Science.gov

Sample records for large sharing networks

  1. Building a Shared Information Network.

    ERIC Educational Resources Information Center

    Stanat, Ruth

    1991-01-01

    Discussion of information needs in a business environment focuses on how to build a shared information network. Highlights include the evolution of corporate intelligence systems; results of a survey that examined the information networking needs of large corporations; and a case study of the development of an information network at Citibank N.A.…

  2. 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.

  3. Multi-modal analysis of functional connectivity and cerebral blood flow reveals shared and unique effects of propofol in large-scale brain networks.

    PubMed

    Qiu, Maolin; Scheinost, Dustin; Ramani, Ramachandran; Constable, R Todd

    2017-03-01

    Anesthesia-induced changes in functional connectivity and cerebral blow flow (CBF) in large-scale brain networks have emerged as key markers of reduced consciousness. However, studies of functional connectivity disagree on which large-scale networks are altered or preserved during anesthesia, making it difficult to find a consensus amount studies. Additionally, pharmacological alterations in CBF could amplify or occlude changes in connectivity due to the shared variance between CBF and connectivity. Here, we used data-driven connectivity methods and multi-modal imaging to investigate shared and unique neural correlates of reduced consciousness for connectivity in large-scale brain networks. Rs-fMRI and CBF data were collected from the same subjects during an awake and deep sedation condition induced by propofol. We measured whole-brain connectivity using the intrinsic connectivity distribution (ICD), a method not reliant on pre-defined seed regions, networks of interest, or connectivity thresholds. The shared and unique variance between connectivity and CBF were investigated. Finally, to account for shared variance, we present a novel extension to ICD that incorporates cerebral blood flow (CBF) as a scaling factor in the calculation of global connectivity, labeled CBF-adjusted ICD). We observed altered connectivity in multiple large-scale brain networks including the default mode (DMN), salience, visual, and motor networks and reduced CBF in the DMN, frontoparietal network, and thalamus. Regional connectivity and CBF were significantly correlated during both the awake and propofol condition. Nevertheless changes in connectivity and CBF between the awake and deep sedation condition were only significantly correlated in a subsystem of the DMN, suggesting that, while there is significant shared variance between the modalities, changes due to propofol are relatively unique. Similar, but less significant, results were observed in the CBF-adjusted ICD analysis, providing

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. Traffic sharing algorithms for hybrid mobile networks

    NASA Technical Reports Server (NTRS)

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

    1995-01-01

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

  9. 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

  10. Sagnac secret sharing over telecom fiber networks.

    PubMed

    Bogdanski, Jan; Ahrens, Johan; Bourennane, Mohamed

    2009-01-19

    We report the first Sagnac quantum secret sharing (in three-and four-party implementations) over 1550 nm single mode fiber (SMF) networks, using a single qubit protocol with phase encoding. Our secret sharing experiment has been based on a single qubit protocol, which has opened the door to practical secret sharing implementation over fiber telecom channels and in free-space. The previous quantum secret sharing proposals were based on multiparticle entangled states, difficult in the practical implementation and not scalable. Our experimental data in the three-party implementation show stable (in regards to birefringence drift) quantum secret sharing transmissions at the total Sagnac transmission loop distances of 55-75 km with the quantum bit error rates (QBER) of 2.3-2.4% for the mean photon number micro?= 0.1 and 1.7-2.1% for micro= 0.3. In the four-party case we have achieved quantum secret sharing transmissions at the total Sagnac transmission loop distances of 45-55 km with the quantum bit error rates (QBER) of 3.0-3.7% for the mean photon number micro= 0.1 and 1.8-3.0% for micro?= 0.3. The stability of quantum transmission has been achieved thanks to our new concept for compensation of SMF birefringence effects in Sagnac, based on a polarization control system and a polarization insensitive phase modulator. The measurement results have showed feasibility of quantum secret sharing over telecom fiber networks in Sagnac configuration, using standard fiber telecom components.

  11. 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.

  12. Data sharing in the Undiagnosed Diseases Network

    PubMed Central

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

    2015-01-01

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

  13. 47 CFR 90.1410 - Network sharing agreement.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... valuation methodology to determine the fair market value of the shared wireless broadband network assets. (j... 47 Telecommunication 5 2012-10-01 2012-10-01 false Network sharing agreement. 90.1410 Section 90... PRIVATE LAND MOBILE RADIO SERVICES 700 MHz Public/Private Partnership § 90.1410 Network sharing...

  14. 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.

  15. 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…

  16. 47 CFR 27.1305 - Shared wireless broadband network.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... interoperable across public safety local and state agencies, jurisdictions, and geographic areas, and that... 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...

  17. 47 CFR 27.1305 - Shared wireless broadband network.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... interoperable across public safety local and state agencies, jurisdictions, and geographic areas, and that... 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...

  18. 47 CFR 27.1305 - Shared wireless broadband network.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... interoperable across public safety local and state agencies, jurisdictions, and geographic areas, and that... 47 Telecommunication 2 2012-10-01 2012-10-01 false Shared wireless broadband network. 27.1305... broadband network. The Shared Wireless Broadband Network developed by the 700 MHz Public/Private...

  19. 47 CFR 90.1405 - Shared wireless broadband network.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... interoperable across public safety local and state agencies, jurisdictions, and geographic areas, and which... 47 Telecommunication 5 2012-10-01 2012-10-01 false Shared wireless broadband network. 90.1405... broadband network. The Shared Wireless Broadband Network developed by the 700 MHz Public/Private...

  20. 47 CFR 90.1405 - Shared wireless broadband network.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... interoperable across public safety local and state agencies, jurisdictions, and geographic areas, and which... 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...

  1. 47 CFR 90.1405 - Shared wireless broadband network.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... interoperable across public safety local and state agencies, jurisdictions, and geographic areas, and which... 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...

  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. 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.…

  4. Impact of network sharing in multi-core architectures.

    SciTech Connect

    Narayanaswamy, G.; Balaji, P.; Feng, W.; Mathematics and Computer Science; Virginia Tech

    2008-01-01

    As commodity components continue to dominate the realm of high-end computing, two hardware trends have emerged as major contributors-high-speed networking technologies and multi-core architectures. Communication middleware such as the Message Passing Interface (MPI) uses the network technology for communicating between processes that reside on different physical nodes, while using shared memory for communicating between processes on different cores within the same node. Thus, two conflicting possibilities arise: (i) with the advent of multi-core architectures, the number of processes that reside on the same physical node and hence share the same physical network can potentially increase significantly, resulting in increased network usage, and (ii) given the increase in intra-node shared-memory communication for processes residing on the same node, the network usage can potentially decrease significantly. In this paper, we address these two conflicting possibilities and study the behavior of network usage in multi-core environments with sample scientific applications. Specifically, we analyze trends that result in increase or decrease of network usage, and we derive insights into application performance based on these. We also study the sharing of different resources in the system in multi-core environments and identify the contribution of the network in this mix. In addition, we study different process allocation strategies and analyze their impact on such network sharing.

  5. 47 CFR 27.1310 - Network sharing agreement.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ....1310 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES 700 MHz Public/Private Partnership § 27.1310 Network sharing... Commission for prior approval. All other modifications must be submitted to the Chiefs of the...

  6. Sharing Information the Cheap Way: A Look at "Caveman Networking."

    ERIC Educational Resources Information Center

    Ekhaml, Leticia

    1994-01-01

    Describes four peer networking projects initiated by school library media specialists in Georgia that promoted curriculum development, collection development and library services, cooperation between school and public libraries to share online resources, and cooperation between public and school libraries to share magazine articles. (LRW)

  7. 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.

  8. 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

  9. Complex Dynamics in Information Sharing Networks

    NASA Astrophysics Data System (ADS)

    Cronin, Bruce

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

  10. 47 CFR 27.1310 - Network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES 700 MHz Public/Private Partnership § 27.1310 Network sharing agreement. The relationship between the Upper 700 MHz D Block licensee, the Public Safety Broadband Licensee... the Commission concerning the 700 MHz Public/Private Partnership. (b) Network specifications...

  11. 47 CFR 90.1410 - Network sharing agreement.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... PRIVATE LAND MOBILE RADIO SERVICES 700 MHz Public/Private Partnership § 90.1410 Network sharing agreement. The relationship between the Upper 700 MHz D Block licensee, the Public Safety Broadband Licensee, and... concerning the 700 MHz Public/Private Partnership. (b) Network specifications that comply with § 27.1305...

  12. 47 CFR 90.1410 - Network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... PRIVATE LAND MOBILE RADIO SERVICES 700 MHz Public/Private Partnership § 90.1410 Network sharing agreement. The relationship between the Upper 700 MHz D Block licensee, the Public Safety Broadband Licensee, and... concerning the 700 MHz Public/Private Partnership. (b) Network specifications that comply with § 27.1305...

  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

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

    2015-07-06

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

  16. 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

  17. Networked Resource Sharing of CD-ROM Information Banks.

    ERIC Educational Resources Information Center

    Gey, Fredric C.

    1992-01-01

    Proposes an infrastructure to provide organizationwide access in a large university to government statistical databases issued on CD-ROM. Topics addressed include characteristics of government numeric data on CD-ROM, networked options, desired characteristics of networked CD-ROM access, hardware and network configurations, a prototype system, and…

  18. 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.

  19. Reservation information sharing enhancement for deflection routing in OBS network.

    PubMed

    Gao, Donghui; Zhang, Hanyi; Zhou, Zhiyu

    2005-03-07

    The resource contention problem is critical in Just-Enough-Time (JET) based optical burst switching (OBS) networks. Although deflection routing (DR) reduces the contention probability in some degree, it does not give much improvement under heavy traffic load. This paper analyzed the inducement causing contention in OBS networks, and proposed Resource Information Sharing Enhancement (RISE) scheme. Theoretical analysis shows that this scheme achieves shorter length of the detour path than normal DR. We simulated this scheme on both full mesh network and practical 14-node NSFNET. The simulation results show that it gives at best 2 orders magnitude improvement in reducing the burst contention probability over its previous routing approaches.

  20. 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

  1. Survivable Lightpath Provisioning in WDM Mesh Networks Under Shared Path Protection and Signal Quality Constraints

    NASA Astrophysics Data System (ADS)

    Yang, Xi; Shen, Lu; Ramamurthy, Byrav

    2005-04-01

    This paper addresses the problem of survivable lightpath provisioning in wavelength-division-multiplexing (WDM) mesh networks, taking into consideration optical-layer protection and some realistic optical signal quality constraints. The investigated networks use sparsely placed optical-electrical-optical (O/E/O) modules for regeneration and wavelength conversion. Given a fixed network topology with a number of sparsely placed O/E/O modules and a set of connection requests, a pair of link-disjoint lightpaths is established for each connection. Due to physical impairments and wavelength continuity,both the working and protection lightpaths need to be regenerated at some intermediate nodes to overcome signal quality degradation and wavelength contention. In the present paper, resource-efficient provisioning solutions are achieved with the objective of maximizing resource sharing. The authors propose a resource-sharing scheme that supports three kinds of resource-sharing scenarios, including a conventional wavelength-link sharing scenario, which shares wavelength links between protection lightpaths, and two new scenarios, which share O/E/O modules between protection lightpaths and between working and protection lightpaths. An integer linear programming (ILP)-based solution approach is used to find optimal solutions. The authors also propose a local optimization heuristic approach and a tabu search heuristic approach to solve this problem for real-world,large mesh networks. Numerical results show that our solution approaches work well under a variety of network settings and achieves a high level of resource-sharing rates (over 60% for O/E/O modules and over 30% for wavelength links), which translate into great savings in network costs.

  2. 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.

  3. Experimental circular quantum secret sharing over telecom fiber network.

    PubMed

    Wei, Ke-Jin; Ma, Hai-Qiang; Yang, Jian-Hui

    2013-07-15

    We present a robust single photon circular quantum secret sharing (QSS) scheme with phase encoding over 50 km single mode fiber network using a circular QSS protocol. Our scheme can automatically provide a perfect compensation of birefringence and remain stable for a long time. A high visibility of 99.3% is obtained. Furthermore, our scheme realizes a polarization insensitive phase modulators. The visibility of this system can be maintained perpetually without any adjustment to the system every time we test the system.

  4. Enabling Interoperable and Selective Data Sharing among Social Networking Sites

    NASA Astrophysics Data System (ADS)

    Shin, Dongwan; Lopes, Rodrigo

    With the widespread use of social networking (SN) sites and even introduction of a social component in non-social oriented services, there is a growing concern over user privacy in general, how to handle and share user profiles across SN sites in particular. Although there have been several proprietary or open source-based approaches to unifying the creation of third party applications, the availability and retrieval of user profile information are still limited to the site where the third party application is run, mostly devoid of the support for data interoperability. In this paper we propose an approach to enabling interopearable and selective data sharing among SN sites. To support selective data sharing, we discuss an authenticated dictionary (ADT)-based credential which enables a user to share only a subset of her information certified by external SN sites with applications running on an SN site. For interoperable data sharing, we propose an extension to the OpenSocial API so that it can provide an open source-based framework for allowing the ADT-based credential to be used seamlessly among different SN sites.

  5. 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.

  6. 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

  7. 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

  8. 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).

  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. 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

  11. 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.

  12. Data sharing in large research consortia: experiences and recommendations from ENGAGE.

    PubMed

    Budin-Ljøsne, Isabelle; Isaeva, Julia; Knoppers, Bartha Maria; Tassé, Anne Marie; Shen, Huei-yi; McCarthy, Mark I; Harris, Jennifer R

    2014-03-01

    Data sharing is essential for the conduct of cutting-edge research and is increasingly required by funders concerned with maximising the scientific yield from research data collections. International research consortia are encouraged to share data intra-consortia, inter-consortia and with the wider scientific community. Little is reported regarding the factors that hinder or facilitate data sharing in these different situations. This paper provides results from a survey conducted in the European Network for Genetic and Genomic Epidemiology (ENGAGE) that collected information from its participating institutions about their data-sharing experiences. The questionnaire queried about potential hurdles to data sharing, concerns about data sharing, lessons learned and recommendations for future collaborations. Overall, the survey results reveal that data sharing functioned well in ENGAGE and highlight areas that posed the most frequent hurdles for data sharing. Further challenges arise for international data sharing beyond the consortium. These challenges are described and steps to help address these are outlined.

  13. Integrative biology identifies shared transcriptional networks in CKD.

    PubMed

    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; Kretzler, Matthias

    2014-11-01

    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.

  14. 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

  15. Perceived serosorting of injection paraphernalia sharing networks among injection drug users in Baltimore, MD.

    PubMed

    Yang, C; Tobin, K; Latkin, C

    2011-01-01

    We examined perceived serosorting of injection paraphernalia sharing networks among a sample of 572 injection drug users (IDUs). There was evidence for serosorting of high-risk injection behaviors among HIV-negative IDUs, as 94% of HIV-negative IDUs shared injection paraphernalia exclusively with perceived HIV-negative networks. However, 82% of HIV-positive IDUs shared injection paraphernalia with perceived HIV-negative networks. The findings indicate a potential risk of rapid HIV transmission. Future prevention efforts targeting IDUs should address the limitation of serosorting, and focus on preventing injection paraphernalia sharing regardless of potential sharing networks' perceived HIV status.

  16. 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.

  17. 47 CFR 90.1415 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 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...

  18. 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...

  19. Information Sharing Challenges For Large Multi-Jurisdiction Issues

    SciTech Connect

    Millard, W. David; Carter, Richard J.

    2002-07-29

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

  20. Large-scale cortical networks and cognition.

    PubMed

    Bressler, S L

    1995-03-01

    The well-known parcellation of the mammalian cerebral cortex into a large number of functionally distinct cytoarchitectonic areas presents a problem for understanding the complex cortical integrative functions that underlie cognition. How do cortical areas having unique individual functional properties cooperate to accomplish these complex operations? Do neurons distributed throughout the cerebral cortex act together in large-scale functional assemblages? This review examines the substantial body of evidence supporting the view that complex integrative functions are carried out by large-scale networks of cortical areas. Pathway tracing studies in non-human primates have revealed widely distributed networks of interconnected cortical areas, providing an anatomical substrate for large-scale parallel processing of information in the cerebral cortex. Functional coactivation of multiple cortical areas has been demonstrated by neurophysiological studies in non-human primates and several different cognitive functions have been shown to depend on multiple distributed areas by human neuropsychological studies. Electrophysiological studies on interareal synchronization have provided evidence that active neurons in different cortical areas may become not only coactive, but also functionally interdependent. The computational advantages of synchronization between cortical areas in large-scale networks have been elucidated by studies using artificial neural network models. Recent observations of time-varying multi-areal cortical synchronization suggest that the functional topology of a large-scale cortical network is dynamically reorganized during visuomotor behavior.

  1. 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.

  2. 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…

  3. 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 ...

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

    ERIC Educational Resources Information Center

    Sadik, Alaa

    2014-01-01

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

  5. 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. In situ exploration of large dynamic networks.

    PubMed

    Hadlak, Steffen; Schulz, Hans-Jörg; Schumann, Heidrun

    2011-12-01

    The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.

  9. 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.

  10. Metabolic network alignment in large scale by network compression.

    PubMed

    Ay, Ferhat; Dang, Michael; Kahveci, Tamer

    2012-03-21

    Metabolic network alignment is a system scale comparative analysis that discovers important similarities and differences across different metabolisms and organisms. Although the problem of aligning metabolic networks has been considered in the past, the computational complexity of the existing solutions has so far limited their use to moderately sized networks. In this paper, we address the problem of aligning two metabolic networks, particularly when both of them are too large to be dealt with using existing methods. We develop a generic framework that can significantly improve the scale of the networks that can be aligned in practical time. Our framework has three major phases, namely the compression phase, the alignment phase and the refinement phase. For the first phase, we develop an algorithm which transforms the given networks to a compressed domain where they are summarized using fewer nodes, termed supernodes, and interactions. In the second phase, we carry out the alignment in the compressed domain using an existing network alignment method as our base algorithm. This alignment results in supernode mappings in the compressed domain, each of which are smaller instances of network alignment problem. In the third phase, we solve each of the instances using the base alignment algorithm to refine the alignment results. We provide a user defined parameter to control the number of compression levels which generally determines the tradeoff between the quality of the alignment versus how fast the algorithm runs. Our experiments on the networks from KEGG pathway database demonstrate that the compression method we propose reduces the sizes of metabolic networks by almost half at each compression level which provides an expected speedup of more than an order of magnitude. We also observe that the alignments obtained by only one level of compression capture the original alignment results with high accuracy. Together, these suggest that our framework results in

  11. A Networked NMR Spectrometer: Configuring a Shared Instrument

    ERIC Educational Resources Information Center

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

    2005-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Wu, Zhengping; Wu, Hao

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

  13. 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.

  14. 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.

  15. 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.

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

    ERIC Educational Resources Information Center

    Doering, David

    1998-01-01

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

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

    ERIC Educational Resources Information Center

    Lipman, Art

    1993-01-01

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

  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. Public Attitudes toward Consent and Data Sharing in Biobank Research: A Large Multi-site Experimental Survey in the US.

    PubMed

    Sanderson, Saskia C; Brothers, Kyle B; Mercaldo, Nathaniel D; Clayton, Ellen Wright; Antommaria, Armand H Matheny; Aufox, Sharon A; Brilliant, Murray H; Campos, Diego; Carrell, David S; Connolly, John; Conway, Pat; Fullerton, Stephanie M; Garrison, Nanibaa' A; Horowitz, Carol R; Jarvik, Gail P; Kaufman, David; Kitchner, Terrie E; Li, Rongling; Ludman, Evette J; McCarty, Catherine A; McCormick, Jennifer B; McManus, Valerie D; Myers, Melanie F; Scrol, Aaron; Williams, Janet L; Shrubsole, Martha J; Schildcrout, Jonathan S; Smith, Maureen E; Holm, Ingrid A

    2017-03-02

    Individuals participating in biobanks and other large research projects are increasingly asked to provide broad consent for open-ended research use and widespread sharing of their biosamples and data. We assessed willingness to participate in a biobank using different consent and data sharing models, hypothesizing that willingness would be higher under more restrictive scenarios. Perceived benefits, concerns, and information needs were also assessed. In this experimental survey, individuals from 11 US healthcare systems in the Electronic Medical Records and Genomics (eMERGE) Network were randomly allocated to one of three hypothetical scenarios: tiered consent and controlled data sharing; broad consent and controlled data sharing; or broad consent and open data sharing. Of 82,328 eligible individuals, exactly 13,000 (15.8%) completed the survey. Overall, 66% (95% CI: 63%-69%) of population-weighted respondents stated they would be willing to participate in a biobank; willingness and attitudes did not differ between respondents in the three scenarios. Willingness to participate was associated with self-identified white race, higher educational attainment, lower religiosity, perceiving more research benefits, fewer concerns, and fewer information needs. Most (86%, CI: 84%-87%) participants would want to know what would happen if a researcher misused their health information; fewer (51%, CI: 47%-55%) would worry about their privacy. The concern that the use of broad consent and open data sharing could adversely affect participant recruitment is not supported by these findings. Addressing potential participants' concerns and information needs and building trust and relationships with communities may increase acceptance of broad consent and wide data sharing in biobank research.

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

    PubMed Central

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

    2016-01-01

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

  3. Large-scale Heterogeneous Network Data Analysis

    DTIC Science & Technology

    2012-07-31

    Data for Multi-Player Influence Maximization on Social Networks.” KDD 2012 (Demo).  Po-Tzu Chang , Yen-Chieh Huang, Cheng-Lun Yang, Shou-De Lin, Pu...Jen Cheng. “Learning-Based Time-Sensitive Re-Ranking for Web Search.” SIGIR 2012 (poster)  Hung -Che Lai, Cheng-Te Li, Yi-Chen Lo, and Shou-De Lin...Exploiting and Evaluating MapReduce for Large-Scale Graph Mining.” ASONAM 2012 (Full, 16% acceptance ratio).  Hsun-Ping Hsieh , Cheng-Te Li, and Shou

  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. Dynamic Spectrum Sharing with Limited Network State Information

    DTIC Science & Technology

    2010-11-01

    possibly different) messages to a set of mobile receivers . We have studied limited feedback schemes for both single-user Orthogonal Frequency...in which the broadcast node sequentially receives feedback from the mobiles and decides when to stop receiving additional feedback and begin data...concerned with the costs and benefits of learning and exchanging Network State Information (NSI) among cooperative nodes in a wireless network. NSI

  6. 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.

  7. 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…

  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. 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…

  10. Micro-to-Micro Communications: Local Networks Help Personal Computers Share Information.

    ERIC Educational Resources Information Center

    Byers, T. J.

    1984-01-01

    Discusses major features of an effective local network of microcomputers within a business: (1) particular transmission medium used; (2) the way data is transmitted over that medium; and (3) the way access to the network is shared among the devices. A data communications glossary is provided. (MBR)

  11. 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.

  12. Social network analysis of food sharing among households in opisthorchiasis endemic villages of Lawa Lake, Thailand.

    PubMed

    Phimpraphai, Waraphon; Tangkawattana, Sirikachorn; Sereerak, Piya; Kasemsuwan, Suwicha; Sripa, Banchob

    2017-05-01

    Consumption of raw fish is a well-documented risk factor for Opisthorchis viverrini infection. Sharing of food, especially raw fish recipes may influence the spread of disease through a community. Using social network analysis of an ego network, we investigated food sharing among households in an Opisthorchis-endemic area. Network centrality properties were used to explain the differences in O. viverrini transmission and control between villages with a low and high prevalence of infection. Information on demography and O. viverrini infection in 2008 from villagers in the Lawa Lake area was extracted from the Tropical Disease Research Center database. The two villages that had the lowest and the highest O. viverrini infection at the household level were recruited. Ten percent of households of each village were randomly sampled. Participatory epidemiology and face-to-face structured interviews guided by a social network questionnaire were used to collect data on livelihood, agricultural patterns, food sources, raw fish eating habits, and other food sharing during daily life and social gatherings. The number of contacts including in-degree and out-degree varied from 0 to 7 in the low-infection village and 0 to 4 in the high-infection village. The mean number of contacts for the food-sharing network among the low- and high-infection villages was 1.64 and 0.73 contacts per household, respectively. Between these villages, the mean number of out-degree (p=0.0125), but not in-degree (p=0.065), was significantly different. Food-sharing differed in numbers of sharing-in and sharing-out between the two villages. Network analysis of food sharing may be of value in designing strategies for opisthorchiasis control at the community level.

  13. The intellectual property management for data sharing in a German liver cancer research network.

    PubMed

    He, Shan; Ganzinger, Matthias; Knaup, Petra

    2012-01-01

    Sharing data in biomedical research networks has great potential benefits including efficient use of resources, avoiding duplicate experiments and promoting collaboration. However, concerns from data producers about difficulties of getting proper acknowledgement for their contributions are becoming obstacles for efficient and network wide data sharing in reality. Effective and convenient ways of intellectual property management and acknowledging contributions to the data producers are required. This paper analyzed the system requirements for intellectual property management in a German liver cancer research network and proposed solutions for facilitating acknowledgement of data contributors using informatics tools instead of pure policy level strategies.

  14. Synchronization of coupled large-scale Boolean networks

    NASA Astrophysics Data System (ADS)

    Li, Fangfei

    2014-03-01

    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.

  15. 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.

  16. Quantum synchronization and quantum state sharing in an irregular complex network

    NASA Astrophysics Data System (ADS)

    Li, Wenlin; Li, Chong; Song, Heshan

    2017-02-01

    We investigate the quantum synchronization phenomenon of the complex network constituted by coupled optomechanical systems and prove that the unknown identical quantum states can be shared or distributed in the quantum network even though the topology is varying. Considering a channel constructed by quantum correlation, we show that quantum synchronization can sustain and maintain high levels in Markovian dissipation for a long time. We also analyze the state-sharing process between two typical complex networks, and the results predict that linked nodes can be directly synchronized, but the whole network will be synchronized only if some specific synchronization conditions are satisfied. Furthermore, we give the synchronization conditions analytically through analyzing network dynamics. This proposal paves the way for studying multi-interaction synchronization and achieving effective quantum information processing in a complex network.

  17. 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…

  18. Shared Authority Control at the Western Library Network.

    ERIC Educational Resources Information Center

    Coyne, Fumiko H.; Mifflin, Ingrid

    1990-01-01

    Examines the cooperative efforts of the Western Library Network (WLN) and its members to maintain authority control over its central bibliographic database. The first section describes authority maintenance activities performed by WLN centrally, and the second describes the contribution of an individual member library--the Washington State…

  19. Freedom and Sharing in the Global Network Society

    ERIC Educational Resources Information Center

    Lankshear, Colin

    2006-01-01

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

  20. 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…

  1. Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli.

    PubMed

    Hodgin, Jeffrey B; Nair, Viji; Zhang, Hongyu; Randolph, Ann; Harris, Raymond C; Nelson, Robert G; Weil, E Jennifer; Cavalcoli, James D; Patel, Jignesh M; Brosius, Frank C; Kretzler, Matthias

    2013-01-01

    Murine models are valuable instruments in defining the pathogenesis of diabetic nephropathy (DN), but they only partially recapitulate disease manifestations of human DN, limiting their utility. To define the molecular similarities and differences between human and murine DN, we performed a cross-species comparison of glomerular transcriptional networks. Glomerular gene expression was profiled in patients with early type 2 DN and in three mouse models (streptozotocin DBA/2, C57BLKS db/db, and eNOS-deficient C57BLKS db/db mice). Species-specific transcriptional networks were generated and compared with a novel network-matching algorithm. Three shared human-mouse cross-species glomerular transcriptional networks containing 143 (Human-DBA STZ), 97 (Human-BKS db/db), and 162 (Human-BKS eNOS(-/-) db/db) gene nodes were generated. Shared nodes across all networks reflected established pathogenic mechanisms of diabetes complications, such as elements of Janus kinase (JAK)/signal transducer and activator of transcription (STAT) and vascular endothelial growth factor receptor (VEGFR) signaling pathways. In addition, novel pathways not previously associated with DN and cross-species gene nodes and pathways unique to each of the human-mouse networks were discovered. The human-mouse shared glomerular transcriptional networks will assist DN researchers in selecting mouse models most relevant to the human disease process of interest. Moreover, they will allow identification of new pathways shared between mice and humans.

  2. 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

  3. Thermodynamics of Large-Scale Heterogeneous Wireless Networks

    DTIC Science & Technology

    2014-03-06

    David Tse, co-PIs: Piyush Gupta, Devavrat Shah In recent years, large wireless networks have become the architecture of choice in many emerging...of this project, we have applied the lessons we learnt about wireless networks to other types of networks such as power networks. More details below. I...transmit powers . II. SYNCHRONIZATION A key issue to enable relaying/multi-hopping in wireless networks is synchronization between different network nodes

  4. 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

  5. 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.

  6. Dyspnea and pain share emotion-related brain network.

    PubMed

    von Leupoldt, Andreas; Sommer, Tobias; Kegat, Sarah; Baumann, Hans Jörg; Klose, Hans; Dahme, Bernhard; Büchel, Christian

    2009-10-15

    The early detection of stimuli signalling threat to an organism is a crucial evolutionary advantage. For example, the perception of aversive bodily sensations such as dyspnea and pain strongly motivates fast adaptive behaviour to ensure survival. Their similarly threatening and motivating characters led to the speculation that both sensations are mediated by common brain areas, which has also been suggested by neuroimaging studies on either dyspnea or pain. By using functional magnetic resonance imaging (fMRI), we formally tested this hypothesis and compared the cortical processing of perceived heat pain and resistive load induced dyspnea in the same group of participants. Here we show that the perception of both aversive sensations is processed in similar brain areas including the insula, dorsal anterior cingulate cortex, amygdala and medial thalamus. These areas have a documented role in the processing of emotions such as fear and anxiety. Thus, the current study highlights the role of a common emotion-related human brain network which underlies the perception of aversive bodily sensations such as dyspnea and pain. This network seems crucial for translating the threatening character of different bodily signals into behavioural consequences that promote survival.

  7. A proxy technique for media content sharing among UPnP-enabled home networks

    NASA Astrophysics Data System (ADS)

    Lee, HyunRyong; Kim, JongWon

    2005-10-01

    We propose a proxy-based scheme for segment-based distributed streaming services among UPnP(universal plug and play)-enabled home networks. We design a "SHARE" module that extends HG (home gateway) with a UPnP-compatible protocol. By relaying SSDP (simple service discovery protocol) messages defined in the UPnP device architecture, the SHARE module provides connectivity that is needed to control other UPnP devices for streaming services among home networks. To provide the streaming services, the SHARE module tries to coordinate the distribution of streaming loads among multiple senders by using many-to-one distributed streaming service. It also tries to minimize the quality degradation of streaming services based on the system and network resource status of each sender by leveraging the UPnP QoS services. That is, pre-allocation of HG resources according to the UPnP QoS services can be used to improve the quality of streaming services. Based on the UPnP components, the SHARE module provides the transparent content sharing to users. Through design-level verifications and partial implementations of the proposed SHARE module, we validate the feasibility of our work.

  8. Network infrastructure for a large radiology environment

    NASA Astrophysics Data System (ADS)

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

    1996-05-01

    As image transmission becomes a more important part of the way radiology departments operate, the need for a high speed network infrastructure has become more important. We have installed a high speed network in the department that uses the latest Asynchronous Transfer Mode (ATM) networking technology combined with Ethernet switching. This network combination is capable of handling a tremendous amount of data traffic while maintaining compatibility with the existing Ethernet environment. These network changes have significantly improved Ethernet throughput on some of the most heavily used segments of the network by effectively isolating common traffic onto different network segments using new network management software and capabilities that are the result of the ATM backbone. Additional capabilities have allowed us to provide a number of serves that would not have been available using older techniques and architecture. Careful planning of the network before any new installations or changes is important for overall network and traffic management. The installation of this high speed network has allowed us to make imaging within the department and throughout the Medical Center and the connected region a reality.

  9. 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.

  10. 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 ...

  11. 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.

  12. 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

  13. 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 students' research…

  14. 47 CFR 27.1315 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... the network sharing agreement. 27.1315 Section 27.1315 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON CARRIER SERVICES MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES 700 MHz Public... Commission staff, one from the Wireless Telecommunications Bureau and one from the Public Safety and...

  15. 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…

  16. SOBR: A High-Performance Shared Output Buffered Router for Networks-on-Chip

    NASA Astrophysics Data System (ADS)

    Chen, Yancang; Xie, Lunguo

    This paper presents a single-cycle shared output buffered router for Networks-on-Chip. In output ports, each input port always has an output virtual-channel (VC) which can be exchanged by VC swapper. Its critical path is only 24 logic gates, and it reduces 9.4% area overhead compared with the classical router.

  17. 47 CFR 90.1415 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 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 agreement... Public Safety Broadband Licensee must negotiate an NSA and such other agreements as the Commission...

  18. 47 CFR 90.1415 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 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 agreement... Public Safety Broadband Licensee must negotiate an NSA and such other agreements as the Commission...

  19. 47 CFR 27.1315 - Establishment, execution, and application of the network sharing agreement.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... COMMISSION (CONTINUED) COMMON CARRIER SERVICES MISCELLANEOUS WIRELESS COMMUNICATIONS SERVICES 700 MHz Public/Private Partnership § 27.1315 Establishment, execution, and application of the network sharing agreement... with the Public Safety Broadband Licensee, and the NSA and related agreements or documents have...

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

    ERIC Educational Resources Information Center

    Zimmerman, Melisa S.

    2014-01-01

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

  1. 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…

  2. 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

    2016-08-09

    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.

  3. 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.

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

    PubMed

    Nolin, David A

    2010-10-01

    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.

  5. 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

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

    PubMed

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

    2014-05-30

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

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

    PubMed Central

    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

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

    PubMed

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

    2016-07-01

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

  9. SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

    PubMed Central

    Ma'ayan, Avi; Jenkins, Sherry L; Webb, Ryan L; Berger, Seth I; Purushothaman, Sudarshan P; Abul-Husn, Noura S; Posner, Jeremy M; Flores, Tony; Iyengar, Ravi

    2009-01-01

    Background Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge. Results SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names. Conclusion SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. The installation may be downloaded from: . The source code can be accessed from: PMID:19154595

  10. Cooperation stimulation strategies for peer-to-peer wireless live video-sharing social networks.

    PubMed

    Lin, W Sabrina; Zhao, H Vicky; Liu, K J Ray

    2010-07-01

    Human behavior analysis in video sharing social networks is an emerging research area, which analyzes the behavior of users who share multimedia content and investigates the impact of human dynamics on video sharing systems. Users watching live streaming in the same wireless network share the same limited bandwidth of backbone connection to the Internet, thus, they might want to cooperate with each other to obtain better video quality. These users form a wireless live-streaming social network. Every user wishes to watch video with high quality while paying as little as possible cost to help others. This paper focuses on providing incentives for user cooperation. We propose a game-theoretic framework to model user behavior and to analyze the optimal strategies for user cooperation simulation in wireless live streaming. We first analyze the Pareto optimality and the time-sensitive bargaining equilibrium of the two-person game. We then extend the solution to the multiuser scenario. We also consider potential selfish users' cheating behavior and malicious users' attacking behavior and analyze the performance of the proposed strategies with the existence of cheating users and malicious attackers. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free and attack resistance, and help provide reliable services for wireless live streaming applications.

  11. 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.

  12. 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

  13. 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

  14. Finding shared decisions in stakeholder networks: An agent-based approach

    NASA Astrophysics Data System (ADS)

    Le Pira, Michela; Inturri, Giuseppe; Ignaccolo, Matteo; Pluchino, Alessandro; Rapisarda, Andrea

    2017-01-01

    We address the problem of a participatory decision-making process where a shared priority list of alternatives has to be obtained while avoiding inconsistent decisions. An agent-based model (ABM) is proposed to mimic this process in different social networks of stakeholders who interact according to an opinion dynamics model. Simulations' results show the efficacy of interaction in finding a transitive and, above all, shared decision. These findings are in agreement with real participation experiences regarding transport planning decisions and can give useful suggestions on how to plan an effective participation process for sustainable policy-making based on opinion consensus.

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

    PubMed

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

    2012-07-15

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

  16. 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

  17. 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…

  18. 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.

  19. 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

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

    PubMed

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

    2014-01-01

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

  1. 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.

  2. Sharing Data In The Global Alzheimer’s Association Interactive Network

    PubMed Central

    Neu, Scott C.; Crawford, Karen L.; Toga, Arthur W.

    2015-01-01

    The Global Alzheimer’s Association Interactive Network (GAAIN) aims to be a shared network of research data, analysis tools, and computational resources for studying the causes of Alzheimer’s disease. Central to its design are policies that honor data ownership, prevent unauthorized data distribution, and respect the boundaries of contributing institutions. The results of data queries are displayed in graphs and summary tables, which protects data ownership while providing sufficient information to view trends in aggregated data and discover new data sets. In this article we report on our progress in sharing data through the integration of geographically-separated and independently-operated Alzheimer’s disease research studies around the world. PMID:26049147

  3. Sharing Data for Public Health Research by Members of an International Online Diabetes Social Network

    PubMed Central

    Weitzman, Elissa R.; Adida, Ben; Kelemen, Skyler; Mandl, Kenneth D.

    2011-01-01

    Background Surveillance and response to diabetes may be accelerated through engaging online diabetes social networks (SNs) in consented research. We tested the willingness of an online diabetes community to share data for public health research by providing members with a privacy-preserving social networking software application for rapid temporal-geographic surveillance of glycemic control. Methods and Findings SN-mediated collection of cross-sectional, member-reported data from an international online diabetes SN entered into a software applicaction we made available in a “Facebook-like” environment to enable reporting, charting and optional sharing of recent hemoglobin A1c values through a geographic display. Self-enrollment by 17% (n = 1,136) of n = 6,500 active members representing 32 countries and 50 US states. Data were current with 83.1% of most recent A1c values reported obtained within the past 90 days. Sharing was high with 81.4% of users permitting data donation to the community display. 34.1% of users also displayed their A1cs on their SN profile page. Users selecting the most permissive sharing options had a lower average A1c (6.8%) than users not sharing with the community (7.1%, p = .038). 95% of users permitted re-contact. Unadjusted aggregate A1c reported by US users closely resembled aggregate 2007–2008 NHANES estimates (respectively, 6.9% and 6.9%, p = 0.85). Conclusions Success within an early adopter community demonstrates that online SNs may comprise efficient platforms for bidirectional communication with and data acquisition from disease populations. Advancing this model for cohort and translational science and for use as a complementary surveillance approach will require understanding of inherent selection and publication (sharing) biases in the data and a technology model that supports autonomy, anonymity and privacy. PMID:21556358

  4. Joint accurate time and stable frequency distribution infrastructure sharing fiber footprint with research network

    NASA Astrophysics Data System (ADS)

    Vojtech, Josef; Slapak, Martin; Skoda, Pavel; Radil, Jan; Havlis, Ondrej; Altmann, Michal; Munster, Petr; Smotlacha, Vladimir; Kundrat, Jan; Velc, Radek; Altmannova, Lada; Hula, Miloslav

    2016-09-01

    In this paper, we present infrastructure for accurate time and stable frequency distribution. It is based on sharing of fibers of research and educational network carrying data traffic. Accurate time and stable frequency transmission uses mainly created dark channels amplified by special bidirectional amplifiers with the same propagation path for both directions. Paper also targets challenges joined with bidirectional transmission, which represents directional non-reciprocities and interaction with parallel data transmissions.

  5. 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

  6. 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.

  7. 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.

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

    NASA Astrophysics Data System (ADS)

    Dang, Zhaohui; Zhang, Yulin

    2013-01-01

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

  9. Learning from examples in large neural networks

    NASA Astrophysics Data System (ADS)

    Sompolinsky, H.; Tishby, N.; Seung, H. S.

    1990-09-01

    A statistical-mechanical theory of learning from examples in layered networks at finite temperature is studied. When the training error is a smooth function of continuously varying weights, the generalization error falls off asymptotically as the inverse number of examples. By analytical and numerical studies of single-layer perceptrons, we show that when the weights are discrete, the generalization error can exhibit a discontinuous transition to perfect generalization. For intermediate sizes of the example set, the state of perfect generalization coexists with a metastable spin-glass state.

  10. 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.

  11. Multisector Health Policy Networks in 15 Large US Cities

    PubMed Central

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

    2016-01-01

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

  12. 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.

  13. 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

  14. 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.

  15. 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

  16. 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.

  17. Learning networks for sustainable, large-scale improvement.

    PubMed

    McCannon, C Joseph; Perla, Rocco J

    2009-05-01

    Large-scale improvement efforts known as improvement networks offer structured opportunities for exchange of information and insights into the adaptation of clinical protocols to a variety of settings.

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

    PubMed

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

    2015-07-27

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

  19. 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

  20. Analyzing large biological datasets with association networks.

    PubMed

    Karpinets, Tatiana V; Park, Byung H; Uberbacher, Edward C

    2012-09-01

    Due to advances in high-throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches that process collected data into new knowledge in a timely manner. In this study, we propose a computational framework for discovering modular structure, relationships and regularities in complex data. The framework utilizes a semantic-preserving vocabulary to convert records of biological annotations of an object, such as an organism, gene, chemical or sequence, into networks (Anets) of the associated annotations. An association between a pair of annotations in an Anet is determined by the similarity of their co-occurrence pattern with all other annotations in the data. This feature captures associations between annotations that do not necessarily co-occur with each other and facilitates discovery of the most significant relationships in the collected data through clustering and visualization of the Anet. To demonstrate this approach, we applied the framework to the analysis of metadata from the Genomes OnLine Database and produced a biological map of sequenced prokaryotic organisms with three major clusters of metadata that represent pathogens, environmental isolates and plant symbionts.

  1. 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.

  2. A large deformation viscoelastic model for double-network hydrogels

    NASA Astrophysics Data System (ADS)

    Mao, Yunwei; Lin, Shaoting; Zhao, Xuanhe; Anand, Lallit

    2017-03-01

    We present a large deformation viscoelasticity model for recently synthesized double network hydrogels which consist of a covalently-crosslinked polyacrylamide network with long chains, and an ionically-crosslinked alginate network with short chains. Such double-network gels are highly stretchable and at the same time tough, because when stretched the crosslinks in the ionically-crosslinked alginate network rupture which results in distributed internal microdamage which dissipates a substantial amount of energy, while the configurational entropy of the covalently-crosslinked polyacrylamide network allows the gel to return to its original configuration after deformation. In addition to the large hysteresis during loading and unloading, these double network hydrogels also exhibit a substantial rate-sensitive response during loading, but exhibit almost no rate-sensitivity during unloading. These features of large hysteresis and asymmetric rate-sensitivity are quite different from the response of conventional hydrogels. We limit our attention to modeling the complex viscoelastic response of such hydrogels under isothermal conditions. Our model is restricted in the sense that we have limited our attention to conditions under which one might neglect any diffusion of the water in the hydrogel - as might occur when the gel has a uniform initial value of the concentration of water, and the mobility of the water molecules in the gel is low relative to the time scale of the mechanical deformation. We also do not attempt to model the final fracture of such double-network hydrogels.

  3. Shared path protection through reconstructing sharable bandwidth based on spectrum segmentation for elastic optical networks

    NASA Astrophysics Data System (ADS)

    Liu, Huanlin; Zhang, Mingjia; Yi, Pengfei; Chen, Yong

    2016-12-01

    In order to address the problems of spectrum fragmentation and low sharing degree of spectrum resources in survivable elastic optical networks, an improved algorithm, called shared path protection by reconstructing sharable bandwidth based on spectrum segmentation (SPP-RSB-SS), is proposed in the paper. In the SPP-RSB-SS algorithm, for reducing the number of spectrum fragmentations and improving the success rate of spectrum allocation, the whole spectrum resource is partitioned into several spectrum segments. And each spectrum segment is allocated to the requests with the same bandwidth requirement in priority. Meanwhile, the protection path with higher spectrum sharing degree is selected through optimizing the link cost function and reconstructing sharable bandwidth. Hence, the protection path can maximize the sharable spectrum usage among multiple protection paths. The simulation results indicate that the SPP-RSB-SS algorithm can increase the sharing degree of protection spectrum effectively. Furthermore, the SPP-RSB-SS algorithm can enhance the spectrum utilization, and reduce the bandwidth blocking probability significantly.

  4. 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

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

    PubMed

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

    2016-07-01

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

  6. 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).

  7. PKI security in large-scale healthcare networks.

    PubMed

    Mantas, Georgios; Lymberopoulos, Dimitrios; Komninos, Nikos

    2012-06-01

    During the past few years a lot of PKI (Public Key Infrastructures) infrastructures have been proposed for healthcare networks in order to ensure secure communication services and exchange of data among healthcare professionals. However, there is a plethora of challenges in these healthcare PKI infrastructures. Especially, there are a lot of challenges for PKI infrastructures deployed over large-scale healthcare networks. In this paper, we propose a PKI infrastructure to ensure security in a large-scale Internet-based healthcare network connecting a wide spectrum of healthcare units geographically distributed within a wide region. Furthermore, the proposed PKI infrastructure facilitates the trust issues that arise in a large-scale healthcare network including multi-domain PKI infrastructures.

  8. One dimensional modeling of blood flow in large networks

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  9. 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.

  10. 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

  11. 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

  12. 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.

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

    NASA Astrophysics Data System (ADS)

    Hessar, Farzad; Roy, Sumit

    2016-06-01

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

  14. Avoiding and tolerating latency in large-scale next-generation shared-memory multiprocessors

    NASA Technical Reports Server (NTRS)

    Probst, David K.

    1993-01-01

    A scalable solution to the memory-latency problem is necessary to prevent the large latencies of synchronization and memory operations inherent in large-scale shared-memory multiprocessors from reducing high performance. We distinguish latency avoidance and latency tolerance. Latency is avoided when data is brought to nearby locales for future reference. Latency is tolerated when references are overlapped with other computation. Latency-avoiding locales include: processor registers, data caches used temporally, and nearby memory modules. Tolerating communication latency requires parallelism, allowing the overlap of communication and computation. Latency-tolerating techniques include: vector pipelining, data caches used spatially, prefetching in various forms, and multithreading in various forms. Relaxing the consistency model permits increased use of avoidance and tolerance techniques. Each model is a mapping from the program text to sets of partial orders on program operations; it is a convention about which temporal precedences among program operations are necessary. Information about temporal locality and parallelism constrains the use of avoidance and tolerance techniques. Suitable architectural primitives and compiler technology are required to exploit the increased freedom to reorder and overlap operations in relaxed models.

  15. 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

  16. Impact of heuristics in clustering large biological networks.

    PubMed

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

    2015-12-01

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

  17. Large-scale P2P network based distributed virtual geographic environment (DVGE)

    NASA Astrophysics Data System (ADS)

    Tan, Xicheng; Yu, Liang; Bian, Fuling

    2007-06-01

    Virtual Geographic Environment has raised full concern as a kind of software information system that helps us understand and analyze the real geographic environment, and it has also expanded to application service system in distributed environment--distributed virtual geographic environment system (DVGE), and gets some achievements. However, limited by the factor of the mass data of VGE, the band width of network, as well as numerous requests and economic, etc. DVGE still faces some challenges and problems which directly cause the current DVGE could not provide the public with high-quality service under current network mode. The Rapid development of peer-to-peer network technology has offered new ideas of solutions to the current challenges and problems of DVGE. Peer-to-peer network technology is able to effectively release and search network resources so as to realize efficient share of information. Accordingly, this paper brings forth a research subject on Large-scale peer-to-peer network extension of DVGE as well as a deep study on network framework, routing mechanism, and DVGE data management on P2P network.

  18. Distributed data networks: a blueprint for Big Data sharing and healthcare analytics.

    PubMed

    Popovic, Jennifer R

    2017-01-01

    This paper defines the attributes of distributed data networks and outlines the data and analytic infrastructure needed to build and maintain a successful network. We use examples from one successful implementation of a large-scale, multisite, healthcare-related distributed data network, the U.S. Food and Drug Administration-sponsored Sentinel Initiative. Analytic infrastructure-development concepts are discussed from the perspective of promoting six pillars of analytic infrastructure: consistency, reusability, flexibility, scalability, transparency, and reproducibility. This paper also introduces one use case for machine learning algorithm development to fully utilize and advance the portfolio of population health analytics, particularly those using multisite administrative data sources.

  19. Uncovering disassortativity in large scale-free networks

    NASA Astrophysics Data System (ADS)

    Litvak, Nelly; van der Hofstad, Remco

    2013-02-01

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

  20. 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.

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

    PubMed

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

    2004-09-16

    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.

  2. Experimental single qubit quantum secret sharing in a fiber network configuration.

    PubMed

    Ma, Hai-Qiang; Wei, Ke-Jin; Yang, Jian-Hui

    2013-11-01

    We present a robust single-photon quantum secret sharing (QSS) scheme with phase encoding in three-party implementations and a design way of more parties over a 50 km single-mode fiber network using a single QSS protocol. This scheme automatically provides perfect compensation for birefringence. A high visibility of 99.4% is obtained over three hours in visibility and stability measurements without any system adjustments, showing good potential for practical systems. Furthermore, polarization-insensitive phase modulators are realized using this system.

  3. 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

  4. Graceful fault tolerance in large networks of microcomputers

    SciTech Connect

    Agrawal, B.K.

    1984-01-01

    This work considers the problem of fault diagnosis in a network of distributed multicomputers, and a strategy for repeated reconfiguration is presented in detail to help improve the degree of fault tolerance. The overall system diagnosability is shown to be enhanced further by constructing a large network with small well-known graphs as its basis and then applying reconfiguration techniques locally in various system partitions and exchanging diagnostic information globally. A detailed description of this new attractive approach is presented along with the diagnostic algorithm suitable for large networks of microcomputers in VLSI based distributed systems. A systematic procedure for defining near-optimal fault-tolerance graph theoretic networks is investigated which is well suited for multicomputer structures. A distributed algorithm along with a new system diagnostic theory is proposed.

  5. A Game-Theoretic Approach for Opportunistic Spectrum Sharing in Cognitive Radio Networks with Incomplete Information

    NASA Astrophysics Data System (ADS)

    Tan, Xuesong Jonathan; Li, Liang; Guo, Wei

    One important issue in cognitive transmission is for multiple secondary users to dynamically acquire spare spectrum from the single primary user. The existing spectrum sharing scheme adopts a deterministic Cournot game to formulate this problem, of which the solution is the Nash equilibrium. This formulation is based on two implicit assumptions. First, each secondary user is willing to fully exchange transmission parameters with all others and hence knows their complete information. Second, the unused spectrum of the primary user for spectrum sharing is always larger than the total frequency demand of all secondary users at the Nash equilibrium. However, both assumptions may not be true in general. To remedy this, the present paper considers a more realistic assumption of incomplete information, i.e., each secondary user may choose to conceal their private information for achieving higher transmission benefit. Following this assumption and given that the unused bandwidth of the primary user is large enough, we adopt a probabilistic Cournot game to formulate an opportunistic spectrum sharing scheme for maximizing the total benefit of all secondary users. Bayesian equilibrium is considered as the solution of this game. Moreover, we prove that a secondary user can improve their expected benefit by actively hiding its transmission parameters and increasing their variance. On the other hand, when the unused spectrum of the primary user is smaller than the maximal total frequency demand of all secondary users at the Bayesian equilibrium, we formulate a constrained optimization problem for the primary user to maximize its profit in spectrum sharing and revise the proposed spectrum sharing scheme to solve this problem heuristically. This provides a unified approach to overcome the aforementioned two limitations of the existing spectrum sharing scheme.

  6. Pythoscape: a framework for generation of large protein similarity networks.

    PubMed

    Barber, Alan E; Babbitt, Patricia C

    2012-11-01

    Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among proteins for which pairwise all-by-all similarity connections have been calculated. Mapping of biological and other information to network nodes or edges enables hypothesis creation about sequence-structure-function relationships across sets of related proteins. Pythoscape provides several options to calculate pairwise similarities for input sequences or structures, applies filters to network edges and defines sets of similar nodes and their associated data as single nodes (termed representative nodes) for compression of network information and output data or formatted files for visualization.

  7. 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

  8. Host Immunity via Mutable Virtualized Large-Scale Network Containers

    DTIC Science & Technology

    2016-07-25

    system for host immunity that combines virtualization , emulation, and mutable network configurations. This system is deployed on a single host, and...entire !Pv4 address space within 5 Host Immunity via Mutable Virtualized Large-Scale Network Containers 45 minutes from a single machine. Second, when...URL, and we call it URL marker. A URL marker records the information about its parent web page’s URL and the user ID who collects the URL. Thus, when

  9. Large-Capacity Three-Party Quantum Digital Secret Sharing Using Three Particular Matrices Coding

    NASA Astrophysics Data System (ADS)

    Lai, Hong; Luo, Ming-Xing; Pieprzyk, Josef; Tao, Li; Liu, Zhi-Ming; Orgun, Mehmet A.

    2016-11-01

    In this paper, we develop a large-capacity quantum digital secret sharing (QDSS) scheme, combined the Fibonacci- and Lucas-valued orbital angular momentum (OAM) entanglement with the recursive Fibonacci and Lucas matrices. To be exact, Alice prepares pairs of photons in the Fibonacci- and Lucas-valued OAM entangled states, and then allocates them to two participants, say, Bob and Charlie, to establish the secret key. Moreover, the available Fibonacci and Lucas values from the matching entangled states are used as the seed for generating the Fibonacci and Lucas matrices. This is achieved because the entries of the Fibonacci and Lucas matrices are recursive. The secret key can only be obtained jointly by Bob and Charlie, who can further recover the secret. Its security is based on the facts that nonorthogonal states are indistinguishable, and Bob or Charlie detects a Fibonacci number, there is still a twofold uncertainty for Charlie' (Bob') detected value. Supported by the Fundamental Research Funds for the Central Universities under Grant No. XDJK2016C043 and the Doctoral Program of Higher Education under Grant No. SWU115091, the National Natural Science Foundation of China under Grant No. 61303039, the Fundamental Research Funds for the Central Universities under Grant No. XDJK2015C153 and the Doctoral Program of Higher Education under Grant No. SWU114112, and the Financial Support the 1000-Plan of Chongqing by Southwest University under Grant No. SWU116007

  10. Predicting Positive and Negative Relationships in Large Social Networks.

    PubMed

    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.

  11. 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

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

    PubMed

    Jiang, Shunrong; Zhu, Xiaoyan; Wang, Liangmin

    2015-09-03

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

  13. 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

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

    PubMed

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

    2010-06-01

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

  15. 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

  16. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  19. 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.

  20. Aberrant intra-salience network dynamic functional connectivity impairs large-scale network interactions in schizophrenia.

    PubMed

    Wang, Xiangpeng; Zhang, Wenwen; Sun, Yujing; Hu, Min; Chen, Antao

    2016-12-01

    Aberrant functional interactions between several large-scale networks, especially the central executive network (CEN), the default mode network (DMN) and the salience network (SN), have been postulated as core pathophysiologic features of schizophrenia; however, the attributing factors of which remain unclear. The study employed resting-state fMRI with 77 participants (42 patients and 35 controls). We performed dynamic functional connectivity (DFC) and functional connectivity (FC) analyses to explore the connectivity patterns of these networks. Furthermore, we performed a structural equation model (SEM) analysis to explore the possible role of the SN in modulating network interactions. The results were as follows: (1) The inter-network connectivity showed decreased connectivity strength and increased time-varying instability in schizophrenia; (2) The SN manifested schizophrenic intra-network dysfunctions in both the FC and DFC patterns; (3) The connectivity properties of the SN were effective in discriminating controls from patients; (4) In patients, the dynamic intra-SN connectivity negatively predicted the inter-network FC, and this effect was mediated by intra-SN connectivity strength. These findings suggest that schizophrenia show systematic deficits in temporal stability of large-scale network connectivity. Furthermore, aberrant network interactions in schizophrenia could be attributed to instable intra-SN connectivity and the dysfunction of the SN may be an intrinsic biomarker of the disease.

  1. 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.

  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. Clustering algorithm for determining community structure in large networks

    NASA Astrophysics Data System (ADS)

    Pujol, Josep M.; Béjar, Javier; Delgado, Jordi

    2006-07-01

    We propose an algorithm to find the community structure in complex networks based on the combination of spectral analysis and modularity optimization. The clustering produced by our algorithm is as accurate as the best algorithms on the literature of modularity optimization; however, the main asset of the algorithm is its efficiency. The best match for our algorithm is Newman’s fast algorithm, which is the reference algorithm for clustering in large networks due to its efficiency. When both algorithms are compared, our algorithm outperforms the fast algorithm both in efficiency and accuracy of the clustering, in terms of modularity. Thus, the results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.

  4. Call Admission Control on Single Node Networks under Output Rate-Controlled Generalized Processor Sharing (ORC-GPS) Scheduler

    NASA Astrophysics Data System (ADS)

    Hanada, Masaki; Nakazato, Hidenori; Watanabe, Hitoshi

    Multimedia applications such as music or video streaming, video teleconferencing and IP telephony are flourishing in packet-switched networks. Applications that generate such real-time data can have very diverse quality-of-service (QoS) requirements. In order to guarantee diverse QoS requirements, the combined use of a packet scheduling algorithm based on Generalized Processor Sharing (GPS) and leaky bucket traffic regulator is the most successful QoS mechanism. GPS can provide a minimum guaranteed service rate for each session and tight delay bounds for leaky bucket constrained sessions. However, the delay bounds for leaky bucket constrained sessions under GPS are unnecessarily large because each session is served according to its associated constant weight until the session buffer is empty. In order to solve this problem, a scheduling policy called Output Rate-Controlled Generalized Processor Sharing (ORC-GPS) was proposed in [17]. ORC-GPS is a rate-based scheduling like GPS, and controls the service rate in order to lower the delay bounds for leaky bucket constrained sessions. In this paper, we propose a call admission control (CAC) algorithm for ORC-GPS, for leaky-bucket constrained sessions with deterministic delay requirements. This CAC algorithm for ORC-GPS determines the optimal values of parameters of ORC-GPS from the deterministic delay requirements of the sessions. In numerical experiments, we compare the CAC algorithm for ORC-GPS with one for GPS in terms of schedulable region and computational complexity.

  5. Spectrum Sharing Based on a Bertrand Game in Cognitive Radio Sensor Networks.

    PubMed

    Zeng, Biqing; Zhang, Chi; Hu, Pianpian; Wang, Shengyu

    2017-01-07

    In the study of power control and allocation based on pricing, the utility of secondary users is usually studied from the perspective of the signal to noise ratio. The study of secondary user utility from the perspective of communication demand can not only promote the secondary users to meet the maximum communication needs, but also to maximize the utilization of spectrum resources, however, research in this area is lacking, so from the viewpoint of meeting the demand of network communication, this paper designs a two stage model to solve spectrum leasing and allocation problem in cognitive radio sensor networks (CRSNs). In the first stage, the secondary base station collects the secondary network communication requirements, and rents spectrum resources from several primary base stations using the Bertrand game to model the transaction behavior of the primary base station and secondary base station. The second stage, the subcarriers and power allocation problem of secondary base stations is defined as a nonlinear programming problem to be solved based on Nash bargaining. The simulation results show that the proposed model can satisfy the communication requirements of each user in a fair and efficient way compared to other spectrum sharing schemes.

  6. Spectrum Sharing Based on a Bertrand Game in Cognitive Radio Sensor Networks

    PubMed Central

    Zeng, Biqing; Zhang, Chi; Hu, Pianpian; Wang, Shengyu

    2017-01-01

    In the study of power control and allocation based on pricing, the utility of secondary users is usually studied from the perspective of the signal to noise ratio. The study of secondary user utility from the perspective of communication demand can not only promote the secondary users to meet the maximum communication needs, but also to maximize the utilization of spectrum resources, however, research in this area is lacking, so from the viewpoint of meeting the demand of network communication, this paper designs a two stage model to solve spectrum leasing and allocation problem in cognitive radio sensor networks (CRSNs). In the first stage, the secondary base station collects the secondary network communication requirements, and rents spectrum resources from several primary base stations using the Bertrand game to model the transaction behavior of the primary base station and secondary base station. The second stage, the subcarriers and power allocation problem of secondary base stations is defined as a nonlinear programming problem to be solved based on Nash bargaining. The simulation results show that the proposed model can satisfy the communication requirements of each user in a fair and efficient way compared to other spectrum sharing schemes. PMID:28067850

  7. 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...

  8. 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.

  9. 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

  10. 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.

  11. Development of Large-Scale Functional Brain Networks in Children

    PubMed Central

    Supekar, Kaustubh; Musen, Mark; Menon, Vinod

    2009-01-01

    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

  12. 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

  13. [EHealth, health networks and electronic health record: towards a culture of sharing and trust].

    PubMed

    Nicolas, L

    2012-09-01

    In Belgium, the computerization of the ambulatory care sector and general practice in particular has been continuously progressing over the last ten years. Although regional differences exist, 75% of the Belgian general practitioners own today a software to assist them in the management of their patients. To date however, general practitioners have been hardly using their electronic system to share or communicate with other actors of the system. The silo culture remains the norm. Aside from certain group practices, computerization has thus not yet contributed to improve in a significant way the quality of care of the patient. The implementation in 2012 of the "shared electronic health record" thanks to the deployment in production of the 5 health networks connected via the federal directory of references is due to deeply change this situation. Communication flows between health care professionals will thus rapidly intensify and the amount of information available at the point of care will increase significantly. This is however only the first step. The future electronic patient record (EPR) will need to make room for a deep integration of the actors around the patient thanks--among other things--to the implementation of a global semantic interoperability strategy. This integration of actors together with the empowerment of the patient are indeed essential preliminary conditions in order to adapt our health system to the enormous challenges that we will all have to face in the next 10 years.

  14. The evaluation of the need to share medical data on the community medical ICT network service in Nagasaki, Japan.

    PubMed

    Matsumoto, Takehiro; Honda, Masayuki

    2013-01-01

    The Community Medical ICT Network service at the Nagasaki, Japan was established in 2009. Medical information network for sharing patients data was investigated focused on the access log data from April of 2009 and October of 2010. The total number of the access to the medical information was 30,914 of 2,213 patients. And the total number of access of the image including diagnostic imaging report, medical examination, treatment and medical documents was 10,278(33.2%), 8,949(28.9%), 6,896(22.3%) and 4,791(15.5%) respectively. These results showed that these medical information had enough valued for sharing in the regional medicine. In conclusion, many types of medical information should be required for sharing in the community medical ICT network in Japan.

  15. 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.

  16. The stress response and immune system share, borrow, and reconfigure their physiological network elements: Evidence from the insects.

    PubMed

    Adamo, Shelley A

    2017-02-01

    The classic biomedical view is that stress hormone effects on the immune system are largely pathological, especially if the stress is chronic. However, more recent interpretations have focused on the potential adaptive function of these effects. This paper examines stress response-immune system interactions from a physiological network perspective, using insects because of their simpler physiology. For example, stress hormones can reduce disease resistance, yet activating an immune response results in the release of stress hormones in both vertebrates and invertebrates. From a network perspective, this phenomenon is consistent with the 'sharing' of the energy-releasing ability of stress hormones by both the stress response and the immune system. Stress-induced immunosuppression is consistent with the stress response 'borrowing' molecular components from the immune system to increase the capacity of stress-relevant physiological processes (i.e. a trade off). The insect stress hormones octopamine and adipokinetic hormone can also 'reconfigure' the immune system to help compensate for the loss of some of the immune system's molecular resources (e.g. apolipophorin III). This view helps explain seemingly maladaptive interactions between the stress response and immune system. The adaptiveness of stress hormone effects on individual immune components may be apparent only from the perspective of the whole organism. These broad principles will apply to both vertebrates and invertebrates.

  17. A cooperation mechanism for pure P2P file-sharing networks to improve application-level QoS

    NASA Astrophysics Data System (ADS)

    Wakamiya, Naoki; Konishi, Junjiro; Murata, Masayuki

    2006-10-01

    To provide application-oriented network services, a variety of overlay networks are deployed over physical IP networks. Since they share and compete for the same physical network resources, their selfish behaviors affect each other and, as a result, their performance deteriorates. In this paper, we propose a mechanism for pureP2P networks of file-sharing applications to cooperate with each other. In our proposal, a cooperative peer first finds another P2P network and establishes a logical link to a cooperative peer in the found network. Both ends of the logical link decide whether they cooperate or not from a viewpoint of the mutualism. When they consider they benefit from the cooperation, messages and files are exchanged among cooperative P2P networks through the logical link. For an efficient and effective cooperation, our mechanism has an algorithm for the selection of cooperative peers and a caching mechanism to avoid putting too much load on cooperative peers and cooperating networks. Simulation results showed that the number of discovered providing peers and the ratio of search hits increased about twice, while the load by the cooperation among P2P networks was reduced about half by caching.

  18. 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

  19. Evidence for Large Complex Networks of Plant Short Silencing RNAs

    PubMed Central

    MacLean, Daniel; Elina, Nataliya; Havecker, Ericka R.; Heimstaedt, Susanne B.; Studholme, David J.; Baulcombe, David C.

    2010-01-01

    Background In plants and animals there are many classes of short RNAs that carry out a wide range of functions within the cell; short silencing RNAs (ssRNAs) of 21–25 nucleotides in length are produced from double-stranded RNA precursors by the protein Dicer and guide nucleases and other proteins to their RNA targets through base pairing interactions. The consequence of this process is degradation of the targeted RNA, suppression of its translation or initiation of secondary ssRNA production. The secondary ssRNAs in turn could then initiate further layers of ssRNA production to form extensive cascades and networks of interacting RNA [1]. Previous empirical analysis in plants established the existence of small secondary ssRNA cascade [2], in which a single instance of this event occurred but it was not known whether there are other more extensive networks of secondary sRNA production. Methodology/Principal Findings We generated a network by predicting targets of ssRNA populations obtained from high-throughput sequencing experiments. The topology of the network shows it to have power law connectivity distribution, to be dissortative, highly clustered and composed of multiple components. We also identify protein families, PPR and ULP1, that act as hubs within the network. Comparison of the repetition of genomic sub-sequences of ssRNA length between Arabidopsis and E.coli suggest that the network structure is made possible by the underlying repetitiveness in the genome sequence. Conclusions/Significance Together our results provide good evidence for the existence of a large, robust ssRNA interaction network with distinct regulatory function. Such a network could have a massive effect on the regulation of gene expression via mediation of transcript levels. PMID:20360863

  20. Evolution of quality at the Organ Center of the Organ Procurement and Transplantation Network/United Network for Organ Sharing.

    PubMed

    Brown, Roger S; Belton, A Matthew; Martin, Judith M; Simmons, Dee Dee; Taylor, Gloria J; Willard, Ellie

    2009-09-01

    One of the goals of the Organ Center of the Organ Procurement and Transplantation Network/United Network for Organ Sharing is to increase the efficiency of equitable organ allocation in the United States. Recognizing the ever-growing need for organ donors and transplants, leaders at the Organ Center increased its commitment to quality improvement initiatives through the development of a quality management team in 2001. The Organ Center began to focus on ways to capture data on processes and pinpoint areas for improvement. As the collection and analysis of data evolved, the Organ Center embraced formal quality standards, such as improvement cycles. Using these cycles, the Organ Center has seen significant improvement. One initiative involving lifesaving heart, lung, and liver placement showed success by doubling the Organ Center's organ placement rate. Another project involving the validation of donor information demonstrated that the accuracy of organ allocation can be improved by 5% on a consistent basis. As stewards for the gift of life and leaders in organ allocation, the Organ Center uses continuous quality improvement to achieve the goal of increasing the efficiency of equitable organ allocation.

  1. 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.

  2. Distributed Coordinated Control of Large-Scale Nonlinear Networks

    DOE PAGES

    Kundu, Soumya; Anghel, Marian

    2015-11-08

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

  3. Modeling Failure Propagation in Large-Scale Engineering Networks

    NASA Astrophysics Data System (ADS)

    Schläpfer, Markus; Shapiro, Jonathan L.

    The simultaneous unavailability of several technical components within large-scale engineering systems can lead to high stress, rendering them prone to cascading events. In order to gain qualitative insights into the failure propagation mechanisms resulting from independent outages, we adopt a minimalistic model representing the components and their interdependencies by an undirected, unweighted network. The failure dynamics are modeled by an anticipated accelerated “wearout” process being dependent on the initial degree of a node and on the number of failed nearest neighbors. The results of the stochastic simulations imply that the influence of the network topology on the speed of the cascade highly depends on how the number of failed nearest neighbors shortens the life expectancy of a node. As a formal description of the decaying networks we propose a continuous-time mean field approximation, estimating the average failure rate of the nearest neighbors of a node based on the degree-degree distribution.

  4. Distributed Coordinated Control of Large-Scale Nonlinear Networks

    SciTech Connect

    Kundu, Soumya; Anghel, Marian

    2015-11-08

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

  5. 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

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

    PubMed

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

    2011-10-01

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

  7. 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…

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

    PubMed

    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 et al., 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.

  9. 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.

  10. 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

  11. 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.

  12. A large fiber sensor network for an acoustic neutrino telescope

    NASA Astrophysics Data System (ADS)

    Buis, Ernst-Jan; Doppenberg, Ed; Lahmann, Robert; Toet, Peter

    2017-03-01

    The scientific prospects of detecting neutrinos with an energy close or even higher than the GKZ cut-off energy has been discussed extensively in literature. It is clear that due to their expected low flux, the detection of these ultra-high energy neutrinos (Ev > 1018 eV) requires a telescope larger than 100 km3. Acoustic detection may provide a way to observe these ultra-high energy cosmic neutrinos, as sound that they induce in the deep sea when neutrinos lose their energy travels undisturbed for many kilometers. To realize a large scale acoustic neutrino telescope, dedicated technology must be developed that allows for a deep sea sensor network. Fiber optic hydrophone technology provides a promising means to establish a large scale sensor network [1] with the proper sensitivity to detect the small signals from the neutrino interactions.

  13. 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.

  14. A Holistic Management Architecture for Large-Scale Adaptive Networks

    DTIC Science & Technology

    2007-09-01

    MANAGEMENT ARCHITECTURE FOR LARGE-SCALE ADAPTIVE NETWORKS by Michael R. Clement September 2007 Thesis Advisor: Alex Bordetsky Second Reader...TECHNOLOGY MANAGEMENT from the NAVAL POSTGRADUATE SCHOOL September 2007 Author: Michael R. Clement Approved by: Dr. Alex ...achieve in life is by His will. Ad Majorem Dei Gloriam. To my parents, my family, and Caitlin: For supporting me, listening to me when I got

  15. 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.

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

    PubMed

    Crespin, Silvio J; García-Villalta, Jorge E

    2014-10-01

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

  17. 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.

  18. 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.

  19. 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.

  20. CytoKavosh: A Cytoscape Plug-In for Finding Network Motifs in Large Biological Networks

    PubMed Central

    Razaghi Moghadam Kashani, Zahra; Salehzadeh-Yazdi, Ali; Khakabimamaghani, Sahand

    2012-01-01

    Network motifs are small connected sub-graphs that have recently gathered much attention to discover structural behaviors of large and complex networks. Finding motifs with any size is one of the most important problems in complex and large networks. It needs fast and reliable algorithms and tools for achieving this purpose. CytoKavosh is one of the best choices for finding motifs with any given size in any complex network. It relies on a fast algorithm, Kavosh, which makes it faster than other existing tools. Kavosh algorithm applies some well known algorithmic features and includes tricky aspects, which make it an efficient algorithm in this field. CytoKavosh is a Cytoscape plug-in which supports us in finding motifs of given size in a network that is formerly loaded into the Cytoscape work-space (directed or undirected). High performance of CytoKavosh is achieved by dynamically linking highly optimized functions of Kavosh's C++ to the Cytoscape Java program, which makes this plug-in suitable for analyzing large biological networks. Some significant attributes of CytoKavosh is efficiency in time usage and memory and having no limitation related to the implementation in motif size. CytoKavosh is implemented in a visual environment Cytoscape that is convenient for the users to interact and create visual options to analyze the structural behavior of a network. This plug-in can work on any given network and is very simple to use and generates graphical results of discovered motifs with any required details. There is no specific Cytoscape plug-in, specific for finding the network motifs, based on original concept. So, we have introduced for the first time, CytoKavosh as the first plug-in, and we hope that this plug-in can be improved to cover other options to make it the best motif-analyzing tool. PMID:22952659

  1. 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…

  2. Reconfigurable hardware applications on NetFPGA for network monitoring in large area sensor networks

    NASA Astrophysics Data System (ADS)

    Belias, A.; Koutsoumpos, V.; Manolopoulos, K.; Kachris, C.

    2013-10-01

    A valuable functionality for sensor networks, distributed in large volumes is the capability to characterize and analyze the data traffic at wire speed and monitor the data prior to committing to permanent storage. As a demonstrator we use a reconfigurable hardware router for real-time monitoring of data before their transmission to further processing and storage. The reconfigurable hardware router is based on the NetFPGA platform. In this study we report on the hardware implementation to monitor web-based network applications and compare our results with a software based network analyzer.

  3. Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics

    NASA Astrophysics Data System (ADS)

    Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo

    We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.

  4. Weighted social networks for a large scale artificial society

    NASA Astrophysics Data System (ADS)

    Fan, Zong Chen; Duan, Wei; Zhang, Peng; Qiu, Xiao Gang

    2016-12-01

    The method of artificial society has provided a powerful way to study and explain how individual behaviors at micro level give rise to the emergence of global social phenomenon. It also creates the need for an appropriate representation of social structure which usually has a significant influence on human behaviors. It has been widely acknowledged that social networks are the main paradigm to describe social structure and reflect social relationships within a population. To generate social networks for a population of interest, considering physical distance and social distance among people, we propose a generation model of social networks for a large-scale artificial society based on human choice behavior theory under the principle of random utility maximization. As a premise, we first build an artificial society through constructing a synthetic population with a series of attributes in line with the statistical (census) data for Beijing. Then the generation model is applied to assign social relationships to each individual in the synthetic population. Compared with previous empirical findings, the results show that our model can reproduce the general characteristics of social networks, such as high clustering coefficient, significant community structure and small-world property. Our model can also be extended to a larger social micro-simulation as an input initial. It will facilitate to research and predict some social phenomenon or issues, for example, epidemic transition and rumor spreading.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  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. Marginalized neural network mixtures for large-scale regression.

    PubMed

    Lazaro-Gredilla, Miguel; Figueiras-Vidal, Aníbal R

    2010-08-01

    For regression tasks, traditional neural networks (NNs) have been superseded by gaussian processes, which provide probabilistic predictions (input-dependent error bars), improved accuracy, and virtually no overfitting. Due to their high computational cost, in scenarios with massive data sets, one has to resort to sparse gaussian processes, which strive to achieve similar performance with much smaller computational effort. In this context, we introduce a mixture of NNs with marginalized output weights that can both provide probabilistic predictions and improve on the performance of sparse gaussian processes, at the same computational cost. The effectiveness of this approach is shown experimentally on some representative large data sets.

  17. 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.

  18. A Modular Ring Architecture for Large Scale Neural Network Implementations

    NASA Astrophysics Data System (ADS)

    Jump, Lance B.; Ligomenides, Panos A.

    1989-11-01

    Constructing fully parallel, large scale, neural networks is complicated by the problems of providing for massive interconnectivity and of overcoming fan in/out limitations in area-efficient VLSI/WSI realizations. A modular, bus switched, neural ring architecture employing primitive ring (pRing) processors is proposed, which solves the fan in/out and connectivity problems by a dynamically reconfigurable communication ring that synchronously serves identical, radially connected, processing elements. It also allows cost versus performance trade-offs by the assignment of variable numbers of logical neurons to each physical processing element.

  19. 78 FR 7464 - Large Scale Networking (LSN) ; Joint Engineering Team (JET)

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-02-01

    ... Technology Research and Development (NITRD) National Coordination Office (NCO), NSF. Reference the NITRD Web... Networking and Information Technology Research and Development ] (NITRD) National Coordination Office (NCO... research networking and networking to support science applications. The JET reports to the Large...

  20. 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.

  1. Methodology for the Improvement of Large District Heating Networks

    NASA Astrophysics Data System (ADS)

    Volkova, Anna; Mashatin, Vladislav; Hlebnikov, Aleksander; Siirde, Andres

    2012-12-01

    The purpose of this paper is to offer a methodology for the evaluation of large district heating networks. The methodology includes an analysis of heat generation and distribution based on the models created in the TERMIS and EnergyPro software Data from the large-scale Tallinn district heating system was used for the approbation of the proposed methodology as a basis of the case study. The effective operation of the district heating system, both at the stage of heat generation and heat distribution, can reduce the cost of heat supplied to the consumers. It can become an important factor for increasing the number of district heating consumers and demand for the heat load, which in turn will allow installing new cogeneration plants, using renewable energy sources and heat pump technologies

  2. 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.

  3. Brief Mental Training Reorganizes Large-Scale Brain Networks

    PubMed Central

    Tang, Yi-Yuan; Tang, Yan; Tang, Rongxiang; Lewis-Peacock, Jarrod A.

    2017-01-01

    Emerging evidences have shown that one form of mental training—mindfulness meditation, can improve attention, emotion regulation and cognitive performance through changing brain activity and structural connectivity. However, whether and how the short-term mindfulness meditation alters large-scale brain networks are not well understood. Here, we applied a novel data-driven technique, the multivariate pattern analysis (MVPA) to resting-state fMRI (rsfMRI) data to identify changes in brain activity patterns and assess the neural mechanisms induced by a brief mindfulness training—integrative body–mind training (IBMT), which was previously reported in our series of randomized studies. Whole brain rsfMRI was performed on an undergraduate group who received 2 weeks of IBMT with 30 min per session (5 h training in total). Classifiers were trained on measures of functional connectivity in this fMRI data, and they were able to reliably differentiate (with 72% accuracy) patterns of connectivity from before vs. after the IBMT training. After training, an increase in positive functional connections (60 connections) were detected, primarily involving bilateral superior/middle occipital gyrus, bilateral frontale operculum, bilateral superior temporal gyrus, right superior temporal pole, bilateral insula, caudate and cerebellum. These results suggest that brief mental training alters the functional connectivity of large-scale brain networks at rest that may involve a portion of the neural circuitry supporting attention, cognitive and affective processing, awareness and sensory integration and reward processing. PMID:28293180

  4. Brief Mental Training Reorganizes Large-Scale Brain Networks.

    PubMed

    Tang, Yi-Yuan; Tang, Yan; Tang, Rongxiang; Lewis-Peacock, Jarrod A

    2017-01-01

    Emerging evidences have shown that one form of mental training-mindfulness meditation, can improve attention, emotion regulation and cognitive performance through changing brain activity and structural connectivity. However, whether and how the short-term mindfulness meditation alters large-scale brain networks are not well understood. Here, we applied a novel data-driven technique, the multivariate pattern analysis (MVPA) to resting-state fMRI (rsfMRI) data to identify changes in brain activity patterns and assess the neural mechanisms induced by a brief mindfulness training-integrative body-mind training (IBMT), which was previously reported in our series of randomized studies. Whole brain rsfMRI was performed on an undergraduate group who received 2 weeks of IBMT with 30 min per session (5 h training in total). Classifiers were trained on measures of functional connectivity in this fMRI data, and they were able to reliably differentiate (with 72% accuracy) patterns of connectivity from before vs. after the IBMT training. After training, an increase in positive functional connections (60 connections) were detected, primarily involving bilateral superior/middle occipital gyrus, bilateral frontale operculum, bilateral superior temporal gyrus, right superior temporal pole, bilateral insula, caudate and cerebellum. These results suggest that brief mental training alters the functional connectivity of large-scale brain networks at rest that may involve a portion of the neural circuitry supporting attention, cognitive and affective processing, awareness and sensory integration and reward processing.

  5. Using Swarming Agents for Scalable Security in Large Network Environments

    SciTech Connect

    Crouse, Michael; White, Jacob L.; Fulp, Errin W.; Berenhaut, Kenneth S.; Fink, Glenn A.; Haack, Jereme N.

    2011-09-23

    The difficulty of securing computer infrastructures increases as they grow in size and complexity. Network-based security solutions such as IDS and firewalls cannot scale because of exponentially increasing computational costs inherent in detecting the rapidly growing number of threat signatures. Hostbased solutions like virus scanners and IDS suffer similar issues, and these are compounded when enterprises try to monitor these in a centralized manner. Swarm-based autonomous agent systems like digital ants and artificial immune systems can provide a scalable security solution for large network environments. The digital ants approach offers a biologically inspired design where each ant in the virtual colony can detect atoms of evidence that may help identify a possible threat. By assembling the atomic evidences from different ant types the colony may detect the threat. This decentralized approach can require, on average, fewer computational resources than traditional centralized solutions; however there are limits to its scalability. This paper describes how dividing a large infrastructure into smaller managed enclaves allows the digital ant framework to effectively operate in larger environments. Experimental results will show that using smaller enclaves allows for more consistent distribution of agents and results in faster response times.

  6. Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

    PubMed Central

    Vértes, Petra E.; Alexander-Bloch, Aaron; Bullmore, Edward T.

    2014-01-01

    Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour. PMID:25180309

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

    PubMed

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

    2011-03-01

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

  8. Post-transplant lymphoproliferative disorder after pancreas transplantation: a United Network for Organ Sharing database analysis.

    PubMed

    Jackson, K; Ruppert, K; Shapiro, R

    2013-01-01

    There are not a great deal of data on post-transplant lymphoproliferative disorder (PTLD) following pancreas transplantation. We analyzed the United Network for Organ Sharing national database of pancreas transplants to identify predictors of PTLD development. A univariate Cox model was generated for each potential predictor, and those at least marginally associated (p < 0.15) with PTLD were entered into a multivariable Cox model. PTLD developed in 43 patients (1.0%) of 4205 pancreas transplants. Mean follow-up time was 4.9 ± 2.2 yr. In the multivariable Cox model, recipient EBV seronegativity (HR 5.52, 95% CI: 2.99-10.19, p < 0.001), not having tacrolimus in the immunosuppressive regimen (HR 6.02, 95% CI: 2.74-13.19, p < 0.001), recipient age (HR 0.96, 95% CI: 0.92-0.99, p = 0.02), non-white ethnicity (HR 0.11, 95% CI: 0.02-0.84, p = 0.03), and HLA mismatching (HR 0.80, 95% CI: 0.67-0.97, p = 0.02) were significantly associated with the development of PTLD. Patient survival was significantly decreased in patients with PTLD, with a one-, three-, and five-yr survival of 91%, 76%, and 70%, compared with 97%, 93%, and 88% in patients without PTLD (p < 0.001). PTLD is an uncommon but potentially lethal complication following pancreas transplantation. Patients with the risk factors identified should be monitored closely for the development of PTLD.

  9. Consume, Modify, Share (CMS): The Interplay between Individual Decisions and Structural Network Properties in the Diffusion of Information.

    PubMed

    Koren, Hila; Kaminer, Ido; Raban, Daphne Ruth

    2016-01-01

    Widely used information diffusion models such as Independent Cascade Model, Susceptible Infected Recovered (SIR) and others fail to acknowledge that information is constantly subject to modification. Some aspects of information diffusion are best explained by network structural characteristics while in some cases strong influence comes from individual decisions. We introduce reinvention, the ability to modify information, as an individual level decision that affects the diffusion process as a whole. Based on a combination of constructs from the Diffusion of Innovations and the Critical Mass Theories, the present study advances the CMS (consume, modify, share) model which accounts for the interplay between network structure and human behavior and interactions. The model's building blocks include processes leading up to and following the formation of a critical mass of information adopters and disseminators. We examine the formation of an inflection point, information reach, sustainability of the diffusion process and collective value creation. The CMS model is tested on two directed networks and one undirected network, assuming weak or strong ties and applying constant and relative modification schemes. While all three networks are designed for disseminating new knowledge they differ in structural properties. Our findings suggest that modification enhances the diffusion of information in networks that support undirected connections and carries the biggest effect when information is shared via weak ties. Rogers' diffusion model and traditional information contagion models are fine tuned. Our results show that modifications not only contribute to a sustainable diffusion process, but also aid information in reaching remote areas of the network. The results point to the importance of cultivating weak ties, allowing reciprocal interaction among nodes and supporting the modification of information in promoting diffusion processes. These results have theoretical and

  10. Consume, Modify, Share (CMS): The Interplay between Individual Decisions and Structural Network Properties in the Diffusion of Information

    PubMed Central

    Koren, Hila; Kaminer, Ido

    2016-01-01

    Widely used information diffusion models such as Independent Cascade Model, Susceptible Infected Recovered (SIR) and others fail to acknowledge that information is constantly subject to modification. Some aspects of information diffusion are best explained by network structural characteristics while in some cases strong influence comes from individual decisions. We introduce reinvention, the ability to modify information, as an individual level decision that affects the diffusion process as a whole. Based on a combination of constructs from the Diffusion of Innovations and the Critical Mass Theories, the present study advances the CMS (consume, modify, share) model which accounts for the interplay between network structure and human behavior and interactions. The model's building blocks include processes leading up to and following the formation of a critical mass of information adopters and disseminators. We examine the formation of an inflection point, information reach, sustainability of the diffusion process and collective value creation. The CMS model is tested on two directed networks and one undirected network, assuming weak or strong ties and applying constant and relative modification schemes. While all three networks are designed for disseminating new knowledge they differ in structural properties. Our findings suggest that modification enhances the diffusion of information in networks that support undirected connections and carries the biggest effect when information is shared via weak ties. Rogers' diffusion model and traditional information contagion models are fine tuned. Our results show that modifications not only contribute to a sustainable diffusion process, but also aid information in reaching remote areas of the network. The results point to the importance of cultivating weak ties, allowing reciprocal interaction among nodes and supporting the modification of information in promoting diffusion processes. These results have theoretical and

  11. Structural study of GeS2 glass: Reverse Monte Carlo modelling for edge-sharing tetrahedral network

    NASA Astrophysics Data System (ADS)

    Itoh, Keiji

    2017-04-01

    The pulsed neutron diffraction and reverse Monte Carlo (RMC) modelling methods were used to investigate the structure of GeS2 glass. The high-resolution real-space neutron data shows that there is a negligible amount of chemical disorder in the structure of GeS2 glass. The RMC modelling was done by fitting to the neutron total structure factor with constraints for the edge- and corner-sharing tetrahedral configurations as well as the nearest neighbour coordination numbers. Two different structure models (two-dimensional layer network and three-dimensional random network) were examined and both the models reproduced the experimental data.

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

    PubMed

    Fripp, Matthias

    2012-06-05

    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.

  13. 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

    2016-05-02

    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.

  14. 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…

  15. Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach

    PubMed Central

    Khan, Jawaad Ullah; Cho, Ho-Shin

    2016-01-01

    In this paper, we propose a data-gathering scheme for hierarchical underwater sensor networks, where multiple Autonomous Underwater Vehicles (AUVs) are deployed over large-scale coverage areas. The deployed AUVs constitute an intermittently connected multihop network through inter-AUV synchronization (in this paper, synchronization means an interconnection between nodes for communication) for forwarding data to the designated sink. In such a scenario, the performance of the multihop communication depends upon the synchronization among the vehicles. The mobility parameters of the vehicles vary continuously because of the constantly changing underwater currents. The variations in the AUV mobility parameters reduce the inter-AUV synchronization frequency contributing to delays in the multihop communication. The proposed scheme improves the AUV synchronization frequency by permitting neighboring AUVs to share their status information via a pre-selected node called an agent-node at the static layer of the network. We evaluate the proposed scheme in terms of the AUV synchronization frequency, vertical delay (node→AUV), horizontal delay (AUV→AUV), end-to-end delay, and the packet loss ratio. Simulation results show that the proposed scheme significantly reduces the aforementioned delays without the synchronization time-out process employed in conventional works. PMID:27706042

  16. Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach.

    PubMed

    Khan, Jawaad Ullah; Cho, Ho-Shin

    2016-09-30

    In this paper, we propose a data-gathering scheme for hierarchical underwater sensor networks, where multiple Autonomous Underwater Vehicles (AUVs) are deployed over large-scale coverage areas. The deployed AUVs constitute an intermittently connected multihop network through inter-AUV synchronization (in this paper, synchronization means an interconnection between nodes for communication) for forwarding data to the designated sink. In such a scenario, the performance of the multihop communication depends upon the synchronization among the vehicles. The mobility parameters of the vehicles vary continuously because of the constantly changing underwater currents. The variations in the AUV mobility parameters reduce the inter-AUV synchronization frequency contributing to delays in the multihop communication. The proposed scheme improves the AUV synchronization frequency by permitting neighboring AUVs to share their status information via a pre-selected node called an agent-node at the static layer of the network. We evaluate the proposed scheme in terms of the AUV synchronization frequency, vertical delay (node→AUV), horizontal delay (AUV→AUV), end-to-end delay, and the packet loss ratio. Simulation results show that the proposed scheme significantly reduces the aforementioned delays without the synchronization time-out process employed in conventional works.

  17. Natural language acquisition in large scale neural semantic networks

    NASA Astrophysics Data System (ADS)

    Ealey, Douglas

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

  18. 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

  19. Just in time connectivity for large spiking networks

    PubMed Central

    Lytton, William W.; Omurtag, Ahmet; Neymotin, Samuel A; Hines, Michael L

    2008-01-01

    The scale of large neuronal network simulations is memory-limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically-relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed – just-in-time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON’s standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory-limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that only added items to the queue when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run. PMID:18533821

  20. Just-in-time connectivity for large spiking networks.

    PubMed

    Lytton, William W; Omurtag, Ahmet; Neymotin, Samuel A; Hines, Michael L

    2008-11-01

    The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.

  1. Dynamic Spectrum Access to the Combined Resource of Commercial and Public Safety Bands Based on a WCDMA Shared Network

    NASA Astrophysics Data System (ADS)

    Jeon, Hyoungsuk; Im, Sooyeol; Kim, Youmin; Kim, Seunghee; Kim, Jinup; Lee, Hyuckjae

    The public safety spectrum is generally under-utilized due to the unique traffic characteristics of bursty and mission critical. This letter considers the application of dynamic spectrum access (DSA) to the combined spectrum of public safety (PS) and commercial (CMR) users in a common shared network that can provide both PS and CMR services. Our scenario includes the 700MHz Public/Private Partnership which was recently issued by the Federal Communications Commission. We first propose an efficient DSA mechanism to coordinate the combined spectrum, and then establish a call admission control that reflects the proposed DSA in a wideband code division multiple access based network. The essentials of our proposed DSA are opportunistic access to the public safety spectrum and priority access to the commercial spectrum. Simulation results show that these schemes are well harmonized in various network environments.

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 75 FR 6560 - Financial Crimes Enforcement Network; Expansion of Special Information Sharing Procedures To...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-02-10

    ... 31 CFR Part 103 RIN 1506-AB04 Financial Crimes Enforcement Network; Expansion of Special Information... Network (``FinCEN''), Treasury. ACTION: Final rule. SUMMARY: FinCEN is issuing this final rule to amend... enforcement agencies, and State and local law enforcement agencies, to submit requests for information...

  7. A Scalable QoS-Aware VoD Resource Sharing Scheme for Next Generation Networks

    NASA Astrophysics Data System (ADS)

    Huang, Chenn-Jung; Luo, Yun-Cheng; Chen, Chun-Hua; Hu, Kai-Wen

    In network-aware concept, applications are aware of network conditions and are adaptable to the varying environment to achieve acceptable and predictable performance. In this work, a solution for video on demand service that integrates wireless and wired networks by using the network aware concepts is proposed to reduce the blocking probability and dropping probability of mobile requests. Fuzzy logic inference system is employed to select appropriate cache relay nodes to cache published video streams and distribute them to different peers through service oriented architecture (SOA). SIP-based control protocol and IMS standard are adopted to ensure the possibility of heterogeneous communication and provide a framework for delivering real-time multimedia services over an IP-based network to ensure interoperability, roaming, and end-to-end session management. The experimental results demonstrate that effectiveness and practicability of the proposed work.

  8. Characterizing Multiple Wireless Sensor Networks for Large-Scale Radio Tomography

    DTIC Science & Technology

    2015-03-01

    Network in Home Automation Network and Smart Grid,” in 2012 International Conference on Complex Systems, Agadir, Morocco, Nov. 2012, pp. 1–6. [13] D. Maas...CHARACTERIZING MULTIPLE WIRELESS SENSOR NETWORKS FOR LARGE-SCALE RADIO TOMOGRAPHY THESIS Tan Van, Captain, USAF AFIT-ENG-MS-15-M-057 DEPARTMENT OF...subject to copyright protection in the United States. AFIT-ENG-MS-15-M-057 CHARACTERIZING MULTIPLE WIRELESS SENSOR NETWORKS FOR LARGE-SCALE RADIO

  9. 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.

  10. Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks

    PubMed Central

    Hu, Xiaohua; Wu, Fang-Xiang

    2007-01-01

    Background Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model. Results In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable. Conclusion Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network. PMID:17764552

  11. Approximating spectral impact of structural perturbations in large networks.

    PubMed

    Milanese, Attilio; Sun, Jie; Nishikawa, Takashi

    2010-04-01

    Determining the effect of structural perturbations on the eigenvalue spectra of networks is an important problem because the spectra characterize not only their topological structures, but also their dynamical behavior, such as synchronization and cascading processes on networks. Here we develop a theory for estimating the change of the largest eigenvalue of the adjacency matrix or the extreme eigenvalues of the graph Laplacian when small but arbitrary set of links are added or removed from the network. We demonstrate the effectiveness of our approximation schemes using both real and artificial networks, showing in particular that we can accurately obtain the spectral ranking of small subgraphs. We also propose a local iterative scheme which computes the relative ranking of a subgraph using only the connectivity information of its neighbors within a few links. Our results may not only contribute to our theoretical understanding of dynamical processes on networks, but also lead to practical applications in ranking subgraphs of real complex networks.

  12. 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…

  13. 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…

  14. 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.

  15. 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.

  16. 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.

  17. GPP Webinar: Solar Utilization in Higher Education Networking & Information Sharing Group: Financing Issues Discussion

    EPA Pesticide Factsheets

    This presentation from a Solar Utilization in Higher Education Networking and Information webinar covers financing and project economics issues related to solar project development in the higher education sector.

  18. 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.

  19. Explicit integration with GPU acceleration for large kinetic networks

    DOE PAGES

    Brock, Benjamin; Belt, Andrew; Billings, Jay Jay; ...

    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

  20. 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.

  1. Explicit integration with GPU acceleration for large kinetic networks

    SciTech Connect

    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.

  2. 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

  3. Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption?

    PubMed Central

    Fegley, Brent D.; Torvik, Vetle I.

    2013-01-01

    The assumption that a name uniquely identifies an entity introduces two types of errors: splitting treats one entity as two or more (because of name variants); lumping treats multiple entities as if they were one (because of shared names). Here we investigate the extent to which splitting and lumping affect commonly-used measures of large-scale named-entity networks within two disambiguated bibliographic datasets: one for co-author names in biomedicine (PubMed, 2003–2007); the other for co-inventor names in U.S. patents (USPTO, 2003–2007). In both cases, we find that splitting has relatively little effect, whereas lumping has a dramatic effect on network measures. For example, in the biomedical co-authorship network, lumping (based on last name and both initials) drives several measures down: the global clustering coefficient by a factor of 4 (from 0.265 to 0.066); degree assortativity by a factor of ∼13 (from 0.763 to 0.06); and average shortest path by a factor of 1.3 (from 5.9 to 4.5). These results can be explained in part by the fact that lumping artificially creates many intransitive relationships and high-degree vertices. This effect of lumping is much less dramatic but persists with measures that give less weight to high-degree vertices, such as the mean local clustering coefficient and log-based degree assortativity. Furthermore, the log-log distribution of collaborator counts follows a much straighter line (power law) with splitting and lumping errors than without, particularly at the low and the high counts. This suggests that part of the power law often observed for collaborator counts in science and technology reflects an artifact: name ambiguity. PMID:23894639

  4. 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

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2010-07-01

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

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

    PubMed

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

    2014-05-07

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

  8. 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.

  9. 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…

  10. 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.

  11. 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.

  12. 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.

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

    PubMed

    Dallest, Kathy; Strachan, Heather; Flett, Gillian

    2009-01-01

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

  14. Dynamic route choice model of large-scale traffic network

    SciTech Connect

    Boyce, D.W.; Lee, D.H.; Janson, B.N.; Berka, S.

    1997-08-01

    Application and extensions of a dynamic network equilibrium model to the Advanced Driver and Vehicle Advisory Navigation Concept (ADVANCE) Network are described in this paper. ADVANCE is a dynamic route guidance field test designed for 800 km{sup 2} in the northwestern suburbs of Chicago. The dynamic route choice model employed in this paper is solved efficiently by a modified version of Janson`s DYMOD algorithm. Realistic traffic engineering-based link delay functions, instead of the simplistic Bureau of Public Roads (BPR) function, are used to estimate link travel times and intersection delays for most types of links and intersections. Further, an expanded intersection representation is utilized, resulting in a network of nearly 23,000 links and 10,000 nodes. Time-dependent link flows, travel times, speeds and queue spillbacks are generated for the ADVANCE Network. The model was solved on a CONVEX-C3880. Convergence and computational results are presented and analyzed.

  15. 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

  16. Algorithms for Data Sharing, Coordination, and Communication in Dynamic Network Settings

    DTIC Science & Technology

    2007-12-03

    Chakraborty, and Nancy Lynch. Clock Synchronization for Wireless Networks. In Teruo Higashino , editor, Principles of Distributed Systems: OPODIS 2004: 8th...Teruo Higashino , editor, Principles of Distributed Systems: OPODIS 2004: 8th International Conference on Principles of Distributed 4 Systems, Grenoble

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

    ERIC Educational Resources Information Center

    Hakkinen, Paivi

    2003-01-01

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

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

    PubMed

    He, Qinbin; Xia, Zhile; Lin, Bin

    2016-11-07

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

  19. 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…

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

    PubMed

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

    2015-03-10

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

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. Analysis of Community Detection Algorithms for Large Scale Cyber Networks

    SciTech Connect

    Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee; Mackey, Patrick S.; Springer, John

    2016-09-30

    The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The next section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.

  6. 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.

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

    PubMed Central

    Yoon, Hong-Jun; Tourassi, Georgia

    2015-01-01

    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. PMID:25973446

  8. 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.

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

    PubMed

    Yoon, Hong-Jun; Tourassi, Georgia

    2014-05-01

    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. 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.

  11. Supporting the Maritime Information Dominance: Optimizing Tactical Network for Biometric Data Sharing in Maritime Interdiction Operations

    DTIC Science & Technology

    2015-03-01

    avoidance DFBA Defense Forensics and Biometrics Agency DOD Department of Defense DRAM dynamic random access memory DSCS defense satellite...transfer biometric data manually from the biometric device via a USB flash memory or CD/RW to connected networks for dissemination to authoritative...Ethernet port. SEEK devices currently operate on Microsoft Windows XP SP3 and the 32 bit version of Windows 7. Onboard memory is capped at two

  12. Generic patterns in the evolution of urban water networks: Evidence from a large Asian city

    NASA Astrophysics Data System (ADS)

    Krueger, Elisabeth; Klinkhamer, Christopher; Urich, Christian; Zhan, Xianyuan; Rao, P. Suresh C.

    2017-03-01

    We examine high-resolution urban infrastructure data using every pipe for the water distribution network (WDN) and sanitary sewer network (SSN) in a large Asian city (≈4 million residents) to explore the structure as well as the spatial and temporal evolution of these infrastructure networks. Network data were spatially disaggregated into multiple subnets to examine intracity topological differences for functional zones of the WDN and SSN, and time-stamped SSN data were examined to understand network evolution over several decades as the city expanded. Graphs were generated using a dual-mapping technique (Hierarchical Intersection Continuity Negotiation), which emphasizes the functional attributes of these networks. Network graphs for WDNs and SSNs are characterized by several network topological metrics, and a double Pareto (power-law) model approximates the node-degree distributions of both water infrastructure networks (WDN and SSN), across spatial and hierarchical scales relevant to urban settings, and throughout their temporal evolution over several decades. These results indicate that generic mechanisms govern the networks' evolution, similar to those of scale-free networks found in nature. Deviations from the general topological patterns are indicative of (1) incomplete establishment of network hierarchies and functional network evolution, (2) capacity for growth (expansion) or densification (e.g., in-fill), and (3) likely network vulnerabilities. We discuss the implications of our findings for the (re-)design of urban infrastructure networks to enhance their resilience to external and internal threats.

  13. Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

    PubMed Central

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

    2016-01-01

    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: (i) 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); (ii) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (iii) 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

  14. Large-scale brain network dynamics supporting adolescent cognitive control.

    PubMed

    Dwyer, Dominic B; Harrison, Ben J; Yücel, Murat; Whittle, Sarah; Zalesky, Andrew; Pantelis, Christos; Allen, Nicholas B; Fornito, Alex

    2014-10-15

    Adolescence is a time when the ability to engage cognitive control is linked to crucial life outcomes. Despite a historical focus on prefrontal cortex functioning, recent evidence suggests that differences between individuals may relate to interactions between distributed brain regions that collectively form a cognitive control network (CCN). Other research points to a spatially distinct and functionally antagonistic system--the default-mode network (DMN)--which typically deactivates during performance of control tasks. This literature implies that individual differences in cognitive control are determined either by activation or functional connectivity of CCN regions, deactivation or functional connectivity of DMN regions, or some combination of both. We tested between these possibilities using a multilevel fMRI characterization of CCN and DMN dynamics, measured during performance of a cognitive control task and during a task-free resting state, in 73 human adolescents. Better cognitive control performance was associated with (1) reduced activation of CCN regions, but not deactivation of the DMN; (2) variations in task-related, but not resting-state, functional connectivity within a distributed network involving both the CCN and DMN; (3) functional segregation of core elements of these two systems; and (4) task-dependent functional integration of a set of peripheral nodes into either one network or the other in response to prevailing stimulus conditions. These results indicate that individual differences in adolescent cognitive control are not solely attributable to the functioning of any single region or network, but are instead dependent on a dynamic and context-dependent interplay between the CCN and DMN.

  15. Data transfer over the wide area network with a large round trip time

    NASA Astrophysics Data System (ADS)

    Matsunaga, H.; Isobe, T.; Mashimo, T.; Sakamoto, H.; Ueda, I.

    2010-04-01

    A Tier-2 regional center is running at the University of Tokyo in Japan. This center receives a large amount of data of the ATLAS experiment from the Tier-1 center in France. Although the link between the two centers has 10Gbps bandwidth, it is not a dedicated link but is shared with other traffic, and the round trip time is 290ms. It is not easy to exploit the available bandwidth for such a link, so-called long fat network. We performed data transfer tests by using GridFTP in various combinations of the parameters, such as the number of parallel streams and the TCP window size. In addition, we have gained experience of the actual data transfer in our production system where the Disk Pool Manager (DPM) is used as the Storage Element and the data transfer is controlled by the File Transfer Service (FTS). We report results of the tests and the daily activity, and discuss the improvement of the data transfer throughput.

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

    PubMed

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

    2006-01-01

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

  17. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

    PubMed

    Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

    2017-04-10

    This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  18. 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

  19. 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.

  20. Autonomous management of a recursive area hierarchy for large scale wireless sensor networks using multiple parents

    SciTech Connect

    Cree, Johnathan Vee; Delgado-Frias, Jose

    2016-03-01

    Large scale wireless sensor networks have been proposed for applications ranging from anomaly detection in an environment to vehicle tracking. Many of these applications require the networks to be distributed across a large geographic area while supporting three to five year network lifetimes. In order to support these requirements large scale wireless sensor networks of duty-cycled devices need a method of efficient and effective autonomous configuration/maintenance. This method should gracefully handle the synchronization tasks duty-cycled networks. Further, an effective configuration solution needs to recognize that in-network data aggregation and analysis presents significant benefits to wireless sensor network and should configure the network in a way such that said higher level functions benefit from the logically imposed structure. NOA, the proposed configuration and maintenance protocol, provides a multi-parent hierarchical logical structure for the network that reduces the synchronization workload. It also provides higher level functions with significant inherent benefits such as but not limited to: removing network divisions that are created by single-parent hierarchies, guarantees for when data will be compared in the hierarchy, and redundancies for communication as well as in-network data aggregation/analysis/storage.

  1. Critical dynamics on a large human Open Connectome network

    NASA Astrophysics Data System (ADS)

    Ódor, Géza

    2016-12-01

    Extended numerical simulations of threshold models have been performed on a human brain network with N =836 733 connected nodes available from the Open Connectome Project. While in the case of simple threshold models a sharp discontinuous phase transition without any critical dynamics arises, variable threshold models exhibit extended power-law scaling regions. This is attributed to fact that Griffiths effects, stemming from the topological or interaction heterogeneity of the network, can become relevant if the input sensitivity of nodes is equalized. I have studied the effects of link directness, as well as the consequence of inhibitory connections. Nonuniversal power-law avalanche size and time distributions have been found with exponents agreeing with the values obtained in electrode experiments of the human brain. The dynamical critical region occurs in an extended control parameter space without the assumption of self-organized criticality.

  2. Critical dynamics on a large human Open Connectome network.

    PubMed

    Ódor, Géza

    2016-12-01

    Extended numerical simulations of threshold models have been performed on a human brain network with N=836733 connected nodes available from the Open Connectome Project. While in the case of simple threshold models a sharp discontinuous phase transition without any critical dynamics arises, variable threshold models exhibit extended power-law scaling regions. This is attributed to fact that Griffiths effects, stemming from the topological or interaction heterogeneity of the network, can become relevant if the input sensitivity of nodes is equalized. I have studied the effects of link directness, as well as the consequence of inhibitory connections. Nonuniversal power-law avalanche size and time distributions have been found with exponents agreeing with the values obtained in electrode experiments of the human brain. The dynamical critical region occurs in an extended control parameter space without the assumption of self-organized criticality.

  3. Density Estimation and Anomaly Detection in Large Social Networks

    DTIC Science & Technology

    2014-07-15

    Observatory generates 1.5 terabytes of data daily [36], and the upcoming Square Kilometer Array (SKA, [19]) is projected to generate an exabyte of...2.4.1 DMD experiment: dynamic textures with missing data As mentioned in the introduction, sensors such as the Solar Data Observatory are generating...Rights Movement and upheaval among southern Democrats. (Best viewed in color.) By looking at the network estimates of the DFS estimator across time (as

  4. Performance of IEEE 1588 in Large-Scale Networks

    DTIC Science & Technology

    2010-11-01

    commercially available Syn1588 network cards from Oregano Systems. They not only feature an IEEE 1588 hardware timestamper, but also a 1 PPS output...of the measurements, which are done per default once a second, can be removed considering the short-term stability of the oscillator. As the Oregano ...PTTI) Meeting 76 ACKNOWLEDGMENTS The authors wish to thank Julien Ridoux, the University of Melbourne, Oregano Systems, and Meinberg for

  5. Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

    DOE PAGES

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

    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

  6. 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.

  7. 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

  8. A Large-Scale Initiative Inviting Patients to Share Personal Fitness Tracker Data with Their Providers: Initial Results

    PubMed Central

    Pevnick, Joshua M.; Fuller, Garth; Duncan, Ray; Spiegel, Brennan M. R.

    2016-01-01

    Background Personal fitness trackers (PFT) have substantial potential to improve healthcare. Objective To quantify and characterize early adopters who shared their PFT data with providers. Methods We used bivariate statistics and logistic regression to compare patients who shared any PFT data vs. patients who did not. Results A patient portal was used to invite 79,953 registered portal users to share their data. Of 66,105 users included in our analysis, 499 (0.8%) uploaded data during an initial 37-day study period. Bivariate and regression analysis showed that early adopters were more likely than non-adopters to be younger, male, white, health system employees, and to have higher BMIs. Neither comorbidities nor utilization predicted adoption. Conclusion Our results demonstrate that patients had little intrinsic desire to share PFT data with their providers, and suggest that patients most at risk for poor health outcomes are least likely to share PFT data. Marketing, incentives, and/or cultural change may be needed to induce such data-sharing. PMID:27846287

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

    PubMed

    Delmonico, F L; Graham, W K

    2006-01-01

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

  10. Scalability of abstraction-network-based quality assurance to large SNOMED hierarchies.

    PubMed

    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.

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

    PubMed

    Padilla, Carmencita David; Therrell, Bradford L

    2012-01-01

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

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

    PubMed

    Begum, Tina; Ghosh, Tapash Chandra

    2014-10-05

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  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. 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.

  16. Real-time, large scale optimization of water network systems using a subdomain approach.

    SciTech Connect

    van Bloemen Waanders, Bart Gustaaf; Biegler, Lorenz T.; Laird, Carl Damon

    2005-03-01

    Certain classes of dynamic network problems can be modeled by a set of hyperbolic partial differential equations describing behavior along network edges and a set of differential and algebraic equations describing behavior at network nodes. In this paper, we demonstrate real-time performance for optimization problems in drinking water networks. While optimization problems subject to partial differential, differential, and algebraic equations can be solved with a variety of techniques, efficient solutions are difficult for large network problems with many degrees of freedom and variable bounds. Sequential optimization strategies can be inefficient for this problem due to the high cost of computing derivatives with respect to many degrees of freedom. Simultaneous techniques can be more efficient, but are difficult because of the need to solve a large nonlinear program; a program that may be too large for current solver. This study describes a dynamic optimization formulation for estimating contaminant sources in drinking water networks, given concentration measurements at various network nodes. We achieve real-time performance by combining an efficient large-scale nonlinear programming algorithm with two problem reduction techniques. D Alembert's principle can be applied to the partial differential equations governing behavior along the network edges (distribution pipes). This allows us to approximate the time-delay relationships between network nodes, removing the need to discretize along the length of the pipes. The efficiency of this approach alone, however, is still dependent on the size of the network and does not scale indefinitely to larger network models. We further reduce the problem size with a subdomain approach and solve smaller inversion problems using a geographic window around the area of contamination. We illustrate the effectiveness of this overall approach and these reduction techniques on an actual metropolitan water network model.

  17. 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.

  18. Oscillations and Synchrony in Large-scale Cortical Network Models

    DTIC Science & Technology

    2008-06-17

    Intrinsic neuronal and circuit properties control the responses of large ensembles of neurons by creating spatiotemporal patterns of ...map-based models) to simulate the intrinsic dynamics of biological neurons . These phenomenological models were designed to capture the main response...function of parameters that affect synaptic interactions and intrinsic states of the neurons . Keywords

  19. 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

  20. Diffusion on complex networks: a way to probe their large-scale topological structures

    NASA Astrophysics Data System (ADS)

    Simonsen, Ingve; Astrup Eriksen, Kasper; Maslov, Sergei; Sneppen, Kim

    2004-05-01

    A diffusion process on complex networks is introduced in order to uncover their large-scale topological structures. This is achieved by focusing on the slowest decaying diffusive modes of the network. The proposed procedure is applied to real-world networks like a friendship network of known modular structure, and an Internet routing network. For the friendship network, its known structure is well reproduced. In case of the Internet, where the structure is far less well known, one indeed finds a modular structure, and modules can roughly be associated with individual countries. Quantitatively, the modular structure of the Internet manifests itself in an approximately 10 times larger participation ratio of its slowest decaying modes as compared to the null model-a random scale-free network. The extreme edges of the Internet are found to correspond to Russian and US military sites.

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

    DOE PAGES

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

    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

  2. Integration and segregation of large-scale brain networks during short-term task automatization

    PubMed Central

    Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F.; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

    2016-01-01

    The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes. PMID:27808095

  3. Understanding the anomalous frequency responses of composite materials using very large random resistor-capacitor networks

    NASA Astrophysics Data System (ADS)

    Aouaichia, Mustapha; McCullen, Nick; Bowen, Chris R.; Almond, Darryl P.; Budd, Chris; Bouamrane, Rachid

    2017-03-01

    In this paper large resistor-capacitor (RC) networks that consist of randomly distributed conductive and capacitive elements which are much larger than those previously explored are studied using an efficient algorithm. We investigate the emergent power-law scaling of the conductance and the percolation and saturation limits of the networks at the high and low frequency bounds in order to compare with a modification of the classical Effective Medium Approximation (EMA) that enables its extension to finite network sizes. It is shown that the new formula provides a simple analytical description of the network response that accurately predicts the effects of finite network size and composition and it agrees well with the new numerical calculations on large networks and is a significant improvement on earlier EMA formulae. Avenues for future improvement and explanation of the formula are highlighted. Finally, the statistical variation of network conductivity with network size is observed and explained. This work provides a deeper insight into the response of large resistor-capacitor networks to understand the AC electrical properties, size effects, composition effects and statistical variation of properties of a range of heterogeneous materials and composite systems.

  4. 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…

  5. Deceased donors with a past history of malignancy: an organ procurement and transplantation network/united network for organ sharing update.

    PubMed

    Kauffman, H Myron; Cherikh, Wida S; McBride, Maureen A; Cheng, Yulin; Hanto, Douglas W

    2007-07-27

    Approximately 2% of deceased donor organ transplants result from donors with a past history of cancer. An analysis of Organ Procurement and Transplantation Network/United Network for Organ Sharing data on 39,455 deceased donors from 2000 to 2005 showed 1069 donors had a PHC, resulting in 2508 transplants, including 1236 kidneys, 891 livers, 199 hearts, 100 lungs, and 82 miscellaneous organs. The most common type of previous cancer in the donor was nonmelanoma skin cancer (n=776) followed by central nervous system malignancies (n=642) and carcinoma of the uterine cervix (n=336). One donor with a glioblastoma multiforme transmitted fatal tumors to three recipients. One donor with a history of melanoma 32 years earlier transmitted a fatal melanoma to a single recipient and, therefore, donors with a history of melanoma should not be used. Donors with a past history of cancer who have a nontraumatic cerebral hemorrhage cause concern because this hemorrhage may be the result of an unrecognized metastatic tumor.

  6. 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:…

  7. 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.

  8. 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

  9. A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research

    PubMed Central

    Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D’Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

    2015-01-01

    Background Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. Objective The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Materials and Methods Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. Results The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Discussion and Conclusion Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient

  10. A fractal model of HIV transmission on complex sociogeographic networks: towards analysis of large data sets.

    PubMed

    Wallace, R

    1993-01-01

    "A paradigm of HIV (human immunodeficiency virus) transmission along very large 'sociogeographic' networks--spatially focused nets of social interaction--is extended to include fractal (dilationally self-similar) structures upon which a metric of 'sociogeographic' distance can be defined.... Techniques are sketched for determining the sociogeographic structure of a large, geographically centered social network, providing a possible empirical basis for predicting forms and rates of spread of the initial, rapid stages of an HIV outbreak for networks not yet infected, and perhaps greatly expanding the utility of routinely collected small-area administrative data sets in the design of mutually reinforcing, multifactorial disease-control strategies."

  11. 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

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

    PubMed

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

    2016-10-01

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

  13. 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.

  14. How large are actor and partner effects of personality on relationship satisfaction? The importance of controlling for shared method variance.

    PubMed

    Orth, Ulrich

    2013-10-01

    Previous research suggests that the personality of a relationship partner predicts not only the individual's own satisfaction with the relationship but also the partner's satisfaction. Based on the actor-partner interdependence model, the present research tested whether actor and partner effects of personality are biased when the same method (e.g., self-report) is used for the assessment of personality and relationship satisfaction and, consequently, shared method variance is not controlled for. Data came from 186 couples, of whom both partners provided self- and partner reports on the Big Five personality traits. Depending on the research design, actor effects were larger than partner effects (when using only self-reports), smaller than partner effects (when using only partner reports), or of about the same size as partner effects (when using self- and partner reports). The findings attest to the importance of controlling for shared method variance in dyadic data analysis.

  15. Large Communities in a Scale-Free Network

    NASA Astrophysics Data System (ADS)

    Alves, Caio; Ribeiro, Rodrigo; Sanchis, Rémy

    2017-01-01

    We prove the existence of a large complete subgraph w.h.p. in a preferential attachment random graph process with an edge-step. That is, we consider a dynamic stochastic process for constructing a graph in which at each step we independently decide, with probability pin (0,1), whether the graph receives a new vertex or a new edge between existing vertices. The connections are then made according to a preferential attachment rule. We prove that the random graph Gt produced by this so-called generalized linear preferential (GLP) model at time t contains a complete subgraph whose vertex set cardinality is given by t^α , where α = (1-ɛ)1-p/2-p, for any small ɛ >0 asymptotically almost surely.

  16. Finite-state neural networks. A step toward the simulation of very large systems

    SciTech Connect

    Kohring, G.A. )

    1991-02-01

    Neural networks composed of neurons with Q{sub N} states and synapses with Q{sub J} states are studied analytically and numerically. Analytically it is shown that these finite-state networks are much more efficient at information storage than networks with continuous synapses. In order to take the utmost advantage of networks with finite-state elements, a multineuron and multisynapse coding scheme is introduced which allows the simulation of networks having 1.0 {times} 10{sup 9} couplings at a speed of 7.1 {times} 10{sup 9} coupling evaluations per second on a single processor of the Cray-YMP. A local learning algorithm is also introduced which allows for the efficient training of large networks with finite-state elements.

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

    PubMed

    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.

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

    PubMed

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

    2016-05-01

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

  19. 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

  20. 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.

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

    PubMed

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

    2013-01-07

    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.

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

    DOE PAGES

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

    2010-01-01

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

  3. 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.

  4. 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

  5. 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

  6. Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

    PubMed

    Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

    2017-01-01

    Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

  7. Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data

    PubMed Central

    Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

    2017-01-01

    Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods. PMID:28166542

  8. 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

  9. Hippocampal Contributions to the Large-Scale Episodic Memory Network Predict Vivid Visual Memories.

    PubMed

    Geib, Benjamin R; Stanley, Matthew L; Wing, Erik A; Laurienti, Paul J; Cabeza, Roberto

    2017-01-01

    A common approach in memory research is to isolate the function(s) of individual brain regions, such as the hippocampus, without addressing how those regions interact with the larger network. To investigate the properties of the hippocampus embedded within large-scale networks, we used functional magnetic resonance imaging and graph theory to characterize complex hippocampal interactions during the active retrieval of vivid versus dim visual memories. The study yielded 4 main findings. First, the right hippocampus displayed greater communication efficiency with the network (shorter path length) and became a more convergent structure for information integration (higher centrality measures) for vivid than dim memories. Second, vivid minus dim differences in our graph theory measures of interest were greater in magnitude for the right hippocampus than for any other region in the 90-region network. Moreover, the right hippocampus significantly reorganized its set of direct connections from dim to vivid memory retrieval. Finally, beyond the hippocampus, communication throughout the whole-brain network was more efficient (shorter global path length) for vivid than dim memories. In sum, our findings illustrate how multivariate network analyses can be used to investigate the roles of specific regions within the large-scale network, while also accounting for global network changes.

  10. 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.

  11. Localization Algorithm Based on a Spring Model (LASM) for Large Scale Wireless Sensor Networks.

    PubMed

    Chen, Wanming; Mei, Tao; Meng, Max Q-H; Liang, Huawei; Liu, Yumei; Li, Yangming; Li, Shuai

    2008-03-15

    A navigation method for a lunar rover based on large scale wireless sensornetworks is proposed. To obtain high navigation accuracy and large exploration area, highnode localization accuracy and large network scale are required. However, thecomputational and communication complexity and time consumption are greatly increasedwith the increase of the network scales. A localization algorithm based on a spring model(LASM) method is proposed to reduce the computational complexity, while maintainingthe localization accuracy in large scale sensor networks. The algorithm simulates thedynamics of physical spring system to estimate the positions of nodes. The sensor nodesare set as particles with masses and connected with neighbor nodes by virtual springs. Thevirtual springs will force the particles move to the original positions, the node positionscorrespondingly, from the randomly set positions. Therefore, a blind node position can bedetermined from the LASM algorithm by calculating the related forces with the neighbornodes. The computational and communication complexity are O(1) for each node, since thenumber of the neighbor nodes does not increase proportionally with the network scale size.Three patches are proposed to avoid local optimization, kick out bad nodes and deal withnode variation. Simulation results show that the computational and communicationcomplexity are almost constant despite of the increase of the network scale size. The time consumption has also been proven to remain almost constant since the calculation steps arealmost unrelated with the network scale size.

  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. 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.

  14. 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.

  15. Mining, modeling, and evaluation of subnetworks from large biomolecular networks and its comparison study.

    PubMed

    Hu, Xiaohua; Ng, Michael; Wu, Fang-Xiang; Sokhansanj, Bahrad A

    2009-03-01

    In this paper, we present a novel method to mine, model, and evaluate a regulatory system executing cellular functions that can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to such a biomolecular network to obtain various subnetworks. Second, computational models are generated for the subnetworks and simulated to predict their behavior in the cellular context. We discuss and evaluate some of the advanced computational modeling approaches, in particular, state-space modeling, probabilistic Boolean network modeling, and fuzzy logic modeling. The modeling and simulation results represent hypotheses that are tested against high-throughput biological datasets (microarrays and/or genetic screens) under normal and perturbation conditions. Experimental results on time-series gene expression data for the human cell cycle indicate that our approach is promising for subnetwork mining and simulation from large biomolecular networks.

  16. DARPA Ensemble-Based Modeling Large Graphs & Applications to Social Networks

    DTIC Science & Technology

    2015-07-29

    processes on social networks. Specific connectivity schemes affect influence propagation and epidemic spread, and is also responsible for Web page...AFRL-OSR-VA-TR-2015-0212 DARPA EnsembleBased Modeling Large Graphs & Applications to Social Networks Zoltan Toroczkai UNIVERSITY OF NOTRE DAME DU LAC...for this collection of information is estimated to average 1 hour per response , including the time for reviewing instructions, searching existing data

  17. 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.

  18. Emergence of Switch-Like Behavior in a Large Family of Simple Biochemical Networks

    PubMed Central

    Siegal-Gaskins, Dan; Mejia-Guerra, Maria Katherine; Smith, Gregory D.; Grotewold, Erich

    2011-01-01

    Bistability plays a central role in the gene regulatory networks (GRNs) controlling many essential biological functions, including cellular differentiation and cell cycle control. However, establishing the network topologies that can exhibit bistability remains a challenge, in part due to the exceedingly large variety of GRNs that exist for even a small number of components. We begin to address this problem by employing chemical reaction network theory in a comprehensive in silico survey to determine the capacity for bistability of more than 40,000 simple networks that can be formed by two transcription factor-coding genes and their associated proteins (assuming only the most elementary biochemical processes). We find that there exist reaction rate constants leading to bistability in ∼90% of these GRN models, including several circuits that do not contain any of the TF cooperativity commonly associated with bistable systems, and the majority of which could only be identified as bistable through an original subnetwork-based analysis. A topological sorting of the two-gene family of networks based on the presence or absence of biochemical reactions reveals eleven minimal bistable networks (i.e., bistable networks that do not contain within them a smaller bistable subnetwork). The large number of previously unknown bistable network topologies suggests that the capacity for switch-like behavior in GRNs arises with relative ease and is not easily lost through network evolution. To highlight the relevance of the systematic application of CRNT to bistable network identification in real biological systems, we integrated publicly available protein-protein interaction, protein-DNA interaction, and gene expression data from Saccharomyces cerevisiae, and identified several GRNs predicted to behave in a bistable fashion. PMID:21589886

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

    PubMed

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

    2012-07-01

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

  20. Emergence of switch-like behavior in a large family of simple biochemical networks.

    PubMed

    Siegal-Gaskins, Dan; Mejia-Guerra, Maria Katherine; Smith, Gregory D; Grotewold, Erich

    2011-05-01

    Bistability plays a central role in the gene regulatory networks (GRNs) controlling many essential biological functions, including cellular differentiation and cell cycle control. However, establishing the network topologies that can exhibit bistability remains a challenge, in part due to the exceedingly large variety of GRNs that exist for even a small number of components. We begin to address this problem by employing chemical reaction network theory in a comprehensive in silico survey to determine the capacity for bistability of more than 40,000 simple networks that can be formed by two transcription factor-coding genes and their associated proteins (assuming only the most elementary biochemical processes). We find that there exist reaction rate constants leading to bistability in ∼90% of these GRN models, including several circuits that do not contain any of the TF cooperativity commonly associated with bistable systems, and the majority of which could only be identified as bistable through an original subnetwork-based analysis. A topological sorting of the two-gene family of networks based on the presence or absence of biochemical reactions reveals eleven minimal bistable networks (i.e., bistable networks that do not contain within them a smaller bistable subnetwork). The large number of previously unknown bistable network topologies suggests that the capacity for switch-like behavior in GRNs arises with relative ease and is not easily lost through network evolution. To highlight the relevance of the systematic application of CRNT to bistable network identification in real biological systems, we integrated publicly available protein-protein interaction, protein-DNA interaction, and gene expression data from Saccharomyces cerevisiae, and identified several GRNs predicted to behave in a bistable fashion.

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

    PubMed

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

    2013-01-01

    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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Computing global structural balance in large-scale signed social networks.

    PubMed

    Facchetti, Giuseppe; Iacono, Giovanni; Altafini, Claudio

    2011-12-27

    Structural balance theory affirms that signed social networks (i.e., graphs whose signed edges represent friendly/hostile interactions among individuals) tend to be organized so as to avoid conflictual situations, corresponding to cycles of negative parity. Using an algorithm for ground-state calculation in large-scale Ising spin glasses, in this paper we compute the global level of balance of very large online social networks and verify that currently available networks are indeed extremely balanced. This property is explainable in terms of the high degree of skewness of the sign distributions on the nodes of the graph. In particular, individuals linked by a large majority of negative edges create mostly "apparent disorder," rather than true "frustration."

  8. Computing global structural balance in large-scale signed social networks

    PubMed Central

    Facchetti, Giuseppe; Iacono, Giovanni; Altafini, Claudio

    2011-01-01

    Structural balance theory affirms that signed social networks (i.e., graphs whose signed edges represent friendly/hostile interactions among individuals) tend to be organized so as to avoid conflictual situations, corresponding to cycles of negative parity. Using an algorithm for ground-state calculation in large-scale Ising spin glasses, in this paper we compute the global level of balance of very large online social networks and verify that currently available networks are indeed extremely balanced. This property is explainable in terms of the high degree of skewness of the sign distributions on the nodes of the graph. In particular, individuals linked by a large majority of negative edges create mostly “apparent disorder,” rather than true “frustration.” PMID:22167802

  9. Shared networks of interpreter services, at relatively low cost, can help providers serve patients with limited english skills.

    PubMed

    Jacobs, Elizabeth A; Leos, Ginelle Sanchez; Rathouz, Paul J; Fu, Paul

    2011-10-01

    Language barriers in health care-a large and growing problem in the United States-contribute to disparities in health care quality and outcomes in populations with limited English proficiency. Providing access to adequate interpreter services has been shown to reduce health disparities in these populations. However, many health care organizations do not provide such services because of the perceived high cost. In this observational study we calculated the costs incurred by a group of California public hospitals that formed a network to make trained interpreters available via videoconference and telephone. We found that encounters in this network where interpreters helped patients and providers communicate lasted an average of 10.6 minutes and cost an average of $24.86 per encounter. Such costs should be weighed against the likely alternatives, such as the opportunity costs of having other hospital staff act as ad hoc interpreters; medical errors that could result from inadequate interpretation; and the fact that not providing such services may leave providers out of compliance with federal law. We also discuss ways in which providers could be compensated for providing interpreter services.

  10. A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks

    PubMed Central

    Xu, Yang; Hu, Xuemei; Hu, Haixiao; Liu, Ming

    2016-01-01

    In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors’ local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption. PMID:27537891

  11. A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks.

    PubMed

    Xu, Yang; Hu, Xuemei; Hu, Haixiao; Liu, Ming

    2016-08-16

    In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors' local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption.

  12. 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.

  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. Structure-preserving model reduction of large-scale logistics networks. Applications for supply chains

    NASA Astrophysics Data System (ADS)

    Scholz-Reiter, B.; Wirth, F.; Dashkovskiy, S.; Makuschewitz, T.; Schönlein, M.; Kosmykov, M.

    2011-12-01

    We investigate the problem of model reduction with a view to large-scale logistics networks, specifically supply chains. Such networks are modeled by means of graphs, which describe the structure of material flow. An aim of the proposed model reduction procedure is to preserve important features within the network. As a new methodology we introduce the LogRank as a measure for the importance of locations, which is based on the structure of the flows within the network. We argue that these properties reflect relative importance of locations. Based on the LogRank we identify subgraphs of the network that can be neglected or aggregated. The effect of this is discussed for a few motifs. Using this approach we present a meta algorithm for structure-preserving model reduction that can be adapted to different mathematical modeling frameworks. The capabilities of the approach are demonstrated with a test case, where a logistics network is modeled as a Jackson network, i.e., a particular type of queueing network.

  15. Analysis of Swedish Forest Owners' Information and Knowledge-Sharing Networks for Decision-Making: Insights for Climate Change Communication and Adaptation.

    PubMed

    André, Karin; Baird, Julia; Gerger Swartling, Åsa; Vulturius, Gregor; Plummer, Ryan

    2017-03-08

    To further the understanding of climate change adaptation processes, more attention needs to be paid to the various contextual factors that shape whether and how climate-related knowledge and information is received and acted upon by actors involved. This study sets out to examine the characteristics of forest owners' in Sweden, the information and knowledge-sharing networks they draw upon for decision-making, and their perceptions of climate risks, their forests' resilience, the need for adaptation, and perceived adaptive capacity. By applying the concept of ego-network analysis, the empirical data was generated by a quantitative survey distributed to 3000 private forest owners' in Sweden in 2014 with a response rate of 31%. The results show that there is a positive correlation, even though it is generally weak, between forest owner climate perceptions and (i) network features, i.e. network size and heterogeneity, and (ii) presence of certain alter groups (i.e. network members or actors). Results indicate that forest owners' social networks currently serve only a minimal function of sharing knowledge of climate change and adaptation. Moreover, considering the fairly infrequent contact between respondents and alter groups, the timing of knowledge sharing is important. In conclusion we suggest those actors that forest owners' most frequently communicate with, especially forestry experts providing advisory services (e.g. forest owner associations, companies, and authorities) have a clear role to communicate both the risks of climate change and opportunities for adaptation. Peers are valuable in connecting information about climate risks and adaptation to the actual forest property.

  16. SHARE and Share Alike

    ERIC Educational Resources Information Center

    Baird, Jeffrey Marshall

    2006-01-01

    This article describes a reading comprehension program adopted at J. E. Cosgriff Memorial Catholic School in Salt Lake City, Utah. The program is called SHARE: Students Helping Achieve Reading Excellence, and involves seventh and eighth grade students teaching first and second graders reading comprehension strategies learned in middle school…

  17. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity

    PubMed Central

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework. PMID:27303272

  18. 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

  19. A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

    NASA Technical Reports Server (NTRS)

    Bhaduri, Kanishka; Srivastava, Ashok N.

    2009-01-01

    This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.

  20. Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.

    PubMed

    Yuan, Yinyin; Li, Chang-Tsun; Windram, Oliver

    2011-04-06

    Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC) method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/).

  1. Soft adaptive fusion of sensor energy for large-scale sensor networks (SAFE)

    NASA Astrophysics Data System (ADS)

    Rababaah, Haroun; Shirkhodaie, Amir

    2009-04-01

    Target tracking for network surveillance systems has gained significant interest especially in sensitive areas such as homeland security, battlefield intelligence, and facility surveillance. Most of the current sensor network protocols do not address the need for multi-sensor fusion-based target tracking schemes, which is crucial for the longevity of the sensor network. In this paper, we present an efficient fusion model for target tracking in a cluster-based large sensor networks. This new scheme is inspired by the image processing techniques by perceiving a sensor network as an energy map of sensor stimuli and applying typical image processing techniques on this map such as: filtering, convolution, clustering, segmentation, etc to achieve high-level perceptions and understanding of the situation. The new fusion model is called Soft Adaptive Fusion of Sensor Energies (SAFE). SAFE performs soft fusion of the energies collected by a local region of sensors in a large-scale sensor network. This local fusion is then transmitted by the head node to a base-station to update the common operation picture with evolving events of interest. Simulated scenarios showed that SAFE is promising by demonstrating a significant improvement in target tracking reliability, uncertainty, and efficiency.

  2. Active self-testing noise measurement sensors for large-scale environmental sensor networks.

    PubMed

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

    2013-12-13

    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.

  3. LGL: creating a map of protein function with an algorithm for visualizing very large biological networks.

    PubMed

    Adai, Alex T; Date, Shailesh V; Wieland, Shannon; Marcotte, Edward M

    2004-06-25

    Networks are proving to be central to the study of gene function, protein-protein interaction, and biochemical pathway data. Visualization of networks is important for their study, but visualization tools are often inadequate for working with very large biological networks. Here, we present an algorithm, called large graph layout (LGL), which can be used to dynamically visualize large networks on the order of hundreds of thousands of vertices and millions of edges. LGL applies a force-directed iterative layout guided by a minimal spanning tree of the network in order to generate coordinates for the vertices in two or three dimensions, which are subsequently visualized and interactively navigated with companion programs. We demonstrate the use of LGL in visualizing an extensive protein map summarizing the results of approximately 21 billion sequence comparisons between 145579 proteins from 50 genomes. Proteins are positioned in the map according to sequence homology and gene fusions, with the map ultimately serving as a theoretical framework that integrates inferences about gene function derived from sequence homology, remote homology, gene fusions, and higher-order fusions. We confirm that protein neighbors in the resulting map are functionally related, and that distinct map regions correspond to distinct cellular systems, enabling a computational strategy for discovering proteins' functions on the basis of the proteins' map positions. Using the map produced by LGL, we infer general functions for 23 uncharacterized protein families.

  4. 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

  5. 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.

  6. Maximum-entropy large-scale structures of Boolean networks optimized for criticality

    NASA Astrophysics Data System (ADS)

    Möller, Marco; Peixoto, Tiago P.

    2015-04-01

    We construct statistical ensembles of modular Boolean networks that are constrained to lie at the critical line between frozen and chaotic dynamic regimes. The ensembles are maximally random given the imposed constraints, and thus represent null models of critical networks. By varying the network density and the entropic cost associated with biased Boolean functions, the ensembles undergo several phase transitions. The observed structures range from fully random to several ordered ones, including a prominent core-periphery-like structure, and an 'attenuated' two-group structure, where the network is divided in two groups of nodes, and one of them has Boolean functions with very low sensitivity. This shows that such simple large-scale structures are the most likely to occur when optimizing for criticality, in the absence of any other constraint or competing optimization criteria.

  7. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

    PubMed

    Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

    2015-01-01

    Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

  8. 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

  9. Mining large-scale response networks reveals ‘topmost activities’ in Mycobacterium tuberculosis infection

    PubMed Central

    Sambarey, Awanti; Prashanthi, Karyala; Chandra, Nagasuma

    2013-01-01

    Mycobacterium tuberculosis owes its high pathogenic potential to its ability to evade host immune responses and thrive inside the macrophage. The outcome of infection is largely determined by the cellular response comprising a multitude of molecular events. The complexity and inter-relatedness in the processes makes it essential to adopt systems approaches to study them. In this work, we construct a comprehensive network of infection-related processes in a human macrophage comprising 1888 proteins and 14,016 interactions. We then compute response networks based on available gene expression profiles corresponding to states of health, disease and drug treatment. We use a novel formulation for mining response networks that has led to identifying highest activities in the cell. Highest activity paths provide mechanistic insights into pathogenesis and response to treatment. The approach used here serves as a generic framework for mining dynamic changes in genome-scale protein interaction networks. PMID:23892477

  10. 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

  11. High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

    PubMed

    Andras, Peter

    2017-01-25

    Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

  12. 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

  13. An Efficient Steady-State Analysis Method for Large Boolean Networks with High Maximum Node Connectivity.

    PubMed

    Hong, Changki; Hwang, Jeewon; Cho, Kwang-Hyun; Shin, Insik

    2015-01-01

    Boolean networks have been widely used to model biological processes lacking detailed kinetic information. Despite their simplicity, Boolean network dynamics can still capture some important features of biological systems such as stable cell phenotypes represented by steady states. For small models, steady states can be determined through exhaustive enumeration of all state transitions. As the number of nodes increases, however, the state space grows exponentially thus making it difficult to find steady states. Over the last several decades, many studies have addressed how to handle such a state space explosion. Recently, increasing attention has been paid to a satisfiability solving algorithm due to its potential scalability to handle large networks. Meanwhile, there still lies a problem in the case of large models with high maximum node connectivity where the satisfiability solving algorithm is known to be computationally intractable. To address the problem, this paper presents a new partitioning-based method that breaks down a given network into smaller subnetworks. Steady states of each subnetworks are identified by independently applying the satisfiability solving algorithm. Then, they are combined to construct the steady states of the overall network. To efficiently apply the satisfiability solving algorithm to each subnetwork, it is crucial to find the best partition of the network. In this paper, we propose a method that divides each subnetwork to be smallest in size and lowest in maximum node connectivity. This minimizes the total cost of finding all steady states in entire subnetworks. The proposed algorithm is compared with others for steady states identification through a number of simulations on both published small models and randomly generated large models with differing maximum node connectivities. The simulation results show that our method can scale up to several hundreds of nodes even for Boolean networks with high maximum node connectivity. The

  14. 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.

  15. 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.

  16. The Use of Online Social Networks by Polish Former Erasmus Students: A Large-Scale Survey

    ERIC Educational Resources Information Center

    Bryla, Pawel

    2014-01-01

    There is an increasing role of online social networks in the life of young Poles. We conducted a large-scale survey among Polish former Erasmus students. We have received 2450 completed questionnaires from alumni of 115 higher education institutions all over Poland. 85.4% of our respondents reported they kept in touch with their former Erasmus…

  17. 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.

  18. 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…

  19. Pediatric liver transplantation for urea cycle disorders and organic acidemias: United Network for Organ Sharing data for 2002-2012.

    PubMed

    Perito, Emily R; Rhee, Sue; Roberts, John Paul; Rosenthal, Philip

    2014-01-01

    Decision making concerning liver transplantation is unique for children with urea cycle disorders (UCDs) and organic acidemias (OAs) because of their immediate high priority on the waiting list, which is not related to the severity of their disease. There are limited national outcome data on which recommendations about liver transplantation for UCDs or OAs can be based. This study was a retrospective analysis of United Network for Organ Sharing data for liver recipients who underwent transplantation at an age < 18 years in 2002-2012. Repeat transplants were excluded. Among the pediatric liver transplants, 5.4% were liver-only for UCDs/OAs. The proportion of transplants for UCDs/OAs increased from 4.3% in 2002-2005 to 7.4% in 2010-2012 (P < 0.001). Ninety-six percent were deceased donor transplants, and 59% of these patients underwent transplantation at <2 years of age. Graft survival improved as the age at transplant increased (P = 0.04). Within 5 years after transplantation, the graft survival rate was 78% for children < 2 years old at transplant and 88% for children ≥ 2 years old at transplant (P = 0.06). Vascular thrombosis caused 44% of the graft losses, and 65% of these losses occurred in children < 2 years old. Patient survival also improved as the age at transplant increased: the 5-year patient survival rate was 88% for children with UCDs/OAs who were <2 years old at transplant and 99% for children who were ≥2 years old at transplant (P = 0.006). At the last-follow-up (54 ± 34.4 months), children who underwent transplantation for UCDs/OAs were more likely to have cognitive and motor delays than children who underwent transplantation for other indications. Cognitive and motor delays for children with UCDs/OAs were associated with metabolic disorders, but they were not predicted by age or weight at transplant, sex, ethnicity, liver graft type (split versus whole), or hospitalization at transplant in univariate and

  20. Shared-risk link group (SRLG)-diverse path provisioning under hybrid service level agreements in wavelength-routed optical mesh networks: formulation and solution approaches

    NASA Astrophysics Data System (ADS)

    Shen, Lu; Yang, Xi; Ramamurthy, Byrav

    2003-10-01

    The static provisioning problem in wavelength-routed optical networks has been studied for many years. However, service providers are still facing the challenges arising from the special requirements for provisioning services at the optical layer. In this paper, we incorporate some realistic constraints into the static provisioning problem, and formulate it under different network resource availability conditions. We consider three classes of shared risk link group (SRLG)-diverse path protection schemes: dedicated, shared, and unprotected. We associate with each connection request a lightpath length constraint and a revenue value. When the network resources are not sufficient to accommodate all the connection requests, the static provisioning problem is formulated as a revenue maximization problem, whose objective is maximizing the total revenue value. When the network has sufficient resources, the problem becomes a capacity minimization problem with the objective of minimizing the number of used wavelength-links. We give integer linear programming (ILP) formulations for these problems. Because solving these ILP problems is extremely time consuming, we propose a tabu search heuristic to solve these problems within a reasonable time. Experimental results are presented to compare the solutions obtained by an ILP solver, the tabu search heuristic and a divide-and-conquer greedy heuristic.

  1. Weighted and directed interactions in evolving large-scale epileptic brain networks.

    PubMed

    Dickten, Henning; Porz, Stephan; Elger, Christian E; Lehnertz, Klaus

    2016-10-06

    Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess-with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics-both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only - in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

  2. A community integration strategy based on an improved modularity density increment for large-scale networks

    NASA Astrophysics Data System (ADS)

    Shang, Ronghua; Zhang, Weitong; Jiao, Licheng; Stolkin, Rustam; Xue, Yu

    2017-03-01

    This paper presents a community integration strategy for large-scale networks, based on pre-partitioning, followed by optimization of an improved modularity density increment Δ D. Our proposed method initially searches for local core nodes in the network, i.e. potential community centers, and expands these communities to include neighbor nodes which have sufficiently high similarity with the core node. In this way, we can effectively exploit the information of the node and structure of the network, to accurately pre-partition the network into communities. Next, we arrange these pre-partitioned communities according to their external connections in descending order. In this way, we can ensure that communities with greater influence are prioritized during the process of community integration. At the same time, this paper proposes an improved modularity density increment Δ D, and shows how to use this as an objective function during the community integration optimization process. During the process of community consolidation, those neighbor communities with few external connections are prioritized for merging, thereby avoiding the fusion errors. Finally, we incorporate global reasoning into the process of local integration. We calculate and compare the improved modularity density increment of each pair of communities, to determine whether or not they should be integrated, effectively improve the accuracy of community integration. Experimental results show that our proposed algorithm can obtain superior community classification results on 5 large-scale networks, as compared with 8 other well known algorithms from the literature.

  3. Weighted and directed interactions in evolving large-scale epileptic brain networks

    NASA Astrophysics Data System (ADS)

    Dickten, Henning; Porz, Stephan; Elger, Christian E.; Lehnertz, Klaus

    2016-10-01

    Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only – in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

  4. Weighted and directed interactions in evolving large-scale epileptic brain networks

    PubMed Central

    Dickten, Henning; Porz, Stephan; Elger, Christian E.; Lehnertz, Klaus

    2016-01-01

    Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only – in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control. PMID:27708381

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. ShakeNet: a portable wireless sensor network for instrumenting large civil structures

    USGS Publications Warehouse

    Kohler, Monica D.; Hao, Shuai; Mishra, Nilesh; Govindan, Ramesh; Nigbor, Robert

    2015-08-03

    We report our findings from a U.S. Geological Survey (USGS) National Earthquake Hazards Reduction Program-funded project to develop and test a wireless, portable, strong-motion network of up to 40 triaxial accelerometers for structural health monitoring. The overall goal of the project was to record ambient vibrations for several days from USGS-instrumented structures. Structural health monitoring has important applications in fields like civil engineering and the study of earthquakes. The emergence of wireless sensor networks provides a promising means to such applications. However, while most wireless sensor networks are still in the experimentation stage, very few take into consideration the realistic earthquake engineering application requirements. To collect comprehensive data for structural health monitoring for civil engineers, high-resolution vibration sensors and sufficient sampling rates should be adopted, which makes it challenging for current wireless sensor network technology in the following ways: processing capabilities, storage limit, and communication bandwidth. The wireless sensor network has to meet expectations set by wired sensor devices prevalent in the structural health monitoring community. For this project, we built and tested an application-realistic, commercially based, portable, wireless sensor network called ShakeNet for instrumentation of large civil structures, especially for buildings, bridges, or dams after earthquakes. Two to three people can deploy ShakeNet sensors within hours after an earthquake to measure the structural response of the building or bridge during aftershocks. ShakeNet involved the development of a new sensing platform (ShakeBox) running a software suite for networking, data collection, and monitoring. Deployments reported here on a tall building and a large dam were real-world tests of ShakeNet operation, and helped to refine both hardware and software. 

  12. 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

  13. 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.

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

    PubMed Central

    van de Groep, Jorik; Gupta, Dhritiman; Verschuuren, Marc A.; M. Wienk, Martijn; 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

  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. CytoModeler: a tool for bridging large-scale network analysis and dynamic quantitative modeling

    PubMed Central

    Xia, Tian; Van Hemert, John; Dickerson, Julie A.

    2011-01-01

    Summary: CytoModeler is an open-source Java application based on the Cytoscape platform. It integrates large-scale network analysis and quantitative modeling by combining omics analysis on the Cytoscape platform, access to deterministic and stochastic simulators, and static and dynamic network context visualizations of simulation results. Availability: Implemented in Java, CytoModeler runs with Cytoscape 2.6 and 2.7. Binaries, documentation and video walkthroughs are freely available at http://vrac.iastate.edu/~jlv/cytomodeler/. Contact: julied@iastate.edu; netscape@iastate.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21511714

  18. 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.

  19. Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

    PubMed

    Hase, Takeshi; Ghosh, Samik; 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.

  20. Functional models for large-scale gene regulation networks: realism and fiction.

    PubMed

    Lagomarsino, Marco Cosentino; Bassetti, Bruno; Castellani, Gastone; Remondini, Daniel

    2009-04-01

    High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as "realistic" and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.

  1. Applying forces to elastic network models of large biomolecules using a haptic feedback device

    NASA Astrophysics Data System (ADS)

    Stocks, M. B.; Laycock, S. D.; Hayward, S.

    2011-03-01

    Elastic network models of biomolecules have proved to be relatively good at predicting global conformational changes particularly in large systems. Software that facilitates rapid and intuitive exploration of conformational change in elastic network models of large biomolecules in response to externally applied forces would therefore be of considerable use, particularly if the forces mimic those that arise in the interaction with a functional ligand. We have developed software that enables a user to apply forces to individual atoms of an elastic network model of a biomolecule through a haptic feedback device or a mouse. With a haptic feedback device the user feels the response to the applied force whilst seeing the biomolecule deform on the screen. Prior to the interactive session normal mode analysis is performed, or pre-calculated normal mode eigenvalues and eigenvectors are loaded. For large molecules this allows the memory and number of calculations to be reduced by employing the idea of the important subspace, a relatively small space of the first M lowest frequency normal mode eigenvectors within which a large proportion of the total fluctuation occurs. Using this approach it was possible to study GroEL on a standard PC as even though only 2.3% of the total number of eigenvectors could be used, they accounted for 50% of the total fluctuation. User testing has shown that the haptic version allows for much more rapid and intuitive exploration of the molecule than the mouse version.

  2. Applying forces to elastic network models of large biomolecules using a haptic feedback device.

    PubMed

    Stocks, M B; Laycock, S D; Hayward, S

    2011-03-01

    Elastic network models of biomolecules have proved to be relatively good at predicting global conformational changes particularly in large systems. Software that facilitates rapid and intuitive exploration of conformational change in elastic network models of large biomolecules in response to externally applied forces would therefore be of considerable use, particularly if the forces mimic those that arise in the interaction with a functional ligand. We have developed software that enables a user to apply forces to individual atoms of an elastic network model of a biomolecule through a haptic feedback device or a mouse. With a haptic feedback device the user feels the response to the applied force whilst seeing the biomolecule deform on the screen. Prior to the interactive session normal mode analysis is performed, or pre-calculated normal mode eigenvalues and eigenvectors are loaded. For large molecules this allows the memory and number of calculations to be reduced by employing the idea of the important subspace, a relatively small space of the first M lowest frequency normal mode eigenvectors within which a large proportion of the total fluctuation occurs. Using this approach it was possible to study GroEL on a standard PC as even though only 2.3% of the total number of eigenvectors could be used, they accounted for 50% of the total fluctuation. User testing has shown that the haptic version allows for much more rapid and intuitive exploration of the molecule than the mouse version.

  3. 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…

  4. 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…

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

    PubMed

    Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

    2016-09-19

    Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

  10. 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

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

    PubMed

    Li, Xing; Chen, Dexin; Li, Chunyan; Wang, Liangmin

    2015-07-03

    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.

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

    DOE PAGES

    Pesce, Lorenzo L.; Lee, Hyong C.; Hereld, Mark; ...

    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

  13. 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.

  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. Large-scale WDM passive optical network based on cyclical AWG.

    PubMed

    Xu, Zhaowen; Cheng, Xiaofei; Yeo, Yong-Kee; Shao, Xu; Zhou, Luying; Zhang, Hongguang

    2012-06-18

    A large scale wavelength division multiplexed passive optical network is proposed and experimentally demonstrated. 124 bidirectional optical channels with 10-Gb/s downstream and 1.25-Gb/s upstream transmission are simultaneously distributed by a single 32*32 cyclic AWG. The effect of the extinction ratio and seeding power to BER performance are experimentally investigated. The selection of the subcarrier frequency is also analyzed by simulation.

  16. User-Friendly Data-Sharing Practices for Fostering Collaboration within a Research Network: Roles of a Vanguard Center for a Community-Based Study.

    PubMed

    Lee, Jae Eun; Sung, Jung Hye; Barnett, M Edwina; Norris, Keith

    2015-12-22

    Although various attempts have been made to build collaborative cultures for data sharing, their effectiveness is still questionable. The Jackson Heart Study (JHS) Vanguard Center (JHSVC) at the NIH-funded Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN) Data Coordinating Center (DCC) may be a new concept in that the data are being shared with a research network where a plethora of scientists/researchers are working together to achieve their common goal. This study describes the current practices to share the JHS data through the mechanism of JHSVC. The JHS is the largest single-site cohort study to prospectively investigate the determinants of cardiovascular disease among African-Americans. It has adopted a formal screened access method through a formalized JHSVC mechanism, in which only a qualified scientist(s) can access the data. The role of the DCC was to help RTRN researchers explore hypothesis-driven ideas to enhance the output and impact of JHS data through customized services, such as feasibility tests, data querying, manuscript proposal development and data analyses for publication. DCC has implemented these various programs to facilitate data utility. A total of 300 investigators attended workshops and/or received training booklets. DCC provided two online and five onsite workshops and developed/distributed more than 250 copies of the booklet to help potential data users understand the structure of and access to the data. Information on data use was also provided through the RTRN website. The DCC efforts led to the production of five active manuscript proposals, seven completed publications, 11 presentations and four NIH grant proposals. These outcomes resulted from activities during the first four years; over the last couple of years, there were few new requests. Our study suggested that DCC-customized services enhanced the accessibility of JHS data and their utility by RTRN researchers and helped to achieve the

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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…

  4. 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.

  5. Complex networks, community structure, and catchment classification in a large-scale river basin

    NASA Astrophysics Data System (ADS)

    Fang, Koren; Sivakumar, Bellie; Woldemeskel, Fitsum M.

    2017-02-01

    This study introduces the concepts of complex networks, especially community structure, to classify catchments in large-scale river basins. The Mississippi River basin (MRB) is considered as a representative large-scale basin, and daily streamflow from a network of 1663 stations are analyzed. Six community structure methods are employed: edge betweenness, greedy algorithm, multilevel modularity optimization, leading eigenvector, label propagation, and walktrap. The influence of correlation threshold (i.e. spatial correlation in flow between stations) on classification (i.e. community formation) is examined. The consistency among the methods in classifying catchments is assessed, using a normalized mutual information (NMI) index. An attempt is also made to explain the community formation in terms of river network/branching and some important catchment/flow properties. The results indicate that the correlation threshold has a notable influence on the number and size of communities identified and that there is a high level of consistency in the performance among the methods (except for the leading eigenvector method at lower thresholds). The results also reveal that only a few communities combine to represent a majority of the catchments, with the 10 largest communities (roughly 4% of the total number of communities) representing almost two-thirds of the catchments. Community formation is found to be influenced not only by geographic proximity but also, more importantly, by the organization of the river network (i.e. main stem and subsequent branching). Some communities are found to exhibit a greater variability in catchment/flow properties within themselves when compared to that of the whole network, thus indicating that such characteristics are unlikely to be a significant influence on community grouping.

  6. GPP Webinar: Solar Utilization in Higher Education Networking & Information Sharing Group: RFP, Contract, and Administrative Issues Discussion

    EPA Pesticide Factsheets

    This presentation from a Solar Utilization in Higher Education Networking and Information webinar covers contracts, Request for Proposals (RFPs), and administrative issues related to solar project development in the higher education sector.

  7. Large Scale Cortical Functional Networks Associated with Slow-Wave and Spindle-Burst-Related Spontaneous Activity

    PubMed Central

    McVea, David A.; Murphy, Timothy H.; Mohajerani, Majid H.

    2016-01-01

    Cortical sensory systems are active with rich patterns of activity during sleep and under light anesthesia. Remarkably, this activity shares many characteristics with those present when the awake brain responds to sensory stimuli. We review two specific forms of such activity: slow-wave activity (SWA) in the adult brain and spindle bursts in developing brain. SWA is composed of 0.5–4 Hz resting potential fluctuations. Although these fluctuations synchronize wide regions of cortex, recent large-scale imaging has shown spatial details of their distribution that reflect underlying cortical structural projections and networks. These networks are regulated, as prior awake experiences alter both the spatial and temporal features of SWA in subsequent sleep. Activity patterns of the immature brain, however, are very different from those of the adult. SWA is absent, and the dominant pattern is spindle bursts, intermittent high frequency oscillations superimposed on slower depolarizations within sensory cortices. These bursts are driven by intrinsic brain activity, which act to generate peripheral inputs, for example via limb twitches. They are present within developing sensory cortex before they are mature enough to exhibit directed movements and respond to external stimuli. Like in the adult, these patterns resemble those evoked by sensory stimulation when awake. It is suggested that spindle-burst activity is generated purposefully by the developing nervous system as a proxy for true external stimuli. While the sleep-related functions of both slow-wave and spindle-burst activity may not be entirely clear, they reflect robust regulated phenomena which can engage select wide-spread cortical circuits. These circuits are similar to those activated during sensory processing and volitional events. We highlight these two patterns of brain activity because both are prominent and well-studied forms of spontaneous activity that will yield valuable insights into brain function in

  8. Flexible sampling large-scale social networks by self-adjustable random walk

    NASA Astrophysics Data System (ADS)

    Xu, Xiao-Ke; Zhu, Jonathan J. H.

    2016-12-01

    Online social networks (OSNs) have become an increasingly attractive gold mine for academic and commercial researchers. However, research on OSNs faces a number of difficult challenges. One bottleneck lies in the massive quantity and often unavailability of OSN population data. Sampling perhaps becomes the only feasible solution to the problems. How to draw samples that can represent the underlying OSNs has remained a formidable task because of a number of conceptual and methodological reasons. Especially, most of the empirically-driven studies on network sampling are confined to simulated data or sub-graph data, which are fundamentally different from real and complete-graph OSNs. In the current study, we propose a flexible sampling method, called Self-Adjustable Random Walk (SARW), and test it against with the population data of a real large-scale OSN. We evaluate the strengths of the sampling method in comparison with four prevailing methods, including uniform, breadth-first search (BFS), random walk (RW), and revised RW (i.e., MHRW) sampling. We try to mix both induced-edge and external-edge information of sampled nodes together in the same sampling process. Our results show that the SARW sampling method has been able to generate unbiased samples of OSNs with maximal precision and minimal cost. The study is helpful for the practice of OSN research by providing a highly needed sampling tools, for the methodological development of large-scale network sampling by comparative evaluations of existing sampling methods, and for the theoretical understanding of human networks by highlighting discrepancies and contradictions between existing knowledge/assumptions of large-scale real OSN data.

  9. Latency-Efficient Communication in Wireless Mesh Networks under Consideration of Large Interference Range

    NASA Astrophysics Data System (ADS)

    Xin, Qin; Yao, Xiaolan; Engelstad, Paal E.

    2010-09-01

    Wireless Mesh Networking is an emerging communication paradigm to enable resilient, cost-efficient and reliable services for the future-generation wireless networks. We study here the minimum-latency communication primitive of gossiping (all-to-all communication) in multi-hop ad-hoc Wireless Mesh Networks (WMNs). Each mesh node in the WMN is initially given a message and the objective is to design a minimum-latency schedule such that each mesh node distributes its message to all other mesh nodes. Minimum-latency gossiping problem is well known to be NP-hard even for the scenario in which the topology of the WMN is known to all mesh nodes in advance. In this paper, we propose a new latency-efficient approximation scheme that can accomplish gossiping task in polynomial time units in any ad-hoc WMN under consideration of Large Interference Range (LIR), e.g., the interference range is much larger than the transmission range. To the best of our knowledge, it is first time to investigate such a scenario in ad-hoc WMNs under LIR, our algorithm allows the labels (e.g., identifiers) of the mesh nodes to be polynomially large in terms of the size of the WMN, which is the first time that the scenario of large labels has been considered in ad-hoc WMNs under LIR. Furthermore, our gossiping scheme can be considered as a framework which can be easily implied to the scenario under consideration of mobility-related issues since we assume that the mesh nodes have no knowledge on the network topology even for its neighboring mesh nodes.

  10. 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

  11. 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

  12. A large scale code resolution service network in the Internet of Things.

    PubMed

    Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan

    2012-11-07

    In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS.

  13. Disrupted Topological Patterns of Large-Scale Network in Conduct Disorder

    PubMed Central

    Jiang, Yali; Liu, Weixiang; Ming, Qingsen; Gao, Yidian; Ma, Ren; Zhang, Xiaocui; Situ, Weijun; Wang, Xiang; Yao, Shuqiao; Huang, Bingsheng

    2016-01-01

    Regional abnormalities in brain structure and function, as well as disrupted connectivity, have been found repeatedly in adolescents with conduct disorder (CD). Yet, the large-scale brain topology associated with CD is not well characterized, and little is known about the systematic neural mechanisms of CD. We employed graphic theory to investigate systematically the structural connectivity derived from cortical thickness correlation in a group of patients with CD (N = 43) and healthy controls (HCs, N = 73). Nonparametric permutation tests were applied for between-group comparisons of graphical metrics. Compared with HCs, network measures including global/local efficiency and modularity all pointed to hypo-functioning in CD, despite of preserved small-world organization in both groups. The hubs distribution is only partially overlapped with each other. These results indicate that CD is accompanied by both impaired integration and segregation patterns of brain networks, and the distribution of highly connected neural network ‘hubs’ is also distinct between groups. Such misconfiguration extends our understanding regarding how structural neural network disruptions may underlie behavioral disturbances in adolescents with CD, and potentially, implicates an aberrant cytoarchitectonic profiles in the brain of CD patients. PMID:27841320

  14. 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

  15. 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 quer