that the disturbances initiate an instability that propagates in the downstream direction by means of alteration of water and sediment division at bifurcations. These adjustments of the bifurcations change the migration and shape of bars, with a feedback to the upstream bifurcation and alteration of the approaching flow to the downstream bifurcation. This way, the morphological effect of a disturbance amplifies in the downstream direction. Thus, the interplay of bifurcation instability and asymmetrical reshaping of bars was found to be essential for propagation of the effects of a disturbance. The study also demonstrated that the large-scale bar statistics are hardly affected.

  2. A Hierarchical Computer Network: An Alternative Approach to Clinical Laboratory Computerization in a Large Hospital

    PubMed Central

    Miller, Robert E.; Steinbach, Glen L.; Dayhoff, Ruth E.

    1980-01-01

    Computerized data handling is recognized as an essential aspect of the modern clinical laboratory in medium and large sized hospitals. However, most currently installed proprietary or “turnkey” systems are often hardware/software-constrained and based on outmoded design concepts which seriously limit the use of the laboratory computer system as an effective patient care, research, and management tool. These short-comings are particularly serious in the large university teaching hospital. Recent improvements in the price/performance ratio for computer hardware, the availability of specialized high-level applications-oriented languages, and advances in data communications have permitted development of powerful computer networks which are economically feasible in the large hospital setting. An operational three-tiered hierarchical network for clinical laboratory data processing is described. The integration of the clinical laboratory data processing function into overall institutional information processing, details of the computer system configuration, and the benefits realized are discussed.

  3. Damage Identification for Large Span Structure Based on Multiscale Inputs to Artificial Neural Networks

    PubMed Central

    Teng, Jun; Cui, Yan

    2014-01-01

    In structural health monitoring system, little research on the damage identification from different types of sensors applied to large span structure has been done in the field. In fact, it is significant to estimate the whole structural safety if the multitype sensors or multiscale measurements are used in application of structural health monitoring and the damage identification for large span structure. A methodology to combine the local and global measurements in noisy environments based on artificial neural network is proposed in this paper. For a real large span structure, the capacity of the methodology is validated, including the decision on damage placement, the discussions on the number of the sensors, and the optimal parameters for artificial neural networks. Furthermore, the noisy environments in different levels are simulated to demonstrate the robustness and effectiveness of the proposed approach. PMID:24977207

  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. PMID:27133613

  5. Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

    PubMed Central

    Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

    2013-01-01

    Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank. PMID:24204833

  6. 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.

  7. 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. 

  8. Large-area soft-imprinted nanowire networks as light trapping transparent conductors.

    PubMed

    van de Groep, Jorik; Gupta, Dhritiman; Verschuuren, Marc A; Wienk, Martijn M; Janssen, Rene A J; Polman, Albert

    2015-01-01

    Using soft-imprint nanolithography, we demonstrate large-area application of engineered two-dimensional polarization-independent networks of silver nanowires as transparent conducting electrodes. These networks have high optical transmittance, low electrical sheet resistance, and at the same time function as a photonic light-trapping structure enhancing optical absorption in the absorber layer of thin-film solar cells. We study the influence of nanowire width and pitch on the network transmittance and sheet resistance, and demonstrate improved performance compared to ITO. Next, we use P3HT-PCBM organic solar cells as a model system to show the realization of nanowire network based functional devices. Using angle-resolved external quantum efficiency measurements, we demonstrate engineered light trapping by coupling to guided modes in the thin absorber layer of the solar cell. Concurrent to the direct observation of controlled light trapping we observe a reduction in photocurrent as a result of increased reflection and parasitic absorption losses; such losses can be minimized by re-optimization of the NW network geometry. Together, these results demonstrate how engineered 2D NW networks can serve as multifunctional structures that unify the functions of a transparent conductor and a light trapping structure. These results are generic and can be applied to any type of optoelectronic device. PMID:26091006

  9. 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

  10. Large-area soft-imprinted nanowire networks as light trapping transparent conductors

    NASA Astrophysics Data System (ADS)

    van de Groep, Jorik; Gupta, Dhritiman; Verschuuren, Marc A.; M. Wienk, Martijn; Janssen, Rene A. J.; Polman, Albert

    2015-06-01

    Using soft-imprint nanolithography, we demonstrate large-area application of engineered two-dimensional polarization-independent networks of silver nanowires as transparent conducting electrodes. These networks have high optical transmittance, low electrical sheet resistance, and at the same time function as a photonic light-trapping structure enhancing optical absorption in the absorber layer of thin-film solar cells. We study the influence of nanowire width and pitch on the network transmittance and sheet resistance, and demonstrate improved performance compared to ITO. Next, we use P3HT-PCBM organic solar cells as a model system to show the realization of nanowire network based functional devices. Using angle-resolved external quantum efficiency measurements, we demonstrate engineered light trapping by coupling to guided modes in the thin absorber layer of the solar cell. Concurrent to the direct observation of controlled light trapping we observe a reduction in photocurrent as a result of increased reflection and parasitic absorption losses; such losses can be minimized by re-optimization of the NW network geometry. Together, these results demonstrate how engineered 2D NW networks can serve as multifunctional structures that unify the functions of a transparent conductor and a light trapping structure. These results are generic and can be applied to any type of optoelectronic device.

  11. 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.

  12. 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

  13. 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

  14. Networks of silicon nanowires: A large-scale atomistic electronic structure analysis

    NASA Astrophysics Data System (ADS)

    Keleş, Ümit; Liedke, Bartosz; Heinig, Karl-Heinz; Bulutay, Ceyhun

    2013-11-01

    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.

  15. A new traffic control design method for large networks with signalized intersections

    NASA Technical Reports Server (NTRS)

    Leininger, G. G.; Colony, D. C.; Seldner, K.

    1979-01-01

    The paper presents a traffic control design technique for application to large traffic networks with signalized intersections. It is shown that the design method adopts a macroscopic viewpoint to establish a new traffic modelling procedure in which vehicle platoons are subdivided into main stream queues and turning queues. Optimization of the signal splits minimizes queue lengths in the steady state condition and improves traffic flow conditions, from the viewpoint of the traveling public. Finally, an application of the design method to a traffic network with thirty-three signalized intersections is used to demonstrate the effectiveness of the proposed technique.

  16. 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.

  17. 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.

  18. 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

  19. 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…

  20. 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…

  1. Large fontanelles are a shared feature of haploinsufficiency of RUNX2 and its co-activator CBFB.

    PubMed

    Goto, Tomohide; Aramaki, Michihiko; Yoshihashi, Hiroshi; Nishimura, Gen; Hasegawa, Yukihiro; Takahashi, Takao; Ishii, Takashi; Fukushima, Yoshimitsu; Kosaki, Kenjiro

    2004-12-01

    CBFB at 16q22 heterodimerizes with either RUNX2 (also known as CBFA1) or RUNX1 (CBFA2) to activate the transcription of downstream molecules. RUNX2 regulates osteoblast differentiation and chondrocyte maturation and its haploinsufficiency leads to cleidocranial dysplasia, characterized large fontanelles, hypoplasia or aplasia of the clavicles, hypoplasia of the distal phalanges, and a wide pubic symphysis. Complete loss of Runx1 or Cbfb in mice is lethal because of the absence of fetal liver hematopoiesis. Fetal rescue in Cbfb(-/-) mice by providing the Cbfb functions in the hematopoietic progenitors leads to wide fontanelle and delayed chondrocyte maturation, presumably resulting from the incomplete function of the transcriptional pathway mediated by the Cbfb-Runx2 heterodimer. The present report describes a patient with a small deletion of chromosome 16q22.1 encompassing CBFB. Skeletal abnormalities included a widely open fontanelle, multiple wormian bones along the sagittal suture, hypoplasia of the distal phalanges, and mildly shortened clavicles. G-banding analysis revealed a shortening of the 16q22.1 band. A fluorescence in situ hybridization analysis, using the BAC probe spanning the CBFB locus at 16q22.1, revealed that the CBFB probe hybridized to only one of the two homologous chromosome 16 regions. Array-comparative genomic hybridization analysis revealed that the deletion spans 1.2 megabases. In reviewing eight previously reported cases of 16q interstitial deletions involving band q22, large cranial sutures were noted in all but one case. Considering the phenotypic similarity of the 16q22 deletion case and Cbfb(-/-) mice rescued for hematopoiesis and the consistency of the phenotype among 16q22 deletion cases, we suggest that the common phenotypic feature of the 16q22 deletion, large fontanelles, can be attributed to a haploinsufficiency of CBFB. PMID:15566413

  2. Teaching Community Networks: A Case Study of Informal Social Support and Information Sharing among Sociology Graduate Students

    ERIC Educational Resources Information Center

    Hunt, Andrea N.; Mair, Christine A.; Atkinson, Maxine P.

    2012-01-01

    Despite the prominence of teaching in academia, we know little about how graduate students learn to teach. We propose the concept of a teaching community network (TCN), an informal social network that facilitates the exchange of teaching-specific resources. We explore the role of TCNs through a case study of a sociology doctoral program at a large…

  3. 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.

  4. 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

  5. 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

  6. 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

  7. Collaboratively Sharing Scientific Data

    NASA Astrophysics Data System (ADS)

    Wang, Fusheng; Vergara-Niedermayr, Cristobal

    Scientific research becomes increasingly reliant on multi-disciplinary, multi-institutional collaboration through sharing experimental data. Indeed, data sharing is mandatory by government research agencies such as NIH. The major hurdles for data sharing come from: i) the lack of data sharing infrastructure to make data sharing convenient for users; ii) users’ fear of losing control of their data; iii) difficulty on sharing schemas and incompatible data from sharing partners; and iv) inconsistent data under schema evolution. In this paper, we develop a collaborative data sharing system SciPort, to support consistency preserved data sharing among multiple distributed organizations. The system first provides Central Server based lightweight data integration architecture, so data and schemas can be conveniently shared across multiple organizations. Through distributed schema management, schema sharing and evolution is made possible, while data consistency is maintained and data compatibility is enforced. With this data sharing system, distributed sites can now consistently share their research data and their associated schemas with much convenience and flexibility. SciPort has been successfully used for data sharing in biomedical research, clinical trials and large scale research collaboration.

  8. 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

  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. Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks.

    PubMed

    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

  11. Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

    DOE PAGESBeta

    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. 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

  13. Near linear time algorithm to detect community structures in large-scale networks

    NASA Astrophysics Data System (ADS)

    Raghavan, Usha Nandini; Albert, Réka; Kumara, Soundar

    2007-09-01

    Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.

  14. 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

  15. 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.

  16. 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.

  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. Exploring the low-energy landscape of large-scale signed social networks

    NASA Astrophysics Data System (ADS)

    Facchetti, G.; Iacono, G.; Altafini, C.

    2012-09-01

    Analogously to a spin glass, a large-scale signed social network is characterized by the presence of disorder, expressed in this context (and in the social network literature) by the concept of structural balance. If, as we have recently shown, the signed social networks currently available have a limited amount of true disorder (or frustration), it is also interesting to investigate how this frustration is organized, by exploring the landscape of near-optimal structural balance. What we obtain in this paper is that while one of the networks analyzed shows a unique valley of minima, and a funneled landscape that gradually and smoothly worsens as we move away from the optimum, another network shows instead several distinct valleys of optimal or near-optimal structural balance, separated by energy barriers determined by internally balanced subcommunities of users, a phenomenon similar to the replica-symmetry breaking of spin glasses. Multiple, essentially isoenergetic, arrangements of these communities are possible. Passing from one valley to another requires one to destroy the internal arrangement of these balanced subcommunities and then to reform it again. It is essentially this process of breaking the internal balance of the subcommunities which gives rise to the energy barriers.

  19. 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.

  20. 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.; Dittman, T.; Feaux, K.; Gallaher, W. W.; Mattioli, G. S.; Mencin, D.; Walls, C. P.

    2013-12-01

    The EarthScope Plate Boundary Observatory (PBO), through a NSF-ARRA supplement, has enhanced the geophysical infrastructure in in the Pacific Northwest by upgrading 232 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 the Pacific Northwest 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 total of 282 PBO stations were upgraded and added to the UNAVCO real-time GPS system, along with addition of 22 new meteorological instruments 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 UNAVCO's data infrastructure included evaluation and purchase of the Trimble Pivot Platform, servers, and additional hardware for archiving the high rate data. UNAVCO staff is working closely with the UNAVCO community to develop data standards, protocols, and a science plan for the use of RT-GPS data.

  1. 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

  2. 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

    2016-01-01

    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

  3. Visualization, documentation, analysis, and communication of large scale gene regulatory networks

    PubMed Central

    Longabaugh, William J.R.; Davidson, Eric H.; Bolouri, Hamid

    2009-01-01

    Summary Genetic regulatory networks (GRNs) are complex, large-scale, and spatially and temporally distributed. These characteristics impose challenging demands on computational GRN modeling tools, and there is a need for custom modeling tools. In this paper, we report on our ongoing development of BioTapestry, an open source, freely available computational tool designed specifically for GRN modeling. We also outline our future development plans, and give some examples of current applications of BioTapestry. PMID:18757046

  4. 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…

  5. 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. PMID:26553014

  6. 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

  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 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.

  9. 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.

  10. 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

  11. 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.

  12. Large-scale changes in network interactions as a physiological signature of spatial neglect

    PubMed Central

    Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L.; Callejas, Alicia; Astafiev, Serguei V.; Metcalf, Nicholas V.; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z.; Carter, Alex R.; Shulman, Gordon L.

    2014-01-01

    The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n = 84) heterogeneous sample of first-ever stroke patients (within 1–2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode

  13. Emotional speech synchronizes brains across listeners and engages large-scale dynamic brain networks

    PubMed Central

    Nummenmaa, Lauri; Saarimäki, Heini; Glerean, Enrico; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P.; Hari, Riitta; Sams, Mikko

    2014-01-01

    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. PMID:25128711

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. The topology of large Open Connectome networks for the human brain.

    PubMed

    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

  1. 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

  2. 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

  3. 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

  4. 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.

  5. BFL: a node and edge betweenness based fast layout algorithm for large scale networks

    PubMed Central

    Hashimoto, Tatsunori B; Nagasaki, Masao; Kojima, Kaname; Miyano, Satoru

    2009-01-01

    Background Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements. Results To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths. Conclusion Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer. PMID:19146673

  6. Aloe vera non-decolorized whole leaf extract-induced large intestinal tumors in F344 rats share similar molecular pathways with human sporadic colorectal tumors.

    PubMed

    Pandiri, Arun R; Sills, Robert C; Hoenerhoff, Mark J; Peddada, Shyamal D; Ton, Thai-Vu T; Hong, Hue-Hua L; Flake, Gordon P; Malarkey, David E; Olson, Greg R; Pogribny, Igor P; Walker, Nigel J; Boudreau, Mary D

    2011-12-01

    Aloe vera is one of the most commonly used botanicals for various prophylactic and therapeutic purposes. Recently, NTP/NCTR has demonstrated a dose-dependent increase in large intestinal tumors in F344 rats chronically exposed to Aloe barbadensis Miller (Aloe vera) non-decolorized whole leaf extract (AVNWLE) in drinking water. The morphological and molecular pathways of AVNWLE-induced large intestinal tumors in the F344 rats were compared to human colorectal cancer (hCRC) literature. Defined histological criteria were used to compare AVNWLE-induced large intestinal tumors with hCRC. The commonly mutated genes (Kras, Ctnnb1, and Tp53) and altered signaling pathways (MAPK, WNT, and TGF-β) important in hCRC were evaluated within AVNWLE-induced large intestinal tumors. Histological evaluation of the large intestinal tumors indicated eight of twelve adenomas (Ads) and four of twelve carcinomas (Cas). Mutation analysis of eight Ads and four Cas identified point mutations in exons 1 and 2 of the Kras gene (two of eight Ads, two of four Cas), and in exon 2 of the Ctnnb1 gene (three of eight Ads, one of four Cas). No Tp53 (exons 5-8) mutations were found in Ads or Cas. Molecular pathways important in hCRC such as MAPK, WNT, and TGF-β signaling were also altered in AVNWLE-induced Ads and Cas. In conclusion, the AVNWLE-induced large intestinal tumors in F344 rats share several similarities with hCRC at the morphological and molecular levels. PMID:21937742

  7. Aloe vera Non-Decolorized Whole Leaf Extract-Induced Large Intestinal Tumors in F344 Rats Share Similar Molecular Pathways with Human Sporadic Colorectal Tumors

    PubMed Central

    Pandiri, Arun R.; Sills, Robert C.; Hoenerhoff, Mark J.; Peddada, Shyamal D.; Ton, Thai-Vu T.; Hong, Hue-Hua L.; Flake, Gordon P.; Malarkey, David E.; Olson, Greg R.; Pogribny, Igor P.; Walker, Nigel J.; Boudreau, Mary D.

    2016-01-01

    Aloe vera is one of the most commonly used botanicals for various prophylactic and therapeutic purposes. Recently, NTP/NCTR has demonstrated a dose-dependent increase in large intestinal tumors in F344 rats chronically exposed to Aloe barbadensis Miller (Aloe vera) non-decolorized whole leaf extract (AVNWLE) in drinking water. The morphological and molecular pathways of AVNWLE-induced large intestinal tumors in the F344 rats were compared to human colorectal cancer (hCRC) literature. Defined histological criteria were used to compare AVNWLE-induced large intestinal tumors with hCRC. The commonly mutated genes (Kras, Ctnnb1, and Tp53) and altered signaling pathways (MAPK, WNT, and TGF-β) important in hCRC were evaluated within AVNWLE-induced large intestinal tumors. Histological evaluation of the large intestinal tumors indicated eight of twelve adenomas (Ads) and four of twelve carcinomas (Cas). Mutation analysis of eight Ads and four Cas identified point mutations in exons 1 and 2 of the Kras gene (two of eight Ads, two of four Cas), and in exon 2 of the Ctnnb1 gene (three of eight Ads, one of four Cas). No Tp53 (exons 5–8) mutations were found in Ads or Cas. Molecular pathways important in hCRC such as MAPK, WNT, and TGF-β signaling were also altered in AVNWLE-induced Ads and Cas. In conclusion, the AVNWLE-induced large intestinal tumors in F344 rats share several similarities with hCRC at the morphological and molecular levels. PMID:21937742

  8. Streaming parallel GPU acceleration of large-scale filter-based spiking neural networks.

    PubMed

    Slażyński, Leszek; Bohte, Sander

    2012-01-01

    The arrival of graphics processing (GPU) cards suitable for massively parallel computing promises affordable large-scale neural network simulation previously only available at supercomputing facilities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of magnitude, the challenge is to develop fine-grained parallel algorithms to fully exploit the particulars of GPUs. Computation in a neural network is inherently parallel and thus a natural match for GPU architectures: given inputs, the internal state for each neuron can be updated in parallel. We show that for filter-based spiking neurons, like the Spike Response Model, the additive nature of membrane potential dynamics enables additional update parallelism. This also reduces the accumulation of numerical errors when using single precision computation, the native precision of GPUs. We further show that optimizing simulation algorithms and data structures to the GPU's architecture has a large pay-off: for example, matching iterative neural updating to the memory architecture of the GPU speeds up this simulation step by a factor of three to five. With such optimizations, we can simulate in better-than-realtime plausible spiking neural networks of up to 50 000 neurons, processing over 35 million spiking events per second. PMID:23098420

  9. Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions.

    PubMed

    Najafi, Mahshid; McMenamin, Brenton W; Simon, Jonathan Z; Pessoa, Luiz

    2016-07-15

    Large-scale analysis of functional MRI data has revealed that brain regions can be grouped into stable "networks" or communities. In many instances, the communities are characterized as relatively disjoint. Although recent work indicates that brain regions may participate in multiple communities (for example, hub regions), the extent of community overlap is poorly understood. To address these issues, here we investigated large-scale brain networks based on "rest" and task human functional MRI data by employing a mixed-membership Bayesian model that allows each brain region to belong to all communities simultaneously with varying membership strengths. The approach allowed us to 1) compare the structure of disjoint and overlapping communities; 2) determine the relationship between functional diversity (how diverse is a region's functional activation repertoire) and membership diversity (how diverse is a region's affiliation to communities); 3) characterize overlapping community structure; 4) characterize the degree of non-modularity in brain networks; 5) study the distribution of "bridges", including bottleneck and hub bridges. Our findings revealed the existence of dense community overlap that was not limited to "special" hubs. Furthermore, the findings revealed important differences between community organization during rest and during specific task states. Overall, we suggest that dense overlapping communities are well suited to capture the flexible and task dependent mapping between brain regions and their functions. PMID:27129758

  10. On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

    NASA Astrophysics Data System (ADS)

    Thanh, Vo Hong; Zunino, Roberto; Priami, Corrado

    2015-06-01

    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.

  11. Large and stable emission current from synthesized carbon nanotube/fiber network

    NASA Astrophysics Data System (ADS)

    Di, Yunsong; Xiao, Mei; Zhang, Xiaobing; Wang, Qilong; Li, Chen; Lei, Wei; Cui, Yunkang

    2014-02-01

    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.

  12. 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.

  13. 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

  14. 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

  15. 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.

  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. 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. PMID:24794073

  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. PMID:22508131

  19. Large networks of vertical multi-layer graphenes with morphology-tunable magnetoresistance.

    PubMed

    Yue, Zengji; Levchenko, Igor; Kumar, Shailesh; Seo, Donghan; Wang, Xiaolin; Dou, Shixue; Ostrikov, Kostya Ken

    2013-10-01

    We report on the comparative study of magnetotransport properties of large-area vertical few-layer graphene networks with different morphologies, measured in a strong (up to 10 T) magnetic field over a wide temperature range. The petal-like and tree-like graphene networks grown by a plasma enhanced CVD process on a thin (500 nm) silicon oxide layer supported by a silicon wafer demonstrate a significant difference in the resistance-magnetic field dependencies at temperatures ranging from 2 to 200 K. This behaviour is explained in terms of the effect of electron scattering at ultra-long reactive edges and ultra-dense boundaries of the graphene nanowalls. Our results pave a way towards three-dimensional vertical graphene-based magnetoelectronic nanodevices with morphology-tuneable anisotropic magnetic properties. PMID:23603856

  20. 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.

  1. Exact mesh shape design of large cable-network antenna reflectors with flexible ring truss supports

    NASA Astrophysics Data System (ADS)

    Liu, Wang; Li, Dong-Xu; Yu, Xin-Zhan; Jiang, Jian-Ping

    2014-04-01

    An exact-designed mesh shape with favorable surface accuracy is of practical significance to the performance of large cable-network antenna reflectors. In this study, a novel design approach that could guide the generation of exact spatial parabolic mesh configurations of such reflector was proposed. By incorporating the traditional force density method with the standard finite element method, this proposed approach had taken the deformation effects of flexible ring truss supports into consideration, and searched for the desired mesh shapes that can satisfy the requirement that all the free nodes are exactly located on the objective paraboloid. Compared with the conventional design method, a remarkable improvement of surface accuracy in the obtained mesh shapes had been demonstrated by numerical examples. The present work would provide a helpful technical reference for the mesh shape design of such cable-network antenna reflector in engineering practice. [Figure not available: see fulltext.

  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. Large networks of vertical multi-layer graphenes with morphology-tunable magnetoresistance

    NASA Astrophysics Data System (ADS)

    Yue, Zengji; Levchenko, Igor; Kumar, Shailesh; Seo, Donghan; Wang, Xiaolin; Dou, Shixue; Ostrikov, Kostya (Ken)

    2013-09-01

    We report on the comparative study of magnetotransport properties of large-area vertical few-layer graphene networks with different morphologies, measured in a strong (up to 10 T) magnetic field over a wide temperature range. The petal-like and tree-like graphene networks grown by a plasma enhanced CVD process on a thin (500 nm) silicon oxide layer supported by a silicon wafer demonstrate a significant difference in the resistance-magnetic field dependencies at temperatures ranging from 2 to 200 K. This behaviour is explained in terms of the effect of electron scattering at ultra-long reactive edges and ultra-dense boundaries of the graphene nanowalls. Our results pave a way towards three-dimensional vertical graphene-based magnetoelectronic nanodevices with morphology-tuneable anisotropic magnetic properties.We report on the comparative study of magnetotransport properties of large-area vertical few-layer graphene networks with different morphologies, measured in a strong (up to 10 T) magnetic field over a wide temperature range. The petal-like and tree-like graphene networks grown by a plasma enhanced CVD process on a thin (500 nm) silicon oxide layer supported by a silicon wafer demonstrate a significant difference in the resistance-magnetic field dependencies at temperatures ranging from 2 to 200 K. This behaviour is explained in terms of the effect of electron scattering at ultra-long reactive edges and ultra-dense boundaries of the graphene nanowalls. Our results pave a way towards three-dimensional vertical graphene-based magnetoelectronic nanodevices with morphology-tuneable anisotropic magnetic properties. Electronic supplementary information (ESI) available: Fig. S1-S6, a schematic of the experimental setup, SEM and TEM characterizations, and details of electrical measurements. See DOI: 10.1039/c3nr00550j

  4. Large-scale topology and the default mode network in the mouse connectome

    PubMed Central

    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.

    2014-01-01

    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

  5. 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…

  6. 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…

  7. Stress-induced alterations in large-scale functional networks of the rodent brain.

    PubMed

    Henckens, Marloes J A G; van der Marel, Kajo; van der Toorn, Annette; Pillai, Anup G; Fernández, Guillén; Dijkhuizen, Rick M; Joëls, Marian

    2015-01-15

    Stress-related psychopathology is associated with altered functioning of large-scale brain networks. Animal research into chronic stress, one of the most prominent environmental risk factors for development of psychopathology, has revealed molecular and cellular mechanisms potentially contributing to human mental disease. However, so far, these studies have not addressed the system-level changes in extended brain networks, thought to critically contribute to mental disorders. We here tested the effects of chronic stress exposure (10 days immobilization) on the structural integrity and functional connectivity patterns in the brain, using high-resolution structural MRI, diffusion kurtosis imaging, and resting-state functional MRI, while confirming the expected changes in neuronal dendritic morphology using Golgi-staining. Stress effectiveness was confirmed by a significantly lower body weight and increased adrenal weight. In line with previous research, stressed animals displayed neuronal dendritic hypertrophy in the amygdala and hypotrophy in the hippocampal and medial prefrontal cortex. Using independent component analysis of resting-state fMRI data, we identified ten functional connectivity networks in the rodent brain. Chronic stress appeared to increase connectivity within the somatosensory, visual, and default mode networks. Moreover, chronic stress exposure was associated with an increased volume and diffusivity of the lateral ventricles, whereas no other volumetric changes were observed. This study shows that chronic stress exposure in rodents induces alterations in functional network connectivity strength which partly resemble those observed in stress-related psychopathology. Moreover, these functional consequences of stress seem to be more prominent than the effects on gross volumetric change, indicating their significance for future research. PMID:25462693

  8. 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. PMID:25969194

  9. 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

  10. EnArchi: A Robust and Manageable Approach for Dynamic Large-scale Sensor Networks

    NASA Astrophysics Data System (ADS)

    Wang, X.; Frolik, J.

    2006-12-01

    Recent advances in wireless, sensing, and embedded systems technologies are poised to enable the use of thousands of low-cost nodes to form a wireless sensor network. These networks promise monitoring of environments that are remote or inaccessible, providing users with critical information with unprecedented spatial and temporal resolution. One major hurdle facing practical, large-scale sensor network deployments is the difficulties of managing the vast number (1000s) of sensor nodes. An emerging and promising approach is to use tiered architectures, where the higher tier (backbone) is formed with a limited number of resource-rich master nodes and a highly-populated, lower tier consisting of energy-constrained, low-cost end nodes. Given that the related networking issues of the backbone are relatively well understood, what is needed, and what is discussed in this presentation, is an approach for the end nodes in the lower tier (an approach dubbed 'EnArchi') that is robust and manageable under the dynamic constraints imposed by the application. In our approach, inspired by Von Neumann, EnArchi end nodes operate as homogeneous, stochastically self- controlled autonomous agents, and like their natural counterparts (e.g., cells) "process information and proceed in their actions based on data received from their environment in light of rules and instructions they hold internally" (quoted from http://www.medaloffreedom.com/JohnvonNeumann.htm). While the individual EnArchi automata are simple in design, as a group they enable emerging complex systems-level behavior. Our work shows EnArchi is amenable to maintaining an overall network-level quality of service in large-scale deployments even under the dynamic scenarios of high sensor fallout rates and/or network replenishment; as far as we are aware of, our approach is the only one appeared in the literature that achieves an energy efficiency close to optimal for this task. In this presentation, we will present our results

  11. 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

  12. Schizophrenic Patients and Their Unaffected Siblings Share Increased Resting-State Connectivity in the Task-Negative Network but Not Its Anticorrelated Task-Positive Network

    PubMed Central

    Liu, Haihong; Kaneko, Yoshio; Ouyang, Xuan; Li, Li; Hao, Yihui; Chen, Eric Y. H.; Jiang, Tianzi; Zhou, Yuan; Liu, Zhening

    2012-01-01

    Background: Abnormal connectivity of the anticorrelated intrinsic networks, the task-negative network (TNN), and the task-positive network (TPN) is implicated in schizophrenia. Comparisons between schizophrenic patients and their unaffected siblings enable further understanding of illness susceptibility and pathophysiology. We examined the resting-state connectivity differences in the intrinsic networks between schizophrenic patients, their unaffected siblings, and healthy controls. Methods: Resting-state functional magnetic resonance images were obtained from 25 individuals in each subject group. The posterior cingulate cortex/precuneus and right dorsolateral prefrontal cortex were used as seed regions to identify the TNN and TPN through functional connectivity analysis. Interregional connectivity strengths were analyzed using overlapped intrinsic networks composed of regions common to all subject groups. Results: Schizophrenic patients and their unaffected siblings showed increased connectivity in the TNN between the bilateral inferior temporal gyri. By contrast, schizophrenic patients alone demonstrated increased connectivity between the posterior cingulate cortex/precuneus and left inferior temporal gyrus and between the ventral medial prefrontal cortex and right lateral parietal cortex in the TNN. Schizophrenic patients exhibited increased connectivity between the left dorsolateral prefrontal cortex and right inferior frontal gyrus in the TPN relative to their unaffected siblings, though this trend only approached statistical significance in comparison to healthy controls. Conclusion: Resting-state hyperconnectivity of the intrinsic networks may disrupt network coordination and thereby contribute to the pathophysiology of schizophrenia. Similar, though milder, hyperconnectivity of the TNN in unaffected siblings of schizophrenic patients may contribute to the identification of schizophrenia endophenotypes and ultimately to the determination of schizophrenia

  13. 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.

  14. A parallel multigrid preconditioner for the simulation of large fracture networks

    SciTech Connect

    Sampath, Rahul S; Barai, Pallab; Nukala, Phani K

    2010-01-01

    Computational modeling of a fracture in disordered materials using discrete lattice models requires the solution of a linear system of equations every time a new lattice bond is broken. Solving these linear systems of equations successively is the most expensive part of fracture simulations using large three-dimensional networks. In this paper, we present a parallel multigrid preconditioned conjugate gradient algorithm to solve these linear systems. Numerical experiments demonstrate that this algorithm performs significantly better than the algorithms previously used to solve this problem.

  15. Designing Long-term Monitoring Networks for Water Quality in Large Watersheds with Simulation Analysis

    NASA Astrophysics Data System (ADS)

    Tan, S.; Shoemaker, C. A.

    2011-12-01

    ability of the model to capture the variability in the flow; the normalized bias, β_n which describes the ability of the model to approximate the mean,; and the Pearson correlation coefficient, r which describes how well the model matches the timing and shape of the hydrographs. The best raingauge network was found using Tabu search as a search algorithm. However, we found that the best combination of rain gauge and in-stream monitoring is found by coupling several combinations of good rain gauge configurations with good in-stream monitoring configurations to pick the best overall combination. Hence the watershed simulation model is used to integrate the factors related to multiple rain, flow, and water quality sensors to assess which combination of spatial and temporal data is most useful given the large variability in weather for a specific watershed.

  16. 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

  17. Calibration strategy of optical measurement network for large-scale and shell-like objects

    NASA Astrophysics Data System (ADS)

    Yin, Yongkai; Peng, Xiang; Liu, Xiaoli; Li, Ameng; Qu, Xinghua

    2012-04-01

    It can be difficult to calibrate the three-dimensional (3D) optical measurement network (OMN) designed to inspect large-scale and shell-like objects. One of the challenges is how to in situ build up a large and precise calibration target, which can be adapted to the desired measurement volume. In this paper, a strategy for in situ calibration of the OMN is presented. First, one of the said objects is chosen to fabricate a large-scale and shell-like calibration target thereon the coded marks are pasted and their coordinates are calculated by using a technique of auto-reconstruction. This results in a highly accurate benchmark-data-set that can cover the large-scale and shell-like measurement volume. Next, all the node 3D sensors of the OMN are calibrated with the established benchmark-data-set. Thus the extrinsic parameters of all node sensors can be unified into a common coordinate system so that the structure parameters and poses of node sensors in the OMN can be determined accurately. The proposed calibration strategy is verified by a group of experiments and a case study for inspecting a large size crucible.

  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. Reversible large-scale modification of cortical networks during neuroprosthetic control.

    PubMed

    Ganguly, Karunesh; Dimitrov, Dragan F; Wallis, Jonathan D; Carmena, Jose M

    2011-05-01

    Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control. PMID:21499255

  20. Combining flux and energy balance analysis to model large-scale biochemical networks.

    PubMed

    Heuett, William J; Qian, Hong

    2006-12-01

    Stoichiometric Network Theory is a constraints-based, optimization approach for quantitative analysis of the phenotypes of large-scale biochemical networks that avoids the use of detailed kinetics. This approach uses the reaction stoichiometric matrix in conjunction with constraints provided by flux balance and energy balance to guarantee mass conserved and thermodynamically allowable predictions. However, the flux and energy balance constraints have not been effectively applied simultaneously on the genome scale because optimization under the combined constraints is non-linear. In this paper, a sequential quadratic programming algorithm that solves the non-linear optimization problem is introduced. A simple example and the system of fermentation in Saccharomyces cerevisiae are used to illustrate the new method. The algorithm allows the use of non-linear objective functions. As a result, we suggest a novel optimization with respect to the heat dissipation rate of a system. We also emphasize the importance of incorporating interactions between a model network and its surroundings. PMID:17245812