The algebra of the general Markov model on phylogenetic trees and networks.

PubMed

Sumner, J G; Holland, B R; Jarvis, P D

2012-04-01

It is known that the Kimura 3ST model of sequence evolution on phylogenetic trees can be extended quite naturally to arbitrary split systems. However, this extension relies heavily on mathematical peculiarities of the associated Hadamard transformation, and providing an analogous augmentation of the general Markov model has thus far been elusive. In this paper, we rectify this shortcoming by showing how to extend the general Markov model on trees to include incompatible edges; and even further to more general network models. This is achieved by exploring the algebra of the generators of the continuous-time Markov chain together with the “splitting” operator that generates the branching process on phylogenetic trees. For simplicity, we proceed by discussing the two state case and then show that our results are easily extended to more states with little complication. Intriguingly, upon restriction of the two state general Markov model to the parameter space of the binary symmetric model, our extension is indistinguishable from the Hadamard approach only on trees; as soon as any incompatible splits are introduced the two approaches give rise to differing probability distributions with disparate structure. Through exploration of a simple example, we give an argument that our extension to more general networks has desirable properties that the previous approaches do not share. In particular, our construction allows for convergent evolution of previously divergent lineages; a property that is of significant interest for biological applications.

Macrostructure from Microstructure: Generating Whole Systems from Ego Networks

PubMed Central

Smith, Jeffrey A.

2014-01-01

This paper presents a new simulation method to make global network inference from sampled data. The proposed simulation method takes sampled ego network data and uses Exponential Random Graph Models (ERGM) to reconstruct the features of the true, unknown network. After describing the method, the paper presents two validity checks of the approach: the first uses the 20 largest Add Health networks while the second uses the Sociology Coauthorship network in the 1990's. For each test, I take random ego network samples from the known networks and use my method to make global network inference. I find that my method successfully reproduces the properties of the networks, such as distance and main component size. The results also suggest that simpler, baseline models provide considerably worse estimates for most network properties. I end the paper by discussing the bounds/limitations of ego network sampling. I also discuss possible extensions to the proposed approach. PMID:25339783

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data

PubMed Central

Koleti, Amar; Terryn, Raymond; Stathias, Vasileios; Chung, Caty; Cooper, Daniel J; Turner, John P; Vidović, Dušica; Forlin, Michele; Kelley, Tanya T; D’Urso, Alessandro; Allen, Bryce K; Torre, Denis; Jagodnik, Kathleen M; Wang, Lily; Jenkins, Sherry L; Mader, Christopher; Niu, Wen; Fazel, Mehdi; Mahi, Naim; Pilarczyk, Marcin; Clark, Nicholas; Shamsaei, Behrouz; Meller, Jarek; Vasiliauskas, Juozas; Reichard, John; Medvedovic, Mario; Ma’ayan, Avi; Pillai, Ajay

2018-01-01

Abstract The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content. PMID:29140462

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

NASA Astrophysics Data System (ADS)

Jha, Devesh K.; Chattopadhyay, Pritthi; Sarkar, Soumik; Ray, Asok

2016-05-01

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot.

Cognitive task analysis of network analysts and managers for network situational awareness

NASA Astrophysics Data System (ADS)

Erbacher, Robert F.; Frincke, Deborah A.; Wong, Pak Chung; Moody, Sarah; Fink, Glenn

2010-01-01

The goal of our project is to create a set of next-generation cyber situational-awareness capabilities with applications to other domains in the long term. The situational-awareness capabilities being developed focus on novel visualization techniques as well as data analysis techniques designed to improve the comprehensibility of the visualizations. The objective is to improve the decision-making process to enable decision makers to choose better actions. To this end, we put extensive effort into ensuring we had feedback from network analysts and managers and understanding what their needs truly are. This paper discusses the cognitive task analysis methodology we followed to acquire feedback from the analysts. This paper also provides the details we acquired from the analysts on their processes, goals, concerns, etc. A final result we describe is the generation of a task-flow diagram.

OpenSoC Fabric

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-08-21

Recent advancements in technology scaling have shown a trend towards greater integration with large-scale chips containing thousands of processors connected to memories and other I/O devices using non-trivial network topologies. Software simulation proves insufficient to study the tradeoffs in such complex systems due to slow execution time, whereas hardware RTL development is too time-consuming. We present OpenSoC Fabric, an on-chip network generation infrastructure which aims to provide a parameterizable and powerful on-chip network generator for evaluating future high performance computing architectures based on SoC technology. OpenSoC Fabric leverages a new hardware DSL, Chisel, which contains powerful abstractions provided by itsmore » base language, Scala, and generates both software (C++) and hardware (Verilog) models from a single code base. The OpenSoC Fabric2 infrastructure is modeled after existing state-of-the-art simulators, offers large and powerful collections of configuration options, and follows object-oriented design and functional programming to make functionality extension as easy as possible.« less

Consistent visualizations of changing knowledge

PubMed Central

Tipney, Hannah J.; Schuyler, Ronald P.; Hunter, Lawrence

2009-01-01

Networks are increasingly used in biology to represent complex data in uncomplicated symbolic form. However, as biological knowledge is continually evolving, so must those networks representing this knowledge. Capturing and presenting this type of knowledge change over time is particularly challenging due to the intimate manner in which researchers customize those networks they come into contact with. The effective visualization of this knowledge is important as it creates insight into complex systems and stimulates hypothesis generation and biological discovery. Here we highlight how the retention of user customizations, and the collection and visualization of knowledge associated provenance supports effective and productive network exploration. We also present an extension of the Hanalyzer system, ReOrient, which supports network exploration and analysis in the presence of knowledge change. PMID:21347184

The APA and the Rise of Pediatric Generalist Network Research

PubMed Central

Wasserman, Richard; Serwint, Janet R.; Kuppermann, Nathan; Srivastava, Rajendu; Dreyer, Benard

2010-01-01

The Academic Pediatric Association (APA – formerly the Ambulatory Pediatric Association) first encouraged multi-institutional collaborative research among its members over thirty years ago. Individual APA members went on subsequently to figure prominently in establishing formal research networks. These enduring collaborations have been established to conduct investigations in a variety of generalist contexts. At present, four generalist networks – Pediatric Research in Office Settings (PROS), the Pediatric Emergency Care Applied Network (PECARN), the COntinuity Research NETwork (CORNET), and Pediatric Research in Inpatient Settings (PRIS) – have a track record of extensive achievement in generating new knowledge aimed at improving the health and health care of children. This review details the history, accomplishments, and future directions of these networks and summarizes the common themes, strengths, challenges and opportunities inherent in pediatric generalist network research. PMID:21282083

Next Generation Space Telescope Integrated Science Module Data System

NASA Technical Reports Server (NTRS)

Schnurr, Richard G.; Greenhouse, Matthew A.; Jurotich, Matthew M.; Whitley, Raymond; Kalinowski, Keith J.; Love, Bruce W.; Travis, Jeffrey W.; Long, Knox S.

1999-01-01

The Data system for the Next Generation Space Telescope (NGST) Integrated Science Module (ISIM) is the primary data interface between the spacecraft, telescope, and science instrument systems. This poster includes block diagrams of the ISIM data system and its components derived during the pre-phase A Yardstick feasibility study. The poster details the hardware and software components used to acquire and process science data for the Yardstick instrument compliment, and depicts the baseline external interfaces to science instruments and other systems. This baseline data system is a fully redundant, high performance computing system. Each redundant computer contains three 150 MHz power PC processors. All processors execute a commercially available real time multi-tasking operating system supporting, preemptive multi-tasking, file management and network interfaces. These six processors in the system are networked together. The spacecraft interface baseline is an extension of the network, which links the six processors. The final selection for Processor busses, processor chips, network interfaces, and high-speed data interfaces will be made during mid 2002.

Fitness consequences of spousal relatedness in 46 small-scale societies.

PubMed

Bailey, Drew H; Hill, Kim R; Walker, Robert S

2014-05-01

Social norms that regulate reproductive and marital decisions generate impressive cross-cultural variation in the prevalence of kin marriages. In some societies, marriages among kin are the norm and this inbreeding creates intensive kinship networks concentrated within communities. In others, especially forager societies, most marriages are between more genealogically and geographically distant individuals, which generates a larger number of kin and affines of lesser relatedness in more extensive kinship networks spread out over multiple communities. Here, we investigate the fitness consequence of kin marriages across a sample of 46 small-scale societies (12,439 marriages). Results show that some non-forager societies (including horticulturalists, agriculturalists and pastoralists), but not foragers, have intensive kinship societies where fitness outcomes (measured as the number of surviving children in genealogies) peak at commonly high levels of spousal relatedness. By contrast, the extensive kinship systems of foragers have worse fitness outcomes at high levels of spousal relatedness. Overall, societies with greater levels of inbreeding showed a more positive relationship between fitness and spousal relatedness. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Discriminative Relational Topic Models.

PubMed

Chen, Ning; Zhu, Jun; Xia, Fei; Zhang, Bo

2015-05-01

Relational topic models (RTMs) provide a probabilistic generative process to describe both the link structure and document contents for document networks, and they have shown promise on predicting network structures and discovering latent topic representations. However, existing RTMs have limitations in both the restricted model expressiveness and incapability of dealing with imbalanced network data. To expand the scope and improve the inference accuracy of RTMs, this paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference (RegBayes) with a regularization parameter to deal with the imbalanced link structure issue in real networks and improve the discriminative ability of learned latent representations; and 3) instead of doing variational approximation with strict mean-field assumptions, we present collapsed Gibbs sampling algorithms for the generalized relational topic models by exploring data augmentation without making restricting assumptions. Under the generic RegBayes framework, we carefully investigate two popular discriminative loss functions, namely, the logistic log-loss and the max-margin hinge loss. Experimental results on several real network datasets demonstrate the significance of these extensions on improving prediction performance.

Cognitive Task Analysis of Network Analysts and Managers for Network Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erbacher, Robert; Frincke, Deborah A.; Wong, Pak C.

The goal of the project was to create a set of next generation cyber situational awareness capabilities with applications to other domains in the long term. The goal is to improve the decision making process such that decision makers can choose better actions. To this end, we put extensive effort into ensuring we had feedback from network analysts and managers and understood what their needs truly were. Consequently, this is the focus of this portion of the research. This paper discusses the methodology we followed to acquire this feedback from the analysts, namely a cognitive task analysis. Additionally, this papermore » provides the details we acquired from the analysts. This essentially provides details on their processes, goals, concerns, the data and meta-data they analyze, etc. A final result we describe is the generation of a task-flow diagram.« less

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D

PubMed Central

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron

2017-01-01

Abstract Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. PMID:28814063

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D.

PubMed

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron; Gümüs, Zeynep H

2017-08-01

Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. © The Authors 2017. Published by Oxford University Press.

Dynamic neural network models of the premotoneuronal circuitry controlling wrist movements in primates.

PubMed

Maier, M A; Shupe, L E; Fetz, E E

2005-10-01

Dynamic recurrent neural networks were derived to simulate neuronal populations generating bidirectional wrist movements in the monkey. The models incorporate anatomical connections of cortical and rubral neurons, muscle afferents, segmental interneurons and motoneurons; they also incorporate the response profiles of four populations of neurons observed in behaving monkeys. The networks were derived by gradient descent algorithms to generate the eight characteristic patterns of motor unit activations observed during alternating flexion-extension wrist movements. The resulting model generated the appropriate input-output transforms and developed connection strengths resembling those in physiological pathways. We found that this network could be further trained to simulate additional tasks, such as experimentally observed reflex responses to limb perturbations that stretched or shortened the active muscles, and scaling of response amplitudes in proportion to inputs. In the final comprehensive network, motor units are driven by the combined activity of cortical, rubral, spinal and afferent units during step tracking and perturbations. The model displayed many emergent properties corresponding to physiological characteristics. The resulting neural network provides a working model of premotoneuronal circuitry and elucidates the neural mechanisms controlling motoneuron activity. It also predicts several features to be experimentally tested, for example the consequences of eliminating inhibitory connections in cortex and red nucleus. It also reveals that co-contraction can be achieved by simultaneous activation of the flexor and extensor circuits without invoking features specific to co-contraction.

The influence of tie strength on evolutionary games on networks: An empirical investigation

NASA Astrophysics Data System (ADS)

Buesser, Pierre; Peña, Jorge; Pestelacci, Enea; Tomassini, Marco

2011-11-01

Extending previous work on unweighted networks, we present here a systematic numerical investigation of standard evolutionary games on weighted networks. In the absence of any reliable model for generating weighted social networks, we attribute weights to links in a few ways supported by empirical data ranging from totally uncorrelated to weighted bipartite networks. The results of the extensive simulation work on standard complex network models show that, except in a case that does not seem to be common in social networks, taking the tie strength into account does not change in a radical manner the long-run steady-state behavior of the studied games. Besides model networks, we also included a real-life case drawn from a coauthorship network. In this case also, taking the weights into account only changes the results slightly with respect to the raw unweighted graph, although to draw more reliable conclusions on real social networks many more cases should be studied as these weighted networks become available.

Statistical mechanics of a cat's cradle

NASA Astrophysics Data System (ADS)

Shen, Tongye; Wolynes, Peter G.

2006-11-01

It is believed that, much like a cat's cradle, the cytoskeleton can be thought of as a network of strings under tension. We show that both regular and random bond-disordered networks having bonds that buckle upon compression exhibit a variety of phase transitions as a function of temperature and extension. The results of self-consistent phonon calculations for the regular networks agree very well with computer simulations at finite temperature. The analytic theory also yields a rigidity onset (mechanical percolation) and the fraction of extended bonds for random networks. There is very good agreement with the simulations by Delaney et al (2005 Europhys. Lett. 72 990). The mean field theory reveals a nontranslationally invariant phase with self-generated heterogeneity of tautness, representing 'antiferroelasticity'.

An ANOVA approach for statistical comparisons of brain networks.

PubMed

Fraiman, Daniel; Fraiman, Ricardo

2018-03-16

The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

Suborbital Science Program

NASA Technical Reports Server (NTRS)

Vachon, Jacques; Curry, Robert E.

2010-01-01

Program Objectives: 1) Satellite Calibration and Validation: Provide methods to perform the cal/val requirements for Earth Observing System satellites. 2) New Sensor Development: Provide methods to reduce risk for new sensor concepts and algorithm development prior to committing sensors to operations. 3) Process Studies: Facilitate the acquisition of high spatial/temporal resolution focused measurements that are required to understand small atmospheric and surface structures which generate powerful Earth system effects. 4) Airborne Networking: Develop disruption-tolerant networking to enable integrated multiple scale measurements of critical environmental features. Dryden Capabilities include: a) Aeronautics history of aircraft developments and milestones. b) Extensive history and experience in instrument integration. c) Extensive history and experience in aircraft modifications. d) Strong background in international deployments. e) Long history of reliable and dependable execution of projects. f) Varied aircraft types providing different capabilities, performance and duration.

Achieving Your Vision: How to Make Sustainable Living a Reality on Campus

ERIC Educational Resources Information Center

Orr, Andrea

2010-01-01

Located on a wildlife reserve in northern California, Butte College is an environmentally conscious community, complete with an extensive solar-power program and a transportation network built to keep cars off its roads. The college generates more solar power than any other community college in the country and is a nationally recognized leader in…

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

PubMed

Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Protein interaction networks from literature mining

NASA Astrophysics Data System (ADS)

Ihara, Sigeo

2005-03-01

The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

The Virtual Research and Extension Communication Network (VRECN): An Interactive Learning and Communication Network for Research and Extension Personnel. Concept Paper for the Food & Agriculture Organisation of the United Nations (FAO).

ERIC Educational Resources Information Center

Richardson, Don

A Virtual Research and Extension Communication Network (VRECN) is a set of networked electronic tools facilitating improvement in communication processes and information sharing among stakeholders involved in agricultural development. In developing countries, research and extension personnel within a ministry of agriculture, in consultation and…

Social networks as embedded complex adaptive systems.

PubMed

Benham-Hutchins, Marge; Clancy, Thomas R

2010-09-01

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 15th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, the authors discuss healthcare social networks as a hierarchy of embedded complex adaptive systems. The authors further examine the use of social network analysis tools as a means to understand complex communication patterns and reduce medical errors.

A multi-phase network situational awareness cognitive task analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erbacher, Robert; Frincke, Deborah A.; Wong, Pak C.

Abstract The goal of our project is to create a set of next-generation cyber situational-awareness capabilities with applications to other domains in the long term. The objective is to improve the decision-making process to enable decision makers to choose better actions. To this end, we put extensive effort into making certain that we had feedback from network analysts and managers and understand what their genuine needs are. This article discusses the cognitive task-analysis methodology that we followed to acquire feedback from the analysts. This article also provides the details we acquired from the analysts on their processes, goals, concerns, themore » data and metadata that they analyze. Finally, we describe the generation of a novel task-flow diagram representing the activities of the target user base.« less

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

GeNN: a code generation framework for accelerated brain simulations

NASA Astrophysics Data System (ADS)

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-01

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

GeNN: a code generation framework for accelerated brain simulations.

PubMed

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-07

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

GeNN: a code generation framework for accelerated brain simulations

PubMed Central

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-01

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/. PMID:26740369

Cortical network reorganization guided by sensory input features.

PubMed

Kilgard, Michael P; Pandya, Pritesh K; Engineer, Navzer D; Moucha, Raluca

2002-12-01

Sensory experience alters the functional organization of cortical networks. Previous studies using behavioral training motivated by aversive or rewarding stimuli have demonstrated that cortical plasticity is specific to salient inputs in the sensory environment. Sensory experience associated with electrical activation of the basal forebrain (BasF) generates similar input specific plasticity. By directly engaging plasticity mechanisms and avoiding extensive behavioral training, BasF stimulation makes it possible to efficiently explore how specific sensory features contribute to cortical plasticity. This review summarizes our observations that cortical networks employ a variety of strategies to improve the representation of the sensory environment. Different combinations of receptive-field, temporal, and spectrotemporal plasticity were generated in primary auditory cortex neurons depending on the pitch, modulation rate, and order of sounds paired with BasF stimulation. Simple tones led to map expansion, while modulated tones altered the maximum cortical following rate. Exposure to complex acoustic sequences led to the development of combination-sensitive responses. This remodeling of cortical response characteristics may reflect changes in intrinsic cellular mechanisms, synaptic efficacy, and local neuronal connectivity. The intricate relationship between the pattern of sensory activation and cortical plasticity suggests that network-level rules alter the functional organization of the cortex to generate the most behaviorally useful representation of the sensory environment.

Generation of oscillating gene regulatory network motifs

NASA Astrophysics Data System (ADS)

van Dorp, M.; Lannoo, B.; Carlon, E.

2013-07-01

Using an improved version of an evolutionary algorithm originally proposed by François and Hakim [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.0304532101 101, 580 (2004)], we generated small gene regulatory networks in which the concentration of a target protein oscillates in time. These networks may serve as candidates for oscillatory modules to be found in larger regulatory networks and protein interaction networks. The algorithm was run for 105 times to produce a large set of oscillating modules, which were systematically classified and analyzed. The robustness of the oscillations against variations of the kinetic rates was also determined, to filter out the least robust cases. Furthermore, we show that the set of evolved networks can serve as a database of models whose behavior can be compared to experimentally observed oscillations. The algorithm found three smallest (core) oscillators in which nonlinearities and number of components are minimal. Two of those are two-gene modules: the mixed feedback loop, already discussed in the literature, and an autorepressed gene coupled with a heterodimer. The third one is a single gene module which is competitively regulated by a monomer and a dimer. The evolutionary algorithm also generated larger oscillating networks, which are in part extensions of the three core modules and in part genuinely new modules. The latter includes oscillators which do not rely on feedback induced by transcription factors, but are purely of post-transcriptional type. Analysis of post-transcriptional mechanisms of oscillation may provide useful information for circadian clock research, as recent experiments showed that circadian rhythms are maintained even in the absence of transcription.

Extension algorithm for generic low-voltage networks

NASA Astrophysics Data System (ADS)

Marwitz, S.; Olk, C.

2018-02-01

Distributed energy resources (DERs) are increasingly penetrating the energy system which is driven by climate and sustainability goals. These technologies are mostly connected to low- voltage electrical networks and change the demand and supply situation in these networks. This can cause critical network states. Network topologies vary significantly and depend on several conditions including geography, historical development, network design or number of network connections. In the past, only some of these aspects were taken into account when estimating the network investment needs for Germany on the low-voltage level. Typically, fixed network topologies are examined or a Monte Carlo approach is used to quantify the investment needs at this voltage level. Recent research has revealed that DERs differ substantially between rural, suburban and urban regions. The low-voltage network topologies have different design concepts in these regions, so that different network topologies have to be considered when assessing the need for network extensions and investments due to DERs. An extension algorithm is needed to calculate network extensions and investment needs for the different typologies of generic low-voltage networks. We therefore present a new algorithm, which is capable of calculating the extension for generic low-voltage networks of any given topology based on voltage range deviations and thermal overloads. The algorithm requires information about line and cable lengths, their topology and the network state only. We test the algorithm on a radial, a loop, and a heavily meshed network. Here we show that the algorithm functions for electrical networks with these topologies. We found that the algorithm is able to extend different networks efficiently by placing cables between network nodes. The main value of the algorithm is that it does not require any information about routes for additional cables or positions for additional substations when it comes to estimating network extension needs.

An architectural approach to create self organizing control systems for practical autonomous robots

NASA Technical Reports Server (NTRS)

Greiner, Helen

1991-01-01

For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.

Method and apparatus for automated, modular, biomass power generation

DOEpatents

Diebold, James P; Lilley, Arthur; Browne, III, Kingsbury; Walt, Robb Ray; Duncan, Dustin; Walker, Michael; Steele, John; Fields, Michael; Smith, Trevor

2013-11-05

Method and apparatus for generating a low tar, renewable fuel gas from biomass and using it in other energy conversion devices, many of which were designed for use with gaseous and liquid fossil fuels. An automated, downdraft gasifier incorporates extensive air injection into the char bed to maintain the conditions that promote the destruction of residual tars. The resulting fuel gas and entrained char and ash are cooled in a special heat exchanger, and then continuously cleaned in a filter prior to usage in standalone as well as networked power systems.

Method and apparatus for automated, modular, biomass power generation

DOEpatents

Diebold, James P [Lakewood, CO; Lilley, Arthur [Finleyville, PA; Browne, Kingsbury III [Golden, CO; Walt, Robb Ray [Aurora, CO; Duncan, Dustin [Littleton, CO; Walker, Michael [Longmont, CO; Steele, John [Aurora, CO; Fields, Michael [Arvada, CO; Smith, Trevor [Lakewood, CO

2011-03-22

Method and apparatus for generating a low tar, renewable fuel gas from biomass and using it in other energy conversion devices, many of which were designed for use with gaseous and liquid fossil fuels. An automated, downdraft gasifier incorporates extensive air injection into the char bed to maintain the conditions that promote the destruction of residual tars. The resulting fuel gas and entrained char and ash are cooled in a special heat exchanger, and then continuously cleaned in a filter prior to usage in standalone as well as networked power systems.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

PubMed Central

Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. PMID:29049295

Formal Models of the Network Co-occurrence Underlying Mental Operations.

PubMed

Bzdok, Danilo; Varoquaux, Gaël; Grisel, Olivier; Eickenberg, Michael; Poupon, Cyril; Thirion, Bertrand

2016-06-01

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition.

Formal Models of the Network Co-occurrence Underlying Mental Operations

PubMed Central

Bzdok, Danilo; Varoquaux, Gaël; Grisel, Olivier; Eickenberg, Michael; Poupon, Cyril; Thirion, Bertrand

2016-01-01

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition. PMID:27310288

Personalized recommendation via an improved NBI algorithm and user influence model in a Microblog network

NASA Astrophysics Data System (ADS)

Lian, Jie; Liu, Yun; Zhang, Zhen-jiang; Gui, Chang-ni

2013-10-01

Bipartite network based recommendations have attracted extensive attentions in recent years. Differing from traditional object-oriented recommendations, the recommendation in a Microblog network has two crucial differences. One is high authority users or one’s special friends usually play a very active role in tweet-oriented recommendation. The other is that the object in a Microblog network corresponds to a set of tweets on same topic instead of an actual and single entity, e.g. goods or movies in traditional networks. Thus repeat recommendations of the tweets in one’s collected topics are indispensable. Therefore, this paper improves network based inference (NBI) algorithm by original link matrix and link weight on resource allocation processes. This paper finally proposes the Microblog recommendation model based on the factors of improved network based inference and user influence model. Adjusting the weights of these two factors could generate the best recommendation results in algorithm accuracy and recommendation personalization.

Getting support in polarized societies: income, social networks, and socioeconomic context.

PubMed

Letki, Natalia; Mieriņa, Inta

2015-01-01

This paper explores how unequal resources and social and economic polarization affects the size of social networks and their use to access resources. We argue that individual resource position generates divergent expectations with regard to the impact of polarization on the size of networks on one hand, and their usefulness for accessing resources on the other. Social and economic polarization encourages reliance on informal networks, but those at the bottom of the social structure are forced to rely on more extensive networks than the wealthy to compensate for their isolated and underprivileged position. At the same time, social and economic polarization limits the resources the poor can access through their networks. We provide evidence consistent with these propositions, based on data derived from the International Social Survey Programme 2001 "Social Networks" dataset combined with contextual information on the levels of economic inequality in particular countries along with whether they experienced postcommunism. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Evaluation Study of a Wireless Multimedia Traffic-Oriented Network Model

NASA Astrophysics Data System (ADS)

Vasiliadis, D. C.; Rizos, G. E.; Vassilakis, C.

2008-11-01

In this paper, a wireless multimedia traffic-oriented network scheme over a fourth generation system (4-G) is presented and analyzed. We conducted an extensive evaluation study for various mobility configurations in order to incorporate the behavior of the IEEE 802.11b standard over a test-bed wireless multimedia network model. In this context, the Quality of Services (QoS) over this network is vital for providing a reliable high-bandwidth platform for data-intensive sources like video streaming. Therefore, the main issues concerned in terms of QoS were the metrics for bandwidth of both dropped and lost packets and their mean packet delay under various traffic conditions. Finally, we used a generic distance-vector routing protocol which was based on an implementation of Distributed Bellman-Ford algorithm. The performance of the test-bed network model has been evaluated by using the simulation environment of NS-2.

Predicting electrocardiogram and arterial blood pressure waveforms with different Echo State Network architectures.

PubMed

Fong, Allan; Mittu, Ranjeev; Ratwani, Raj; Reggia, James

2014-01-01

Alarm fatigue caused by false alarms and alerts is an extremely important issue for the medical staff in Intensive Care Units. The ability to predict electrocardiogram and arterial blood pressure waveforms can potentially help the staff and hospital systems better classify a patient's waveforms and subsequent alarms. This paper explores the use of Echo State Networks, a specific type of neural network for mining, understanding, and predicting electrocardiogram and arterial blood pressure waveforms. Several network architectures are designed and evaluated. The results show the utility of these echo state networks, particularly ones with larger integrated reservoirs, for predicting electrocardiogram waveforms and the adaptability of such models across individuals. The work presented here offers a unique approach for understanding and predicting a patient's waveforms in order to potentially improve alarm generation. We conclude with a brief discussion of future extensions of this research.

Mapping Extension's Networks: Using Social Network Analysis to Explore Extension's Outreach

ERIC Educational Resources Information Center

Bartholomay, Tom; Chazdon, Scott; Marczak, Mary S.; Walker, Kathrin C.

2011-01-01

The University of Minnesota Extension conducted a social network analysis (SNA) to examine its outreach to organizations external to the University of Minnesota. The study found that its outreach network was both broad in its reach and strong in its connections. The study found that SNA offers a unique method for describing and measuring Extension…

Generation Dependent Ultrafast Charge Separation and Recombination in a Pyrene-Viologen Family of Dendrons.

PubMed

Gong, Zheng; Bao, Jianhua; Nagai, Keiji; Iyoda, Tomokazu; Kawauchi, Takehiro; Piotrowiak, Piotr

2016-05-12

The ability of a dendritic network to intercept electrons and extend the lifetime of a short-lived photoinduced charge separated (CS) state was investigated in a homologous family of methyl viologen (MV(2+)) dendrons spanning four generations, G0 through G3. The CS state in the parent pyrene-methylene-viologen G0 system with a single acceptor exhibits an extremely short lifetime of τ = 0.72 ps. The expansion of the viologen network introduces slower components to the recombination kinetics by allowing the injected electron to migrate further away from the donor. The long-lived fraction of the population increases monotonically in the order G3 > G2 > G1 > G0, while the respective recombination rates decrease. In the highest generation of the dendron ∼14% of the CS state population experiences a 10-fold or greater lifetime extension. Long range tunneling across multiple viologen units and sequential site-to-site hopping both contribute to the overall effect. The large excess energy deposited in the apical viologen upon charge separation and the presence of an extended network of low lying π-orbitals likely facilitate shuttling the electron further down the dendron.

The Social Network Classroom

NASA Astrophysics Data System (ADS)

Bunus, Peter

Online social networking is an important part in the everyday life of college students. Despite the increasing popularity of online social networking among students and faculty members, its educational benefits are largely untested. This paper presents our experience in using social networking applications and video content distribution websites as a complement of traditional classroom education. In particular, the solution has been based on effective adaptation, extension and integration of Facebook, Twitter, Blogger YouTube and iTunes services for delivering educational material to students on mobile platforms like iPods and 3 rd generation mobile phones. The goals of the proposed educational platform, described in this paper, are to make the learning experience more engaging, to encourage collaborative work and knowledge sharing among students, and to provide an interactive platform for the educators to reach students and deliver lecture material in a totally new way.

Secure Multicast Tree Structure Generation Method for Directed Diffusion Using A* Algorithms

NASA Astrophysics Data System (ADS)

Kim, Jin Myoung; Lee, Hae Young; Cho, Tae Ho

The application of wireless sensor networks to areas such as combat field surveillance, terrorist tracking, and highway traffic monitoring requires secure communication among the sensor nodes within the networks. Logical key hierarchy (LKH) is a tree based key management model which provides secure group communication. When a sensor node is added or evicted from the communication group, LKH updates the group key in order to ensure the security of the communications. In order to efficiently update the group key in directed diffusion, we propose a method for secure multicast tree structure generation, an extension to LKH that reduces the number of re-keying messages by considering the addition and eviction ratios of the history data. For the generation of the proposed key tree structure the A* algorithm is applied, in which the branching factor at each level can take on different value. The experiment results demonstrate the efficiency of the proposed key tree structure against the existing key tree structures of fixed branching factors.

Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

PubMed

Jin, Ick Hoon; Yuan, Ying; Liang, Faming

2013-10-01

Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the intractable normalizing constant and model degeneracy. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the intractable normalizing constant and model degeneracy issues encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

Fine-tuning gene networks using simple sequence repeats

PubMed Central

Egbert, Robert G.; Klavins, Eric

2012-01-01

The parameters in a complex synthetic gene network must be extensively tuned before the network functions as designed. Here, we introduce a simple and general approach to rapidly tune gene networks in Escherichia coli using hypermutable simple sequence repeats embedded in the spacer region of the ribosome binding site. By varying repeat length, we generated expression libraries that incrementally and predictably sample gene expression levels over a 1,000-fold range. We demonstrate the utility of the approach by creating a bistable switch library that programmatically samples the expression space to balance the two states of the switch, and we illustrate the need for tuning by showing that the switch’s behavior is sensitive to host context. Further, we show that mutation rates of the repeats are controllable in vivo for stability or for targeted mutagenesis—suggesting a new approach to optimizing gene networks via directed evolution. This tuning methodology should accelerate the process of engineering functionally complex gene networks. PMID:22927382

Improved personalized recommendation based on a similarity network

NASA Astrophysics Data System (ADS)

Wang, Ximeng; Liu, Yun; Xiong, Fei

2016-08-01

A recommender system helps individual users find the preferred items rapidly and has attracted extensive attention in recent years. Many successful recommendation algorithms are designed on bipartite networks, such as network-based inference or heat conduction. However, most of these algorithms define the resource-allocation methods for an average allocation. That is not reasonable because average allocation cannot indicate the user choice preference and the influence between users which leads to a series of non-personalized recommendation results. We propose a personalized recommendation approach that combines the similarity function and bipartite network to generate a similarity network that improves the resource-allocation process. Our model introduces user influence into the recommender system and states that the user influence can make the resource-allocation process more reasonable. We use four different metrics to evaluate our algorithms for three benchmark data sets. Experimental results show that the improved recommendation on a similarity network can obtain better accuracy and diversity than some competing approaches.

Networked Learning for Agricultural Extension: A Framework for Analysis and Two Cases

ERIC Educational Resources Information Center

Kelly, Nick; Bennett, John McLean; Starasts, Ann

2017-01-01

Purpose: This paper presents economic and pedagogical motivations for adopting information and communications technology (ICT)- mediated learning networks in agricultural education and extension. It proposes a framework for networked learning in agricultural extension and contributes a theoretical and case-based rationale for adopting the…

A generalized locomotion CPG architecture based on oscillatory building blocks.

PubMed

Yang, Zhijun; França, Felipe M G

2003-07-01

Neural oscillation is one of the most extensively investigated topics of artificial neural networks. Scientific approaches to the functionalities of both natural and artificial intelligences are strongly related to mechanisms underlying oscillatory activities. This paper concerns itself with the assumption of the existence of central pattern generators (CPGs), which are the plausible neural architectures with oscillatory capabilities, and presents a discrete and generalized approach to the functionality of locomotor CPGs of legged animals. Based on scheduling by multiple edge reversal (SMER), a primitive and deterministic distributed algorithm, it is shown how oscillatory building block (OBB) modules can be created and, hence, how OBB-based networks can be formulated as asymmetric Hopfield-like neural networks for the generation of complex coordinated rhythmic patterns observed among pairs of biological motor neurons working during different gait patterns. It is also shown that the resulting Hopfield-like network possesses the property of reproducing the whole spectrum of different gaits intrinsic to the target locomotor CPGs. Although the new approach is not restricted to the understanding of the neurolocomotor system of any particular animal, hexapodal and quadrupedal gait patterns are chosen as illustrations given the wide interest expressed by the ongoing research in the area.

Extending Case-Based Reasoning (CBR) Approaches to Semi-automated Network Alert Reporting

DTIC Science & Technology

2013-04-01

connecting to the domain is likely infected with malware, or may have been exposed to malicious code. -- Detailed Information: The Sourcefire VRT ...to be generated by malware. After applying an extensive whitelist, the VRT pulls out the most commonly visited domains and adds them to its...malicious software. The VRT recommends ClamAV for Windows 3.0. 39 -- Contributors: Sourcefire Vulnerability Research Team -- Additional

Twitter Chats: Connect, Foster, and Engage Internal Extension Networks

ERIC Educational Resources Information Center

Seger, Jamie; Hill, Paul; Stafne, Eric; Swadley, Emy

2017-01-01

The eXtension Educational Technology Learning Network (EdTechLN) has found Twitter to be an effective form of informal communication for routinely engaging network members. Twitter chats provide Extension professionals an opportunity to reach and engage one other. As the EdTechLN's experimentation with Twitter chats has demonstrated, the use of…

Rapid molecular evolution across amniotes of the IIS/TOR network

PubMed Central

McGaugh, Suzanne E.; Bronikowski, Anne M.; Kuo, Chih-Horng; Reding, Dawn M.; Addis, Elizabeth A.; Flagel, Lex E.; Janzen, Fredric J.

2015-01-01

The insulin/insulin-like signaling and target of rapamycin (IIS/TOR) network regulates lifespan and reproduction, as well as metabolic diseases, cancer, and aging. Despite its vital role in health, comparative analyses of IIS/TOR have been limited to invertebrates and mammals. We conducted an extensive evolutionary analysis of the IIS/TOR network across 66 amniotes with 18 newly generated transcriptomes from nonavian reptiles and additional available genomes/transcriptomes. We uncovered rapid and extensive molecular evolution between reptiles (including birds) and mammals: (i) the IIS/TOR network, including the critical nodes insulin receptor substrate (IRS) and phosphatidylinositol 3-kinase (PI3K), exhibit divergent evolutionary rates between reptiles and mammals; (ii) compared with a proxy for the rest of the genome, genes of the IIS/TOR extracellular network exhibit exceptionally fast evolutionary rates; and (iii) signatures of positive selection and coevolution of the extracellular network suggest reptile- and mammal-specific interactions between members of the network. In reptiles, positively selected sites cluster on the binding surfaces of insulin-like growth factor 1 (IGF1), IGF1 receptor (IGF1R), and insulin receptor (INSR); whereas in mammals, positively selected sites clustered on the IGF2 binding surface, suggesting that these hormone-receptor binding affinities are targets of positive selection. Further, contrary to reports that IGF2R binds IGF2 only in marsupial and placental mammals, we found positively selected sites clustered on the hormone binding surface of reptile IGF2R that suggest that IGF2R binds to IGF hormones in diverse taxa and may have evolved in reptiles. These data suggest that key IIS/TOR paralogs have sub- or neofunctionalized between mammals and reptiles and that this network may underlie fundamental life history and physiological differences between these amniote sister clades. PMID:25991861

Rapid molecular evolution across amniotes of the IIS/TOR network.

PubMed

McGaugh, Suzanne E; Bronikowski, Anne M; Kuo, Chih-Horng; Reding, Dawn M; Addis, Elizabeth A; Flagel, Lex E; Janzen, Fredric J; Schwartz, Tonia S

2015-06-02

The insulin/insulin-like signaling and target of rapamycin (IIS/TOR) network regulates lifespan and reproduction, as well as metabolic diseases, cancer, and aging. Despite its vital role in health, comparative analyses of IIS/TOR have been limited to invertebrates and mammals. We conducted an extensive evolutionary analysis of the IIS/TOR network across 66 amniotes with 18 newly generated transcriptomes from nonavian reptiles and additional available genomes/transcriptomes. We uncovered rapid and extensive molecular evolution between reptiles (including birds) and mammals: (i) the IIS/TOR network, including the critical nodes insulin receptor substrate (IRS) and phosphatidylinositol 3-kinase (PI3K), exhibit divergent evolutionary rates between reptiles and mammals; (ii) compared with a proxy for the rest of the genome, genes of the IIS/TOR extracellular network exhibit exceptionally fast evolutionary rates; and (iii) signatures of positive selection and coevolution of the extracellular network suggest reptile- and mammal-specific interactions between members of the network. In reptiles, positively selected sites cluster on the binding surfaces of insulin-like growth factor 1 (IGF1), IGF1 receptor (IGF1R), and insulin receptor (INSR); whereas in mammals, positively selected sites clustered on the IGF2 binding surface, suggesting that these hormone-receptor binding affinities are targets of positive selection. Further, contrary to reports that IGF2R binds IGF2 only in marsupial and placental mammals, we found positively selected sites clustered on the hormone binding surface of reptile IGF2R that suggest that IGF2R binds to IGF hormones in diverse taxa and may have evolved in reptiles. These data suggest that key IIS/TOR paralogs have sub- or neofunctionalized between mammals and reptiles and that this network may underlie fundamental life history and physiological differences between these amniote sister clades.

The applications of deep neural networks to sdBV classification

NASA Astrophysics Data System (ADS)

Boudreaux, Thomas M.

2017-12-01

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and we show that two separate paradigms of deep learning - the Artificial Neural Network And the Convolutional Neural Network - can both be used to classify this synthetic data effectively. And that additionally this classification can be performed at relatively high levels of accuracy with minimal time spent adjusting network hyperparameters.

Inferring Phylogenetic Networks Using PhyloNet.

PubMed

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

PubMed Central

Vesperini, Fabio; Schuller, Björn

2017-01-01

In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases. PMID:28182121

Role and mechanism of the maturation cleavage of VP0 in poliovirus assembly: structure of the empty capsid assembly intermediate at 2.9 A resolution.

PubMed Central

Basavappa, R.; Syed, R.; Flore, O.; Icenogle, J. P.; Filman, D. J.; Hogle, J. M.

1994-01-01

The crystal structure of the P1/Mahoney poliovirus empty capsid has been determined at 2.9 A resolution. The empty capsids differ from mature virions in that they lack the viral RNA and have yet to undergo a stabilizing maturation cleavage of VP0 to yield the mature capsid proteins VP4 and VP2. The outer surface and the bulk of the protein shell are very similar to those of the mature virion. The major differences between the 2 structures are focused in a network formed by the N-terminal extensions of the capsid proteins on the inner surface of the shell. In the empty capsids, the entire N-terminal extension of VP1, as well as portions corresponding to VP4 and the N-terminal extension of VP2, are disordered, and many stabilizing interactions that are present in the mature virion are missing. In the empty capsid, the VP0 scissile bond is located some 20 A away from the positions in the mature virion of the termini generated by VP0 cleavage. The scissile bond is located on the rim of a trefoil-shaped depression in the inner surface of the shell that is highly reminiscent of an RNA binding site in bean pod mottle virus. The structure suggests plausible (and ultimately testable) models for the initiation of encapsidation, for the RNA-dependent autocatalytic cleavage of VP0, and for the role of the cleavage in establishing the ordered N-terminal network and in generating stable virions. PMID:7849583

A Clonal Genetic Screen for Mutants Causing Defects in Larval Tracheal Morphogenesis in Drosophila

PubMed Central

Baer, Magdalena M.; Bilstein, Andreas; Leptin, Maria

2007-01-01

The initial establishment of the tracheal network in the Drosophila embryo is beginning to be understood in great detail, both in its genetic control cascades and in its cell biological events. By contrast, the vast expansion of the system during larval growth, with its extensive ramification of preexisting tracheal branches, has been analyzed less well. The mutant phenotypes of many genes involved in this process are probably not easy to reveal, as these genes may be required for other functions at earlier developmental stages. We therefore conducted a screen for defects in individual clonal homozygous mutant cells in the tracheal network of heterozygous larvae using the mosaic analysis with a repressible cell marker (MARCM) system to generate marked, recombinant mitotic clones. We describe the identification of a set of mutants with distinct phenotypic effects. In particular we found a range of defects in terminal cells, including failure in lumen formation and reduced or extensive branching. Other mutations affect cell growth, cell shape, and cell migration. PMID:17603107

Energy efficient flexible hybrid wavelength division multiplexing-time division multiplexing passive optical network with pay as you grow deployment

NASA Astrophysics Data System (ADS)

Garg, Amit Kumar; Madavi, Amresh Ashok; Janyani, Vijay

2017-02-01

A flexible hybrid wavelength division multiplexing-time division multiplexing passive optical network architecture that allows dual rate signals to be sent at 1 and 10 Gbps to each optical networking unit depending upon the traffic load is proposed. The proposed design allows dynamic wavelength allocation with pay-as-you-grow deployment capability. This architecture is capable of providing up to 40 Gbps of equal data rates to all optical distribution networks (ODNs) and up to 70 Gbps of a asymmetrical data rate to the specific ODN. The proposed design handles broadcasting capability with simultaneous point-to-point transmission, which further reduces energy consumption. In this architecture, each module sends a wavelength to each ODN, thus making the architecture fully flexible; this flexibility allows network providers to use only required OLT components and switch off others. The design is also reliable to any module or TRx failure and provides services without any service disruption. Dynamic wavelength allocation and pay-as-you-grow deployment support network extensibility and bandwidth scalability to handle future generation access networks.

FNV: light-weight flash-based network and pathway viewer.

PubMed

Dannenfelser, Ruth; Lachmann, Alexander; Szenk, Mariola; Ma'ayan, Avi

2011-04-15

Network diagrams are commonly used to visualize biochemical pathways by displaying the relationships between genes, proteins, mRNAs, microRNAs, metabolites, regulatory DNA elements, diseases, viruses and drugs. While there are several currently available web-based pathway viewers, there is still room for improvement. To this end, we have developed a flash-based network viewer (FNV) for the visualization of small to moderately sized biological networks and pathways. Written in Adobe ActionScript 3.0, the viewer accepts simple Extensible Markup Language (XML) formatted input files to display pathways in vector graphics on any web-page providing flexible layout options, interactivity with the user through tool tips, hyperlinks and the ability to rearrange nodes on the screen. FNV was utilized as a component in several web-based systems, namely Genes2Networks, Lists2Networks, KEA, ChEA and PathwayGenerator. In addition, FVN can be used to embed pathways inside pdf files for the communication of pathways in soft publication materials. FNV is available for use and download along with the supporting documentation and sample networks at http://www.maayanlab.net/FNV. avi.maayan@mssm.edu.

Transport link scanner: simulating geographic transport network expansion through individual investments

NASA Astrophysics Data System (ADS)

Jacobs-Crisioni, C.; Koopmans, C. C.

2016-07-01

This paper introduces a GIS-based model that simulates the geographic expansion of transport networks by several decision-makers with varying objectives. The model progressively adds extensions to a growing network by choosing the most attractive investments from a limited choice set. Attractiveness is defined as a function of variables in which revenue and broader societal benefits may play a role and can be based on empirically underpinned parameters that may differ according to private or public interests. The choice set is selected from an exhaustive set of links and presumably contains those investment options that best meet private operator's objectives by balancing the revenues of additional fare against construction costs. The investment options consist of geographically plausible routes with potential detours. These routes are generated using a fine-meshed regularly latticed network and shortest path finding methods. Additionally, two indicators of the geographic accuracy of the simulated networks are introduced. A historical case study is presented to demonstrate the model's first results. These results show that the modelled networks reproduce relevant results of the historically built network with reasonable accuracy.

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

PubMed

Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

2017-07-15

There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity. Copyright © 2017 Elsevier Inc. All rights reserved.

Sensory-evoked perturbations of locomotor activity by sparse sensory input: a computational study

PubMed Central

Brownstone, Robert M.

2015-01-01

Sensory inputs from muscle, cutaneous, and joint afferents project to the spinal cord, where they are able to affect ongoing locomotor activity. Activation of sensory input can initiate or prolong bouts of locomotor activity depending on the identity of the sensory afferent activated and the timing of the activation within the locomotor cycle. However, the mechanisms by which afferent activity modifies locomotor rhythm and the distribution of sensory afferents to the spinal locomotor networks have not been determined. Considering the many sources of sensory inputs to the spinal cord, determining this distribution would provide insights into how sensory inputs are integrated to adjust ongoing locomotor activity. We asked whether a sparsely distributed set of sensory inputs could modify ongoing locomotor activity. To address this question, several computational models of locomotor central pattern generators (CPGs) that were mechanistically diverse and generated locomotor-like rhythmic activity were developed. We show that sensory inputs restricted to a small subset of the network neurons can perturb locomotor activity in the same manner as seen experimentally. Furthermore, we show that an architecture with sparse sensory input improves the capacity to gate sensory information by selectively modulating sensory channels. These data demonstrate that sensory input to rhythm-generating networks need not be extensively distributed. PMID:25673740

Experimental demonstration of OSPF-TE extensions in muiti-domain OBS networks connected by GMPLS network

NASA Astrophysics Data System (ADS)

Tian, Chunlei; Yin, Yawei; Wu, Jian; Lin, Jintong

2008-11-01

The interworking network of Generalized Multi-Protocol Label Switching (GMPLS) and Optical Burst Switching (OBS) is attractive network architecture for the future IP/DWDM network nowadays. In this paper, OSPF-TE extensions for multi-domain Optical Burst Switching networks connected by GMPLS controlled WDM network are proposed, the corresponding experimental results such as the advertising latency are also presented by using an OBS network testbed. The experimental results show that it works effectively on the OBS/GMPLS networks.

A note on adding viscoelasticity to earthquake simulators

USGS Publications Warehouse

Pollitz, Fred

2017-01-01

Here, I describe how time‐dependent quasi‐static stress transfer can be implemented in an earthquake simulator code that is used to generate long synthetic seismicity catalogs. Most existing seismicity simulators use precomputed static stress interaction coefficients to rapidly implement static stress transfer in fault networks with typically tens of thousands of fault patches. The extension to quasi‐static deformation, which accounts for viscoelasticity of Earth’s ductile lower crust and mantle, involves the precomputation of additional interaction coefficients that represent time‐dependent stress transfer among the model fault patches, combined with defining and evolving additional state variables that track this stress transfer. The new approach is illustrated with application to a California‐wide synthetic fault network.

3-D components of a biological neural network visualized in computer generated imagery. I - Macular receptive field organization

NASA Technical Reports Server (NTRS)

Ross, Muriel D.; Cutler, Lynn; Meyer, Glenn; Lam, Tony; Vaziri, Parshaw

1990-01-01

Computer-assisted, 3-dimensional reconstructions of macular receptive fields and of their linkages into a neural network have revealed new information about macular functional organization. Both type I and type II hair cells are included in the receptive fields. The fields are rounded, oblong, or elongated, but gradations between categories are common. Cell polarizations are divergent. Morphologically, each calyx of oblong and elongated fields appears to be an information processing site. Intrinsic modulation of information processing is extensive and varies with the kind of field. Each reconstructed field differs in detail from every other, suggesting that an element of randomness is introduced developmentally and contributes to endorgan adaptability.

Complex network view of evolving manifolds

NASA Astrophysics Data System (ADS)

da Silva, Diamantino C.; Bianconi, Ginestra; da Costa, Rui A.; Dorogovtsev, Sergey N.; Mendes, José F. F.

2018-03-01

We study complex networks formed by triangulations and higher-dimensional simplicial complexes representing closed evolving manifolds. In particular, for triangulations, the set of possible transformations of these networks is restricted by the condition that at each step, all the faces must be triangles. Stochastic application of these operations leads to random networks with different architectures. We perform extensive numerical simulations and explore the geometries of growing and equilibrium complex networks generated by these transformations and their local structural properties. This characterization includes the Hausdorff and spectral dimensions of the resulting networks, their degree distributions, and various structural correlations. Our results reveal a rich zoo of architectures and geometries of these networks, some of which appear to be small worlds while others are finite dimensional with Hausdorff dimension equal or higher than the original dimensionality of their simplices. The range of spectral dimensions of the evolving triangulations turns out to be from about 1.4 to infinity. Our models include simplicial complexes representing manifolds with evolving topologies, for example, an h -holed torus with a progressively growing number of holes. This evolving graph demonstrates features of a small-world network and has a particularly heavy-tailed degree distribution.

Stochastic approximation methods-Powerful tools for simulation and optimization: A survey of some recent work on multi-agent systems and cyber-physical systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yin, George; Wang, Le Yi; Zhang, Hongwei

2014-12-10

Stochastic approximation methods have found extensive and diversified applications. Recent emergence of networked systems and cyber-physical systems has generated renewed interest in advancing stochastic approximation into a general framework to support algorithm development for information processing and decisions in such systems. This paper presents a survey on some recent developments in stochastic approximation methods and their applications. Using connected vehicles in platoon formation and coordination as a platform, we highlight some traditional and new methodologies of stochastic approximation algorithms and explain how they can be used to capture essential features in networked systems. Distinct features of networked systems with randomlymore » switching topologies, dynamically evolving parameters, and unknown delays are presented, and control strategies are provided.« less

Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks.

PubMed

Lin, Haifeng; Bai, Di; Gao, Demin; Liu, Yunfei

2016-07-30

In Rechargeable Wireless Sensor Networks (R-WSNs), in order to achieve the maximum data collection rate it is critical that sensors operate in very low duty cycles because of the sporadic availability of energy. A sensor has to stay in a dormant state in most of the time in order to recharge the battery and use the energy prudently. In addition, a sensor cannot always conserve energy if a network is able to harvest excessive energy from the environment due to its limited storage capacity. Therefore, energy exploitation and energy saving have to be traded off depending on distinct application scenarios. Since higher data collection rate or maximum data collection rate is the ultimate objective for sensor deployment, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving the data generating rate in R-WSNs. In this work, we propose an algorithm based on data aggregation to compute an upper data generation rate by maximizing it as an optimization problem for a network, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. At the same time, a topology controlling scheme is adopted for improving the network's performance. Through extensive simulation and experiments, we demonstrate that our algorithm is efficient at maximizing the data collection rate in rechargeable wireless sensor networks.

Biological network extraction from scientific literature: state of the art and challenges.

PubMed

Li, Chen; Liakata, Maria; Rebholz-Schuhmann, Dietrich

2014-09-01

Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

ITEP: an integrated toolkit for exploration of microbial pan-genomes.

PubMed

Benedict, Matthew N; Henriksen, James R; Metcalf, William W; Whitaker, Rachel J; Price, Nathan D

2014-01-03

Comparative genomics is a powerful approach for studying variation in physiological traits as well as the evolution and ecology of microorganisms. Recent technological advances have enabled sequencing large numbers of related genomes in a single project, requiring computational tools for their integrated analysis. In particular, accurate annotations and identification of gene presence and absence are critical for understanding and modeling the cellular physiology of newly sequenced genomes. Although many tools are available to compare the gene contents of related genomes, new tools are necessary to enable close examination and curation of protein families from large numbers of closely related organisms, to integrate curation with the analysis of gain and loss, and to generate metabolic networks linking the annotations to observed phenotypes. We have developed ITEP, an Integrated Toolkit for Exploration of microbial Pan-genomes, to curate protein families, compute similarities to externally-defined domains, analyze gene gain and loss, and generate draft metabolic networks from one or more curated reference network reconstructions in groups of related microbial species among which the combination of core and variable genes constitute the their "pan-genomes". The ITEP toolkit consists of: (1) a series of modular command-line scripts for identification, comparison, curation, and analysis of protein families and their distribution across many genomes; (2) a set of Python libraries for programmatic access to the same data; and (3) pre-packaged scripts to perform common analysis workflows on a collection of genomes. ITEP's capabilities include de novo protein family prediction, ortholog detection, analysis of functional domains, identification of core and variable genes and gene regions, sequence alignments and tree generation, annotation curation, and the integration of cross-genome analysis and metabolic networks for study of metabolic network evolution. ITEP is a powerful, flexible toolkit for generation and curation of protein families. ITEP's modular design allows for straightforward extension as analysis methods and tools evolve. By integrating comparative genomics with the development of draft metabolic networks, ITEP harnesses the power of comparative genomics to build confidence in links between genotype and phenotype and helps disambiguate gene annotations when they are evaluated in both evolutionary and metabolic network contexts.

HBCUs Research Conference Agenda and Abstracts

NASA Technical Reports Server (NTRS)

Dutta, Sunil (Compiler)

1997-01-01

The purpose of this Historically Black Colleges and Universities (HBCUS) Research Conference was to provide an opportunity for principal investigators and their students to present research progress reports. The abstracts included in this report indicate the range and quality of research topics such as aeropropulsion, space propulsion, space power, fluid dynamics, designs, structures and materials being funded through grants from Lewis Research Center to HBCUS. The conference generated extensive networking between students, principal investigators, Lewis technical monitors, and other Lewis researchers.

HBCUs Research Conference Agenda and Abstracts

NASA Technical Reports Server (NTRS)

Dutta, Sunil (Compiler)

1998-01-01

The purpose of this Historically Black Colleges and Universities (HBCUs) Research Conference was to provide an opportunity for principal investigators and their students to present research progress reports. The abstracts included in this report indicate the range and quality of research topics such as aeropropulsion, space propulsion, space power, fluid dynamics, designs, structures and materials being funded through grants from Lewis Research Center to HBCUs. The conference generated extensive networking between students, principal investigators, Lewis technical monitors, and other Lewis researchers.

HBCUs Research Conference agenda and abstracts

NASA Technical Reports Server (NTRS)

Dutta, Sunil (Compiler)

1995-01-01

The purpose of this Historically Black Colleges and Universities (HBCUs) Research conference was to provide an opportunity for principal investigators and their students to present research progress reports. The abstracts included in this report indicate the range and quality of research topics such as aeropropulsion, space propulsion, space power, fluid dynamics, designs, structures and materials being funded through grants from Lewis Research Center to HBCUs. The conference generated extensive networking between students, principal investigators, Lewis technical monitors, and other Lewis researchers.

XCEDE: An Extensible Schema For Biomedical Data

PubMed Central

Gadde, Syam; Aucoin, Nicole; Grethe, Jeffrey S.; Keator, David B.; Marcus, Daniel S.; Pieper, Steve

2013-01-01

The XCEDE (XML-based Clinical and Experimental Data Exchange) XML schema, developed by members of the BIRN (Biomedical Informatics Research Network), provides an extensive metadata hierarchy for storing, describing and documenting the data generated by scientific studies. Currently at version 2.0, the XCEDE schema serves as a specification for the exchange of scientific data between databases, analysis tools, and web services. It provides a structured metadata hierarchy, storing information relevant to various aspects of an experiment (project, subject, protocol, etc.). Each hierarchy level also provides for the storage of data provenance information allowing for a traceable record of processing and/or changes to the underlying data. The schema is extensible to support the needs of various data modalities and to express types of data not originally envisioned by the developers. The latest version of the XCEDE schema and manual are available from http://www.xcede.org/ PMID:21479735

Supporting EarthScope Cyber-Infrastructure with a Modern GPS Science Data System

NASA Astrophysics Data System (ADS)

Webb, F. H.; Bock, Y.; Kedar, S.; Jamason, P.; Fang, P.; Dong, D.; Owen, S. E.; Prawirodirjo, L.; Squibb, M.

2008-12-01

Building on NASA's investment in the measurement of crustal deformation from continuous GPS, we are developing and implementing a Science Data System (SDS) that will provide mature, long-term Earth Science Data Records (ESDR's). This effort supports NASA's Earth Surface and Interiors (ESI) focus area and provide NASA's component to the EarthScope PBO. This multi-year development is sponsored by NASA's Making Earth System data records for Use in Research Environments (MEaSUREs) program. The SDS integrates the generation of ESDRs with data analysis and exploration, product generation, and modeling tools based on daily GPS data that include GPS networks in western North America and a component of NASA's Global GPS Network (GGN) for terrestrial reference frame definition. The system is expandable to multiple regional and global networks. The SDS builds upon mature data production, exploration, and analysis algorithms developed under NASA's REASoN, ACCESS, and SENH programs. This SDS provides access to positions, time series, velocity fields, and strain measurements derived from continuous GPS data obtained at tracking stations in both the Plate Boundary Observatory and other regional Western North America GPS networks, dating back to 1995. The SDS leverages the IT and Web Services developments carried out under the SCIGN/REASoN and ACCESS projects, which have streamlined access to data products for researchers and modelers, and which have created a prototype an on-the-fly interactive research environment through a modern data portal, GPS Explorer. This IT system has been designed using modern IT tools and principles in order to be extensible to any geographic location, scale, natural hazard, and combination of geophysical sensor and related data. We have built upon open GIS standards, particularly those of the OGC, and have used the principles of Web Service-based Service Oriented Architectures to provide scalability and extensibility to new services and capabilities.

Filtering in Hybrid Dynamic Bayesian Networks

NASA Technical Reports Server (NTRS)

Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin

2000-01-01

We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).

Network evolution induced by asynchronous stimuli through spike-timing-dependent plasticity.

PubMed

Yuan, Wu-Jie; Zhou, Jian-Fang; Zhou, Changsong

2013-01-01

In sensory neural system, external asynchronous stimuli play an important role in perceptual learning, associative memory and map development. However, the organization of structure and dynamics of neural networks induced by external asynchronous stimuli are not well understood. Spike-timing-dependent plasticity (STDP) is a typical synaptic plasticity that has been extensively found in the sensory systems and that has received much theoretical attention. This synaptic plasticity is highly sensitive to correlations between pre- and postsynaptic firings. Thus, STDP is expected to play an important role in response to external asynchronous stimuli, which can induce segregative pre- and postsynaptic firings. In this paper, we study the impact of external asynchronous stimuli on the organization of structure and dynamics of neural networks through STDP. We construct a two-dimensional spatial neural network model with local connectivity and sparseness, and use external currents to stimulate alternately on different spatial layers. The adopted external currents imposed alternately on spatial layers can be here regarded as external asynchronous stimuli. Through extensive numerical simulations, we focus on the effects of stimulus number and inter-stimulus timing on synaptic connecting weights and the property of propagation dynamics in the resulting network structure. Interestingly, the resulting feedforward structure induced by stimulus-dependent asynchronous firings and its propagation dynamics reflect both the underlying property of STDP. The results imply a possible important role of STDP in generating feedforward structure and collective propagation activity required for experience-dependent map plasticity in developing in vivo sensory pathways and cortices. The relevance of the results to cue-triggered recall of learned temporal sequences, an important cognitive function, is briefly discussed as well. Furthermore, this finding suggests a potential application for examining STDP by measuring neural population activity in a cultured neural network.

Fault-tolerant bandwidth reservation strategies for data transfers in high-performance networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zuo, Liudong; Zhu, Michelle M.; Wu, Chase Q.

2016-11-22

Many next-generation e-science applications need fast and reliable transfer of large volumes of data with guaranteed performance, which is typically enabled by the bandwidth reservation service in high-performance networks. One prominent issue in such network environments with large footprints is that node and link failures are inevitable, hence potentially degrading the quality of data transfer. We consider two generic types of bandwidth reservation requests (BRRs) concerning data transfer reliability: (i) to achieve the highest data transfer reliability under a given data transfer deadline, and (ii) to achieve the earliest data transfer completion time while satisfying a given data transfer reliabilitymore » requirement. We propose two periodic bandwidth reservation algorithms with rigorous optimality proofs to optimize the scheduling of individual BRRs within BRR batches. The efficacy of the proposed algorithms is illustrated through extensive simulations in comparison with scheduling algorithms widely adopted in production networks in terms of various performance metrics.« less

Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

PubMed

Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

2016-12-01

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

Motorized CPM/CAM physiotherapy device with sliding-mode Fuzzy Neural Network control loop.

PubMed

Ho, Hung-Jung; Chen, Tien-Chi

2009-11-01

Continuous passive motion (CPM) and controllable active motion (CAM) physiotherapy devices promote rehabilitation of damaged joints. This paper presents a computerized CPM/CAM system that obviates the need for mechanical resistance devices such as springs. The system is controlled by a computer which performs sliding-mode Fuzzy Neural Network (FNN) calculations online. CAM-type resistance force is generated by the active performance of an electric motor which is controlled so as to oppose the motion of the patient's leg. A force sensor under the patient's foot on the device pedal provides data for feedback in a sliding-mode FNN control loop built around the motor. Via an active impedance control feedback system, the controller drives the motor to behave similarly to a damped spring by generating and controlling the amplitude and direction of the pedal force in relation to the patient's leg. Experiments demonstrate the high sensitivity and speed of the device. The PC-based feedback nature of the control loop means that sophisticated auto-adaptable CPM/CAM custom-designed physiotherapy becomes possible. The computer base also allows extensive data recording, data analysis and network-connected remote patient monitoring.

Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints

PubMed Central

Cheng, Jianjun; Leng, Mingwei; Li, Longjie; Zhou, Hanhai; Chen, Xiaoyun

2014-01-01

Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods. PMID:25329660

From grid cells to place cells with realistic field sizes

PubMed Central

2017-01-01

While grid cells in the medial entorhinal cortex (MEC) of rodents have multiple, regularly arranged firing fields, place cells in the cornu ammonis (CA) regions of the hippocampus mostly have single spatial firing fields. Since there are extensive projections from MEC to the CA regions, many models have suggested that a feedforward network can transform grid cell firing into robust place cell firing. However, these models generate place fields that are consistently too small compared to those recorded in experiments. Here, we argue that it is implausible that grid cell activity alone can be transformed into place cells with robust place fields of realistic size in a feedforward network. We propose two solutions to this problem. Firstly, weakly spatially modulated cells, which are abundant throughout EC, provide input to downstream place cells along with grid cells. This simple model reproduces many place cell characteristics as well as results from lesion studies. Secondly, the recurrent connections between place cells in the CA3 network generate robust and realistic place fields. Both mechanisms could work in parallel in the hippocampal formation and this redundancy might account for the robustness of place cell responses to a range of disruptions of the hippocampal circuitry. PMID:28750005

Performance evaluation of secured DICOM image communication with next generation internet protocol IPv6

NASA Astrophysics Data System (ADS)

Yu, Fenghai; Zhang, Jianguo; Chen, Xiaomeng; Huang, H. K.

2005-04-01

Next Generation Internet (NGI) technology with new communication protocol IPv6 emerges as a potential solution for low-cost and high-speed networks for image data transmission. IPv6 is designed to solve many of the problems of the current version of IP (known as IPv4) with regard to address depletion, security, autoconfiguration, extensibility, and more. We choose CTN (Central Test Node) DICOM software developed by The Mallinckrodt Institute of Radiology to implement IPv6/IPv4 enabled DICOM communication software on different operating systems (Windows/Linux), and used this DICOM software to evaluate the performance of the IPv6/IPv4 enabled DICOM image communication with different security setting and environments. We compared the security communications of IPsec with SSL/TLS on different TCP/IP protocols (IPv6/IPv4), and find that there are some trade-offs to choose security solution between IPsec and SSL/TLS in the security implementation of IPv6/IPv4 communication networks.

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

PubMed

Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

2018-01-01

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

Applications of artificial neural networks in medical science.

PubMed

Patel, Jigneshkumar L; Goyal, Ramesh K

2007-09-01

Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

An intermediate level of abstraction for computational systems chemistry.

PubMed

Andersen, Jakob L; Flamm, Christoph; Merkle, Daniel; Stadler, Peter F

2017-12-28

Computational techniques are required for narrowing down the vast space of possibilities to plausible prebiotic scenarios, because precise information on the molecular composition, the dominant reaction chemistry and the conditions for that era are scarce. The exploration of large chemical reaction networks is a central aspect in this endeavour. While quantum chemical methods can accurately predict the structures and reactivities of small molecules, they are not efficient enough to cope with large-scale reaction systems. The formalization of chemical reactions as graph grammars provides a generative system, well grounded in category theory, at the right level of abstraction for the analysis of large and complex reaction networks. An extension of the basic formalism into the realm of integer hyperflows allows for the identification of complex reaction patterns, such as autocatalysis, in large reaction networks using optimization techniques.This article is part of the themed issue 'Reconceptualizing the origins of life'. © 2017 The Author(s).

Data-centric multiobjective QoS-aware routing protocol for body sensor networks.

PubMed

Razzaque, Md Abdur; Hong, Choong Seon; Lee, Sungwon

2011-01-01

In this paper, we address Quality-of-Service (QoS)-aware routing issue for Body Sensor Networks (BSNs) in delay and reliability domains. We propose a data-centric multiobjective QoS-Aware routing protocol, called DMQoS, which facilitates the system to achieve customized QoS services for each traffic category differentiated according to the generated data types. It uses modular design architecture wherein different units operate in coordination to provide multiple QoS services. Their operation exploits geographic locations and QoS performance of the neighbor nodes and implements a localized hop-by-hop routing. Moreover, the protocol ensures (almost) a homogeneous energy dissipation rate for all routing nodes in the network through a multiobjective Lexicographic Optimization-based geographic forwarding. We have performed extensive simulations of the proposed protocol, and the results show that DMQoS has significant performance improvements over several state-of-the-art approaches.

A secure file manager for UNIX

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeVries, R.G.

1990-12-31

The development of a secure file management system for a UNIX-based computer facility with supercomputers and workstations is described. Specifically, UNIX in its usual form does not address: (1) Operation which would satisfy rigorous security requirements. (2) Online space management in an environment where total data demands would be many times the actual online capacity. (3) Making the file management system part of a computer network in which users of any computer in the local network could retrieve data generated on any other computer in the network. The characteristics of UNIX can be exploited to develop a portable, secure filemore » manager which would operate on computer systems ranging from workstations to supercomputers. Implementation considerations making unusual use of UNIX features, rather than requiring extensive internal system changes, are described, and implementation using the Cray Research Inc. UNICOS operating system is outlined.« less

Perspectives and limitations of QKD integration in metropolitan area networks.

PubMed

Aleksic, Slavisa; Hipp, Florian; Winkler, Dominic; Poppe, Andreas; Schrenk, Bernhard; Franzl, Gerald

2015-04-20

Quantum key distribution (QKD) systems have already reached a reasonable level of maturity. However, a smooth integration and a wide adoption of commercial QKD systems in metropolitan area networks has still remained challenging because of technical and economical obstacles. Mainly the need for dedicated fibers and the strong dependence of the secret key rate on both loss budget and background noise in the quantum channel hinder a practical, flexible and robust implementation of QKD in current and next-generation optical metro networks. In this paper, we discuss these obstacles and present approaches to share existing fiber infrastructures among quantum and classical channels. Particularly, a proposal for a smooth integration of QKD in optical metro networks, which implies removing spurious background photons caused by optical transmitters, amplifiers and nonlinear effects in fibers, is presented and discussed. We determine and characterize impairments on quantum channels caused by many classical telecom channels at practically used power levels coexisting within the same fiber. Extensive experimental results are presented and indicate that a practical integration of QKD in conventional optical metro networks is possible.

HBCUs/OMUs Research Conference Agenda and Abstracts

NASA Technical Reports Server (NTRS)

Dutta, Sunil (Compiler)

2000-01-01

The purpose of this Historically Black Colleges and Universities (HBCUs) Research Conference was to provide an opportunity for principal investigators and their students to present research progress reports. The Abstracts included in this report indicate the range and quality of research topics such as aeropropulsion, space propulsion, space power, fluid dynamics, designs, structures and materials being funded through grants from Glenn Research Center to HBCUs. The conference generated extensive networking between students, principal investigators, Glenn technical monitors, and other Glenn researchers.

HBCUs/OMUs Research Conference Agenda and Abstracts

NASA Technical Reports Server (NTRS)

Dutta, Sunil (Compiler)

2003-01-01

The purpose of this Historically Black Colleges and Universities (HBCUs/OMUs) Research Conference was to provide an opportunity for principal investigators and their students to present research progress reports. The abstracts included in this report indicate the range and quality of research topics such as aeropropulsion, space propulsion, space power, fluid dynamics, designs, structures and materials being funded through grants from Glenn Research Center to HBCUs. The conference generated extensive networking between students, principal investigators, Glenn technical monitors, and other Glenn researchers.

HBCUs/OMUs Research Conference Agenda and Abstracts

NASA Technical Reports Server (NTRS)

Dutta, Sunil (Compiler)

2001-01-01

The purpose of this Historically Black Colleges and Universities (HBCUs) Research Conference was to provide an opportunity for principal investigators and their students to present research progress reports. The abstracts included in this report indicate the range and quality of research topics such as aeropropulsion, space propulsion, space power, fluid dynamics, designs, structures and materials being funded through grants from Glenn Research Center to HBCUs. The conference generated extensive networking between students, principal investigators, Glenn technical monitors, and other Glenn researchers.

On Human Resource Diversity in Distributed Energy Technology

NASA Technical Reports Server (NTRS)

Kalu, A.; Emrich, C.; Ventre, G.; Acosta, Roberto J.

2003-01-01

The purpose of this Historically Black Colleges and Universities (HBCUs/OMUs) Research Conference was to provide an opportunity for principal investigators and their students to present research progress reports. The abstracts included in this report indicate the range and quality of research topics such as aeropropulsion, space propulsion, space power, fluid dynamics, designs, structures and materials being funded through grants from Glenn Research Center to HBCUs. The conference generated extensive networking between students, principal investigators, Glenn technical monitors, and other Glenn researchers.

Definition, analysis and development of an optical data distribution network for integrated avionics and control systems

NASA Technical Reports Server (NTRS)

Burns, R. R.

1981-01-01

The potential and functional requirements of fiber optic bus designs for next generation aircraft are assessed. State-of-the-art component evaluations and projections were used in the system study. Complex networks were decomposed into dedicated structures, star buses, and serial buses for detailed analysis. Comparisons of dedicated links, star buses, and serial buses with and without full duplex operation and with considerations for terminal to terminal communication requirements were obtained. This baseline was then used to consider potential extensions of busing methods to include wavelength multiplexing and optical switches. Example buses were illustrated for various areas of the aircraft as potential starting points for more detail analysis as the platform becomes definitized.

A Petri Net model for distributed energy system

NASA Astrophysics Data System (ADS)

Konopko, Joanna

2015-12-01

Electrical networks need to evolve to become more intelligent, more flexible and less costly. The smart grid is the next generation power energy, uses two-way flows of electricity and information to create a distributed automated energy delivery network. Building a comprehensive smart grid is a challenge for system protection, optimization and energy efficient. Proper modeling and analysis is needed to build an extensive distributed energy system and intelligent electricity infrastructure. In this paper, the whole model of smart grid have been proposed using Generalized Stochastic Petri Nets (GSPN). The simulation of created model is also explored. The simulation of the model has allowed the analysis of how close the behavior of the model is to the usage of the real smart grid.

Citizen Science to Support Community-based Flood Early Warning and Resilience Building

NASA Astrophysics Data System (ADS)

Paul, J. D.; Buytaert, W.; Allen, S.; Ballesteros-Cánovas, J. A.; Bhusal, J.; Cieslik, K.; Clark, J.; Dewulf, A.; Dhital, M. R.; Hannah, D. M.; Liu, W.; Nayaval, J. L.; Schiller, A.; Smith, P. J.; Stoffel, M.; Supper, R.

2017-12-01

In Disaster Risk Management, an emerging shift has been noted from broad-scale, top-down assessments towards more participatory, community-based, bottom-up approaches. Combined with technologies for robust and low-cost sensor networks, a citizen science approach has recently emerged as a promising direction in the provision of extensive, real-time information for flood early warning systems. Here we present the framework and initial results of a major new international project, Landslide EVO, aimed at increasing local resilience against hydrologically induced disasters in western Nepal by exploiting participatory approaches to knowledge generation and risk governance. We identify three major technological developments that strongly support our approach to flood early warning and resilience building in Nepal. First, distributed sensor networks, participatory monitoring, and citizen science hold great promise in complementing official monitoring networks and remote sensing by generating site-specific information with local buy-in, especially in data-scarce regions. Secondly, the emergence of open source, cloud-based risk analysis platforms supports the construction of a modular, distributed, and potentially decentralised data processing workflow. Finally, linking data analysis platforms to social computer networks and ICT (e.g. mobile phones, tablets) allows tailored interfaces and people-centred decision- and policy-support systems to be built. Our proposition is that maximum impact is created if end-users are involved not only in data collection, but also over the entire project life-cycle, including the analysis and provision of results. In this context, citizen science complements more traditional knowledge generation practices, and also enhances multi-directional information provision, risk management, early-warning systems and local resilience building.

Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks

PubMed Central

Lin, Haifeng; Bai, Di; Gao, Demin; Liu, Yunfei

2016-01-01

In Rechargeable Wireless Sensor Networks (R-WSNs), in order to achieve the maximum data collection rate it is critical that sensors operate in very low duty cycles because of the sporadic availability of energy. A sensor has to stay in a dormant state in most of the time in order to recharge the battery and use the energy prudently. In addition, a sensor cannot always conserve energy if a network is able to harvest excessive energy from the environment due to its limited storage capacity. Therefore, energy exploitation and energy saving have to be traded off depending on distinct application scenarios. Since higher data collection rate or maximum data collection rate is the ultimate objective for sensor deployment, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving the data generating rate in R-WSNs. In this work, we propose an algorithm based on data aggregation to compute an upper data generation rate by maximizing it as an optimization problem for a network, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. At the same time, a topology controlling scheme is adopted for improving the network’s performance. Through extensive simulation and experiments, we demonstrate that our algorithm is efficient at maximizing the data collection rate in rechargeable wireless sensor networks. PMID:27483282

Coordinated Scheduling for Interdependent Electric Power and Natural Gas Infrastructures

DOE PAGES

Zlotnik, Anatoly; Roald, Line; Backhaus, Scott; ...

2016-03-24

The extensive installation of gas-fired power plants in many parts of the world has led electric systems to depend heavily on reliable gas supplies. The use of gas-fired generators for peak load and reserve provision causes high intraday variability in withdrawals from high-pressure gas transmission systems. Such variability can lead to gas price fluctuations and supply disruptions that affect electric generator dispatch, electricity prices, and threaten the security of power systems and gas pipelines. These infrastructures function on vastly different spatio-temporal scales, which prevents current practices for separate operations and market clearing from being coordinated. Here in this article, wemore » apply new techniques for control of dynamic gas flows on pipeline networks to examine day-ahead scheduling of electric generator dispatch and gas compressor operation for different levels of integration, spanning from separate forecasting, and simulation to combined optimal control. We formulate multiple coordination scenarios and develop tractable physically accurate computational implementations. These scenarios are compared using an integrated model of test networks for power and gas systems with 24 nodes and 24 pipes, respectively, which are coupled through gas-fired generators. The analysis quantifies the economic efficiency and security benefits of gas-electric coordination and dynamic gas system operation.« less

Extension Disaster Education Network (EDEN): Preparing Families for Disaster

ERIC Educational Resources Information Center

Washburn, Carolyn; Saunders, Kristine

2010-01-01

According to the American Red Cross (n.d.), less than half of Americans have an emergency preparedness plan in place. Therefore, it is critical that the Cooperative Extension System takes a role in encouraging the development of family preparedness plans. The Extension Disaster Education Network (EDEN) has developed a family and consumer sciences…

A community resource benchmarking predictions of peptide binding to MHC-I molecules.

PubMed

Peters, Bjoern; Bui, Huynh-Hoa; Frankild, Sune; Nielson, Morten; Lundegaard, Claus; Kostem, Emrah; Basch, Derek; Lamberth, Kasper; Harndahl, Mikkel; Fleri, Ward; Wilson, Stephen S; Sidney, John; Lund, Ole; Buus, Soren; Sette, Alessandro

2006-06-09

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation.

PubMed

Xue, Yuan; Xu, Tao; Zhang, Han; Long, L Rodney; Huang, Xiaolei

2018-05-03

Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L 1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic is trained by maximizing a multi-scale loss function, while the segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

Advancing Nucleosynthesis in Core-Collapse Supernovae Models Using 2D CHIMERA Simulations

NASA Astrophysics Data System (ADS)

Harris, J. A.; Hix, W. R.; Chertkow, M. A.; Bruenn, S. W.; Lentz, E. J.; Messer, O. B.; Mezzacappa, A.; Blondin, J. M.; Marronetti, P.; Yakunin, K.

2014-01-01

The deaths of massive stars as core-collapse supernovae (CCSN) serve as a crucial link in understanding galactic chemical evolution since the birth of the universe via the Big Bang. We investigate CCSN in polar axisymmetric simulations using the multidimensional radiation hydrodynamics code CHIMERA. Computational costs have traditionally constrained the evolution of the nuclear composition in CCSN models to, at best, a 14-species α-network. However, the limited capacity of the α-network to accurately evolve detailed composition, the neutronization and the nuclear energy generation rate has fettered the ability of prior CCSN simulations to accurately reproduce the chemical abundances and energy distributions as known from observations. These deficits can be partially ameliorated by "post-processing" with a more realistic network. Lagrangian tracer particles placed throughout the star record the temporal evolution of the initial simulation and enable the extension of the nuclear network evolution by incorporating larger systems in post-processing nucleosynthesis calculations. We present post-processing results of the four ab initio axisymmetric CCSN 2D models of Bruenn et al. (2013) evolved with the smaller α-network, and initiated from stellar metallicity, non-rotating progenitors of mass 12, 15, 20, and 25 M⊙ from Woosley & Heger (2007). As a test of the limitations of post-processing, we provide preliminary results from an ongoing simulation of the 15 M⊙ model evolved with a realistic 150 species nuclear reaction network in situ. With more accurate energy generation rates and an improved determination of the thermodynamic trajectories of the tracer particles, we can better unravel the complicated multidimensional "mass-cut" in CCSN simulations and probe for less energetically significant nuclear processes like the νp-process and the r-process, which require still larger networks.

A study of knowledge supernetworks and network robustness in different business incubators

NASA Astrophysics Data System (ADS)

Zhang, Haihong; Wu, Wenqing; Zhao, Liming

2016-04-01

As the most important intangible resource of the new generation of business incubators, knowledge has been studied extensively, particularly with respect to how it spreads among incubating firms through knowledge networks. However, these homogeneous networks do not adequately describe the heterogeneity of incubating firms in different types of business incubators. To solve the problem of heterogeneity, the notion of a knowledge supernetwork has been used both to construct a knowledge interaction model among incubating firms and to distinguish social network relationships from knowledge network relationships. The process of knowledge interaction and network evolution can then be simulated with a few rules for incubating firms regarding knowledge innovation/absorption, social network connection, and entry and exit, among other aspects. Knowledge and networks have been used as performance indicators to evaluate the evolution of knowledge supernetworks. Moreover, we study the robustness of incubating firms' social networks by employing four types of attack strategies. Based on our simulation results, we conclude that there have been significant knowledge interaction and network evolution among incubating firms on a periodic basis and that both specialized and diversified business incubators have every advantage necessary in terms of both knowledge and networks to cultivate start-up companies. As far as network robustness is concerned, there is no obvious difference between the two types of business incubators with respect to the stability of their network structures, but specialized business incubators have stronger network communication abilities than diversified business incubators.

FTP Extensions for Variable Protocol Specification

NASA Technical Reports Server (NTRS)

Allman, Mark; Ostermann, Shawn

2000-01-01

The specification for the File Transfer Protocol (FTP) assumes that the underlying network protocols use a 32-bit network address and a 16-bit transport address (specifically IP version 4 and TCP). With the deployment of version 6 of the Internet Protocol, network addresses will no longer be 32-bits. This paper species extensions to FTP that will allow the protocol to work over a variety of network and transport protocols.

True Randomness from Big Data.

PubMed

Papakonstantinou, Periklis A; Woodruff, David P; Yang, Guang

2016-09-26

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests.

True Randomness from Big Data

NASA Astrophysics Data System (ADS)

Papakonstantinou, Periklis A.; Woodruff, David P.; Yang, Guang

2016-09-01

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests.

True Randomness from Big Data

PubMed Central

Papakonstantinou, Periklis A.; Woodruff, David P.; Yang, Guang

2016-01-01

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests. PMID:27666514

A Computational Framework for 3D Mechanical Modeling of Plant Morphogenesis with Cellular Resolution

PubMed Central

Gilles, Benjamin; Hamant, Olivier; Boudaoud, Arezki; Traas, Jan; Godin, Christophe

2015-01-01

The link between genetic regulation and the definition of form and size during morphogenesis remains largely an open question in both plant and animal biology. This is partially due to the complexity of the process, involving extensive molecular networks, multiple feedbacks between different scales of organization and physical forces operating at multiple levels. Here we present a conceptual and modeling framework aimed at generating an integrated understanding of morphogenesis in plants. This framework is based on the biophysical properties of plant cells, which are under high internal turgor pressure, and are prevented from bursting because of the presence of a rigid cell wall. To control cell growth, the underlying molecular networks must interfere locally with the elastic and/or plastic extensibility of this cell wall. We present a model in the form of a three dimensional (3D) virtual tissue, where growth depends on the local modulation of wall mechanical properties and turgor pressure. The model shows how forces generated by turgor-pressure can act both cell autonomously and non-cell autonomously to drive growth in different directions. We use simulations to explore lateral organ formation at the shoot apical meristem. Although different scenarios lead to similar shape changes, they are not equivalent and lead to different, testable predictions regarding the mechanical and geometrical properties of the growing lateral organs. Using flower development as an example, we further show how a limited number of gene activities can explain the complex shape changes that accompany organ outgrowth. PMID:25569615

Geologic Seafloor Mapping Defines Extensive Paleochannel Network Offshore of the Delmarva Peninsula, U.S.A: Implications for Mid-Atlantic Bight Evolution since the Pliocene

NASA Astrophysics Data System (ADS)

Brothers, L. L.; Foster, D. S.; Pendleton, E. A.; Thieler, E. R.; Baldwin, W. E.; Sweeney, E. M.

2017-12-01

Nearly 10,000 km of geophysical data and seafloor grab samples along with photo and video data from more than 200 seafloor stations are used to interpret seafloor and shallow subsurface geology on the Delmarva Peninsula's inner continental shelf. These USGS data are supplemented with existing National Oceanic Atmospheric Administration hydrographic survey data and Bureau of Ocean Energy Management Wind Energy Area seismic reflection profile data to support one of the most data-rich and extensive inner continental shelf studies on the U.S. Atlantic coast. Using chirp, multi-channel boomer and sparker seismic reflection profile data, we map an extensive paleochannel network from 500 meters to 30 kilometers offshore of the modern Delmarva coastline. Fluvial erosional surfaces relating to six sea-level lowstands are identified at two-way travel times between 0.01 and 0.12 ms. Paleochannels exhibit up to 30 meters of relief and the discrete complexes can be >25 kilometers wide. Based on areal distribution, stratigraphic relationships and amino acid dating results from earlier borehole studies, we interpret the infilled channels as Late Tertiary and Quaternary courses of the Delaware, Susquehanna, Potomac and York Rivers. Our study generates a detailed illustration of major river systems' paleochannel frequency, distribution and geometry and provides new insight into how coastal river systems evolve in low-gradient passive margins.

A Complex Network Approach to Distributional Semantic Models

PubMed Central

Utsumi, Akira

2015-01-01

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

Where-Fi: a dynamic energy-efficient multimedia distribution framework for MANETs

NASA Astrophysics Data System (ADS)

Mohapatra, Shivajit; Carbunar, Bogdan; Pearce, Michael; Chaudhri, Rohit; Vasudevan, Venu

2008-01-01

Next generation mobile ad-hoc applications will revolve around users' need for sharing content/presence information with co-located devices. However, keeping such information fresh requires frequent meta-data exchanges, which could result in significant energy overheads. To address this issue, we propose distributed algorithms for energy efficient dissemination of presence and content usage information between nodes in mobile ad-hoc networks. First, we introduce a content dissemination protocol (called CPMP) for effectively distributing frequent small meta-data updates between co-located devices using multicast. We then develop two distributed algorithms that use the CPMP protocol to achieve "phase locked" wake up cycles for all the participating nodes in the network. The first algorithm is designed for fully-connected networks and then extended in the second to handle hidden terminals. The "phase locked" schedules are then exploited to adaptively transition the network interface to a deep sleep state for energy savings. We have implemented a prototype system (called "Where-Fi") on several Motorola Linux-based cell phone models. Our experimental results show that for all network topologies our algorithms were able to achieve "phase locking" between nodes even in the presence of hidden terminals. Moreover, we achieved battery lifetime extensions of as much as 28% for fully connected networks and about 20% for partially connected networks.

A Petri Net model for distributed energy system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konopko, Joanna

2015-12-31

Electrical networks need to evolve to become more intelligent, more flexible and less costly. The smart grid is the next generation power energy, uses two-way flows of electricity and information to create a distributed automated energy delivery network. Building a comprehensive smart grid is a challenge for system protection, optimization and energy efficient. Proper modeling and analysis is needed to build an extensive distributed energy system and intelligent electricity infrastructure. In this paper, the whole model of smart grid have been proposed using Generalized Stochastic Petri Nets (GSPN). The simulation of created model is also explored. The simulation of themore » model has allowed the analysis of how close the behavior of the model is to the usage of the real smart grid.« less

Maximum Interconnectedness and Availability for Directional Airborne Range Extension Networks

DTIC Science & Technology

2016-08-29

2 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS I. INTRODUCTION Tactical military networks both on land and at sea often have restricted transmission ...ranges due to limits on terminal transmission power , geographic features that block line-of-sight, and poor over-the-horizon signal propagation...IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 1 Maximum Interconnectedness and Availability for Directional Airborne Range Extension Networks Thomas

OpenFlow Extensions for Programmable Quantum Networks

DTIC Science & Technology

2017-06-19

Extensions for Programmable Quantum Networks by Venkat Dasari, Nikolai Snow, and Billy Geerhart Computational and Information Sciences Directorate...distribution is unlimited. 1 1. Introduction Quantum networks and quantum computing have been receiving a surge of interest recently.1–3 However, there has...communicate using entangled particles and perform calculations using quantum logic gates. Additionally, quantum computing uses a quantum bit (qubit

Cutting the Wires: Modularization of Cellular Networks for Experimental Design

PubMed Central

Lang, Moritz; Summers, Sean; Stelling, Jörg

2014-01-01

Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future. PMID:24411264

Optimization of an FPGA Trigger Based on an Artificial Neural Network for the Detection of Neutrino-Induced Air Showers

NASA Astrophysics Data System (ADS)

Szadkowski, Zbigniew; Głas, Dariusz; Pytel, Krzysztof; Wiedeński, Michał

2017-06-01

Neutrinos play a fundamental role in the understanding of the origin of ultrahigh-energy cosmic rays. They interact through charged and neutral currents in the atmosphere generating extensive air showers. However, the very low rate of events potentially generated by neutrinos is a significant challenge for detection techniques and requires both sophisticated algorithms and high-resolution hardware. Air showers initiated by protons and muon neutrinos at various altitudes, angles, and energies were simulated in CORSIKA and the Auger OffLine event reconstruction platforms, giving analog-to-digital convertor (ADC) patterns in Auger water Cherenkov detectors on the ground. The proton interaction cross section is high, so proton “old” showers start their development early in the atmosphere. In contrast to this, neutrinos can generate “young” showers deeply in the atmosphere relatively close to the detectors. Differences between “old” proton and “young” neutrino showers are visible in attenuation factors of ADC waveforms. For the separation of “old” proton and “young” neutrino ADC traces, many three-layer artificial neural networks (ANNs) were tested. They were trained in MATLAB (in a dedicated way -only “old” proton and “young” neutrino showers as patterns) by simulated ADC traces according to the Levenberg-Marquardt algorithm. Unexpectedly, the recognition efficiency is found to be almost independent of the size of the networks. The ANN trigger based on a selected 8-6-1 network was tested in the Cyclone V E FPGA 5CEFA9F31I7, the heart of prototype front-end boards developed for testing new algorithms in the Pierre Auger surface detectors.

Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems.

PubMed

Ihme, Matthias; Marsden, Alison L; Pitsch, Heinz

2008-02-01

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 x 500 grid point discretization of the parameter space.

Physiological pathways regulating the activity of magnocellular neurosecretory cells.

PubMed

Leng, G; Brown, C H; Russell, J A

1999-04-01

Magnocellular oxytocin and vasopressin cells are among the most extensively studied neurons in the brain; their large size and high synthetic capacity, their discrete, homogeneous distribution and the anatomical separation of their terminals from their cell bodies, and the ability to determine their neuronal output readily by measurements of hormone concentration in the plasma, combine to make these systems amenable to a wide range of fundamental investigations. While vasopressin cells have intrinsic burst-generating properties, oxytocin cells are organized within local pattern-generating networks. In this review we consider the rôle played by particular afferent pathways in the regulation of the activity of oxytocin and vasopressin cells. For both cell types, the effects of changes in the activity of synaptic input can be complex.

Topological Transitions in Mitochondrial Membranes controlled by Apoptotic Proteins

NASA Astrophysics Data System (ADS)

Hwee Lai, Ghee; Sanders, Lori K.; Mishra, Abhijit; Schmidt, Nathan W.; Wong, Gerard C. L.; Ivashyna, Olena; Schlesinger, Paul H.

2010-03-01

The Bcl-2 family comprises pro-apoptotic proteins, capable of permeabilizing the mitochondrial membrane, and anti-apoptotic members interacting in an antagonistic fashion to regulate programmed cell death (apoptosis). They offer potential therapeutic targets to re-engage cellular suicide in tumor cells but the extensive network of implicated protein-protein interactions has impeded full understanding of the decision pathway. We show, using synchrotron x-ray diffraction, that pro-apoptotic proteins interact with mitochondrial-like model membranes to generate saddle-splay (negative Gaussian) curvature topologically required for pore formation, while anti-apoptotic proteins can deactivate curvature generation by molecules drastically different from Bcl-2 family members and offer evidence for membrane-curvature mediated interactions general enough to affect very disparate systems.

Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks

NASA Astrophysics Data System (ADS)

Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.

2012-04-01

Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.

The prevention and control of avian influenza: the avian influenza coordinated agriculture project.

PubMed

Cardona, C; Slemons, R; Perez, D

2009-04-01

The Avian Influenza Coordinated Agriculture Project (AICAP) entitled "Prevention and Control of Avian Influenza in the US" strives to be a significant point of reference for the poultry industry and the general public in matters related to the biology, risks associated with, and the methods used to prevent and control avian influenza. To this end, AICAP has been remarkably successful in generating research data, publications through an extensive network of university- and agency-based researchers, and extending findings to stakeholders. An overview of the highlights of AICAP research is presented.

NASA's mobile satellite development program

NASA Technical Reports Server (NTRS)

Rafferty, William; Dessouky, Khaled; Sue, Miles

1988-01-01

A Mobile Satellite System (MSS) will provide data and voice communications over a vast geographical area to a large population of mobile users. A technical overview is given of the extensive research and development studies and development performed under NASA's mobile satellite program (MSAT-X) in support of the introduction of a U.S. MSS. The critical technologies necessary to enable such a system are emphasized: vehicle antennas, modulation and coding, speech coders, networking and propagation characterization. Also proposed is a first, and future generation MSS architecture based upon realized ground segment equipment and advanced space segment studies.

Historical voices of resistance: crossing boundaries to praxis through documentary filmmaking for the public.

PubMed

Kagan, Paula N

2009-01-01

This article contextualizes my forthcoming study of a particular instance of resistance in nursing history, the Cassandra Radical Feminist Nurses Network, and examines how nursing history can be produced as public media to advance progressive ideas about nurses and transformative and emancipatory nursing and healthcare. It argues that nurse-generated documentary filmmaking is a natural extension of theory and practice, linking several disciplinary and conceptual fields to support a praxis that is situated at the intersection of nursing, critical theory, and the humanities.

Correlation mapping microscopy

NASA Astrophysics Data System (ADS)

McGrath, James; Alexandrov, Sergey; Owens, Peter; Subhash, Hrebesh M.; Leahy, Martin J.

2015-03-01

Changes in the microcirculation are associated with conditions such as Raynauds disease. Current modalities used to assess the microcirculation such as nailfold capillaroscopy are limited due to their depth ambiguity. A correlation mapping technique was recently developed to extend the capabilities of Optical Coherence Tomography to generate depth resolved images of the microcirculation. Here we present the extension of this technique to microscopy modalities, including confocal microscopy. It is shown that this correlation mapping microscopy technique can extend the capabilities of conventional microscopy to enable mapping of vascular networks in vivo with high spatial resolution.

Detecting causality in policy diffusion processes.

PubMed

Grabow, Carsten; Macinko, James; Silver, Diana; Porfiri, Maurizio

2016-08-01

A universal question in network science entails learning about the topology of interaction from collective dynamics. Here, we address this question by examining diffusion of laws across US states. We propose two complementary techniques to unravel determinants of this diffusion process: information-theoretic union transfer entropy and event synchronization. In order to systematically investigate their performance on law activity data, we establish a new stochastic model to generate synthetic law activity data based on plausible networks of interactions. Through extensive parametric studies, we demonstrate the ability of these methods to reconstruct networks, varying in size, link density, and degree heterogeneity. Our results suggest that union transfer entropy should be preferred for slowly varying processes, which may be associated with policies attending to specific local problems that occur only rarely or with policies facing high levels of opposition. In contrast, event synchronization is effective for faster enactment rates, which may be related to policies involving Federal mandates or incentives. This study puts forward a data-driven toolbox to explain the determinants of legal activity applicable to political science, across dynamical systems, information theory, and complex networks.

Practical End-to-End Performance Testing Tool for High Speed 3G-Based Networks

NASA Astrophysics Data System (ADS)

Shinbo, Hiroyuki; Tagami, Atsushi; Ano, Shigehiro; Hasegawa, Toru; Suzuki, Kenji

High speed IP communication is a killer application for 3rd generation (3G) mobile systems. Thus 3G network operators should perform extensive tests to check whether expected end-to-end performances are provided to customers under various environments. An important objective of such tests is to check whether network nodes fulfill requirements to durations of processing packets because a long duration of such processing causes performance degradation. This requires testers (persons who do tests) to precisely know how long a packet is hold by various network nodes. Without any tool's help, this task is time-consuming and error prone. Thus we propose a multi-point packet header analysis tool which extracts and records packet headers with synchronized timestamps at multiple observation points. Such recorded packet headers enable testers to calculate such holding durations. The notable feature of this tool is that it is implemented on off-the shelf hardware platforms, i.e., lap-top personal computers. The key challenges of the implementation are precise clock synchronization without any special hardware and a sophisticated header extraction algorithm without any drop.

Energy-efficient rings mechanism for greening multisegment fiber-wireless access networks

NASA Astrophysics Data System (ADS)

Gong, Xiaoxue; Guo, Lei; Hou, Weigang; Zhang, Lincong

2013-07-01

Through integrating advantages of optical and wireless communications, the Fiber-Wireless (FiWi) has become a promising solution for the "last-mile" broadband access. In particular, greening FiWi has attained extensive attention, because the access network is a main energy contributor in the whole infrastructure. However, prior solutions of greening FiWi shut down or sleep unused/minimally used optical network units for a single segment, where we deploy only one optical linear terminal. We propose a green mechanism referred to as energy-efficient ring (EER) for multisegment FiWi access networks. We utilize an integer linear programming model and a generic algorithm to generate clusters, each having the shortest distance of fully connected segments of its own. Leveraging the backtracking method for each cluster, we then connect segments through fiber links, and the shortest distance fiber ring is constructed. Finally, we sleep low load segments and forward affected traffic to other active segments on the same fiber ring by our sleeping scheme. Experimental results show that our EER mechanism significantly reduces the energy consumption at the slightly additional cost of deploying fiber links.

Detecting causality in policy diffusion processes

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Macinko, James; Silver, Diana; Porfiri, Maurizio

2016-08-01

A universal question in network science entails learning about the topology of interaction from collective dynamics. Here, we address this question by examining diffusion of laws across US states. We propose two complementary techniques to unravel determinants of this diffusion process: information-theoretic union transfer entropy and event synchronization. In order to systematically investigate their performance on law activity data, we establish a new stochastic model to generate synthetic law activity data based on plausible networks of interactions. Through extensive parametric studies, we demonstrate the ability of these methods to reconstruct networks, varying in size, link density, and degree heterogeneity. Our results suggest that union transfer entropy should be preferred for slowly varying processes, which may be associated with policies attending to specific local problems that occur only rarely or with policies facing high levels of opposition. In contrast, event synchronization is effective for faster enactment rates, which may be related to policies involving Federal mandates or incentives. This study puts forward a data-driven toolbox to explain the determinants of legal activity applicable to political science, across dynamical systems, information theory, and complex networks.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Warfighter Information Network - Tactical (WIN-T) Increment 2: Second Follow-on Operational Test and Evaluation

DTIC Science & Technology

2015-05-01

HNW line-of-sight network is mounted on a 10-meter telescoping mast located just aft of the TCN’s cab. The flat plate Range Throughput Extension Kit... TAC – Tactical Command Post ATH – At-the-Halt PoP – Point of Presence SNE – Soldier Network Extension NOSC – Network Operations & Security...Survivability/Lethality Analysis Directorate (ARL/SLAD) conducted a Cooperative Vulnerability and Penetration Assessment on WIN-T Increment 2. The Army

An Evolutionary Game Theory Model of Spontaneous Brain Functioning.

PubMed

Madeo, Dario; Talarico, Agostino; Pascual-Leone, Alvaro; Mocenni, Chiara; Santarnecchi, Emiliano

2017-11-22

Our brain is a complex system of interconnected regions spontaneously organized into distinct networks. The integration of information between and within these networks is a continuous process that can be observed even when the brain is at rest, i.e. not engaged in any particular task. Moreover, such spontaneous dynamics show predictive value over individual cognitive profile and constitute a potential marker in neurological and psychiatric conditions, making its understanding of fundamental importance in modern neuroscience. Here we present a theoretical and mathematical model based on an extension of evolutionary game theory on networks (EGN), able to capture brain's interregional dynamics by balancing emulative and non-emulative attitudes among brain regions. This results in the net behavior of nodes composing resting-state networks identified using functional magnetic resonance imaging (fMRI), determining their moment-to-moment level of activation and inhibition as expressed by positive and negative shifts in BOLD fMRI signal. By spontaneously generating low-frequency oscillatory behaviors, the EGN model is able to mimic functional connectivity dynamics, approximate fMRI time series on the basis of initial subset of available data, as well as simulate the impact of network lesions and provide evidence of compensation mechanisms across networks. Results suggest evolutionary game theory on networks as a new potential framework for the understanding of human brain network dynamics.

Interior Western U. S. Deformation Coming into Focus with Maturing Survey and Continuous GPS Networks

NASA Astrophysics Data System (ADS)

Thatcher, W. R.; Svarc, J. L.

2010-12-01

Active Basin and Range (BR) extension produces spectacular fault-generated topography spread over ~800 km in the interior western US. However, present-day deformation rates are relatively low east of the San Andreas fault system and Cascadia subduction zone and ± 1 mm/yr precision in GPS velocity is needed to pinpoint the major faults that accommodate extension. Typically, 3 years of continuous (CGPS) operation and 8 years of sparsely repeated survey (SGPS) network occupations are required to attain this precision. U. S. Geological Survey (USGS) survey networks and Plate Boundary Observatory (PBO) continuous station records are now long enough to meet these requirements. USGS and PBO station coverage is generally complementary. PBO coverage established since 2005 within the ~1000 km x 1500 km deforming zone is relatively complete at ~100 km station spacing; USGS survey-mode profiles established during 1999-2003 are dense; typical station spacing is ~25 km and results are sufficiently precise to determine localized velocity gradients of 1 mm/yr or greater (over ~25-50 km distances) across mapped active strike-slip and normal faults traversed by the SGPS profiles. The great majority of geologically young faults appear to slip at rates less than 1 mm/yr but rates are measurably higher near the western and eastern edges of the BR. There is a marked transition in NE California from strike-slip faulting at rates of ~ 4 mm/yr across the northern Walker Lane zone to pure extension north of about Mt. Lassen. This distinct boundary is apparently related to the prevalence of strike-slip tractions on the San Andreas plate boundary south of the Mendocino triple junction (MTJ) to tensile stresses caused by Cascadia slab retreat north of the MTJ. A horizontal extension rate of 3 mm/yr is observed across the north-striking Hat Creek and related normal faults immediately north of Lassen, but this extension decreases to no more than 1 mm/yr in the Klamath Basin, about 150 km to the north. Extension rates could be as high as ~1 mm/yr across the Surprise Valley fault (near the California-Nevada border) and the Steens Mountain-Pueblo Mountains fault (SE Oregon). But elsewhere in Oregon, Idaho and Montana, extension rates are < 1 mm/yr and only on the eastern edge of the BR, across the Wasatch and related faults in central Utah, do rates reach 3 mm/yr.

CD-loop Extension in Zika Virus Envelope Protein Key for Stability and Pathogenesis.

PubMed

Gallichotte, Emily N; Dinnon, Kenneth H; Lim, Xin-Ni; Ng, Thiam-Seng; Lim, Elisa X Y; Menachery, Vineet D; Lok, Shee-Mei; Baric, Ralph S

2017-12-05

With severe disease manifestations including microcephaly, congenital malformation, and Guillain-Barré syndrome, Zika virus (ZIKV) remains a persistent global public health threat. Despite antigenic similarities with dengue viruses, structural studies have suggested the extended CD-loop and hydrogen-bonding interaction network within the ZIKV envelope protein contribute to stability differences between the viral families. This enhanced stability may lead to the augmented infection, disease manifestation, and persistence in body fluids seen following ZIKV infection. To examine the role of these motifs in infection, we generated a series of ZIKV recombinant viruses that disrupted the hydrogen-bonding network (350A, 351A, and 350A/351A) or the CD-loop extension (Δ346). Our results demonstrate a key role for the ZIKV extended CD-loop in cell-type-dependent replication, virion stability, and in vivo pathogenesis. Importantly, the Δ346 mutant maintains similar antigenicity to wild-type virus, opening the possibility for its use as a live-attenuated vaccine platform for ZIKV and other clinically relevant flaviviruses. © The Author(s) 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Hybrid generative-discriminative approach to age-invariant face recognition

NASA Astrophysics Data System (ADS)

Sajid, Muhammad; Shafique, Tamoor

2018-03-01

Age-invariant face recognition is still a challenging research problem due to the complex aging process involving types of facial tissues, skin, fat, muscles, and bones. Most of the related studies that have addressed the aging problem are focused on generative representation (aging simulation) or discriminative representation (feature-based approaches). Designing an appropriate hybrid approach taking into account both the generative and discriminative representations for age-invariant face recognition remains an open problem. We perform a hybrid matching to achieve robustness to aging variations. This approach automatically segments the eyes, nose-bridge, and mouth regions, which are relatively less sensitive to aging variations compared with the rest of the facial regions that are age-sensitive. The aging variations of age-sensitive facial parts are compensated using a demographic-aware generative model based on a bridged denoising autoencoder. The age-insensitive facial parts are represented by pixel average vector-based local binary patterns. Deep convolutional neural networks are used to extract relative features of age-sensitive and age-insensitive facial parts. Finally, the feature vectors of age-sensitive and age-insensitive facial parts are fused to achieve the recognition results. Extensive experimental results on morphological face database II (MORPH II), face and gesture recognition network (FG-NET), and Verification Subset of cross-age celebrity dataset (CACD-VS) demonstrate the effectiveness of the proposed method for age-invariant face recognition well.

Trapping in scale-free networks with hierarchical organization of modularity.

PubMed

Zhang, Zhongzhi; Lin, Yuan; Gao, Shuyang; Zhou, Shuigeng; Guan, Jihong; Li, Mo

2009-11-01

A wide variety of real-life networks share two remarkable generic topological properties: scale-free behavior and modular organization, and it is natural and important to study how these two features affect the dynamical processes taking place on such networks. In this paper, we investigate a simple stochastic process--trapping problem, a random walk with a perfect trap fixed at a given location, performed on a family of hierarchical networks that exhibit simultaneously striking scale-free and modular structure. We focus on a particular case with the immobile trap positioned at the hub node having the largest degree. Using a method based on generating functions, we determine explicitly the mean first-passage time (MFPT) for the trapping problem, which is the mean of the node-to-trap first-passage time over the entire network. The exact expression for the MFPT is calculated through the recurrence relations derived from the special construction of the hierarchical networks. The obtained rigorous formula corroborated by extensive direct numerical calculations exhibits that the MFPT grows algebraically with the network order. Concretely, the MFPT increases as a power-law function of the number of nodes with the exponent much less than 1. We demonstrate that the hierarchical networks under consideration have more efficient structure for transport by diffusion in contrast with other analytically soluble media including some previously studied scale-free networks. We argue that the scale-free and modular topologies are responsible for the high efficiency of the trapping process on the hierarchical networks.

Dependency-based Siamese long short-term memory network for learning sentence representations

PubMed Central

Zhu, Wenhao; Ni, Jianyue; Wei, Baogang; Lu, Zhiguo

2018-01-01

Textual representations play an important role in the field of natural language processing (NLP). The efficiency of NLP tasks, such as text comprehension and information extraction, can be significantly improved with proper textual representations. As neural networks are gradually applied to learn the representation of words and phrases, fairly efficient models of learning short text representations have been developed, such as the continuous bag of words (CBOW) and skip-gram models, and they have been extensively employed in a variety of NLP tasks. Because of the complex structure generated by the longer text lengths, such as sentences, algorithms appropriate for learning short textual representations are not applicable for learning long textual representations. One method of learning long textual representations is the Long Short-Term Memory (LSTM) network, which is suitable for processing sequences. However, the standard LSTM does not adequately address the primary sentence structure (subject, predicate and object), which is an important factor for producing appropriate sentence representations. To resolve this issue, this paper proposes the dependency-based LSTM model (D-LSTM). The D-LSTM divides a sentence representation into two parts: a basic component and a supporting component. The D-LSTM uses a pre-trained dependency parser to obtain the primary sentence information and generate supporting components, and it also uses a standard LSTM model to generate the basic sentence components. A weight factor that can adjust the ratio of the basic and supporting components in a sentence is introduced to generate the sentence representation. Compared with the representation learned by the standard LSTM, the sentence representation learned by the D-LSTM contains a greater amount of useful information. The experimental results show that the D-LSTM is superior to the standard LSTM for sentences involving compositional knowledge (SICK) data. PMID:29513748

Optimizing the network topology of block copolymer liquid crystal elastomers for enhanced extensibility and toughness

NASA Astrophysics Data System (ADS)

Nowak, Christian; Escobedo, Fernando A.

2017-08-01

Molecular simulations are used to study the effect of synthesis conditions on the tensile response of liquid-crystalline elastomers formed by block copolymer chains. Remarkably, it is found that despite the significant presence of trapped entanglements, these networks can exhibit the sawtooth tensile response previously predicted for ideal unentangled networks. It is also found that the monomer concentration during crosslinking can be tuned to limit the extent of entanglements and inhomogeneities while also maximizing network extensibility. It is predicted that networks synthesized at a "critical" concentration will have the greatest toughness.

Neural network classification of myoelectric signal for prosthesis control.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1991-12-01

An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions. Copyright © 1991. Published by Elsevier Ltd.

The relevance of network micro-structure for neural dynamics.

PubMed

Pernice, Volker; Deger, Moritz; Cardanobile, Stefano; Rotter, Stefan

2013-01-01

The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previous studies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neurons in recurrent networks. However, typically very simple random network models are considered in such studies. Here we use a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much more variable than commonly used network models, and which therefore promise to sample the space of recurrent networks in a more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology in simulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive dataset of networks and neuronal simulations we assess statistical relations between features of the network structure and the spiking activity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics of both single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistent relations between activity characteristics like spike-train irregularity or correlations and network properties, for example the distributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that it is possible to estimate structural characteristics of the network from activity data. We also assess higher order correlations of spiking activity in the various networks considered here, and find that their occurrence strongly depends on the network structure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpret spike train recordings from neural circuits.

Revalidation of the Score for Neonatal Acute Physiology in the Vermont Oxford Network.

PubMed

Zupancic, John A F; Richardson, Douglas K; Horbar, Jeffrey D; Carpenter, Joseph H; Lee, Shoo K; Escobar, Gabriel J

2007-01-01

Our specific objectives were (1) to document the performance of the revised Score for Neonatal Acute Physiology and the revised Score for Neonatal Acute Physiology Perinatal Extension in predicting death in the Vermont Oxford Network, compared with published normative values; (2) to determine whether this performance could be improved through recalibration of the weights for individual score items; (3) to determine the impact of including congenital anomalies in the predictive model; and (4) to compare performance against that of the Vermont Oxford Network risk adjustment, separately and in combination. Fifty-eight Vermont Oxford Network centers collected data prospectively for the revised Score for Neonatal Acute Physiology in the first 12 hours after admission of infants in 2002. Data were collected for 10,469 infants, and analyses were undertaken for 9897 who met inclusion criteria. The median revised Score for Neonatal Acute Physiology was 5, and the mean birth weight was 1951 g. Recalibration of the revised Score for Neonatal Acute Physiology and revised Score for Neonatal Acute Physiology Perinatal Extension resulted in minimal changes in their discriminatory abilities. The Vermont Oxford Network risk adjustment performed similarly, compared with the revised Score for Neonatal Acute Physiology Perinatal Extension. Current score performance was similar to that observed previously, which suggests that the revised Score for Neonatal Acute Physiology and revised Score for Neonatal Acute Physiology Perinatal Extension have not decalibrated over the 7 years since the first cohort was assembled, despite advances in neonatal care during that period. Addition of congenital anomalies to the revised Score for Neonatal Acute Physiology Perinatal Extension improved discrimination significantly, particularly for infants with birth weights of >1500 g. The Vermont Oxford Network risk adjustment performed similarly, compared with the revised Score for Neonatal Acute Physiology Perinatal Extension.

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa

NASA Astrophysics Data System (ADS)

Hwang, Sohyun; Kim, Chan Yeong; Ji, Sun-Gou; Go, Junhyeok; Kim, Hanhae; Yang, Sunmo; Kim, Hye Jin; Cho, Ara; Yoon, Sang Sun; Lee, Insuk

2016-05-01

Pseudomonas aeruginosa is a Gram-negative bacterium of clinical significance. Although the genome of PAO1, a prototype strain of P. aeruginosa, has been extensively studied, approximately one-third of the functional genome remains unknown. With the emergence of antibiotic-resistant strains of P. aeruginosa, there is an urgent need to develop novel antibiotic and anti-virulence strategies, which may be facilitated by an approach that explores P. aeruginosa gene function in systems-level models. Here, we present a genome-wide functional network of P. aeruginosa genes, PseudomonasNet, which covers 98% of the coding genome, and a companion web server to generate functional hypotheses using various network-search algorithms. We demonstrate that PseudomonasNet-assisted predictions can effectively identify novel genes involved in virulence and antibiotic resistance. Moreover, an antibiotic-resistance network based on PseudomonasNet reveals that P. aeruginosa has common modular genetic organisations that confer increased or decreased resistance to diverse antibiotics, which accounts for the pervasiveness of cross-resistance across multiple drugs. The same network also suggests that P. aeruginosa has developed mechanism of trade-off in resistance across drugs by altering genetic interactions. Taken together, these results clearly demonstrate the usefulness of a genome-scale functional network to investigate pathogenic systems in P. aeruginosa.

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

NASA Technical Reports Server (NTRS)

Knox, W. Bradley; Mengshoel, Ole

2009-01-01

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

New Constraints on Upper Mantle Structure Underlying the Diamondiferous Central Slave Craton, Canada, from Teleseismic Body Wave Tomography

NASA Astrophysics Data System (ADS)

Esteve, C.; Schaeffer, A. J.; Audet, P.

2017-12-01

Over the past number of decades, the Slave Craton (Canada) has been extensively studied for its diamondiferous kimberlites. Not only are diamonds a valuable resource, but their kimberlitic host rocks provide an otherwise unique direct source of information on the deep upper mantle (and potentially transition zone). Many of the Canadian Diamond mines are located within the Slave Craton. As a result of the propensity for diamondiferous kimberlites, it is imperative to probe the deep mantle structure beneath the Slave Craton. This work is further motivated by the increase in high-quality broadband seismic data across the Northern Canadian Cordillera over the past decade. To this end we have generated a P and S body wave tomography model of the Slave Craton and its surroundings. Furthermore, tomographic inversion techniques are growing ever more capable of producing high resolution Earth models which capture detailed structure and dynamics across a range of scale lengths. Here, we present preliminary results on the structure of the upper mantle underlying the Slave Craton. These results are generated using data from eight different seismic networks such as the Canadian National Seismic Network (CNSN), Yukon Northwest Seismic Network (YNSN), older Portable Observatories for Lithospheric Analysis and Reseach Investigating Seismicity (POLARIS), Regional Alberta Observatory for Earthquake Studies Network (RV), USArray Transportable Array (TA), older Canadian Northwest Experiment (CANOE), Batholith Broadband (XY) and the Yukon Observatory (YO). This regional model brings new insights about the upper mantle structure beneath the Slave Craton, Canada.

Meteorological Processes Affecting the Transport of Emissions from the Navajo Generating Station to Grand Canyon National Park.

NASA Astrophysics Data System (ADS)

Lindsey, Charles G.; Chen, Jun; Dye, Timothy S.; Willard Richards, L.; Blumenthal, Donald L.

1999-08-01

During the 1990 Navajo Generating Station (NGS) Winter Visibility Study, a network of surface and upper-air meteorological measurement systems was operated in and around Grand Canyon National Park to investigate atmospheric processes in complex terrain that affected the transport of emissions from the nearby NGS. This network included 15 surface monitoring stations, eight balloon sounding stations (equipped with a mix of rawinsonde, tethersonde, and Airsonde sounding systems), three Doppler radar wind profilers, and four Doppler sodars. Measurements were made from 10 January through 31 March 1990. Data from this network were used to prepare objectively analyzed wind fields, trajectories, and streak lines to represent transport of emissions from the NGS, and to prepare isentropic analyses of the data. The results of these meteorological analyses were merged in the form of a computer animation that depicted the streak line analyses along with measurements of perfluorocarbon tracer, SO2, and sulfate aerosol concentrations, as well as visibility measurements collected by an extensive surface monitoring network. These analyses revealed that synoptic-scale circulations associated with the passage of low pressure systems followed by the formation of high pressure ridges accompanied the majority of cases when NGS emittants appeared to be transported to the Grand Canyon. The authors' results also revealed terrain influences on transport within the topography of the study area, especially mesoscale flows inside the Lake Powell basin and along the plain above the Marble Canyon.

Generating functions and stability study of multivariate self-excited epidemic processes

NASA Astrophysics Data System (ADS)

Saichev, A. I.; Sornette, D.

2011-09-01

We present a stability study of the class of multivariate self-excited Hawkes point processes, that can model natural and social systems, including earthquakes, epileptic seizures and the dynamics of neuron assemblies, bursts of exchanges in social communities, interactions between Internet bloggers, bank network fragility and cascading of failures, national sovereign default contagion, and so on. We present the general theory of multivariate generating functions to derive the number of events over all generations of various types that are triggered by a mother event of a given type. We obtain the stability domains of various systems, as a function of the topological structure of the mutual excitations across different event types. We find that mutual triggering tends to provide a significant extension of the stability (or subcritical) domain compared with the case where event types are decoupled, that is, when an event of a given type can only trigger events of the same type.

Cutting the wires: modularization of cellular networks for experimental design.

PubMed

Lang, Moritz; Summers, Sean; Stelling, Jörg

2014-01-07

Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

A Genetic Algorithm for the Generation of Packetization Masks for Robust Image Communication

PubMed Central

Zapata-Quiñones, Katherine; Duran-Faundez, Cristian; Gutiérrez, Gilberto; Lecuire, Vincent; Arredondo-Flores, Christopher; Jara-Lipán, Hugo

2017-01-01

Image interleaving has proven to be an effective solution to provide the robustness of image communication systems when resource limitations make reliable protocols unsuitable (e.g., in wireless camera sensor networks); however, the search for optimal interleaving patterns is scarcely tackled in the literature. In 2008, Rombaut et al. presented an interesting approach introducing a packetization mask generator based in Simulated Annealing (SA), including a cost function, which allows assessing the suitability of a packetization pattern, avoiding extensive simulations. In this work, we present a complementary study about the non-trivial problem of generating optimal packetization patterns. We propose a genetic algorithm, as an alternative to the cited work, adopting the mentioned cost function, then comparing it to the SA approach and a torus automorphism interleaver. In addition, we engage the validation of the cost function and provide results attempting to conclude about its implication in the quality of reconstructed images. Several scenarios based on visual sensor networks applications were tested in a computer application. Results in terms of the selected cost function and image quality metric PSNR show that our algorithm presents similar results to the other approaches. Finally, we discuss the obtained results and comment about open research challenges. PMID:28452934

Evolving the NASA Near Earth Network for the Next Generation of Human Space Flight

NASA Technical Reports Server (NTRS)

Roberts, Christopher J.; Carter, David L.; Hudiburg, John J.; Tye, Robert N.; Celeste, Peter B.

2014-01-01

The purpose of this paper is to present the planned development and evolution of the NASA Near Earth Network (NEN) launch communications services in support of the next generation of human space flight programs. Following the final space shuttle mission in 2011, the two NEN launch communications stations were decommissioned. Today, NASA is developing the next generation of human space flight systems focused on exploration missions beyond low-earth orbit, and supporting the emerging market for commercial crew and cargo human space flight services. The NEN is leading a major initiative to develop a modern high data rate launch communications ground architecture with support from the Kennedy Space Center Ground Systems Development and Operations Program and in partnership with the U.S. Air Force (USAF) Eastern Range. This initiative, the NEN Launch Communications Stations (LCS) development project, successfully completed its System Requirements Review in November 2013. This paper provides an overview of the LCS project and a summary of its progress. The LCS ground architecture, concept of operations, and driving requirements to support the new heavy-lift Space Launch System and Orion Multi-Purpose Crew Vehicle for Exploration Mission-1 are presented. Finally, potential future extensions to the ground architecture beyond EM-1 are discussed.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.

PubMed

Megchelenbrink, Wout; Huynen, Martijn; Marchiori, Elena

2014-01-01

Constraint-based models of metabolic networks are typically underdetermined, because they contain more reactions than metabolites. Therefore the solutions to this system do not consist of unique flux rates for each reaction, but rather a space of possible flux rates. By uniformly sampling this space, an estimated probability distribution for each reaction's flux in the network can be obtained. However, sampling a high dimensional network is time-consuming. Furthermore, the constraints imposed on the network give rise to an irregularly shaped solution space. Therefore more tailored, efficient sampling methods are needed. We propose an efficient sampling algorithm (called optGpSampler), which implements the Artificial Centering Hit-and-Run algorithm in a different manner than the sampling algorithm implemented in the COBRA Toolbox for metabolic network analysis, here called gpSampler. Results of extensive experiments on different genome-scale metabolic networks show that optGpSampler is up to 40 times faster than gpSampler. Application of existing convergence diagnostics on small network reconstructions indicate that optGpSampler converges roughly ten times faster than gpSampler towards similar sampling distributions. For networks of higher dimension (i.e. containing more than 500 reactions), we observed significantly better convergence of optGpSampler and a large deviation between the samples generated by the two algorithms. optGpSampler for Matlab and Python is available for non-commercial use at: http://cs.ru.nl/~wmegchel/optGpSampler/.

XML Encoding of Features Describing Rule-Based Modeling of Reaction Networks with Multi-Component Molecular Complexes

PubMed Central

Blinov, Michael L.; Moraru, Ion I.

2011-01-01

Multi-state molecules and multi-component complexes are commonly involved in cellular signaling. Accounting for molecules that have multiple potential states, such as a protein that may be phosphorylated on multiple residues, and molecules that combine to form heterogeneous complexes located among multiple compartments, generates an effect of combinatorial complexity. Models involving relatively few signaling molecules can include thousands of distinct chemical species. Several software tools (StochSim, BioNetGen) are already available to deal with combinatorial complexity. Such tools need information standards if models are to be shared, jointly evaluated and developed. Here we discuss XML conventions that can be adopted for modeling biochemical reaction networks described by user-specified reaction rules. These could form a basis for possible future extensions of the Systems Biology Markup Language (SBML). PMID:21464833

The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations.

PubMed

Hutton, Brian; Salanti, Georgia; Caldwell, Deborah M; Chaimani, Anna; Schmid, Christopher H; Cameron, Chris; Ioannidis, John P A; Straus, Sharon; Thorlund, Kristian; Jansen, Jeroen P; Mulrow, Cynthia; Catalá-López, Ferrán; Gøtzsche, Peter C; Dickersin, Kay; Boutron, Isabelle; Altman, Douglas G; Moher, David

2015-06-02

The PRISMA statement is a reporting guideline designed to improve the completeness of reporting of systematic reviews and meta-analyses. Authors have used this guideline worldwide to prepare their reviews for publication. In the past, these reports typically compared 2 treatment alternatives. With the evolution of systematic reviews that compare multiple treatments, some of them only indirectly, authors face novel challenges for conducting and reporting their reviews. This extension of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement was developed specifically to improve the reporting of systematic reviews incorporating network meta-analyses. A group of experts participated in a systematic review, Delphi survey, and face-to-face discussion and consensus meeting to establish new checklist items for this extension statement. Current PRISMA items were also clarified. A modified, 32-item PRISMA extension checklist was developed to address what the group considered to be immediately relevant to the reporting of network meta-analyses. This document presents the extension and provides examples of good reporting, as well as elaborations regarding the rationale for new checklist items and the modification of previously existing items from the PRISMA statement. It also highlights educational information related to key considerations in the practice of network meta-analysis. The target audience includes authors and readers of network meta-analyses, as well as journal editors and peer reviewers.

Real-time video streaming using H.264 scalable video coding (SVC) in multihomed mobile networks: a testbed approach

NASA Astrophysics Data System (ADS)

Nightingale, James; Wang, Qi; Grecos, Christos

2011-03-01

Users of the next generation wireless paradigm known as multihomed mobile networks expect satisfactory quality of service (QoS) when accessing streamed multimedia content. The recent H.264 Scalable Video Coding (SVC) extension to the Advanced Video Coding standard (AVC), offers the facility to adapt real-time video streams in response to the dynamic conditions of multiple network paths encountered in multihomed wireless mobile networks. Nevertheless, preexisting streaming algorithms were mainly proposed for AVC delivery over multipath wired networks and were evaluated by software simulation. This paper introduces a practical, hardware-based testbed upon which we implement and evaluate real-time H.264 SVC streaming algorithms in a realistic multihomed wireless mobile networks environment. We propose an optimised streaming algorithm with multi-fold technical contributions. Firstly, we extended the AVC packet prioritisation schemes to reflect the three-dimensional granularity of SVC. Secondly, we designed a mechanism for evaluating the effects of different streamer 'read ahead window' sizes on real-time performance. Thirdly, we took account of the previously unconsidered path switching and mobile networks tunnelling overheads encountered in real-world deployments. Finally, we implemented a path condition monitoring and reporting scheme to facilitate the intelligent path switching. The proposed system has been experimentally shown to offer a significant improvement in PSNR of the received stream compared with representative existing algorithms.

Layered Ensemble Architecture for Time Series Forecasting.

PubMed

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

Allocation and management issues in multiple-transaction open access transmission networks

NASA Astrophysics Data System (ADS)

Tao, Shu

This thesis focuses on some key issues related to allocation and management by the independent grid operator (IGO) of unbundled services in multiple-transaction open access transmission networks. The three unbundled services addressed in the thesis are transmission real power losses, reactive power support requirements from generation sources, and transmission congestion management. We develop the general framework that explicitly represents multiple transactions undertaken simultaneously in the transmission grid. This framework serves as the basis for formulating various problems treated in the thesis. We use this comprehensive framework to develop a physical-flow-based mechanism to allocate the total transmission losses to each transaction using the system. An important property of the allocation scheme is its capability to effectively deal with counter flows that result in the presence of specific transactions. Using the loss allocation results as the basis, we construct the equivalent loss compensation concept and apply it to develop flexible and effective procedures for compensating losses in multiple-transaction networks. We present a new physical-flow-based mechanism for allocating the reactive power support requirements provided by generators in multiple-transaction networks. The allocatable reactive support requirements are formulated as the sum of two specific components---the voltage magnitude variation component and the voltage angle variation component. The formulation utilizes the multiple-transaction framework and makes use of certain simplifying approximations. The formulation leads to a natural allocation as a function of the amount of each transaction. The physical interpretation of each allocation as a sensitivity of the reactive output of a generator is discussed. We propose a congestion management allocation scheme for multiple-transaction networks. The proposed scheme determines the allocation of congestion among the transactions on a physical-flow basis. It also proposes a congestion relief scheme that removes the congestion attributed to each transaction on the network in a least-cost manner to the IGO and determines the appropriate transmission charges to each transaction for its transmission usage. The thesis provides a compendium of problems that are natural extensions of the research results reported here and appear to be good candidates for future work.

An In-Depth Analysis of the Chung-Lu Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winlaw, M.; DeSterck, H.; Sanders, G.

2015-10-28

In the classic Erd}os R enyi random graph model [5] each edge is chosen with uniform probability and the degree distribution is binomial, limiting the number of graphs that can be modeled using the Erd}os R enyi framework [10]. The Chung-Lu model [1, 2, 3] is an extension of the Erd}os R enyi model that allows for more general degree distributions. The probability of each edge is no longer uniform and is a function of a user-supplied degree sequence, which by design is the expected degree sequence of the model. This property makes it an easy model to work withmore » theoretically and since the Chung-Lu model is a special case of a random graph model with a given degree sequence, many of its properties are well known and have been studied extensively [2, 3, 13, 8, 9]. It is also an attractive null model for many real-world networks, particularly those with power-law degree distributions and it is sometimes used as a benchmark for comparison with other graph generators despite some of its limitations [12, 11]. We know for example, that the average clustering coe cient is too low relative to most real world networks. As well, measures of a nity are also too low relative to most real-world networks of interest. However, despite these limitations or perhaps because of them, the Chung-Lu model provides a basis for comparing new graph models.« less

Ryu-Takayanagi formula for symmetric random tensor networks

NASA Astrophysics Data System (ADS)

Chirco, Goffredo; Oriti, Daniele; Zhang, Mingyi

2018-06-01

We consider the special case of random tensor networks (RTNs) endowed with gauge symmetry constraints on each tensor. We compute the Rényi entropy for such states and recover the Ryu-Takayanagi (RT) formula in the large-bond regime. The result provides first of all an interesting new extension of the existing derivations of the RT formula for RTNs. Moreover, this extension of the RTN formalism brings it in direct relation with (tensorial) group field theories (and spin networks), and thus provides new tools for realizing the tensor network/geometry duality in the context of background-independent quantum gravity, and for importing quantum gravity tools into tensor network research.

Artificial Neural Networks in Policy Research: A Current Assessment.

ERIC Educational Resources Information Center

Woelfel, Joseph

1993-01-01

Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

Early Correlated Network Activity in the Hippocampus: Its Putative Role in Shaping Neuronal Circuits.

PubMed

Griguoli, Marilena; Cherubini, Enrico

2017-01-01

Synchronized neuronal activity occurring at different developmental stages in various brain structures represents a hallmark of developmental circuits. This activity, which differs in its specific patterns among animal species may play a crucial role in de novo formation and in shaping neuronal networks. In the rodent hippocampus in vitro , the so-called giant depolarizing potentials (GDPs) constitute a primordial form of neuronal synchrony preceding more organized forms of activity such as oscillations in the theta and gamma frequency range. GDPs are generated at the network level by the interaction of the neurotransmitters glutamate and GABA which, immediately after birth, exert both a depolarizing and excitatory action on their targets. GDPs are triggered by GABAergic interneurons, which in virtue of their extensive axonal branching operate as functional hubs to synchronize large ensembles of cells. Intrinsic bursting activity, driven by a persistent sodium conductance and facilitated by the low expression of Kv7.2 and Kv7.3 channel subunits, responsible for I M , exerts a permissive role in GDP generation. Here, we discuss how GDPs are generated in a probabilistic way when neuronal excitability within a local circuit reaches a certain threshold and how GDP-associated calcium transients act as coincident detectors for enhancing synaptic strength at emerging GABAergic and glutamatergic synapses. We discuss the possible in vivo correlate of this activity. Finally, we debate recent data showing how, in several animal models of neuropsychiatric disorders including autism, a GDPs dysfunction is associated to morphological alterations of neuronal circuits and behavioral deficits reminiscent of those observed in patients.

Equipment Management for Sensor Networks: Linking Physical Infrastructure and Actions to Observational Data

NASA Astrophysics Data System (ADS)

Jones, A. S.; Horsburgh, J. S.; Matos, M.; Caraballo, J.

2015-12-01

Networks conducting long term monitoring using in situ sensors need the functionality to track physical equipment as well as deployments, calibrations, and other actions related to site and equipment maintenance. The observational data being generated by sensors are enhanced if direct linkages to equipment details and actions can be made. This type of information is typically recorded in field notebooks or in static files, which are rarely linked to observations in a way that could be used to interpret results. However, the record of field activities is often relevant to analysis or post-processing of the observational data. We have developed an underlying database schema and deployed a web interface for recording and retrieving information on physical infrastructure and related actions for observational networks. The database schema for equipment was designed as an extension to the Observations Data Model 2 (ODM2), a community-developed information model for spatially discrete, feature based earth observations. The core entities of ODM2 describe location, observed variable, and timing of observations, and the equipment extension contains entities to provide additional metadata specific to the inventory of physical infrastructure and associated actions. The schema is implemented in a relational database system for storage and management with an associated web interface. We designed the web-based tools for technicians to enter and query information on the physical equipment and actions such as site visits, equipment deployments, maintenance, and calibrations. These tools were implemented for the iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) ecohydrologic observatory, and we anticipate that they will be useful for similar large-scale monitoring networks desiring to link observing infrastructure to observational data to increase the quality of sensor-based data products.

Neurofilament tail phosphorylation: identity of the RT-97 phosphoepitope and regulation in neurons by cross-talk among proline-directed kinases.

PubMed

Veeranna; Lee, Ju-Hyun; Pareek, Tej K; Jaffee, Howard; Boland, Barry; Vinod, K Yaragudri; Amin, Niranjana; Kulkarni, Ashok B; Pant, Harish C; Nixon, Ralph A

2008-10-01

As axons myelinate, establish a stable neurofilament network, and expand in caliber, neurofilament proteins are extensively phosphorylated along their C-terminal tails, which is recognized by the monoclonal antibody, RT-97. Here, we demonstrate in vivo that RT-97 immunoreactivity (IR) is generated by phosphorylation at KSPXK or KSPXXXK motifs and requires flanking lysines at specific positions. extracellular signal regulated kinase 1,2 (ERK1,2) and pERK1,2 levels increase in parallel with phosphorylation at the RT-97 epitope during early postnatal brain development. Purified ERK1,2 generated RT-97 on both KSP motifs on recombinant NF-H tail domain proteins, while cdk5 phosphorylated only KSPXK motifs. RT-97 epitope generation in primary hippocampal neurons was regulated by extensive cross-talk among ERK1,2, c-Jun N-terminal kinase 1,2 (JNK1,2) and cdk5. Inhibition of both ERK1,2 and JNK1,2 completely blocked RT-97 generation. Cdk5 influenced RT-97 generation indirectly by modulating JNK activation. In mice, cdk5 gene deletion did not significantly alter RT-97 IR or ERK1,2 and JNK activation. In mice lacking the cdk5 activator P35, the partial suppression of cdk5 activity increased RT-97 IR by activating ERK1,2. Thus, cdk5 influences RT-97 epitope generation partly by modulating ERKs and JNKs, which are the two principal kinases regulating neurofilament phosphorylation. The regulation of a single target by multiple protein kinases underscores the importance of monitoring other relevant kinases when the activity of a particular one is blocked.

Time-Reversal Based Range Extension Technique for Ultra-Wideband (UWB) Sensors and Applications in Tactical Communications and Networking

DTIC Science & Technology

2008-10-16

signal from the signal generator is also used to synchronize DSO to record the data of the received signal. The tapped -delay-line model of CIR will...between each filter tap . The output y(t) — h(t) *x(t) is then uniformly sampled with sampling period Ts. 1 ’s follows the relation Ta/Th — q, where q... eft ) ProbtagPake ^ p(l> ’HO PriHretuHg Figure 5.5: An equivalent block diagram of channel estimation The success of recovery relies on the

A simple model of bipartite cooperation for ecological and organizational networks.

PubMed

Saavedra, Serguei; Reed-Tsochas, Felix; Uzzi, Brian

2009-01-22

In theoretical ecology, simple stochastic models that satisfy two basic conditions about the distribution of niche values and feeding ranges have proved successful in reproducing the overall structural properties of real food webs, using species richness and connectance as the only input parameters. Recently, more detailed models have incorporated higher levels of constraint in order to reproduce the actual links observed in real food webs. Here, building on previous stochastic models of consumer-resource interactions between species, we propose a highly parsimonious model that can reproduce the overall bipartite structure of cooperative partner-partner interactions, as exemplified by plant-animal mutualistic networks. Our stochastic model of bipartite cooperation uses simple specialization and interaction rules, and only requires three empirical input parameters. We test the bipartite cooperation model on ten large pollination data sets that have been compiled in the literature, and find that it successfully replicates the degree distribution, nestedness and modularity of the empirical networks. These properties are regarded as key to understanding cooperation in mutualistic networks. We also apply our model to an extensive data set of two classes of company engaged in joint production in the garment industry. Using the same metrics, we find that the network of manufacturer-contractor interactions exhibits similar structural patterns to plant-animal pollination networks. This surprising correspondence between ecological and organizational networks suggests that the simple rules of cooperation that generate bipartite networks may be generic, and could prove relevant in many different domains, ranging from biological systems to human society.

Building Extraction from Remote Sensing Data Using Fully Convolutional Networks

NASA Astrophysics Data System (ADS)

Bittner, K.; Cui, S.; Reinartz, P.

2017-05-01

Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM) using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.

Phylogenetic trends in respiratory rhythmogenesis: insights from ectothermic vertebrates.

PubMed

Kinkead, Richard

2009-08-31

Understanding the neural substrate driving breathing has puzzled physiologists for more than a century. The discovery of the pre-Bötzinger complex (preBötC) in newborn rodents as a structure with a unique physiological function in respiratory rhythm generation was an important progress in respiratory neurobiology that stimulated much research. Owing to the extensive literature describing the location, organisation, and function of the preBötC mainly in newborn rodents, this structure has become the point of reference in studies addressing respiratory rhythm generation in other mammals and various classes of vertebrates. This paper reviews recent progress made in non-mammalian vertebrates in our understanding of the location and function of the neural networks driving respiratory activity. As in newborn rodents, data from lampreys, air breathing fish, and amphibians show that the production of eupnea is the result of interactions between multiple (at least two) rhythmogenic networks. These networks are located in anatomically distinct areas and show different functional properties in terms of their ability to produce (or not) bursting activity in the absence of synaptic inputs (e.g. pacemaker neurons) and their sensitivity to specific neuromodulators such as substance P, somatostatin, and opioids. Current data indicate that respiratory rhythmogenesis is a phylogenetically ancient function that was highly conserved throughout evolution and that a comparative approach remains important to derive broader biological principles and a more comprehensive view.

Generative model selection using a scalable and size-independent complex network classifier

NASA Astrophysics Data System (ADS)

Motallebi, Sadegh; Aliakbary, Sadegh; Habibi, Jafar

2013-12-01

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree for model selection. Our proposed method, which is named "Generative Model Selection for Complex Networks," outperforms existing methods with respect to accuracy, scalability, and size-independence.

A Bitslice Implementation of Anderson's Attack on A5/1

NASA Astrophysics Data System (ADS)

Bulavintsev, Vadim; Semenov, Alexander; Zaikin, Oleg; Kochemazov, Stepan

2018-03-01

The A5/1 keystream generator is a part of Global System for Mobile Communications (GSM) protocol, employed in cellular networks all over the world. Its cryptographic resistance was extensively analyzed in dozens of papers. However, almost all corresponding methods either employ a specific hardware or require an extensive preprocessing stage and significant amounts of memory. In the present study, a bitslice variant of Anderson's Attack on A5/1 is implemented. It requires very little computer memory and no preprocessing. Moreover, the attack can be made even more efficient by harnessing the computing power of modern Graphics Processing Units (GPUs). As a result, using commonly available GPUs this method can quite efficiently recover the secret key using only 64 bits of keystream. To test the performance of the implementation, a volunteer computing project was launched. 10 instances of A5/1 cryptanalysis have been successfully solved in this project in a single week.

Providing Internet Access to High-Resolution Lunar Images

NASA Technical Reports Server (NTRS)

Plesea, Lucian

2008-01-01

The OnMoon server is a computer program that provides Internet access to high-resolution Lunar images, maps, and elevation data, all suitable for use in geographical information system (GIS) software for generating images, maps, and computational models of the Moon. The OnMoon server implements the Open Geospatial Consortium (OGC) Web Map Service (WMS) server protocol and supports Moon-specific extensions. Unlike other Internet map servers that provide Lunar data using an Earth coordinate system, the OnMoon server supports encoding of data in Moon-specific coordinate systems. The OnMoon server offers access to most of the available high-resolution Lunar image and elevation data. This server can generate image and map files in the tagged image file format (TIFF) or the Joint Photographic Experts Group (JPEG), 8- or 16-bit Portable Network Graphics (PNG), or Keyhole Markup Language (KML) format. Image control is provided by use of the OGC Style Layer Descriptor (SLD) protocol. Full-precision spectral arithmetic processing is also available, by use of a custom SLD extension. This server can dynamically add shaded relief based on the Lunar elevation to any image layer. This server also implements tiled WMS protocol and super-overlay KML for high-performance client application programs.

Generalized hamming networks and applications.

PubMed

Koutroumbas, Konstantinos; Kalouptsidis, Nicholas

2005-09-01

In this paper the classical Hamming network is generalized in various ways. First, for the Hamming maxnet, a generalized model is proposed, which covers under its umbrella most of the existing versions of the Hamming Maxnet. The network dynamics are time varying while the commonly used ramp function may be replaced by a much more general non-linear function. Also, the weight parameters of the network are time varying. A detailed convergence analysis is provided. A bound on the number of iterations required for convergence is derived and its distribution functions are given for the cases where the initial values of the nodes of the Hamming maxnet stem from the uniform and the peak distributions. Stabilization mechanisms aiming to prevent the node(s) with the maximum initial value diverging to infinity or decaying to zero are described. Simulations demonstrate the advantages of the proposed extension. Also, a rough comparison between the proposed generalized scheme as well as the original Hamming maxnet and its variants is carried out in terms of the time required for convergence, in hardware implementations. Finally, the other two parts of the Hamming network, namely the competitors generating module and the decoding module, are briefly considered in the framework of various applications such as classification/clustering, vector quantization and function optimization.

Physical explosion analysis in heat exchanger network design

NASA Astrophysics Data System (ADS)

Pasha, M.; Zaini, D.; Shariff, A. M.

2016-06-01

The failure of shell and tube heat exchangers is being extensively experienced by the chemical process industries. This failure can create a loss of production for long time duration. Moreover, loss of containment through heat exchanger could potentially lead to a credible event such as fire, explosion and toxic release. There is a need to analyse the possible worst case effect originated from the loss of containment of the heat exchanger at the early design stage. Physical explosion analysis during the heat exchanger network design is presented in this work. Baker and Prugh explosion models are deployed for assessing the explosion effect. Microsoft Excel integrated with process design simulator through object linking and embedded (OLE) automation for this analysis. Aspen HYSYS V (8.0) used as a simulation platform in this work. A typical heat exchanger network of steam reforming and shift conversion process was presented as a case study. It is investigated from this analysis that overpressure generated from the physical explosion of each heat exchanger can be estimated in a more precise manner by using Prugh model. The present work could potentially assist the design engineer to identify the critical heat exchanger in the network at the preliminary design stage.

Emergence of event cascades in inhomogeneous networks

NASA Astrophysics Data System (ADS)

Onaga, Tomokatsu; Shinomoto, Shigeru

2016-09-01

There is a commonality among contagious diseases, tweets, and neuronal firings that past events facilitate the future occurrence of events. The spread of events has been extensively studied such that the systems exhibit catastrophic chain reactions if the interaction represented by the ratio of reproduction exceeds unity; however, their subthreshold states are not fully understood. Here, we report that these systems are possessed by nonstationary cascades of event-occurrences already in the subthreshold regime. Event cascades can be harmful in some contexts, when the peak-demand causes vaccine shortages, heavy traffic on communication lines, but may be beneficial in other contexts, such that spontaneous activity in neural networks may be used to generate motion or store memory. Thus it is important to comprehend the mechanism by which such cascades appear, and consider controlling a system to tame or facilitate fluctuations in the event-occurrences. The critical interaction for the emergence of cascades depends greatly on the network structure in which individuals are connected. We demonstrate that we can predict whether cascades may emerge, given information about the interactions between individuals. Furthermore, we develop a method of reallocating connections among individuals so that event cascades may be either impeded or impelled in a network.

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

NASA Technical Reports Server (NTRS)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

Automatic decomposition of kinetic models of signaling networks minimizing the retroactivity among modules.

PubMed

Saez-Rodriguez, Julio; Gayer, Stefan; Ginkel, Martin; Gilles, Ernst Dieter

2008-08-15

The modularity of biochemical networks in general, and signaling networks in particular, has been extensively studied over the past few years. It has been proposed to be a useful property to analyze signaling networks: by decomposing the network into subsystems, more manageable units are obtained that are easier to analyze. While many powerful algorithms are available to identify modules in protein interaction networks, less attention has been paid to signaling networks de.ned as chemical systems. Such a decomposition would be very useful as most quantitative models are de.ned using the latter, more detailed formalism. Here, we introduce a novel method to decompose biochemical networks into modules so that the bidirectional (retroactive) couplings among the modules are minimized. Our approach adapts a method to detect community structures, and applies it to the so-called retroactivity matrix that characterizes the couplings of the network. Only the structure of the network, e.g. in SBML format, is required. Furthermore, the modularized models can be loaded into ProMoT, a modeling tool which supports modular modeling. This allows visualization of the models, exploiting their modularity and easy generation of models of one or several modules for further analysis. The method is applied to several relevant cases, including an entangled model of the EGF-induced MAPK cascade and a comprehensive model of EGF signaling, demonstrating its ability to uncover meaningful modules. Our approach can thus help to analyze large networks, especially when little a priori knowledge on the structure of the network is available. The decomposition algorithms implemented in MATLAB (Mathworks, Inc.) are freely available upon request. ProMoT is freely available at http://www.mpi-magdeburg.mpg.de/projects/promot. Supplementary data are available at Bioinformatics online.

Generative model selection using a scalable and size-independent complex network classifier

DOE Office of Scientific and Technical Information (OSTI.GOV)

Motallebi, Sadegh, E-mail: motallebi@ce.sharif.edu; Aliakbary, Sadegh, E-mail: aliakbary@ce.sharif.edu; Habibi, Jafar, E-mail: jhabibi@sharif.edu

2013-12-15

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree formore » model selection. Our proposed method, which is named “Generative Model Selection for Complex Networks,” outperforms existing methods with respect to accuracy, scalability, and size-independence.« less

Structural changes in socio-affective networks: Multi-modal MRI findings in long-term meditation practitioners.

PubMed

Engen, Haakon G; Bernhardt, Boris C; Skottnik, Leon; Ricard, Matthieu; Singer, Tania

2017-08-31

Our goal was to assess the effects of long-term mental training in socio-affective skills on structural brain networks. We studied a group of long-term meditation practitioners (LTMs) who have focused on cultivating socio-affective skills using loving-kindness and compassion meditation for an average of 40k h, comparing these to meditation-naïve controls. To maximize homogeneity of prior practice, LTMs were included only if they had undergone extensive full-time meditation retreats in the same center. MRI-based cortical thickness analysis revealed increased thickness in the LTM cohort relative to meditation-native controls in fronto-insular cortices. To identify functional networks relevant for the generation of socio-affective states, structural imaging analysis were complemented by fMRI analysis in LTMs, showing amplitude increases during a loving-kindness meditation session relative to non-meditative rest in multiple prefrontal and insular regions bilaterally. Importantly, functional findings partially overlapped with regions of cortical thickness increases in the left ventrolateral prefrontal cortex and anterior insula, suggesting that these regions may play a central role in the generation of emotional states relevant for the meditative practice. Our multi-modal MRI approach revealed structural changes in LTMs who have cultivated loving-kindness and compassion for a significant period of their life in functional networks activated by these practices. These preliminary cross-sectional findings motivate future longitudinal work studying brain plasticity following the regular practice of skills aiming at enhancing human altruism and prosocial motivation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

PubMed

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

PubMed Central

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-01-01

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning.

PubMed

Song, Youyi; Zhang, Ling; Chen, Siping; Ni, Dong; Lei, Baiying; Wang, Tianfu

2015-10-01

In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.

Solution to urn models of pairwise interaction with application to social, physical, and biological sciences

NASA Astrophysics Data System (ADS)

Pickering, William; Lim, Chjan

2017-07-01

We investigate a family of urn models that correspond to one-dimensional random walks with quadratic transition probabilities that have highly diverse applications. Well-known instances of these two-urn models are the Ehrenfest model of molecular diffusion, the voter model of social influence, and the Moran model of population genetics. We also provide a generating function method for diagonalizing the corresponding transition matrix that is valid if and only if the underlying mean density satisfies a linear differential equation and express the eigenvector components as terms of ordinary hypergeometric functions. The nature of the models lead to a natural extension to interaction between agents in a general network topology. We analyze the dynamics on uncorrelated heterogeneous degree sequence networks and relate the convergence times to the moments of the degree sequences for various pairwise interaction mechanisms.

Artificial neural networks for document analysis and recognition.

PubMed

Marinai, Simone; Gori, Marco; Soda, Giovanni; Society, Computer

2005-01-01

Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.

Security Services Discovery by ATM Endsystems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sholander, Peter; Tarman, Thomas

This contribution proposes strawman techniques for Security Service Discovery by ATM endsystems in ATM networks. Candidate techniques include ILMI extensions, ANS extensions and new ATM anycast addresses. Another option is a new protocol based on an IETF service discovery protocol, such as Service Location Protocol (SLP). Finally, this contribution provides strawman requirements for Security-Based Routing in ATM networks.

Textural-Contextual Labeling and Metadata Generation for Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Kiang, Richard K.

1999-01-01

Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed.

The guitar chord-generating algorithm based on complex network

NASA Astrophysics Data System (ADS)

Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais

2016-02-01

This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.

Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups

PubMed Central

2017-01-01

Background Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. The success of a DHSN is based on the number of participants and their consistent creation of externalities through the generation of new content. To promote network growth, it would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities. Objective The aim of the study was to investigate the feasibility of developing predictive models that identify potential superusers in real time. This study examined associations between posting behavior, 4 demographic variables, and 20 indication-specific variables. Methods Data were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs. Of these, 2 were designed to assist in the treatment of addictions (problem drinking and smoking cessation), and 2 for mental health (depressive disorder, panic disorder). To analyze posting behavior, 10 models were developed, and negative binomial regressions were conducted to examine associations between number of posts, and demographic and indication-specific variables. Results The DHSNs varied in number of days active (3658-5210), number of registrants (5049-52,396), number of actors (1085-8452), and number of posts (16,231-521,997). In the sample, all 10 models had low R2 values (.013-.086) with limited statistically significant demographic and indication-specific variables. Conclusions Very few variables were associated with social network engagement. Although some variables were statistically significant, they did not appear to be practically significant. Based on the large number of study participants, variation in DHSN theme, and extensive time-period, we did not find strong evidence that demographic characteristics or indication severity sufficiently explain the variability in number of posts per actor. Researchers should investigate alternative models that identify superusers or other individuals who create social network externalities. PMID:28213340

Regulation of Mitochondrial Structure and Dynamics by the Cytoskeleton and Mechanical Factors.

PubMed

Bartolák-Suki, Erzsébet; Imsirovic, Jasmin; Nishibori, Yuichiro; Krishnan, Ramaswamy; Suki, Béla

2017-08-21

Mitochondria supply cells with energy in the form of ATP, guide apoptosis, and contribute to calcium buffering and reactive oxygen species production. To support these diverse functions, mitochondria form an extensive network with smaller clusters that are able to move along microtubules aided by motor proteins. Mitochondria are also associated with the actin network, which is involved in cellular responses to various mechanical factors. In this review, we discuss mitochondrial structure and function in relation to the cytoskeleton and various mechanical factors influencing cell functions. We first summarize the morphological features of mitochondria with an emphasis on fission and fusion as well as how network properties govern function. We then review the relationship between the mitochondria and the cytoskeletal structures, including mechanical interactions. We also discuss how stretch and its dynamic pattern affect mitochondrial structure and function. Finally, we present preliminary data on how extracellular matrix stiffness influences mitochondrial morphology and ATP generation. We conclude by discussing the more general role that mitochondria may play in mechanobiology and how the mechanosensitivity of mitochondria may contribute to the development of several diseases and aging.

Flash crashes, bursts, and black swans: parallels between financial markets and healthcare systems.

PubMed

West, Bruce J; Clancy, Thomas R

2010-11-01

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 16th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, Dr Clancy, the editor of this column, and co-author, Dr West, discuss how the collapse of global financial markets in 2008 may provide valuable insight into mechanisms of complex system behavior in healthcare. Dr West, a physicist and expert in the field of complex systems and network science, is author of a chapter in the book, On the Edge: Nursing in the Age of Complexity (Lindberg C, Nash S, Linberg C. Bordertown, NJ: Plexus Press; 2008) and his most recent book, Disrupted Networks: From Physics to Climate Change (West BJ, Scafetta N. Singapore: Disrupted Networks, World Scientific Publishing; 2010).

Activation of YUCCA5 by the Transcription Factor TCP4 Integrates Developmental and Environmental Signals to Promote Hypocotyl Elongation in Arabidopsis.

PubMed

Challa, Krishna Reddy; Aggarwal, Pooja; Nath, Utpal

2016-09-05

Cell expansion is an essential process in plant morphogenesis and is regulated by the coordinated action of environmental stimuli and endogenous factors, such as the phytohormones auxin and brassinosteroid. Although the biosynthetic pathways that generate these hormones and their downstream signaling mechanisms have been extensively studied, the upstream transcriptional network that modulates their levels and connects their action to cell morphogenesis is less clear. Here we show that the miR319-regulated TCP (TEOSINTE BRANCHED 1, CYCLODEA, PROLIFERATING CELL FACTORS) transcription factors, notably TCP4, directly activate YUCCA5 transcription and integrate the auxin response to a brassinosteroid-dependent molecular circuit that promotes cell elongation in Arabidopsis hypocotyls. Further, TCP4 modulates the common transcriptional network downstream to auxin-BR signaling, which is also triggered by environmental cues, such as light, to promote cell expansion. Our study links TCP function with the hormone response during cell morphogenesis and shows that developmental and environmental signals converge on a common transcriptional network to promote cell elongation. {copyright, serif} 2016 American Society of Plant Biologists. All rights reserved.

Integrated renewable energy networks

NASA Astrophysics Data System (ADS)

Mansouri Kouhestani, F.; Byrne, J. M.; Hazendonk, P.; Brown, M. B.; Spencer, L.

2015-12-01

This multidisciplinary research is focused on studying implementation of diverse renewable energy networks. Our modern economy now depends heavily on large-scale, energy-intensive technologies. A transition to low carbon, renewable sources of energy is needed. We will develop a procedure for designing and analyzing renewable energy systems based on the magnitude, distribution, temporal characteristics, reliability and costs of the various renewable resources (including biomass waste streams) in combination with various measures to control the magnitude and timing of energy demand. The southern Canadian prairies are an ideal location for developing renewable energy networks. The region is blessed with steady, westerly winds and bright sunshine for more hours annually than Houston Texas. Extensive irrigation agriculture provides huge waste streams that can be processed biologically and chemically to create a range of biofuels. The first stage involves mapping existing energy and waste flows on a neighbourhood, municipal, and regional level. Optimal sites and combinations of sites for solar and wind electrical generation, such as ridges, rooftops and valley walls, will be identified. Geomatics based site and grid analyses will identify best locations for energy production based on efficient production and connectivity to regional grids.

Perspectives for computational modeling of cell replacement for neurological disorders

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aimone, James B.; Weick, Jason P.

In mathematical modeling of anatomically-constrained neural networks we provide significant insights regarding the response of networks to neurological disorders or injury. Furthermore, a logical extension of these models is to incorporate treatment regimens to investigate network responses to intervention. The addition of nascent neurons from stem cell precursors into damaged or diseased tissue has been used as a successful therapeutic tool in recent decades. Interestingly, models have been developed to examine the incorporation of new neurons into intact adult structures, particularly the dentate granule neurons of the hippocampus. These studies suggest that the unique properties of maturing neurons, can impactmore » circuit behavior in unanticipated ways. In this perspective, we review the current status of models used to examine damaged CNS structures with particular focus on cortical damage due to stroke. Secondly, we suggest that computational modeling of cell replacement therapies can be made feasible by implementing approaches taken by current models of adult neurogenesis. The development of these models is critical for generating hypotheses regarding transplant therapies and improving outcomes by tailoring transplants to desired effects.« less

Perspectives for computational modeling of cell replacement for neurological disorders

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aimone, James B.; Weick, Jason P.

Mathematical modeling of anatomically-constrained neural networks has provided significant insights regarding the response of networks to neurological disorders or injury. A logical extension of these models is to incorporate treatment regimens to investigate network responses to intervention. The addition of nascent neurons from stem cell precursors into damaged or diseased tissue has been used as a successful therapeutic tool in recent decades. Interestingly, models have been developed to examine the incorporation of new neurons into intact adult structures, particularly the dentate granule neurons of the hippocampus. These studies suggest that the unique properties of maturing neurons, can impact circuit behaviormore » in unanticipated ways. In this perspective, we review the current status of models used to examine damaged CNS structures with particular focus on cortical damage due to stroke. Secondly, we suggest that computational modeling of cell replacement therapies can be made feasible by implementing approaches taken by current models of adult neurogenesis. The development of these models is critical for generating hypotheses regarding transplant therapies and improving outcomes by tailoring transplants to desired effects.« less

Regulation of Mitochondrial Structure and Dynamics by the Cytoskeleton and Mechanical Factors

PubMed Central

Bartolák-Suki, Erzsébet; Imsirovic, Jasmin; Nishibori, Yuichiro; Krishnan, Ramaswamy; Suki, Béla

2017-01-01

Mitochondria supply cells with energy in the form of ATP, guide apoptosis, and contribute to calcium buffering and reactive oxygen species production. To support these diverse functions, mitochondria form an extensive network with smaller clusters that are able to move along microtubules aided by motor proteins. Mitochondria are also associated with the actin network, which is involved in cellular responses to various mechanical factors. In this review, we discuss mitochondrial structure and function in relation to the cytoskeleton and various mechanical factors influencing cell functions. We first summarize the morphological features of mitochondria with an emphasis on fission and fusion as well as how network properties govern function. We then review the relationship between the mitochondria and the cytoskeletal structures, including mechanical interactions. We also discuss how stretch and its dynamic pattern affect mitochondrial structure and function. Finally, we present preliminary data on how extracellular matrix stiffness influences mitochondrial morphology and ATP generation. We conclude by discussing the more general role that mitochondria may play in mechanobiology and how the mechanosensitivity of mitochondria may contribute to the development of several diseases and aging. PMID:28825689

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

PubMed Central

Li, Jingjing; Shi, Minyi; Ma, Zhihai; Zhao, Shuchun; Euskirchen, Ghia; Ziskin, Jennifer; Urban, Alexander; Hallmayer, Joachim; Snyder, Michael

2014-01-01

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology. PMID:25549968

Virtual Vision

NASA Astrophysics Data System (ADS)

Terzopoulos, Demetri; Qureshi, Faisal Z.

Computer vision and sensor networks researchers are increasingly motivated to investigate complex multi-camera sensing and control issues that arise in the automatic visual surveillance of extensive, highly populated public spaces such as airports and train stations. However, they often encounter serious impediments to deploying and experimenting with large-scale physical camera networks in such real-world environments. We propose an alternative approach called "Virtual Vision", which facilitates this type of research through the virtual reality simulation of populated urban spaces, camera sensor networks, and computer vision on commodity computers. We demonstrate the usefulness of our approach by developing two highly automated surveillance systems comprising passive and active pan/tilt/zoom cameras that are deployed in a virtual train station environment populated by autonomous, lifelike virtual pedestrians. The easily reconfigurable virtual cameras distributed in this environment generate synthetic video feeds that emulate those acquired by real surveillance cameras monitoring public spaces. The novel multi-camera control strategies that we describe enable the cameras to collaborate in persistently observing pedestrians of interest and in acquiring close-up videos of pedestrians in designated areas.

Cascading failures with local load redistribution in interdependent Watts-Strogatz networks

NASA Astrophysics Data System (ADS)

Hong, Chen; Zhang, Jun; Du, Wen-Bo; Sallan, Jose Maria; Lordan, Oriol

2016-05-01

Cascading failures of loads in isolated networks have been studied extensively over the last decade. Since 2010, such research has extended to interdependent networks. In this paper, we study cascading failures with local load redistribution in interdependent Watts-Strogatz (WS) networks. The effects of rewiring probability and coupling strength on the resilience of interdependent WS networks have been extensively investigated. It has been found that, for small values of the tolerance parameter, interdependent networks are more vulnerable as rewiring probability increases. For larger values of the tolerance parameter, the robustness of interdependent networks firstly decreases and then increases as rewiring probability increases. Coupling strength has a different impact on robustness. For low values of coupling strength, the resilience of interdependent networks decreases with the increment of the coupling strength until it reaches a certain threshold value. For values of coupling strength above this threshold, the opposite effect is observed. Our results are helpful to understand and design resilient interdependent networks.

The narrow gap between norms and cooperative behaviour in a reindeer herding community

PubMed Central

2018-01-01

Cooperation evolves on social networks and is shaped, in part, by norms: beliefs and expectations about the behaviour of others or of oneself. Networks of cooperative social partners and associated norms are vital for pastoralists, such as Saami reindeer herders in northern Norway. However, little is known quantitatively about how norms structure pastoralists' social networks or shape cooperation. Saami herders reported their social networks and participated in field experiments, allowing us to gauge the overlap between reported and emergent cooperation. We show that individuals' perceptions of reciprocal cooperation within their social networks exceeded actual reciprocity, although both occurred frequently and were concentrated within herding groups. Herders with more extensive cooperation networks received more rewards in an economic game. Although herders overestimated reciprocal helping, cooperation in this community was still extensive, suggesting that perceived norms potentially allow network structures promoting cooperation to emerge and be maintained. PMID:29515842

Recent research in network problems with applications

NASA Technical Reports Server (NTRS)

Thompson, G. L.

1980-01-01

The capabilities of network codes and their extensions are surveyed in regard to specially structured integer programming problems which are solved by using the solutions of a series of ordinary network problems.

Artificial Neural Network as the FPGA Trigger in the Cyclone V based Front-End for a Detection of Neutrino-Origin Showers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Szadkowski, Zbigniew; Glas, Dariusz; Pytel, Krzysztof

Neutrinos play a fundamental role in the understanding of the origin of ultra-high-energy cosmic rays. They interact through charged and neutral currents in the atmosphere generating extensive air showers. However, their a very low rate of events potentially generated by neutrinos is a significant challenge for a detection technique and requires both sophisticated algorithms and high-resolution hardware. A trigger based on a artificial neural network was implemented into the Cyclone{sup R} V E FPGA 5CEFA9F31I7 - the heart of the prototype Front-End boards developed for tests of new algorithms in the Pierre Auger surface detectors. Showers for muon and taumore » neutrino initiating particles on various altitudes, angles and energies were simulated in CORSICA and Offline platforms giving pattern of ADC traces in Auger water Cherenkov detectors. The 3-layer 12-8-1 neural network was taught in MATLAB by simulated ADC traces according the Levenberg-Marquardt algorithm. Results show that a probability of a ADC traces generation is very low due to a small neutrino cross-section. Nevertheless, ADC traces, if occur, for 1-10 EeV showers are relatively short and can be analyzed by 16-point input algorithm. We optimized the coefficients from MATLAB to get a maximal range of potentially registered events and for fixed-point FPGA processing to minimize calculation errors. New sophisticated triggers implemented in Cyclone{sup R} V E FPGAs with large amount of DSP blocks, embedded memory running with 120 - 160 MHz sampling may support a discovery of neutrino events in the Pierre Auger Observatory. (authors)« less

Topology Design for Directional Range Extension Networks with Antenna Blockage

DTIC Science & Technology

2017-03-19

introduced by pod-based antenna blockages. Using certain modeling approximations, the paper presents a quantitative analysis showing design trade-offs...parameters. Sec- tion IV develops quantitative relationships among key design elements and performance metrics. Section V considers some implications of the...Topology Design for Directional Range Extension Networks with Antenna Blockage Thomas Shake MIT Lincoln Laboratory shake@ll.mit.edu Abstract

Effect of composition and temperature on the second harmonic generation in silver phosphate glasses

NASA Astrophysics Data System (ADS)

Konidakis, I.; Psilodimitrakopoulos, S.; Kosma, K.; Lemonis, A.; Stratakis, E.

2018-01-01

We herein employ nonlinear laser imaging microscopy to explicitly study the dynamics of second harmonic generation (SHG) in silver iodide phosphate glasses. While glasses of this family have gained extensive scientific attention over the years due to their superior conducting properties, considerably less attention has been paid to their unique nonlinear optical characteristics. In the present study, firstly, it is demonstrated that SHG signal intensity is enhanced upon increasing silver content due to the random formation of silver microstructures within the glass network. Secondly, the SHG temperature dynamics were explored near the glass transition temperature (Tg) regime, where significant glass relaxation phenomena occur. It is found that heating towards the Tg improves the SHG efficiency, whereas above Tg, the capacity of glasses to generate second harmonic radiation is drastically suppressed. The novel findings of this work are considered important in terms of the potential employment of these glasses for the realization of advanced photonic applications like optical-switches and wavelength conversion devices.

Travelling and splitting of a wave of hedgehog expression involved in spider-head segmentation.

PubMed

Kanayama, Masaki; Akiyama-Oda, Yasuko; Nishimura, Osamu; Tarui, Hiroshi; Agata, Kiyokazu; Oda, Hiroki

2011-10-11

During development segmentation is a process that generates a spatial periodic pattern. Peak splitting of waves of gene expression is a mathematically predicted, simple strategy accounting for this type of process, but it has not been well characterized biologically. Here we show temporally repeated splitting of gene expression into stripes that is associated with head axis growth in the spider Achaearanea embryo. Preceding segmentation, a wave of hedgehog homologue gene expression is observed to travel posteriorly during development stage 6. This stripe, co-expressing an orthodenticle homologue, undergoes two cycles of splitting and shifting accompanied by convergent extension, serving as a generative zone for the head segments. The two orthodenticle and odd-paired homologues are identified as targets of Hedgehog signalling, and evidence suggests that their activities mediate feedback to maintain the head generative zone and to promote stripe splitting in this zone. We propose that the 'stripe-splitting' strategy employs genetic components shared with Drosophila blastoderm subdivision, which are required for participation in an autoregulatory signalling network.

Advances in Artificial Neural Networks - Methodological Development and Application

USDA-ARS?s Scientific Manuscript database

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

A two-stage flow-based intrusion detection model for next-generation networks.

PubMed

Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

A two-stage flow-based intrusion detection model for next-generation networks

PubMed Central

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results. PMID:29329294

A framework on the emergence and effectiveness of global health networks

PubMed Central

Shiffman, Jeremy; Quissell, Kathryn; Schmitz, Hans Peter; Pelletier, David L; Smith, Stephanie L; Berlan, David; Gneiting, Uwe; Van Slyke, David; Mergel, Ines; Rodriguez, Mariela; Walt, Gill

2016-01-01

Since 1990 mortality and morbidity decline has been more extensive for some conditions prevalent in low- and middle-income countries than for others. One reason may be differences in the effectiveness of global health networks, which have proliferated in recent years. Some may be more capable than others in attracting attention to a condition, in generating funding, in developing interventions and in convincing national governments to adopt policies. This article introduces a supplement on the emergence and effectiveness of global health networks. The supplement examines networks concerned with six global health problems: tuberculosis (TB), pneumonia, tobacco use, alcohol harm, maternal mortality and newborn deaths. This article presents a conceptual framework delineating factors that may shape why networks crystallize more easily surrounding some issues than others, and once formed, why some are better able than others to shape policy and public health outcomes. All supplement papers draw on this framework. The framework consists of 10 factors in three categories: (1) features of the networks and actors that comprise them, including leadership, governance arrangements, network composition and framing strategies; (2) conditions in the global policy environment, including potential allies and opponents, funding availability and global expectations concerning which issues should be prioritized; (3) and characteristics of the issue, including severity, tractability and affected groups. The article also explains the design of the project, which is grounded in comparison of networks surrounding three matched issues: TB and pneumonia, tobacco use and alcohol harm, and maternal and newborn survival. Despite similar burden and issue characteristics, there has been considerably greater policy traction for the first in each pair. The supplement articles aim to explain the role of networks in shaping these differences, and collectively represent the first comparative effort to understand the emergence and effectiveness of global health networks. PMID:26318679

Characterization of emergent synaptic topologies in noisy neural networks

NASA Astrophysics Data System (ADS)

Miller, Aaron James

Learned behaviors are one of the key contributors to an animal's ultimate survival. It is widely believed that the brain's microcircuitry undergoes structural changes when a new behavior is learned. In particular, motor learning, during which an animal learns a sequence of muscular movements, often requires precisely-timed coordination between muscles and becomes very natural once ingrained. Experiments show that neurons in the motor cortex exhibit precisely-timed spike activity when performing a learned motor behavior, and constituent stereotypical elements of the behavior can last several hundred milliseconds. The subject of this manuscript concerns how organized synaptic structures that produce stereotypical spike sequences emerge from random, dynamical networks. After a brief introduction in Chapter 1, we begin Chapter 2 by introducing a spike-timing-dependent plasticity (STDP) rule that defines how the activity of the network drives changes in network topology. The rule is then applied to idealized networks of leaky integrate-and-fire neurons (LIF). These neurons are not subjected to the variability that typically characterize neurons in vivo. In noiseless networks, synapses develop closed loops of strong connectivity that reproduce stereotypical, precisely-timed spike patterns from an initially random network. We demonstrate the characteristics of the asymptotic synaptic configuration are dependent on the statistics of the initial random network. The spike timings of the neurons simulated in Chapter 2 are generated exactly by a computationally economical, nonlinear mapping which is extended to LIF neurons injected with fluctuating current in Chapter 3. Development of an economical mapping that incorporates noise provides a practical solution to the long simulation times required to produce asymptotic synaptic topologies in networks with STDP in the presence of realistic neuronal variability. The mapping relies on generating numerical solutions to the dynamics of a LIF neuron subjected to Gaussian white noise (GWN). The system reduces to the Ornstein-Uhlenbeck first passage time problem, the solution of which we build into the mapping method of Chapter 2. We demonstrate that simulations using the stochastic mapping have reduced computation time compared to traditional Runge-Kutta methods by more than a factor of 150. In Chapter 4, we use the stochastic mapping to study the dynamics of emerging synaptic topologies in noisy networks. With the addition of membrane noise, networks with dynamical synapses can admit states in which the distribution of the synaptic weights is static under spontaneous activity, but the random connectivity between neurons is dynamical. The widely cited problem of instabilities in networks with STDP is avoided with the implementation of a synaptic decay and an activation threshold on each synapse. When such networks are presented with stimulus modeled by a focused excitatory current, chain-like networks can emerge with the addition of an axon-remodeling plasticity rule, a topological constraint on the connectivity modeling the finite resources available to each neuron. The emergent topologies are the result of an iterative stochastic process. The dynamics of the growth process suggest a strong interplay between the network topology and the spike sequences they produce during development. Namely, the existence of an embedded spike sequence alters the distribution of synaptic weights through the entire network. The roles of model parameters that affect the interplay between network structure and activity are elucidated. Finally, we propose two mathematical growth models, which are complementary, that capture the essence of the growth dynamics observed in simulations. In Chapter 5, we present an extension of the stochastic mapping that allows the possibility of neuronal cooperation. We demonstrate that synaptic topologies admitting stereotypical sequences can emerge in yet higher, biologically realistic levels of membrane potential variability when neurons cooperate to innervate shared targets. The structure that is most robust to the variability is that of a synfire chain. The principles of growth dynamics detailed in Chapter 4 are the same that sculpt the emergent synfire topologies. We conclude by discussing avenues for extensions of these results.

Network topologies and convergent aetiologies arising from deletions and duplications observed in individuals with autism.

PubMed

Noh, Hyun Ji; Ponting, Chris P; Boulding, Hannah C; Meader, Stephen; Betancur, Catalina; Buxbaum, Joseph D; Pinto, Dalila; Marshall, Christian R; Lionel, Anath C; Scherer, Stephen W; Webber, Caleb

2013-06-01

Autism Spectrum Disorders (ASD) are highly heritable and characterised by impairments in social interaction and communication, and restricted and repetitive behaviours. Considering four sets of de novo copy number variants (CNVs) identified in 181 individuals with autism and exploiting mouse functional genomics and known protein-protein interactions, we identified a large and significantly interconnected interaction network. This network contains 187 genes affected by CNVs drawn from 45% of the patients we considered and 22 genes previously implicated in ASD, of which 192 form a single interconnected cluster. On average, those patients with copy number changed genes from this network possess changes in 3 network genes, suggesting that epistasis mediated through the network is extensive. Correspondingly, genes that are highly connected within the network, and thus whose copy number change is predicted by the network to be more phenotypically consequential, are significantly enriched among patients that possess only a single ASD-associated network copy number changed gene (p = 0.002). Strikingly, deleted or disrupted genes from the network are significantly enriched in GO-annotated positive regulators (2.3-fold enrichment, corrected p = 2×10(-5)), whereas duplicated genes are significantly enriched in GO-annotated negative regulators (2.2-fold enrichment, corrected p = 0.005). The direction of copy change is highly informative in the context of the network, providing the means through which perturbations arising from distinct deletions or duplications can yield a common outcome. These findings reveal an extensive ASD-associated molecular network, whose topology indicates ASD-relevant mutational deleteriousness and that mechanistically details how convergent aetiologies can result extensively from CNVs affecting pathways causally implicated in ASD.

2016 All Bugs Good and Bad Webinar Series - eXtension

Science.gov Websites

Urban Agriculture, Texas A&M AgriLife Extension and Clemson Cooperative Extension. Series / NIFA This work is supported by the USDA National Institute of Food and Agriculture, New Technologies Internationalizing Extension Network Literacy Program Evaluation Volunteer Administration Women in Agriculture

How Are Television Networks Involved in Distance Learning?

ERIC Educational Resources Information Center

Bucher, Katherine

1996-01-01

Reviews the involvement of various television networks in distance learning, including public broadcasting stations, Cable in the Classroom, Arts and Entertainment Network, Black Entertainment Television, C-SPAN, CNN (Cable News Network), The Discovery Channel, The Learning Channel, Mind Extension University, The Weather Channel, National Teacher…

MERI: an ultra-long-baseline Moon-Earth radio interferometer.

NASA Astrophysics Data System (ADS)

Burns, J. O.

Radiofrequency aperture synthesis, pioneered by Ryle and his colleagues at Cambridge in the 1960's, has evolved to ever longer baselines and larger arrays in recent years. The limiting resolution at a given frequency for modern ground-based very-long-baseline interferometry is simply determined by the physical diameter of the Earth. A second-generation, totally space-based VLB network was proposed recently by a group at the Naval Research Laboratory. The next logical extension of space-based VLBI would be a station or stations on the Moon. The Moon could serve as an outpost or even the primary correlator station for an extended array of space-based antennas.

Supporting Weather Data

NASA Technical Reports Server (NTRS)

2004-01-01

Since its founding in 1992, Global Science & Technology, Inc. (GST), of Greenbelt, Maryland, has been developing technologies and providing services in support of NASA scientific research. GST specialties include scientific analysis, science data and information systems, data visualization, communications, networking and Web technologies, computer science, and software system engineering. As a longtime contractor to Goddard Space Flight Center s Earth Science Directorate, GST scientific, engineering, and information technology staff have extensive qualifications with the synthesis of satellite, in situ, and Earth science data for weather- and climate-related projects. GST s experience in this arena is end-to-end, from building satellite ground receiving systems and science data systems, to product generation and research and analysis.

Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations

NASA Technical Reports Server (NTRS)

Shapiro, Bruce E.; Levchenko, Andre; Meyerowitz, Elliot M.; Wold, Barbara J.; Mjolsness, Eric D.

2003-01-01

Cellerator describes single and multi-cellular signal transduction networks (STN) with a compact, optionally palette-driven, arrow-based notation to represent biochemical reactions and transcriptional activation. Multi-compartment systems are represented as graphs with STNs embedded in each node. Interactions include mass-action, enzymatic, allosteric and connectionist models. Reactions are translated into differential equations and can be solved numerically to generate predictive time courses or output as systems of equations that can be read by other programs. Cellerator simulations are fully extensible and portable to any operating system that supports Mathematica, and can be indefinitely nested within larger data structures to produce highly scaleable models.

Generative models for network neuroscience: prospects and promise

PubMed Central

Betzel, Richard F.

2017-01-01

Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modelling approach for network neuroscience. PMID:29187640

Do-it-yourself networks: a novel method of generating weighted networks.

PubMed

Shanafelt, D W; Salau, K R; Baggio, J A

2017-11-01

Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes.

Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data.

PubMed

Erdoğdu, Utku; Tan, Mehmet; Alhajj, Reda; Polat, Faruk; Rokne, Jon; Demetrick, Douglas

2013-01-01

The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framework could successfully complement the actual sample generation from real cases. This argument is validated in this paper by describing a framework that integrates multiple models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a highly demanding area that has not received attention. The three perspectives employed in the process are based on models that are not closely related. The independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The first model is based on the Probabilistic Boolean Network (PBN) representation of the gene regulatory network underlying the given gene expression data. The second model integrates Hierarchical Markov Model (HIMM) and the third model employs a genetic algorithm in the process. Each model learns as much as possible characteristics of the domain being analysed and tries to incorporate the learned characteristics in generating new samples. In other words, the models base their analysis on domain knowledge implicitly present in the data itself. The developed framework has been extensively tested by checking how the new samples complement the original samples. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.

Towards Integrating Distributed Energy Resources and Storage Devices in Smart Grid.

PubMed

Xu, Guobin; Yu, Wei; Griffith, David; Golmie, Nada; Moulema, Paul

2017-02-01

Internet of Things (IoT) provides a generic infrastructure for different applications to integrate information communication techniques with physical components to achieve automatic data collection, transmission, exchange, and computation. The smart grid, as one of typical applications supported by IoT, denoted as a re-engineering and a modernization of the traditional power grid, aims to provide reliable, secure, and efficient energy transmission and distribution to consumers. How to effectively integrate distributed (renewable) energy resources and storage devices to satisfy the energy service requirements of users, while minimizing the power generation and transmission cost, remains a highly pressing challenge in the smart grid. To address this challenge and assess the effectiveness of integrating distributed energy resources and storage devices, in this paper we develop a theoretical framework to model and analyze three types of power grid systems: the power grid with only bulk energy generators, the power grid with distributed energy resources, and the power grid with both distributed energy resources and storage devices. Based on the metrics of the power cumulative cost and the service reliability to users, we formally model and analyze the impact of integrating distributed energy resources and storage devices in the power grid. We also use the concept of network calculus, which has been traditionally used for carrying out traffic engineering in computer networks, to derive the bounds of both power supply and user demand to achieve a high service reliability to users. Through an extensive performance evaluation, our data shows that integrating distributed energy resources conjointly with energy storage devices can reduce generation costs, smooth the curve of bulk power generation over time, reduce bulk power generation and power distribution losses, and provide a sustainable service reliability to users in the power grid.

Towards Integrating Distributed Energy Resources and Storage Devices in Smart Grid

PubMed Central

Xu, Guobin; Yu, Wei; Griffith, David; Golmie, Nada; Moulema, Paul

2017-01-01

Internet of Things (IoT) provides a generic infrastructure for different applications to integrate information communication techniques with physical components to achieve automatic data collection, transmission, exchange, and computation. The smart grid, as one of typical applications supported by IoT, denoted as a re-engineering and a modernization of the traditional power grid, aims to provide reliable, secure, and efficient energy transmission and distribution to consumers. How to effectively integrate distributed (renewable) energy resources and storage devices to satisfy the energy service requirements of users, while minimizing the power generation and transmission cost, remains a highly pressing challenge in the smart grid. To address this challenge and assess the effectiveness of integrating distributed energy resources and storage devices, in this paper we develop a theoretical framework to model and analyze three types of power grid systems: the power grid with only bulk energy generators, the power grid with distributed energy resources, and the power grid with both distributed energy resources and storage devices. Based on the metrics of the power cumulative cost and the service reliability to users, we formally model and analyze the impact of integrating distributed energy resources and storage devices in the power grid. We also use the concept of network calculus, which has been traditionally used for carrying out traffic engineering in computer networks, to derive the bounds of both power supply and user demand to achieve a high service reliability to users. Through an extensive performance evaluation, our data shows that integrating distributed energy resources conjointly with energy storage devices can reduce generation costs, smooth the curve of bulk power generation over time, reduce bulk power generation and power distribution losses, and provide a sustainable service reliability to users in the power grid1. PMID:29354654

Mechanisms of Innovation Diffusion under Information Abundance and Information Scarcity--On the Contribution of Social Networks in Group vs. Individual Extension Approaches in Semi-Arid Kenya

ERIC Educational Resources Information Center

Darr, Dietrich; Pretzsch, Jurgen

2008-01-01

Purpose: The objective of this paper is to assess the effectiveness of innovation diffusion under group-oriented and individual-oriented extension. Current theoretical notions of innovation diffusion in social networks shall be briefly reviewed, and the concepts of "search" and "innovation" vis-a-vis "transfer" and…

Non-harmful insertion of data mimicking computer network attacks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Neil, Joshua Charles; Kent, Alexander; Hash, Jr, Curtis Lee

Non-harmful data mimicking computer network attacks may be inserted in a computer network. Anomalous real network connections may be generated between a plurality of computing systems in the network. Data mimicking an attack may also be generated. The generated data may be transmitted between the plurality of computing systems using the real network connections and measured to determine whether an attack is detected.

Real-Time Dynamics of Emerging Actin Networks in Cell-Mimicking Compartments

PubMed Central

Deshpande, Siddharth; Pfohl, Thomas

2015-01-01

Understanding the cytoskeletal functionality and its relation to other cellular components and properties is a prominent question in biophysics. The dynamics of actin cytoskeleton and its polymorphic nature are indispensable for the proper functioning of living cells. Actin bundles are involved in cell motility, environmental exploration, intracellular transport and mechanical stability. Though the viscoelastic properties of actin-based structures have been extensively probed, the underlying microstructure dynamics, especially their disassembly, is not fully understood. In this article, we explore the rich dynamics and emergent properties exhibited by actin bundles within flow-free confinements using a microfluidic set-up and epifluorescence microscopy. After forming entangled actin filaments within cell-sized quasi two-dimensional confinements, we induce their bundling using three different fundamental mechanisms: counterion condensation, depletion interactions and specific protein-protein interactions. Intriguingly, long actin filaments form emerging networks of actin bundles via percolation leading to remarkable properties such as stress generation and spindle-like intermediate structures. Simultaneous sharing of filaments in different links of the network is an important parameter, as short filaments do not form networks but segregated clusters of bundles instead. We encounter a hierarchical process of bundling and its subsequent disassembly. Additionally, our study suggests that such percolated networks are likely to exist within living cells in a dynamic fashion. These observations render a perspective about differential cytoskeletal responses towards numerous stimuli. PMID:25785606

Neural network modeling of the kinetics of SO2 removal by fly ash-based sorbent.

PubMed

Raymond-Ooi, E H; Lee, K T; Mohamed, A R; Chu, K H

2006-01-01

The mechanistic modeling of the sulfation reaction between fly ash-based sorbent and SO2 is a challenging task due to a variety reasons including the complexity of the reaction itself and the inability to measure some of the key parameters of the reaction. In this work, the possibility of modeling the sulfation reaction kinetics using a purely data-driven neural network was investigated. Experiments on SO2 removal by a sorbent prepared from coal fly ash/CaO/CaSO4 were conducted using a fixed bed reactor to generate a database to train and validate the neural network model. Extensive SO2 removal data points were obtained by varying three process variables, namely, SO2 inlet concentration (500-2000 mg/L), reaction temperature (60-80 degreesC), and relative humidity (50-70%), as a function of reaction time (0-60 min). Modeling results show that the neural network can provide excellent fits to the SO2 removal data after considerable training and can be successfully used to predict the extent of SO2 removal as a function of time even when the process variables are outside the training domain. From a modeling standpoint, the suitably trained and validated neural network with excellent interpolation and extrapolation properties could have immediate practical benefits in the absence of a theoretical model.

Effects of traffic generation patterns on the robustness of complex networks

NASA Astrophysics Data System (ADS)

Wu, Jiajing; Zeng, Junwen; Chen, Zhenhao; Tse, Chi K.; Chen, Bokui

2018-02-01

Cascading failures in communication networks with heterogeneous node functions are studied in this paper. In such networks, the traffic dynamics are highly dependent on the traffic generation patterns which are in turn determined by the locations of the hosts. The data-packet traffic model is applied to Barabási-Albert scale-free networks to study the cascading failures in such networks and to explore the effects of traffic generation patterns on network robustness. It is found that placing the hosts at high-degree nodes in a network can make the network more robust against both intentional attacks and random failures. It is also shown that the traffic generation pattern plays an important role in network design.

SpectralNET – an application for spectral graph analysis and visualization

PubMed Central

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-01-01

Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request. PMID:16236170

SpectralNET--an application for spectral graph analysis and visualization.

PubMed

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-10-19

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Bioengineering vascularized tissue constructs using an injectable cell-laden enzymatically crosslinked collagen hydrogel derived from dermal extracellular matrix.

PubMed

Kuo, Kuan-Chih; Lin, Ruei-Zeng; Tien, Han-Wen; Wu, Pei-Yun; Li, Yen-Cheng; Melero-Martin, Juan M; Chen, Ying-Chieh

2015-11-01

Tissue engineering promises to restore or replace diseased or damaged tissue by creating functional and transplantable artificial tissues. The development of artificial tissues with large dimensions that exceed the diffusion limitation will require nutrients and oxygen to be delivered via perfusion instead of diffusion alone over a short time period. One approach to perfusion is to vascularize engineered tissues, creating a de novo three-dimensional (3D) microvascular network within the tissue construct. This significantly shortens the time of in vivo anastomosis, perfusion and graft integration with the host. In this study, we aimed to develop injectable allogeneic collagen-phenolic hydroxyl (collagen-Ph) hydrogels that are capable of controlling a wide range of physicochemical properties, including stiffness, water absorption and degradability. We tested whether collagen-Ph hydrogels could support the formation of vascularized engineered tissue graft by human blood-derived endothelial colony-forming cells (ECFCs) and bone marrow-derived mesenchymal stem cells (MSC) in vivo. First, we studied the growth of adherent ECFCs and MSCs on or in the hydrogels. To examine the potential formation of functional vascular networks in vivo, a liquid pre-polymer solution of collagen-Ph containing human ECFCs and MSCs, horseradish peroxidase and hydrogen peroxide was injected into the subcutaneous space or abdominal muscle defect of an immunodeficient mouse before gelation, to form a 3D cell-laden polymerized construct. These results showed that extensive human ECFC-lined vascular networks can be generated within 7 days, the engineered vascular density inside collagen-Ph hydrogel constructs can be manipulated through refinable mechanical properties and proteolytic degradability, and these networks can form functional anastomoses with the existing vasculature to further support the survival of host muscle tissues. Finally, optimized conditions of the cell-laden collagen-Ph hydrogel resulted in not only improving the long-term differentiation of transplanted MSCs into mineralized osteoblasts, but the collagen-Ph hydrogel also improved an increased of adipocytes within the vascularized bioengineered tissue in a mouse after 1 month of implantation. We reported a method for preparing autologous extracellular matrix scaffolds, murine collagen-Ph hydrogels, and demonstrated its suitability for use in supporting human progenitor cell-based formation of 3D vascular networks in vitro and in vivo. Results showed extensive human vascular networks can be generated within 7 days, engineered vascular density inside collagen-Ph constructs can be manipulated through refinable mechanical properties and proteolytic degradability, and these networks can form functional anastomoses with existing vasculature to further support the survival of host muscle tissues. Moreover, optimized conditions of cell-laden collagen-Ph hydrogel resulted in not only improving the long-term differentiation of transplanted MSCs into mineralized osteoblasts, but the collagen-Ph hydrogel also improved an increased of adipocytes within the vascularized bioengineered tissue in a mouse. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Controls on stream network branching angles, tested using landscape evolution models

NASA Astrophysics Data System (ADS)

Theodoratos, Nikolaos; Seybold, Hansjörg; Kirchner, James W.

2016-04-01

Stream networks are striking landscape features. The topology of stream networks has been extensively studied, but their geometry has received limited attention. Analyses of nearly 1 million stream junctions across the contiguous United States [1] have revealed that stream branching angles vary systematically with climate and topographic gradients at continental scale. Stream networks in areas with wet climates and gentle slopes tend to have wider branching angles than in areas with dry climates or steep slopes, but the mechanistic linkages underlying these empirical correlations remain unclear. Under different climatic and topographic conditions different runoff generation mechanisms and, consequently, transport processes are dominant. Models [2] and experiments [3] have shown that the relative strength of channel incision versus diffusive hillslope transport controls the spacing between valleys, an important geometric property of stream networks. We used landscape evolution models (LEMs) to test whether similar factors control network branching angles as well. We simulated stream networks using a wide range of hillslope diffusion and channel incision parameters. The resulting branching angles vary systematically with the parameters, but by much less than the regional variability in real-world stream networks. Our results suggest that the competition between hillslope and channeling processes influences branching angles, but that other mechanisms may also be needed to account for the variability in branching angles observed in the field. References: [1] H. Seybold, D. H. Rothman, and J. W. Kirchner, 2015, Climate's watermark in the geometry of river networks, Submitted manuscript. [2] J. T. Perron, W. E. Dietrich, and J. W. Kirchner, 2008, Controls on the spacing of first-order valleys, Journal of Geophysical Research, 113, F04016. [3] K. E. Sweeney, J. J. Roering, and C. Ellis, 2015, Experimental evidence for hillslope control of landscape scale, Science, 349(6243), 51-53.

Predicting Slag Generation in Sub-Scale Test Motors Using a Neural Network

NASA Technical Reports Server (NTRS)

Wiesenberg, Brent

1999-01-01

Generation of slag (aluminum oxide) is an important issue for the Reusable Solid Rocket Motor (RSRM). Thiokol performed testing to quantify the relationship between raw material variations and slag generation in solid propellants by testing sub-scale motors cast with propellant containing various combinations of aluminum fuel and ammonium perchlorate (AP) oxidizer particle sizes. The test data were analyzed using statistical methods and an artificial neural network. This paper primarily addresses the neural network results with some comparisons to the statistical results. The neural network showed that the particle sizes of both the aluminum and unground AP have a measurable effect on slag generation. The neural network analysis showed that aluminum particle size is the dominant driver in slag generation, about 40% more influential than AP. The network predictions of the amount of slag produced during firing of sub-scale motors were 16% better than the predictions of a statistically derived empirical equation. Another neural network successfully characterized the slag generated during full-scale motor tests. The success is attributable to the ability of neural networks to characterize multiple complex factors including interactions that affect slag generation.

Design and performance of a sericin-alginate interpenetrating network hydrogel for cell and drug delivery.

PubMed

Zhang, Yeshun; Liu, Jia; Huang, Lei; Wang, Zheng; Wang, Lin

2015-07-24

Although alginate hydrogels have been extensively studied for tissue engineering applications, their utilization is limited by poor mechanical strength, rapid drug release, and a lack of cell adhesive ability. Aiming to improve these properties, we employ the interpenetrating hydrogel design rationale. Using alginate and sericin (a natural protein with many unique properties and a major component of silkworm silk), we develop an interpenetrating polymer network (IPN) hydrogel comprising interwoven sericin and alginate double networks. By adjusting the sericin-to-alginate ratios, IPNs' mechanical strength can be adjusted to meet stiffness requirements for various tissue repairs. The IPNs with high sericin content show increased stability during degradation, avoiding pure alginate's early collapse. These IPNs have high swelling ratios, benefiting various applications such as drug delivery. The IPNs sustain controlled drug release with the adjustable rates. Furthermore, these IPNs are adhesive to cells, supporting cell proliferation, long-term survival and migration. Notably, the IPNs inherit sericin's photoluminescent property, enabling bioimaging in vivo. Together, our study indicates that the sericin-alginate IPN hydrogels may serve as a versatile platform for delivering cells and drugs, and suggests that sericin may be a building block broadly applicable for generating IPN networks with other biomaterials for diverse tissue engineering applications.

Design and performance of a sericin-alginate interpenetrating network hydrogel for cell and drug delivery

PubMed Central

Zhang, Yeshun; Liu, Jia; Huang, Lei; Wang, Zheng; Wang, Lin

2015-01-01

Although alginate hydrogels have been extensively studied for tissue engineering applications, their utilization is limited by poor mechanical strength, rapid drug release, and a lack of cell adhesive ability. Aiming to improve these properties, we employ the interpenetrating hydrogel design rationale. Using alginate and sericin (a natural protein with many unique properties and a major component of silkworm silk), we develop an interpenetrating polymer network (IPN) hydrogel comprising interwoven sericin and alginate double networks. By adjusting the sericin-to-alginate ratios, IPNs’ mechanical strength can be adjusted to meet stiffness requirements for various tissue repairs. The IPNs with high sericin content show increased stability during degradation, avoiding pure alginate’s early collapse. These IPNs have high swelling ratios, benefiting various applications such as drug delivery. The IPNs sustain controlled drug release with the adjustable rates. Furthermore, these IPNs are adhesive to cells, supporting cell proliferation, long-term survival and migration. Notably, the IPNs inherit sericin’s photoluminescent property, enabling bioimaging in vivo. Together, our study indicates that the sericin-alginate IPN hydrogels may serve as a versatile platform for delivering cells and drugs, and suggests that sericin may be a building block broadly applicable for generating IPN networks with other biomaterials for diverse tissue engineering applications. PMID:26205586

Design and performance of a sericin-alginate interpenetrating network hydrogel for cell and drug delivery

NASA Astrophysics Data System (ADS)

Zhang, Yeshun; Liu, Jia; Huang, Lei; Wang, Zheng; Wang, Lin

2015-07-01

Although alginate hydrogels have been extensively studied for tissue engineering applications, their utilization is limited by poor mechanical strength, rapid drug release, and a lack of cell adhesive ability. Aiming to improve these properties, we employ the interpenetrating hydrogel design rationale. Using alginate and sericin (a natural protein with many unique properties and a major component of silkworm silk), we develop an interpenetrating polymer network (IPN) hydrogel comprising interwoven sericin and alginate double networks. By adjusting the sericin-to-alginate ratios, IPNs’ mechanical strength can be adjusted to meet stiffness requirements for various tissue repairs. The IPNs with high sericin content show increased stability during degradation, avoiding pure alginate’s early collapse. These IPNs have high swelling ratios, benefiting various applications such as drug delivery. The IPNs sustain controlled drug release with the adjustable rates. Furthermore, these IPNs are adhesive to cells, supporting cell proliferation, long-term survival and migration. Notably, the IPNs inherit sericin’s photoluminescent property, enabling bioimaging in vivo. Together, our study indicates that the sericin-alginate IPN hydrogels may serve as a versatile platform for delivering cells and drugs, and suggests that sericin may be a building block broadly applicable for generating IPN networks with other biomaterials for diverse tissue engineering applications.

Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

PubMed Central

Moghadas, Amin A.; Shadaram, Mehdi

2010-01-01

In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG). The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system. PMID:22163416

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

Applying Strategic Visualization(Registered Trademark) to Lunar and Planetary Mission Design

NASA Technical Reports Server (NTRS)

Frassanito, John R.; Cooke, D. R.

2002-01-01

NASA teams, such as the NASA Exploration Team (NEXT), utilize advanced computational visualization processes to develop mission designs and architectures for lunar and planetary missions. One such process, Strategic Visualization (trademark), is a tool used extensively to help mission designers visualize various design alternatives and present them to other participants of their team. The participants, which may include NASA, industry, and the academic community, are distributed within a virtual network. Consequently, computer animation and other digital techniques provide an efficient means to communicate top-level technical information among team members. Today,Strategic Visualization(trademark) is used extensively both in the mission design process within the technical community, and to communicate the value of space exploration to the general public. Movies and digital images have been generated and shown on nationally broadcast television and the Internet, as well as in magazines and digital media. In our presentation will show excerpts of a computer-generated animation depicting the reference Earth/Moon L1 Libration Point Gateway architecture. The Gateway serves as a staging corridor for human expeditions to the lunar poles and other surface locations. Also shown are crew transfer systems and current reference lunar excursion vehicles as well as the Human and robotic construction of an inflatable telescope array for deployment to the Sun/Earth Libration Point.

What Presidents Need To Know about the Impact of Networking.

ERIC Educational Resources Information Center

Leadership Abstracts, 1993

1993-01-01

Many colleges and universities are undergoing cultural changes as a result of extensive voice, data, and video networking. Local area networks link large portions of most campuses, and national networks have evolved from specialized services for researchers in computer-related disciplines to general utilities on many campuses. Campuswide systems…

The Roland Maze Project school-based extensive air shower network

NASA Astrophysics Data System (ADS)

Feder, J.; Jȩdrzejczak, K.; Karczmarczyk, J.; Lewandowski, R.; Swarzyński, J.; Szabelska, B.; Szabelski, J.; Wibig, T.

2006-01-01

We plan to construct the large area network of extensive air shower detectors placed on the roofs of high school buildings in the city of Łódź. Detection points will be connected by INTERNET to the central server and their work will be synchronized by GPS. The main scientific goal of the project are studies of ultra high energy cosmic rays. Using existing town infrastructure (INTERNET, power supply, etc.) will significantly reduce the cost of the experiment. Engaging high school students in the research program should significantly increase their knowledge of science and modern technologies, and can be a very efficient way of science popularisation. We performed simulations of the projected network capabilities of registering Extensive Air Showers and reconstructing energies of primary particles. Results of the simulations and the current status of project realisation will be presented.

System crash as dynamics of complex networks.

PubMed

Yu, Yi; Xiao, Gaoxi; Zhou, Jie; Wang, Yubo; Wang, Zhen; Kurths, Jürgen; Schellnhuber, Hans Joachim

2016-10-18

Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the "peculiar" dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed.

Perceptual reversals during binocular rivalry: ERP components and their concomitant source differences.

PubMed

Britz, Juliane; Pitts, Michael A

2011-11-01

We used an intermittent stimulus presentation to investigate event-related potential (ERP) components associated with perceptual reversals during binocular rivalry. The combination of spatiotemporal ERP analysis with source imaging and statistical parametric mapping of the concomitant source differences yielded differences in three time windows: reversals showed increased activity in early visual (∼120 ms) and in inferior frontal and anterior temporal areas (∼400-600 ms) and decreased activity in the ventral stream (∼250-350 ms). The combination of source imaging and statistical parametric mapping suggests that these differences were due to differences in generator strength and not generator configuration, unlike the initiation of reversals in right inferior parietal areas. These results are discussed within the context of the extensive network of brain areas that has been implicated in the initiation, implementation, and appraisal of bistable perceptual reversals. Copyright © 2011 Society for Psychophysiological Research.

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

In recent years, spatial and temporal flux data coverage improved significantly and on multiple scales, from a single station to continental networks, due to standardization, automation, and management of the data collection, and better handling of the extensive amounts of generated data. However, operating budgets for flux research items, such as labor, travel, and hardware, are becoming more difficult to acquire and sustain. With more stations and networks, larger data flows from each station, and smaller operating budgets, modern tools are required to effectively and efficiently handle the entire process, including sharing data among collaborative groups. On one hand, such tools can maximize time dedicated to publications answering research questions, and minimize time and expenses spent on data acquisition, processing, quality control and overall station management. On the other hand, cross-sharing the stations with external collaborators may help leverage available funding, and promote data analyses and publications. A new low-cost, advanced system, FluxSuite, utilizes a combination of hardware, software and web-services 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: (i) The system can be easily incorporated into a new flux station, or as un upgrade to many presently operating flux stations, via weatherized remotely-accessible microcomputer, SmartFlux 2, with fully digital inputs (ii) Each next-generation station will measure all parameters needed for flux computations in a digital and PTP time-synchronized mode, accepting digital signals from a number of anemometers and data loggers (iii) The field microcomputer will calculate final fully-processed flux rates in real time, including computation-intensive Fourier transforms, spectra, co-spectra, multiple rotations, stationarity, footprint, etc. (iv) Final fluxes, radiation, weather and soil data will 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.

Generative adversarial networks for brain lesion detection

NASA Astrophysics Data System (ADS)

Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

2017-02-01

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Networks within networks: The neuronal control of breathing

PubMed Central

Garcia, Alfredo J.; Zanella, Sebastien; Koch, Henner; Doi, Atsushi; Ramirez, Jan-Marino

2013-01-01

Breathing emerges through complex network interactions involving neurons distributed throughout the nervous system. The respiratory rhythm generating network is composed of micro networks functioning within larger networks to generate distinct rhythms and patterns that characterize breathing. The pre-Bötzinger complex, a rhythm generating network located within the ventrolateral medulla assumes a core function without which respiratory rhythm generation and breathing cease altogether. It contains subnetworks with distinct synaptic and intrinsic membrane properties that give rise to different types of respiratory rhythmic activities including eupneic, sigh, and gasping activities. While critical aspects of these rhythmic activities are preserved when isolated in in vitro preparations, the pre-Bötzinger complex functions in the behaving animal as part of a larger network that receives important inputs from areas such as the pons and parafacial nucleus. The respiratory network is also an integrator of modulatory and sensory inputs that imbue the network with the important ability to adapt to changes in the behavioral, metabolic, and developmental conditions of the organism. This review summarizes our current understanding of these interactions and relates the emerging concepts to insights gained in other rhythm generating networks. PMID:21333801

How citation distortions create unfounded authority: analysis of a citation network

PubMed Central

2009-01-01

Objective To understand belief in a specific scientific claim by studying the pattern of citations among papers stating it. Design A complete citation network was constructed from all PubMed indexed English literature papers addressing the belief that β amyloid, a protein accumulated in the brain in Alzheimer’s disease, is produced by and injures skeletal muscle of patients with inclusion body myositis. Social network theory and graph theory were used to analyse this network. Main outcome measures Citation bias, amplification, and invention, and their effects on determining authority. Results The network contained 242 papers and 675 citations addressing the belief, with 220 553 citation paths supporting it. Unfounded authority was established by citation bias against papers that refuted or weakened the belief; amplification, the marked expansion of the belief system by papers presenting no data addressing it; and forms of invention such as the conversion of hypothesis into fact through citation alone. Extension of this network into text within grants funded by the National Institutes of Health and obtained through the Freedom of Information Act showed the same phenomena present and sometimes used to justify requests for funding. Conclusion Citation is both an impartial scholarly method and a powerful form of social communication. Through distortions in its social use that include bias, amplification, and invention, citation can be used to generate information cascades resulting in unfounded authority of claims. Construction and analysis of a claim specific citation network may clarify the nature of a published belief system and expose distorted methods of social citation. PMID:19622839

Identification of PEG-induced water stress responsive transcripts using co-expression network in Eucalyptus grandis.

PubMed

Ghosh Dasgupta, Modhumita; Dharanishanthi, Veeramuthu

2017-09-05

Ecophysiological studies in Eucalyptus have shown that water is the principal factor limiting stem growth. Effect of water deficit conditions on physiological and biochemical parameters has been extensively reported in Eucalyptus. The present study was conducted to identify major polyethylene glycol induced water stress responsive transcripts in Eucalyptus grandis using gene co-expression network. A customized array representing 3359 water stress responsive genes was designed to document their expression in leaves of E. grandis cuttings subjected to -0.225MPa of PEG treatment. The differentially expressed transcripts were documented and significantly co-expressed transcripts were used for construction of network. The co-expression network was constructed with 915 nodes and 3454 edges with degree ranging from 2 to 45. Ninety four GO categories and 117 functional pathways were identified in the network. MCODE analysis generated 27 modules and module 6 with 479 nodes and 1005 edges was identified as the biologically relevant network. The major water responsive transcripts represented in the module included dehydrin, osmotin, LEA protein, expansin, arabinogalactans, heat shock proteins, major facilitator proteins, ARM repeat proteins, raffinose synthase, tonoplast intrinsic protein and transcription factors like DREB2A, ARF9, AGL24, UNE12, WLIM1 and MYB66, MYB70, MYB 55, MYB 16 and MYB 103. The coordinated analysis of gene expression patterns and coexpression networks developed in this study identified an array of transcripts that may regulate PEG induced water stress responses in E. grandis. Copyright © 2017 Elsevier B.V. All rights reserved.

Local-Area-Network Simulator

NASA Technical Reports Server (NTRS)

Gibson, Jim; Jordan, Joe; Grant, Terry

1990-01-01

Local Area Network Extensible Simulator (LANES) computer program provides method for simulating performance of high-speed local-area-network (LAN) technology. Developed as design and analysis software tool for networking computers on board proposed Space Station. Load, network, link, and physical layers of layered network architecture all modeled. Mathematically models according to different lower-layer protocols: Fiber Distributed Data Interface (FDDI) and Star*Bus. Written in FORTRAN 77.

Automated Run-Time Mission and Dialog Generation

DTIC Science & Technology

2007-03-01

Processing, Social Network Analysis, Simulation, Automated Scenario Generation 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified...9 D. SOCIAL NETWORKS...13 B. MISSION AND DIALOG GENERATION.................................................13 C. SOCIAL NETWORKS

Experiences in Interagency and International Interfaces for Mission Support

NASA Technical Reports Server (NTRS)

Dell, G. T.; Mitchell, W. J.; Thompson, T. W.; Cappellari, J. O., Jr.; Flores-Amaya, F.

1996-01-01

The Flight Dynamics Division (FDD) of the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GFSC) provides extensive support and products for Space Shuttle missions, expendable launch vehicle launches, and routine on-orbit operations for a variety of spacecraft. A major challenge in providing support for these missions is defining and generating the products required for mission support and developing the method by which these products are exchanged between supporting agencies. As interagency and international cooperation has increased in the space community, the FDD customer base has grown and with it the number and variety of external interfaces and product definitions. Currently, the FDD has working interfaces with the NASA Space and Ground Networks, the Johnson Space Center, the White Sands Complex, the Jet propulsion Laboratory (including the Deep Space Network), the United States Air Force, the Centre National d'Etudes Spatiales, the German Spaceflight Operations Center, the European Space Agency, and the National Space Development Agency of Japan. With the increasing spectrum of possible data product definitions and delivery methods, the FDD is using its extensive interagency experience to improve its support of established customers and to provide leadership in adapting/developing new interfaces. This paper describes the evolution of the interfaces between the FDD and its customers, discusses many of the joint activities ith these customers, and summarizes key lessons learned that can be applied to current and future support.

Quantifying capability of a local seismic network in terms of locations and focal mechanism solutions of weak earthquakes

NASA Astrophysics Data System (ADS)

Fojtíková, Lucia; Kristeková, Miriam; Málek, Jiří; Sokos, Efthimios; Csicsay, Kristián; Zahradník, Jiří

2016-01-01

Extension of permanent seismic networks is usually governed by a number of technical, economic, logistic, and other factors. Planned upgrade of the network can be justified by theoretical assessment of the network capability in terms of reliable estimation of the key earthquake parameters (e.g., location and focal mechanisms). It could be useful not only for scientific purposes but also as a concrete proof during the process of acquisition of the funding needed for upgrade and operation of the network. Moreover, the theoretical assessment can also identify the configuration where no improvement can be achieved with additional stations, establishing a tradeoff between the improvement and additional expenses. This paper presents suggestion of a combination of suitable methods and their application to the Little Carpathians local seismic network (Slovakia, Central Europe) monitoring epicentral zone important from the point of seismic hazard. Three configurations of the network are considered: 13 stations existing before 2011, 3 stations already added in 2011, and 7 new planned stations. Theoretical errors of the relative location are estimated by a new method, specifically developed in this paper. The resolvability of focal mechanisms determined by waveform inversion is analyzed by a recent approach based on 6D moment-tensor error ellipsoids. We consider potential seismic events situated anywhere in the studied region, thus enabling "mapping" of the expected errors. Results clearly demonstrate that the network extension remarkably decreases the errors, mainly in the planned 23-station configuration. The already made three-station extension of the network in 2011 allowed for a few real data examples. Free software made available by the authors enables similar application in any other existing or planned networks.

Heralded entangling quantum gate via cavity-assisted photon scattering

NASA Astrophysics Data System (ADS)

Borges, Halyne S.; Rossatto, Daniel Z.; Luiz, Fabrício S.; Villas-Boas, Celso J.

2018-01-01

We theoretically investigate the generation of heralded entanglement between two identical atoms via cavity-assisted photon scattering in two different configurations, namely, either both atoms confined in the same cavity or trapped into locally separated ones. Our protocols are given by a very simple and elegant single-step process, the key mechanism of which is a controlled-phase-flip gate implemented by impinging a single photon on single-sided cavities. In particular, when the atoms are localized in remote cavities, we introduce a single-step parallel quantum circuit instead of the serial process extensively adopted in the literature. We also show that such parallel circuit can be straightforwardly applied to entangle two macroscopic clouds of atoms. Both protocols proposed here predict a high entanglement degree with a success probability close to unity for state-of-the-art parameters. Among other applications, our proposal and its extension to multiple atom-cavity systems step toward a suitable route for quantum networking, in particular for quantum state transfer, quantum teleportation, and nonlocal quantum memory.

The importance of structural softening for the evolution and architecture of passive margins

PubMed Central

Duretz, T.; Petri, B.; Mohn, G.; Schmalholz, S. M.; Schenker, F. L.; Müntener, O.

2016-01-01

Lithospheric extension can generate passive margins that bound oceans worldwide. Detailed geological and geophysical studies in present and fossil passive margins have highlighted the complexity of their architecture and their multi-stage deformation history. Previous modeling studies have shown the significant impact of coarse mechanical layering of the lithosphere (2 to 4 layer crust and mantle) on passive margin formation. We built upon these studies and design high-resolution (~100–300 m) thermo-mechanical numerical models that incorporate finer mechanical layering (kilometer scale) mimicking tectonically inherited heterogeneities. During lithospheric extension a variety of extensional structures arises naturally due to (1) structural softening caused by necking of mechanically strong layers and (2) the establishment of a network of weak layers across the deforming multi-layered lithosphere. We argue that structural softening in a multi-layered lithosphere is the main cause for the observed multi-stage evolution and architecture of magma-poor passive margins. PMID:27929057

A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

PubMed Central

Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin

2016-01-01

Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay. PMID:27618044

Resource Aware Intelligent Network Services (RAINS) Final Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lehman, Tom; Yang, Xi

The Resource Aware Intelligent Network Services (RAINS) project conducted research and developed technologies in the area of cyber infrastructure resource modeling and computation. The goal of this work was to provide a foundation to enable intelligent, software defined services which spanned the network AND the resources which connect to the network. A Multi-Resource Service Plane (MRSP) was defined, which allows resource owners/managers to locate and place themselves from a topology and service availability perspective within the dynamic networked cyberinfrastructure ecosystem. The MRSP enables the presentation of integrated topology views and computation results which can include resources across the spectrum ofmore » compute, storage, and networks. The RAINS project developed MSRP includes the following key components: i) Multi-Resource Service (MRS) Ontology/Multi-Resource Markup Language (MRML), ii) Resource Computation Engine (RCE), iii) Modular Driver Framework (to allow integration of a variety of external resources). The MRS/MRML is a general and extensible modeling framework that allows for resource owners to model, or describe, a wide variety of resource types. All resources are described using three categories of elements: Resources, Services, and Relationships between the elements. This modeling framework defines a common method for the transformation of cyber infrastructure resources into data in the form of MRML models. In order to realize this infrastructure datification, the RAINS project developed a model based computation system, i.e. “RAINS Computation Engine (RCE)”. The RCE has the ability to ingest, process, integrate, and compute based on automatically generated MRML models. The RCE interacts with the resources thru system drivers which are specific to the type of external network or resource controller. The RAINS project developed a modular and pluggable driver system which facilities a variety of resource controllers to automatically generate, maintain, and distribute MRML based resource descriptions. Once all of the resource topologies are absorbed by the RCE, a connected graph of the full distributed system topology is constructed, which forms the basis for computation and workflow processing. The RCE includes a Modular Computation Element (MCE) framework which allows for tailoring of the computation process to the specific set of resources under control, and the services desired. The input and output of an MCE are both model data based on MRS/MRML ontology and schema. Some of the RAINS project accomplishments include: Development of general and extensible multi-resource modeling framework; Design of a Resource Computation Engine (RCE) system which includes the following key capabilities; Absorb a variety of multi-resource model types and build integrated models; Novel architecture which uses model based communications across the full stack for all Flexible provision of abstract or intent based user facing interfaces; Workflow processing based on model descriptions; Release of the RCE as an open source software; Deployment of RCE in the University of Maryland/Mid-Atlantic Crossroad ScienceDMZ in prototype mode with a plan under way to transition to production; Deployment at the Argonne National Laboratory DTN Facility in prototype mode; Selection of RCE by the DOE SENSE (SDN for End-to-end Networked Science at the Exascale) project as the basis for their orchestration service.« less

Functional mapping of language networks in the normal brain using a word-association task.

PubMed

Ghosh, Shantanu; Basu, Amrita; Kumaran, Senthil S; Khushu, Subash

2010-08-01

Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI) in normal human subjects. Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2*-weighted gradient-echo echo-planar imaging (GE-EPI) sequence (TR 4523 ms, TE 64 ms, flip angle 90°) with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s) with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2) with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD) signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Single subject analysis of the functional data (FWE-corrected, P≤0.001) revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG), superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG), anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001) revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Group data analysis revealed a cerebellar-occipital-fusiform-thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these areas facilitate language comprehension by activating a semantic association network of words processed postlexical access. This finding is important when assessing the extent of cognitive damage and/or recovery and can be used for presurgical planning after optimization.

Distributed Framework for Dynamic Telescope and Instrument Control

NASA Astrophysics Data System (ADS)

Ames, Troy J.; Case, Lynne

2002-12-01

Traditionally, instrument command and control systems have been developed specifically for a single instrument. Such solutions are frequently expensive and are inflexible to support the next instrument development effort. NASA Goddard Space Flight Center is developing an extensible framework, known as Instrument Remote Control (IRC) that applies to any kind of instrument that can be controlled by a computer. IRC combines the platform independent processing capabilities of Java with the power of the Extensible Markup Language (XML). A key aspect of the architecture is software that is driven by an instrument description, written using the Instrument Markup Language (IML). IML is an XML dialect used to describe graphical user interfaces to control and monitor the instrument, command sets and command formats, data streams, communication mechanisms, and data processing algorithms. The IRC framework provides the ability to communicate to components anywhere on a network using the JXTA protocol for dynamic discovery of distributed components. JXTA (see http://www.jxta.org) is a generalized protocol that allows any devices connected by a network to communicate in a peer-to-peer manner. IRC uses JXTA to advertise a device?s IML and discover devices of interest on the network. Devices can join or leave the network and thus join or leave the instrument control environment of IRC. Currently, several astronomical instruments are working with the IRC development team to develop custom components for IRC to control their instruments. These instruments include: High resolution Airborne Wideband Camera (HAWC), a first light instrument for the Stratospheric Observatory for Infrared Astronomy (SOFIA); Submillimeter And Far Infrared Experiment (SAFIRE), a principal investigator instrument for SOFIA; and Fabry-Perot Interferometer Bolometer Research Experiment (FIBRE), a prototype of the SAFIRE instrument, used at the Caltech Submillimeter Observatory (CSO). Most recently, we have been working with the Submillimetre High Angular Resolution Camera IInd Generation (SHARCII) at the CSO to investigate using IRC capabilities with the SHARC instrument.

Distributed Framework for Dynamic Telescope and Instrument Control

NASA Technical Reports Server (NTRS)

Ames, Troy J.; Case, Lynne

2002-01-01

Traditionally, instrument command and control systems have been developed specifically for a single instrument. Such solutions are frequently expensive and are inflexible to support the next instrument development effort. NASA Goddard Space Flight Center is developing an extensible framework, known as Instrument Remote Control (IRC) that applies to any kind of instrument that can be controlled by a computer. IRC combines the platform independent processing capabilities of Java with the power of the Extensible Markup Language (XML). A key aspect of the architecture is software that is driven by an instrument description, written using the Instrument Markup Language (IML). IML is an XML dialect used to describe graphical user interfaces to control and monitor the instrument, command sets and command formats, data streams, communication mechanisms, and data processing algorithms. The IRC framework provides the ability to communicate to components anywhere on a network using the JXTA protocol for dynamic discovery of distributed components. JXTA (see httD://www.jxta.org,) is a generalized protocol that allows any devices connected by a network to communicate in a peer-to-peer manner. IRC uses JXTA to advertise a device's IML and discover devices of interest on the network. Devices can join or leave the network and thus join or leave the instrument control environment of IRC. Currently, several astronomical instruments are working with the IRC development team to develop custom components for IRC to control their instruments. These instruments include: High resolution Airborne Wideband Camera (HAWC), a first light instrument for the Stratospheric Observatory for Infrared Astronomy (SOFIA); Submillimeter And Far Infrared Experiment (SAFIRE), a Principal Investigator instrument for SOFIA; and Fabry-Perot Interferometer Bolometer Research Experiment (FIBRE), a prototype of the SAFIRE instrument, used at the Caltech Submillimeter Observatory (CSO). Most recently, we have been working with the Submillimetre High Angular Resolution Camera IInd Generation (SHARCII) at the CSO to investigate using IRC capabilities with the SHARC instrument.

circlncRNAnet: an integrated web-based resource for mapping functional networks of long or circular forms of noncoding RNAs.

PubMed

Wu, Shao-Min; Liu, Hsuan; Huang, Po-Jung; Chang, Ian Yi-Feng; Lee, Chi-Ching; Yang, Chia-Yu; Tsai, Wen-Sy; Tan, Bertrand Chin-Ming

2018-01-01

Despite their lack of protein-coding potential, long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) have emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation. Given their widespread target spectrum, as well as extensive modes of action, a complete understanding of their biological relevance will depend on integrative analyses of systems data at various levels. While a handful of publicly available databases have been reported, existing tools do not fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest. Through an integrated and streamlined design, circlncRNAnet aims to broaden the understanding of ncRNA candidates by testing in silico several hypotheses of ncRNA-based functions, on the basis of large-scale RNA-seq data. This web server is implemented with several features that represent advances in the bioinformatics of ncRNAs: (1) a flexible framework that accepts and processes user-defined next-generation sequencing-based expression data; (2) multiple analytic modules that assign and productively assess the regulatory networks of user-selected ncRNAs by cross-referencing extensively curated databases; (3) an all-purpose, information-rich workflow design that is tailored to all types of ncRNAs. Outputs on expression profiles, co-expression networks and pathways, and molecular interactomes, are dynamically and interactively displayed according to user-defined criteria. In short, users may apply circlncRNAnet to obtain, in real time, multiple lines of functionally relevant information on circRNAs/lncRNAs of their interest. In summary, circlncRNAnet provides a "one-stop" resource for in-depth analyses of ncRNA biology. circlncRNAnet is freely available at http://app.cgu.edu.tw/circlnc/. © The Authors 2017. Published by Oxford University Press.

Universal bounds on current fluctuations.

PubMed

Pietzonka, Patrick; Barato, Andre C; Seifert, Udo

2016-05-01

For current fluctuations in nonequilibrium steady states of Markovian processes, we derive four different universal bounds valid beyond the Gaussian regime. Different variants of these bounds apply to either the entropy change or any individual current, e.g., the rate of substrate consumption in a chemical reaction or the electron current in an electronic device. The bounds vary with respect to their degree of universality and tightness. A universal parabolic bound on the generating function of an arbitrary current depends solely on the average entropy production. A second, stronger bound requires knowledge both of the thermodynamic forces that drive the system and of the topology of the network of states. These two bounds are conjectures based on extensive numerics. An exponential bound that depends only on the average entropy production and the average number of transitions per time is rigorously proved. This bound has no obvious relation to the parabolic bound but it is typically tighter further away from equilibrium. An asymptotic bound that depends on the specific transition rates and becomes tight for large fluctuations is also derived. This bound allows for the prediction of the asymptotic growth of the generating function. Even though our results are restricted to networks with a finite number of states, we show that the parabolic bound is also valid for three paradigmatic examples of driven diffusive systems for which the generating function can be calculated using the additivity principle. Our bounds provide a general class of constraints for nonequilibrium systems.

Maximum Power Game as a Physical and Social Extension of Classical Games

NASA Astrophysics Data System (ADS)

Kim, Pilwon

2017-03-01

We consider an electric circuit in which the players participate as resistors and adjust their resistance in pursuit of individual maximum power. The maximum power game(MPG) becomes very complicated in a circuit which is indecomposable into serial/parallel components, yielding a nontrivial power distribution at equilibrium. Depending on the circuit topology, MPG covers a wide range of phenomena: from a social dilemma in which the whole group loses to a well-coordinated situation in which the individual pursuit of power promotes the collective outcomes. We also investigate a situation where each player in the circuit has an intrinsic heat waste. Interestingly, it is this individual inefficiency which can keep them from the collective failure in power generation. When coping with an efficient opponent with small intrinsic resistance, a rather inefficient player gets more power than efficient one. A circuit with multiple voltage inputs forms the network-based maximum power game. One of our major interests is to figure out, in what kind of the networks the pursuit for private power leads to greater total power. It turns out that the circuits with the scale-free structure is one of the good candidates which generates as much power as close to the possible maximum total.

Artificial neural networks for AC losses prediction in superconducting round filaments

NASA Astrophysics Data System (ADS)

Leclerc, J.; Makong Hell, L.; Lorin, C.; Masson, P. J.

2016-06-01

An extensive and fast method to estimate superconducting AC losses within a superconducting round filament carrying an AC current and subjected to an elliptical magnetic field (both rotating and oscillating) is presented. Elliptical fields are present in rotating machine stators and being able to accurately predict AC losses in fully superconducting machines is paramount to generating realistic machine designs. The proposed method relies on an analytical scaling law (ASL) combined with two artificial neural network (ANN) estimators taking 9 input parameters representing the superconductor, external field and transport current characteristics. The ANNs are trained with data generated by finite element (FE) computations with a commercial software (FlexPDE) based on the widely accepted H-formulation. After completion, the model is validated through comparison with additional randomly chosen data points and compared for simple field configurations to other predictive models. The loss estimation discrepancy is about 3% on average compared to the FEA analysis. The main advantages of the model compared to FE simulations is the fast computation time (few milliseconds) which allows it to be used in iterated design processes of fully superconducting machines. In addition, the proposed model provides a higher level of fidelity than the scaling laws existing in literature usually only considering pure AC field.

75 FR 49870 - Effects on Broadband Communications Networks of Damage to or Failure of Network Equipment or...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-16

... FEDERAL COMMUNICATIONS COMMISSION 47 CFR Chapter I [PS Docket No. 10-92; DA 10-1357] Effects on Broadband Communications Networks of Damage to or Failure of Network Equipment or Severe Overload AGENCY: Federal Communications Commission ACTION: Proposed rule; extension of reply comment date. SUMMARY: This...

LANES 1 Users' Guide

NASA Technical Reports Server (NTRS)

Jordan, J.

1985-01-01

This document is intended for users of the Local Area Network Extensible Simulator, version I. This simulator models the performance of a Fiber Optic network under a variety of loading conditions and network characteristics. The options available to the user for defining the network conditions are described in this document. Computer hardware and software requirements are also defined.

Generalized Adaptive Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1993-01-01

Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

Extensive video-game experience alters cortical networks for complex visuomotor transformations.

PubMed

Granek, Joshua A; Gorbet, Diana J; Sergio, Lauren E

2010-10-01

Using event-related functional magnetic resonance imaging (fMRI), we examined the effect of video-game experience on the neural control of increasingly complex visuomotor tasks. Previously, skilled individuals have demonstrated the use of a more efficient movement control brain network, including the prefrontal, premotor, primary sensorimotor and parietal cortices. Our results extend and generalize this finding by documenting additional prefrontal cortex activity in experienced video gamers planning for complex eye-hand coordination tasks that are distinct from actual video-game play. These changes in activation between non-gamers and extensive gamers are putatively related to the increased online control and spatial attention required for complex visually guided reaching. These data suggest that the basic cortical network for processing complex visually guided reaching is altered by extensive video-game play. Crown Copyright © 2009. Published by Elsevier Srl. All rights reserved.

Generating Seismograms with Deep Neural Networks

NASA Astrophysics Data System (ADS)

Krischer, L.; Fichtner, A.

2017-12-01

The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of neural networks by estimating the quality and uncertainties of the generated seismograms.

A toolbox for discrete modelling of cell signalling dynamics.

PubMed

Paterson, Yasmin Z; Shorthouse, David; Pleijzier, Markus W; Piterman, Nir; Bendtsen, Claus; Hall, Benjamin A; Fisher, Jasmin

2018-06-18

In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.

Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes.

PubMed

Väremo, Leif; Scheele, Camilla; Broholm, Christa; Mardinoglu, Adil; Kampf, Caroline; Asplund, Anna; Nookaew, Intawat; Uhlén, Mathias; Pedersen, Bente Klarlund; Nielsen, Jens

2015-05-12

Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Physical and biological regulation of neuron regenerative growth and network formation on recombinant dragline silks

DOE PAGES

An, Bo; Tang-Schomer, Min D.; Huang, Wenwen; ...

2015-02-11

In this paper, recombinant spider silks produced in transgenic goat milk were studied as cell culture matrices for neuronal growth. Major ampullate spidroin 1 (MaSp1) supported neuronal growth, axon extension and network connectivity, with cell morphology comparable to the gold standard poly-lysine. In addition, neurons growing on MaSp1 films had increased neural cell adhesion molecule (NCAM) expression at both mRNA and protein levels. The results indicate that MaSp1 films present useful surface charge and substrate stiffness to support the growth of primary rat cortical neurons. Moreover, a putative neuron-specific surface binding sequence GRGGL within MaSp1 may contribute to the biologicalmore » regulation of neuron growth. These findings indicate that MaSp1 could regulate neuron growth through its physical and biological features. Finally, this dual regulation mode of MaSp1 could provide an alternative strategy for generating functional silk materials for neural tissue engineering.« less

Physical and biological regulation of neuron regenerative growth and network formation on recombinant dragline silks

DOE Office of Scientific and Technical Information (OSTI.GOV)

An, Bo; Tang-Schomer, Min D.; Huang, Wenwen

In this paper, recombinant spider silks produced in transgenic goat milk were studied as cell culture matrices for neuronal growth. Major ampullate spidroin 1 (MaSp1) supported neuronal growth, axon extension and network connectivity, with cell morphology comparable to the gold standard poly-lysine. In addition, neurons growing on MaSp1 films had increased neural cell adhesion molecule (NCAM) expression at both mRNA and protein levels. The results indicate that MaSp1 films present useful surface charge and substrate stiffness to support the growth of primary rat cortical neurons. Moreover, a putative neuron-specific surface binding sequence GRGGL within MaSp1 may contribute to the biologicalmore » regulation of neuron growth. These findings indicate that MaSp1 could regulate neuron growth through its physical and biological features. Finally, this dual regulation mode of MaSp1 could provide an alternative strategy for generating functional silk materials for neural tissue engineering.« less

Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication.

PubMed

Allard, Pierre-Marie; Péresse, Tiphaine; Bisson, Jonathan; Gindro, Katia; Marcourt, Laurence; Pham, Van Cuong; Roussi, Fanny; Litaudon, Marc; Wolfender, Jean-Luc

2016-03-15

Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.

Time-variant Lagrangian transport formulation reduces aggregation bias of water and solute mean travel time in heterogeneous catchments

NASA Astrophysics Data System (ADS)

Danesh-Yazdi, Mohammad; Botter, Gianluca; Foufoula-Georgiou, Efi

2017-05-01

Lack of hydro-bio-chemical data at subcatchment scales necessitates adopting an aggregated system approach for estimating water and solute transport properties, such as residence and travel time distributions, at the catchment scale. In this work, we show that within-catchment spatial heterogeneity, as expressed in spatially variable discharge-storage relationships, can be appropriately encapsulated within a lumped time-varying stochastic Lagrangian formulation of transport. This time (variability) for space (heterogeneity) substitution yields mean travel times (MTTs) that are not significantly biased to the aggregation of spatial heterogeneity. Despite the significant variability of MTT at small spatial scales, there exists a characteristic scale above which the MTT is not impacted by the aggregation of spatial heterogeneity. Extensive simulations of randomly generated river networks reveal that the ratio between the characteristic scale and the mean incremental area is on average independent of river network topology and the spatial arrangement of incremental areas.

Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

NASA Astrophysics Data System (ADS)

Kemp, Z. D. C.

2018-04-01

Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.

Regulation of the Hippocampal Network by VGLUT3-Positive CCK- GABAergic Basket Cells

PubMed Central

Fasano, Caroline; Rocchetti, Jill; Pietrajtis, Katarzyna; Zander, Johannes-Friedrich; Manseau, Frédéric; Sakae, Diana Y.; Marcus-Sells, Maya; Ramet, Lauriane; Morel, Lydie J.; Carrel, Damien; Dumas, Sylvie; Bolte, Susanne; Bernard, Véronique; Vigneault, Erika; Goutagny, Romain; Ahnert-Hilger, Gudrun; Giros, Bruno; Daumas, Stéphanie; Williams, Sylvain; El Mestikawy, Salah

2017-01-01

Hippocampal interneurons release the inhibitory transmitter GABA to regulate excitation, rhythm generation and synaptic plasticity. A subpopulation of GABAergic basket cells co-expresses the GABA/glycine vesicular transporters (VIAAT) and the atypical type III vesicular glutamate transporter (VGLUT3); therefore, these cells have the ability to signal with both GABA and glutamate. GABAergic transmission by basket cells has been extensively characterized but nothing is known about the functional implications of VGLUT3-dependent glutamate released by these cells. Here, using VGLUT3-null mice we observed that the loss of VGLUT3 results in a metaplastic shift in synaptic plasticity at Shaeffer’s collaterals – CA1 synapses and an altered theta oscillation. These changes were paralleled by the loss of a VGLUT3-dependent inhibition of GABAergic current in CA1 pyramidal layer. Therefore presynaptic type III metabotropic could be activated by glutamate released from VGLUT3-positive interneurons. This putative presynaptic heterologous feedback mechanism inhibits local GABAergic tone and regulates the hippocampal neuronal network. PMID:28559797

Reciprocal Relationship between Internet Addiction and Network-Related Maladaptive Cognition among Chinese College Freshmen: A Longitudinal Cross-Lagged Analysis

PubMed Central

Han, Piguo; Wang, Peng; Lin, Qingnan; Tian, Yu; Gao, Fengqiang; Chen, Yingmin

2017-01-01

This study explored the reciprocal relationship between Internet addiction (IA) and network-related maladaptive cognition (NMC) in Chinese college freshmen. A short-term longitudinal survey with a sample of 213 college freshmen was conducted in Shandong province, China. The results revealed that IA can significantly predict the generation and development of NMCs, and that when such maladaptive cognitions have been established, they can further adversely affect the extent of the students’ IA. A vicious cycle was observed between these two variables, with IA having predictive priority in its relationship with NMC. This study also determined that the relationship between these two variables was the same for both males and females; therefore, the final model we established can be extensively applied to Chinese college freshmen, regardless of gender. Understanding the reciprocal relationship between these two variables can assist in interventions in IA at the outset of students’ college life. PMID:28690575

Cdx and T Brachyury Co-activate Growth Signaling in the Embryonic Axial Progenitor Niche.

PubMed

Amin, Shilu; Neijts, Roel; Simmini, Salvatore; van Rooijen, Carina; Tan, Sander C; Kester, Lennart; van Oudenaarden, Alexander; Creyghton, Menno P; Deschamps, Jacqueline

2016-12-20

In vertebrate embryos, anterior tissues are generated early, followed by the other axial structures that emerge sequentially from a posterior growth zone. The genetic network driving posterior axial elongation in mice, and its disturbance in mutants with posterior truncation, is not yet fully understood. Here, we show that the combined expression of Cdx2 and T Brachyury is essential to establish the core signature of posterior axial progenitors. Cdx2 and T Brachyury are required for extension of a similar trunk portion of the axis. Simultaneous loss of function of these two genes disrupts axial elongation to a much greater extent than each single mutation alone. We identify and validate common targets for Cdx2 and T Brachyury in vivo, including Wnt and Fgf pathway components active in the axial progenitor niche. Our data demonstrate that integration of the Cdx/Hox and T Brachyury transcriptional networks controls differential axial growth during vertebrate trunk elongation. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Neural network potential for Al-Mg-Si alloys

NASA Astrophysics Data System (ADS)

Kobayashi, Ryo; Giofré, Daniele; Junge, Till; Ceriotti, Michele; Curtin, William A.

2017-10-01

The 6000 series Al alloys, which include a few percent of Mg and Si, are important in automotive and aviation industries because of their low weight, as compared to steels, and the fact their strength can be greatly improved through engineered precipitation. To enable atomistic-level simulations of both the processing and performance of this important alloy system, a neural network (NN) potential for the ternary Al-Mg-Si has been created. Training of the NN uses an extensive database of properties computed using first-principles density functional theory, including complex precipitate phases in this alloy. The NN potential accurately reproduces most of the pure Al properties relevant to the mechanical behavior as well as heat of solution, solute-solute, and solute-vacancy interaction energies, and formation energies of small solute clusters and precipitates that are required for modeling the early stage of precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of NN methods to generate useful potentials in complex alloy systems.

Scaling tunable network model to reproduce the density-driven superlinear relation

NASA Astrophysics Data System (ADS)

Gao, Liang; Shan, Xiaoya; Qin, Yuhao; Yu, Senbin; Xu, Lida; Gao, Zi-You

2018-03-01

Previous works have shown the universality of allometric scaling under total and density values at the city level, but our understanding of the size effects of regions on the universality of allometric scaling remains inadequate. Here, we revisit the scaling relations between the gross domestic production (GDP) and the population based on the total and density values and first reveal that the allometric scaling under density values for different regions is universal. The scaling exponent β under the density value is in the range of (1.0, 2.0], which unexpectedly exceeds the range observed by Pan et al. [Nat. Commun. 4, 1961 (2013)]. For the wider range, we propose a network model based on a 2D lattice space with the spatial correlation factor α as a parameter. Numerical experiments prove that the generated scaling exponent β in our model is fully tunable by the spatial correlation factor α. Our model will furnish a general platform for extensive urban and regional studies.

Samba: a real-time motion capture system using wireless camera sensor networks.

PubMed

Oh, Hyeongseok; Cha, Geonho; Oh, Songhwai

2014-03-20

There is a growing interest in 3D content following the recent developments in 3D movies, 3D TVs and 3D smartphones. However, 3D content creation is still dominated by professionals, due to the high cost of 3D motion capture instruments. The availability of a low-cost motion capture system will promote 3D content generation by general users and accelerate the growth of the 3D market. In this paper, we describe the design and implementation of a real-time motion capture system based on a portable low-cost wireless camera sensor network. The proposed system performs motion capture based on the data-driven 3D human pose reconstruction method to reduce the computation time and to improve the 3D reconstruction accuracy. The system can reconstruct accurate 3D full-body poses at 16 frames per second using only eight markers on the subject's body. The performance of the motion capture system is evaluated extensively in experiments.

Physical and biological regulation of neuron regenerative growth and network formation on recombinant dragline silks.

PubMed

An, Bo; Tang-Schomer, Min; Huang, Wenwen; He, Jiuyang; Jones, Justin; Lewis, Randolph V; Kaplan, David L

2015-04-01

Recombinant spider silks produced in transgenic goat milk were studied as cell culture matrices for neuronal growth. Major ampullate spidroin 1 (MaSp1) supported neuronal growth, axon extension and network connectivity, with cell morphology comparable to the gold standard poly-lysine. In addition, neurons growing on MaSp1 films had increased neural cell adhesion molecule (NCAM) expression at both mRNA and protein levels. The results indicate that MaSp1 films present useful surface charge and substrate stiffness to support the growth of primary rat cortical neurons. Moreover, a putative neuron-specific surface binding sequence GRGGL within MaSp1 may contribute to the biological regulation of neuron growth. These findings indicate that MaSp1 could regulate neuron growth through its physical and biological features. This dual regulation mode of MaSp1 could provide an alternative strategy for generating functional silk materials for neural tissue engineering. Copyright © 2015 Elsevier Ltd. All rights reserved.

Samba: A Real-Time Motion Capture System Using Wireless Camera Sensor Networks

PubMed Central

Oh, Hyeongseok; Cha, Geonho; Oh, Songhwai

2014-01-01

There is a growing interest in 3D content following the recent developments in 3D movies, 3D TVs and 3D smartphones. However, 3D content creation is still dominated by professionals, due to the high cost of 3D motion capture instruments. The availability of a low-cost motion capture system will promote 3D content generation by general users and accelerate the growth of the 3D market. In this paper, we describe the design and implementation of a real-time motion capture system based on a portable low-cost wireless camera sensor network. The proposed system performs motion capture based on the data-driven 3D human pose reconstruction method to reduce the computation time and to improve the 3D reconstruction accuracy. The system can reconstruct accurate 3D full-body poses at 16 frames per second using only eight markers on the subject's body. The performance of the motion capture system is evaluated extensively in experiments. PMID:24658618

New preemptive scheduling for OBS networks considering cascaded wavelength conversion

NASA Astrophysics Data System (ADS)

Gao, Xingbo; Bassiouni, Mostafa A.; Li, Guifang

2009-05-01

In this paper we introduce a new preemptive scheduling technique for next generation optical burst-switched networks considering the impact of cascaded wavelength conversions. It has been shown that when optical bursts are transmitted all optically from source to destination, each wavelength conversion performed along the lightpath may cause certain signal-to-noise deterioration. If the distortion of the signal quality becomes significant enough, the receiver would not be able to recover the original data. Accordingly, subject to this practical impediment, we improve a recently proposed fair channel scheduling algorithm to deal with the fairness problem and aim at burst loss reduction simultaneously in optical burst switching. In our scheme, the dynamic priority associated with each burst is based on a constraint threshold and the number of already conducted wavelength conversions among other factors for this burst. When contention occurs, a new arriving superior burst may preempt another scheduled one according to their priorities. Extensive simulation results have shown that the proposed scheme further improves fairness and achieves burst loss reduction as well.

The collateral network concept: a reassessment of the anatomy of spinal cord perfusion.

PubMed

Etz, Christian D; Kari, Fabian A; Mueller, Christoph S; Silovitz, Daniel; Brenner, Robert M; Lin, Hung-Mo; Griepp, Randall B

2011-04-01

Prevention of paraplegia after repair of thoracoabdominal aortic aneurysm requires understanding the anatomy and physiology of the spinal cord blood supply. Recent laboratory studies and clinical observations suggest that a robust collateral network must exist to explain preservation of spinal cord perfusion when segmental vessels are interrupted. An anatomic study was undertaken. Twelve juvenile Yorkshire pigs underwent aortic cannulation and infusion of a low-viscosity acrylic resin at physiologic pressures. After curing of the resin and digestion of all organic tissue, the anatomy of the blood supply to the spinal cord was studied grossly and with light and electron microscopy. All vascular structures at least 8 μm in diameter were preserved. Thoracic and lumbar segmental arteries give rise not only to the anterior spinal artery but to an extensive paraspinous network feeding the erector spinae, iliopsoas, and associated muscles. The anterior spinal artery, mean diameter 134 ± 20 μm, is connected at multiple points to repetitive circular epidural arteries with mean diameters of 150 ± 26 μm. The capacity of the paraspinous muscular network is 25-fold the capacity of the circular epidural arterial network and anterior spinal artery combined. Extensive arterial collateralization is apparent between the intraspinal and paraspinous networks, and within each network. Only 75% of all segmental arteries provide direct anterior spinal artery-supplying branches. The anterior spinal artery is only one component of an extensive paraspinous and intraspinal collateral vascular network. This network provides an anatomic explanation of the physiological resiliency of spinal cord perfusion when segmental arteries are sacrificed during thoracoabdominal aortic aneurysm repair. Copyright © 2011 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.

Reconstructing the world trade multiplex: The role of intensive and extensive biases

NASA Astrophysics Data System (ADS)

Mastrandrea, Rossana; Squartini, Tiziano; Fagiolo, Giorgio; Garlaschelli, Diego

2014-12-01

In economic and financial networks, the strength of each node has always an important economic meaning, such as the size of supply and demand, import and export, or financial exposure. Constructing null models of networks matching the observed strengths of all nodes is crucial in order to either detect interesting deviations of an empirical network from economically meaningful benchmarks or reconstruct the most likely structure of an economic network when the latter is unknown. However, several studies have proved that real economic networks and multiplexes topologically differ from configurations inferred only from node strengths. Here we provide a detailed analysis of the world trade multiplex by comparing it to an enhanced null model that simultaneously reproduces the strength and the degree of each node. We study several temporal snapshots and almost 100 layers (commodity classes) of the multiplex and find that the observed properties are systematically well reproduced by our model. Our formalism allows us to introduce the (static) concept of extensive and intensive bias, defined as a measurable tendency of the network to prefer either the formation of extra links or the reinforcement of link weights, with respect to a reference case where only strengths are enforced. Our findings complement the existing economic literature on (dynamic) intensive and extensive trade margins. More generally, they show that real-world multiplexes can be strongly shaped by layer-specific local constraints.

Potential of social media as a tool to combat foodborne illness.

PubMed

Chapman, Benjamin; Raymond, Benjamin; Powell, Douglas

2014-07-01

The use of social media platforms, such as Facebook and Twitter, has been increasing substantially in recent years and has affected the way that people access information online. Social media rely on high levels of interaction and user-generated context shared through established and evolving social networks. Health information providers must know how to successfully participate through social media in order to meet the needs of these online audiences. This article reviews the current research on the use of social media for public health communication and suggests potential frameworks for developing social media strategies. The extension to food safety risk communication is explored, considering the potential of social media as a tool to combat foodborne illness.

Monitoring storm tide and flooding from Hurricane Sandy along the Atlantic coast of the United States, October 2012

USGS Publications Warehouse

McCallum, Brian E.; Wicklein, Shaun M.; Reiser, Robert G.; Busciolano, Ronald J.; Morrison, Jonathan; Verdi, Richard J.; Painter, Jaime A.; Frantz, Eric R.; Gotvald, Anthony J.

2013-01-01

The U.S. Geological Survey (USGS) deployed a temporary monitoring network of water-level and barometric pressure sensors at 224 locations along the Atlantic coast from Virginia to Maine to continuously record the timing, areal extent, and magnitude of hurricane storm tide and coastal flooding generated by Hurricane Sandy. These records were greatly supplemented by an extensive post-flood high-water mark (HWM) flagging and surveying campaign from November to December 2012 involving more than 950 HWMs. Both efforts were undertaken as part of a coordinated federal emergency response as outlined by the Stafford Act under a directed mission assignment by the Federal Emergency Management Agency (FEMA).

Advanced power analysis methodology targeted to the optimization of a digital pixel readout chip design and its critical serial powering system

NASA Astrophysics Data System (ADS)

Marconi, S.; Orfanelli, S.; Karagounis, M.; Hemperek, T.; Christiansen, J.; Placidi, P.

2017-02-01

A dedicated power analysis methodology, based on modern digital design tools and integrated with the VEPIX53 simulation framework developed within RD53 collaboration, is being used to guide vital choices for the design and optimization of the next generation ATLAS and CMS pixel chips and their critical serial powering circuit (shunt-LDO). Power consumption is studied at different stages of the design flow under different operating conditions. Significant effort is put into extensive investigations of dynamic power variations in relation with the decoupling seen by the powering network. Shunt-LDO simulations are also reported to prove the reliability at the system level.

Performance effects of irregular communications patterns on massively parallel multiprocessors

NASA Technical Reports Server (NTRS)

Saltz, Joel; Petiton, Serge; Berryman, Harry; Rifkin, Adam

1991-01-01

A detailed study of the performance effects of irregular communications patterns on the CM-2 was conducted. The communications capabilities of the CM-2 were characterized under a variety of controlled conditions. In the process of carrying out the performance evaluation, extensive use was made of a parameterized synthetic mesh. In addition, timings with unstructured meshes generated for aerodynamic codes and a set of sparse matrices with banded patterns on non-zeroes were performed. This benchmarking suite stresses the communications capabilities of the CM-2 in a range of different ways. Benchmark results demonstrate that it is possible to make effective use of much of the massive concurrency available in the communications network.

Harvesting electrostatic energy using super-hydrophobic surfaces

NASA Astrophysics Data System (ADS)

Pociecha, Dominik; Zylka, Pawel

2016-11-01

Almost all environments are now being extensively populated by miniaturized, nano-powered electronic sensor devices communicated together through wireless sensor networks building Internet of Things (IoT). Various energy harvesting techniques are being more and more frequently proposed for battery-less powering of such remote, unattended, implantable or wearable sensors or other low-power electronic gadgets. Energy harvesting relays on extracting energy from the ambient sources readily accessible at the sensor location and converting it into electrical power. The paper exploits possibility of generating electric energy safely accessible for nano-power electronics using tribo-electric and electrostatic induction phenomena displayed at super-hydrophobic surfaces impinged by water droplets. Mechanism of such interaction is discussed and illustrated by experimental results.

Biometric identity management for standard mobile medical networks.

PubMed

Egner, Alexandru; Soceanu, Alexandru; Moldoveanu, Florica

2012-01-01

The explosion of healthcare costs over the last decade has prompted the ICT industry to respond with solutions for reducing costs while improving healthcare quality. The ISO/IEEE 11073 family of standards recently released is the first step towards interoperability of mobile medical devices used in patient environments. The standards do not, however, tackle security problems, such as identity management, or the secure exchange of medical data. This paper proposes an enhancement of the ISO/IEEE 11073-20601 protocol with an identity management system based on biometry. The paper describes a novel biometric-based authentication process, together with the biometric key generation algorithm. The proposed extension of the ISO/IEEE 11073-20601 is also presented.

Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics.

PubMed

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity.

The transmission process: A combinatorial stochastic process for the evolution of transmission trees over networks.

PubMed

Sainudiin, Raazesh; Welch, David

2016-12-07

We derive a combinatorial stochastic process for the evolution of the transmission tree over the infected vertices of a host contact network in a susceptible-infected (SI) model of an epidemic. Models of transmission trees are crucial to understanding the evolution of pathogen populations. We provide an explicit description of the transmission process on the product state space of (rooted planar ranked labelled) binary transmission trees and labelled host contact networks with SI-tags as a discrete-state continuous-time Markov chain. We give the exact probability of any transmission tree when the host contact network is a complete, star or path network - three illustrative examples. We then develop a biparametric Beta-splitting model that directly generates transmission trees with exact probabilities as a function of the model parameters, but without explicitly modelling the underlying contact network, and show that for specific values of the parameters we can recover the exact probabilities for our three example networks through the Markov chain construction that explicitly models the underlying contact network. We use the maximum likelihood estimator (MLE) to consistently infer the two parameters driving the transmission process based on observations of the transmission trees and use the exact MLE to characterize equivalence classes over the space of contact networks with a single initial infection. An exploratory simulation study of the MLEs from transmission trees sampled from three other deterministic and four random families of classical contact networks is conducted to shed light on the relation between the MLEs of these families with some implications for statistical inference along with pointers to further extensions of our models. The insights developed here are also applicable to the simplest models of "meme" evolution in online social media networks through transmission events that can be distilled from observable actions such as "likes", "mentions", "retweets" and "+1s" along with any concomitant comments. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics

PubMed Central

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity. PMID:25569445

Sensitivity of directed networks to the addition and pruning of edges and vertices

NASA Astrophysics Data System (ADS)

Goltsev, A. V.; Timár, G.; Mendes, J. F. F.

2017-08-01

Directed networks have various topologically different extensive components, in contrast to a single giant component in undirected networks. We study the sensitivity (response) of the sizes of these extensive components in directed complex networks to the addition and pruning of edges and vertices. We introduce the susceptibility, which quantifies this sensitivity. We show that topologically different parts of a directed network have different sensitivity to the addition and pruning of edges and vertices and, therefore, they are characterized by different susceptibilities. These susceptibilities diverge at the critical point of the directed percolation transition, signaling the appearance (or disappearance) of the giant strongly connected component in the infinite size limit. We demonstrate this behavior in randomly damaged real and synthetic directed complex networks, such as the World Wide Web, Twitter, the Caenorhabditis elegans neural network, directed Erdős-Rényi graphs, and others. We reveal a nonmonotonic dependence of the sensitivity to random pruning of edges or vertices in the case of C. elegans and Twitter that manifests specific structural peculiarities of these networks. We propose the measurements of the susceptibilities during the addition or pruning of edges and vertices as a new method for studying structural peculiarities of directed networks.

Effective Use of Facebook for Extension Professionals

ERIC Educational Resources Information Center

Mains, Mark; Jenkins-Howard, Brooke; Stephenson, Laura

2013-01-01

As the use of social media increases, Extension is challenged to stay relevant with cliental by using digital tools. This article illustrates how Facebook can be part of Extension's repertoire of methods for communication, program implementation, education, and marketing. This allows professionals to build social networking capacity with…

Design principles of electrical synaptic plasticity.

PubMed

O'Brien, John

2017-09-08

Essentially all animals with nervous systems utilize electrical synapses as a core element of communication. Electrical synapses, formed by gap junctions between neurons, provide rapid, bidirectional communication that accomplishes tasks distinct from and complementary to chemical synapses. These include coordination of neuron activity, suppression of voltage noise, establishment of electrical pathways that define circuits, and modulation of high order network behavior. In keeping with the omnipresent demand to alter neural network function in order to respond to environmental cues and perform tasks, electrical synapses exhibit extensive plasticity. In some networks, this plasticity can have dramatic effects that completely remodel circuits or remove the influence of certain cell types from networks. Electrical synaptic plasticity occurs on three distinct time scales, ranging from milliseconds to days, with different mechanisms accounting for each. This essay highlights principles that dictate the properties of electrical coupling within networks and the plasticity of the electrical synapses, drawing examples extensively from retinal networks. Copyright © 2017 The Author. Published by Elsevier B.V. All rights reserved.

Parameterization of Keeling's network generation algorithm.

PubMed

Badham, Jennifer; Abbass, Hussein; Stocker, Rob

2008-09-01

Simulation is increasingly being used to examine epidemic behaviour and assess potential management options. The utility of the simulations rely on the ability to replicate those aspects of the social structure that are relevant to epidemic transmission. One approach is to generate networks with desired social properties. Recent research by Keeling and his colleagues has generated simulated networks with a range of properties, and examined the impact of these properties on epidemic processes occurring over the network. However, published work has included only limited analysis of the algorithm itself and the way in which the network properties are related to the algorithm parameters. This paper identifies some relationships between the algorithm parameters and selected network properties (mean degree, degree variation, clustering coefficient and assortativity). Our approach enables users of the algorithm to efficiently generate a network with given properties, thereby allowing realistic social networks to be used as the basis of epidemic simulations. Alternatively, the algorithm could be used to generate social networks with a range of property values, enabling analysis of the impact of these properties on epidemic behaviour.

System and method for generating a relationship network

DOEpatents

Franks, Kasian; Myers, Cornelia A; Podowski, Raf M

2015-05-05

A computer-implemented system and process for generating a relationship network is disclosed. The system provides a set of data items to be related and generates variable length data vectors to represent the relationships between the terms within each data item. The system can be used to generate a relationship network for documents, images, or any other type of file. This relationship network can then be queried to discover the relationships between terms within the set of data items.

System and method for generating a relationship network

DOEpatents

Franks, Kasian [Kensington, CA; Myers, Cornelia A [St. Louis, MO; Podowski, Raf M [Pleasant Hill, CA

2011-07-26

A computer-implemented system and process for generating a relationship network is disclosed. The system provides a set of data items to be related and generates variable length data vectors to represent the relationships between the terms within each data item. The system can be used to generate a relationship network for documents, images, or any other type of file. This relationship network can then be queried to discover the relationships between terms within the set of data items.

Cooperation-Induced Topological Complexity: A Promising Road to Fault Tolerance and Hebbian Learning

DTIC Science & Technology

2012-03-16

topological complexity a way to compare the efficiency of a scale-free network to the random network of Erdos and Renyi . All this is extensively dis- cussed in...an excellent review paper byArenas et al. (2008) showing very interesting comparisons of Erdos– Renyi networks and scale- free networks as a function

Criteria of Effectiveness for Network Delivery of Citizens Information through Libraries. Final Report.

ERIC Educational Resources Information Center

Chen, Ching-chih; Hernon, Peter

This two-part publication reports on a study of consumer information delivery by library and non-library networks, which involved an extensive literature review, a telephone survey of 620 library networks, the development of an assessment model for the effectiveness of network information delivery, the development of an in-depth guide for…

On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks †

PubMed Central

Wang, Qiu; Dai, Hong-Ning; Li, Xuran; Wang, Hao; Xiao, Hong

2016-01-01

The security and privacy of underwater acoustic sensor networks has received extensive attention recently due to the proliferation of underwater activities. This paper proposes an analytical model to investigate the eavesdropping attacks in underwater acoustic sensor networks. Our analytical framework considers the impacts of various underwater acoustic channel conditions (such as the acoustic signal frequency, spreading factor and wind speed) and different hydrophones (isotropic hydrophones and array hydrophones) in terms of network nodes and eavesdroppers. We also conduct extensive simulations to evaluate the effectiveness and the accuracy of our proposed model. Empirical results show that our proposed model is quite accurate. In addition, our results also imply that the eavesdropping probability heavily depends on both the underwater acoustic channel conditions and the features of hydrophones. PMID:27213379

Generation of Hot Water from Hot-Dry for Heavy-Oil Recovery in Northern Alberta, Canada

NASA Astrophysics Data System (ADS)

Pathak, V.; Babadagli, T.; Majorowicz, J. A.; Unsworth, M. J.

2011-12-01

The focus of prior applications of hot-dry-rock (HDR) technology was mostly aimed at generating electricity. In northern Alberta, the thermal gradient is low and, therefore, this technology is not suitable for electricity generation. On the other hand, the cost of steam and hot water, and environmental impacts, are becoming critical issues in heavy-oil and bitumen recovery in Alberta. Surface generation of steam or hot-water accounts for six percent of Canada's natural gas consumption and about 50 million tons of CO2 emission. Lowered cost and environmental impacts are critical in the widespread use of steam (for in-situ recovery) and hot-water (for surface extraction of bitumen) in this region. This paper provides an extensive analysis of hot-water generation to be used in heavy-oil/bitumen recovery. We tested different modeling approaches used to determine the amount of energy produced during HDR by history matching to example field data. The most suitable numerical and analytical models were used to apply the data obtained from different regions containing heavy-oil/bitumen deposits in northern Alberta. The heat generation capacity of different regions was determined and the use of this energy (in the form of hot-water) for surface extraction processes was evaluated. Original temperature gradients were applied as well as realistic basement formation characteristics through an extensive hydro thermal analysis in the region including an experimental well drilled to the depth of 2,500m. Existing natural fractures and possible hydraulic fracturing scenarios were evaluated from the heat generation capacity and the economics points of view. The main problem was modeling difficulties, especially determination and representation of fracture network characteristics. A sensitivity analysis was performed for the selected high temperature gradient regions in Alberta. In this practice, the characteristics of hydraulic fractures, injection rate, depth, the distance between injection and production wells and formation thickness were used as variables and an optimization study was carried out based on these variables. The results showed that the hot water (50 C at surface) needed in Fort McMurray for extraction could be obtained at lower costs than the generation of it using natural gas.

Analysis on Voltage Profile of Distribution Network with Distributed Generation

NASA Astrophysics Data System (ADS)

Shao, Hua; Shi, Yujie; Yuan, Jianpu; An, Jiakun; Yang, Jianhua

2018-02-01

Penetration of distributed generation has some impacts on a distribution network in load flow, voltage profile, reliability, power loss and so on. After the impacts and the typical structures of the grid-connected distributed generation are analyzed, the back/forward sweep method of the load flow calculation of the distribution network is modelled including distributed generation. The voltage profiles of the distribution network affected by the installation location and the capacity of distributed generation are thoroughly investigated and simulated. The impacts on the voltage profiles are summarized and some suggestions to the installation location and the capacity of distributed generation are given correspondingly.

Data Verification Tools for Minimizing Management Costs of Dense Air-Quality Monitoring Networks.

PubMed

Miskell, Georgia; Salmond, Jennifer; Alavi-Shoshtari, Maryam; Bart, Mark; Ainslie, Bruce; Grange, Stuart; McKendry, Ian G; Henshaw, Geoff S; Williams, David E

2016-01-19

Aiming at minimizing the costs, both of capital expenditure and maintenance, of an extensive air-quality measurement network, we present simple statistical methods that do not require extensive training data sets for automated real-time verification of the reliability of data delivered by a spatially dense hybrid network of both low-cost and reference ozone measurement instruments. Ozone is a pollutant that has a relatively smooth spatial spread over a large scale although there can be significant small-scale variations. We take advantage of these characteristics and demonstrate detection of instrument calibration drift within a few days using a rolling 72 h comparison of hourly averaged data from the test instrument with that from suitably defined proxies. We define the required characteristics of the proxy measurements by working from a definition of the network purpose and specification, in this case reliable determination of the proportion of hourly averaged ozone measurements that are above a threshold in any given day, and detection of calibration drift of greater than ±30% in slope or ±5 parts-per-billion in offset. By analyzing results of a study of an extensive deployment of low-cost instruments in the Lower Fraser Valley, we demonstrate that proxies can be established using land-use criteria and that simple statistical comparisons can identify low-cost instruments that are not stable and therefore need replacing. We propose that a minimal set of compliant reference instruments can be used to verify the reliability of data from a much more extensive network of low-cost devices.

Bidirectional QoS support for novelty detection applications based on hierarchical wireless sensor network model

NASA Astrophysics Data System (ADS)

Edwards, Mark; Hu, Fei; Kumar, Sunil

2004-10-01

The research on the Novelty Detection System (NDS) (called as VENUS) at the authors' universities has generated exciting results. For example, we can detect an abnormal behavior (such as cars thefts from the parking lot) from a series of video frames based on the cognitively motivated theory of habituation. In this paper, we would like to describe the implementation strategies of lower layer protocols for using large-scale Wireless Sensor Networks (WSN) to NDS with Quality-of-Service (QoS) support. Wireless data collection framework, consisting of small and low-power sensor nodes, provides an alternative mechanism to observe the physical world, by using various types of sensing capabilities that include images (and even videos using Panoptos), sound and basic physical measurements such as temperature. We do not want to lose any 'data query command' packets (in the downstream direction: sink-to-sensors) or have any bit-errors in them since they are so important to the whole sensor network. In the upstream direction (sensors-to-sink), we may tolerate the loss of some sensing data packets. But the 'interested' sensing flow should be assigned a higher priority in terms of multi-hop path choice, network bandwidth allocation, and sensing data packet generation frequency (we hope to generate more sensing data packet for that novel event in the specified network area). The focus of this paper is to investigate MAC-level Quality of Service (QoS) issue in Wireless Sensor Networks (WSN) for Novelty Detection applications. Although QoS has been widely studied in other types of networks including wired Internet, general ad hoc networks and mobile cellular networks, we argue that QoS in WSN has its own characteristics. In wired Internet, the main QoS parameters include delay, jitter and bandwidth. In mobile cellular networks, two most common QoS metrics are: handoff call dropping probability and new call blocking probability. Since the main task of WSN is to detect and report events, the most important QoS parameters should include sensing data packet transmission reliability, lifetime extension degree from sensor sleeping control, event detection latency, congestion reduction level through removal of redundant sensing data. In this paper, we will focus on the following bi-directional QoS topics: (1) Downstream (sink-to-sensor) QoS: Reliable data query command forwarding to particular sensor(s). In other words, we do not want to lose the query command packets; (2) Upstream (sensor-to-sink) QoS: transmission of sensed data with priority control. The more interested data that can help in novelty detection should be transmitted on an optimal path with higher reliability. We propose the use of Differentiated Data Collection. Due to the large-scale nature and resource constraints of typical wireless sensor networks, such as limited energy, small memory (typically RAM < 4K bytes) and short communication range, the above problems become even more challenging. Besides QoS support issue, we will also describe our low-energy Sensing Data Transmission network Architecture. Our research results show the scalability and energy-efficiency of our proposed WSN QoS schemes.

Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups.

PubMed

van Mierlo, Trevor; Li, Xinlong; Hyatt, Douglas; Ching, Andrew T

2017-02-17

Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. The success of a DHSN is based on the number of participants and their consistent creation of externalities through the generation of new content. To promote network growth, it would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities. The aim of the study was to investigate the feasibility of developing predictive models that identify potential superusers in real time. This study examined associations between posting behavior, 4 demographic variables, and 20 indication-specific variables. Data were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs. Of these, 2 were designed to assist in the treatment of addictions (problem drinking and smoking cessation), and 2 for mental health (depressive disorder, panic disorder). To analyze posting behavior, 10 models were developed, and negative binomial regressions were conducted to examine associations between number of posts, and demographic and indication-specific variables. The DHSNs varied in number of days active (3658-5210), number of registrants (5049-52,396), number of actors (1085-8452), and number of posts (16,231-521,997). In the sample, all 10 models had low R 2 values (.013-.086) with limited statistically significant demographic and indication-specific variables. Very few variables were associated with social network engagement. Although some variables were statistically significant, they did not appear to be practically significant. Based on the large number of study participants, variation in DHSN theme, and extensive time-period, we did not find strong evidence that demographic characteristics or indication severity sufficiently explain the variability in number of posts per actor. Researchers should investigate alternative models that identify superusers or other individuals who create social network externalities. ©Trevor van Mierlo, Xinlong Li, Douglas Hyatt, Andrew T Ching. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.02.2017.

A Knowledge Generation Model via the Hypernetwork

PubMed Central

Liu, Jian-Guo; Yang, Guang-Yong; Hu, Zhao-Long

2014-01-01

The influence of the statistical properties of the network on the knowledge diffusion has been extensively studied. However, the structure evolution and the knowledge generation processes are always integrated simultaneously. By introducing the Cobb-Douglas production function and treating the knowledge growth as a cooperative production of knowledge, in this paper, we present two knowledge-generation dynamic evolving models based on different evolving mechanisms. The first model, named “HDPH model,” adopts the hyperedge growth and the hyperdegree preferential attachment mechanisms. The second model, named “KSPH model,” adopts the hyperedge growth and the knowledge stock preferential attachment mechanisms. We investigate the effect of the parameters on the total knowledge stock of the two models. The hyperdegree distribution of the HDPH model can be theoretically analyzed by the mean-field theory. The analytic result indicates that the hyperdegree distribution of the HDPH model obeys the power-law distribution and the exponent is . Furthermore, we present the distributions of the knowledge stock for different parameters . The findings indicate that our proposed models could be helpful for deeply understanding the scientific research cooperation. PMID:24626143

A knowledge generation model via the hypernetwork.

PubMed

Liu, Jian-Guo; Yang, Guang-Yong; Hu, Zhao-Long

2014-01-01

The influence of the statistical properties of the network on the knowledge diffusion has been extensively studied. However, the structure evolution and the knowledge generation processes are always integrated simultaneously. By introducing the Cobb-Douglas production function and treating the knowledge growth as a cooperative production of knowledge, in this paper, we present two knowledge-generation dynamic evolving models based on different evolving mechanisms. The first model, named "HDPH model," adopts the hyperedge growth and the hyperdegree preferential attachment mechanisms. The second model, named "KSPH model," adopts the hyperedge growth and the knowledge stock preferential attachment mechanisms. We investigate the effect of the parameters (α,β) on the total knowledge stock of the two models. The hyperdegree distribution of the HDPH model can be theoretically analyzed by the mean-field theory. The analytic result indicates that the hyperdegree distribution of the HDPH model obeys the power-law distribution and the exponent is γ = 2 + 1/m. Furthermore, we present the distributions of the knowledge stock for different parameters (α,β). The findings indicate that our proposed models could be helpful for deeply understanding the scientific research cooperation.

Toward next-generation optical networks: a network operator perspective based on experimental tests and economic analysis

NASA Astrophysics Data System (ADS)

Xiao, Xiaojun; Du, Chunsheng; Zhou, Rongsheng

2004-04-01

As a result of data traffic"s exponential growth, network is currently evolving from fixed circuit switched services to dynamic packet switched services, which has brought unprecedented changes to the existing transport infrastructure. It is generally agreed that automatic switched optical network (ASON) is one of the promising solutions for the next generation optical networks. In this paper, we present the results of our experimental tests and economic analysis on ASON. The intention of this paper is to present our perspective, in terms of evolution strategy toward ASON, on next generation optical networks. It is shown through experimental tests that the performance of current Pre-standard ASON enabled equipments satisfies the basic requirements of network operators and is ready for initial deployment. The results of the economic analysis show that network operators can be benefit from the deployment of ASON from three sides. Firstly, ASON can reduce the CAPEX for network expanding by integrating multiple ADM & DCS into one box. Secondly, ASON can reduce the OPEX for network operation by introducing automatic resource control scheme. Finally, ASON can increase margin revenue by providing new optical network services such as Bandwidth on Demand, optical VPN etc. Finally, the evolution strategy is proposed as our perspective toward next generation optical networks. We hope the evolution strategy introduced may be helpful for the network operators to gracefully migrate their fixed ring based legacy networks to next generation dynamic mesh based network.

Error Recovery in the Time-Triggered Paradigm with FTT-CAN.

PubMed

Marques, Luis; Vasconcelos, Verónica; Pedreiras, Paulo; Almeida, Luís

2018-01-11

Data networks are naturally prone to interferences that can corrupt messages, leading to performance degradation or even to critical failure of the corresponding distributed system. To improve resilience of critical systems, time-triggered networks are frequently used, based on communication schedules defined at design-time. These networks offer prompt error detection, but slow error recovery that can only be compensated with bandwidth overprovisioning. On the contrary, the Flexible Time-Triggered (FTT) paradigm uses online traffic scheduling, which enables a compromise between error detection and recovery that can achieve timely recovery with a fraction of the needed bandwidth. This article presents a new method to recover transmission errors in a time-triggered Controller Area Network (CAN) network, based on the Flexible Time-Triggered paradigm, namely FTT-CAN. The method is based on using a server (traffic shaper) to regulate the retransmission of corrupted or omitted messages. We show how to design the server to simultaneously: (1) meet a predefined reliability goal, when considering worst case error recovery scenarios bounded probabilistically by a Poisson process that models the fault arrival rate; and, (2) limit the direct and indirect interference in the message set, preserving overall system schedulability. Extensive simulations with multiple scenarios, based on practical and randomly generated systems, show a reduction of two orders of magnitude in the average bandwidth taken by the proposed error recovery mechanism, when compared with traditional approaches available in the literature based on adding extra pre-defined transmission slots.

Error Recovery in the Time-Triggered Paradigm with FTT-CAN

PubMed Central

Pedreiras, Paulo; Almeida, Luís

2018-01-01

Data networks are naturally prone to interferences that can corrupt messages, leading to performance degradation or even to critical failure of the corresponding distributed system. To improve resilience of critical systems, time-triggered networks are frequently used, based on communication schedules defined at design-time. These networks offer prompt error detection, but slow error recovery that can only be compensated with bandwidth overprovisioning. On the contrary, the Flexible Time-Triggered (FTT) paradigm uses online traffic scheduling, which enables a compromise between error detection and recovery that can achieve timely recovery with a fraction of the needed bandwidth. This article presents a new method to recover transmission errors in a time-triggered Controller Area Network (CAN) network, based on the Flexible Time-Triggered paradigm, namely FTT-CAN. The method is based on using a server (traffic shaper) to regulate the retransmission of corrupted or omitted messages. We show how to design the server to simultaneously: (1) meet a predefined reliability goal, when considering worst case error recovery scenarios bounded probabilistically by a Poisson process that models the fault arrival rate; and, (2) limit the direct and indirect interference in the message set, preserving overall system schedulability. Extensive simulations with multiple scenarios, based on practical and randomly generated systems, show a reduction of two orders of magnitude in the average bandwidth taken by the proposed error recovery mechanism, when compared with traditional approaches available in the literature based on adding extra pre-defined transmission slots. PMID:29324723

Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit.

PubMed

Eriksson, O; Brinne, B; Zhou, Y; Björkegren, J; Tegnér, J

2009-03-01

Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein-protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a 'tearing-and-zooming' approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between state-variables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits. [Includes supplementary material].

A Hybrid Lifetime Extended Directional Approach for WBANs

PubMed Central

Li, Changle; Yuan, Xiaoming; Yang, Li; Song, Yueyang

2015-01-01

Wireless Body Area Networks (WBANs) can provide real-time and reliable health monitoring, attributing to the human-centered and sensor interoperability properties. WBANs have become a key component of the ubiquitous eHealth (electronic health) revolution that prospers on the basis of information and communication technologies. The prime consideration in WBAN is how to maximize the network lifetime with battery-powered sensor nodes in energy constraint. Novel solutions in Medium Access Control (MAC) protocols are imperative to satisfy the particular BAN scenario and the need of excellent energy efficiency in healthcare applications. In this paper, we propose a hybrid Lifetime Extended Directional Approach (LEDA) MAC protocol based on IEEE 802.15.6 to reduce energy consumption and prolong network lifetime. The LEDA MAC protocol takes full advantages of directional superiority in energy saving that employs multi-beam directional mode in Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) and single-beam directional mode in Time Division Multiple Access (TDMA) for alternative in data reservation and transmission according to the traffic varieties. Moreover, the impacts of some inherent problems of directional antennas such as deafness and hidden terminal problem can be decreased owing to that all nodes generate individual beam according to user priorities designated. Furthermore, LEDA MAC employs a Dynamic Polled Allocation Period (DPAP) for burst data transmissions to increase the network reliability and adaptability. Extensive analysis and simulation results show that the proposed LEDA MAC protocol achieves extended network lifetime with improved performance compared with IEEE 802.15.6. PMID:26556357

Modulation for emergent networks: serotonin and dopamine.

PubMed

Weng, Juyang; Paslaski, Stephen; Daly, James; VanDam, Courtland; Brown, Jacob

2013-05-01

In autonomous learning, value-sensitive experiences can improve the efficiency of learning. A learning network needs be motivated so that the limited computational resources and the limited lifetime are devoted to events that are of high value for the agent to compete in its environment. The neuromodulatory system of the brain is mainly responsible for developing such a motivation system. Although reinforcement learning has been extensively studied, many existing models are symbolic whose internal nodes or modules have preset meanings. Neural networks have been used to automatically generate internal emergent representations. However, modeling an emergent motivational system for neural networks is still a great challenge. By emergent, we mean that the internal representations emerge autonomously through interactions with the external environments. This work proposes a generic emergent modulatory system for emergent networks, which includes two subsystems - the serotonin system and the dopamine system. The former signals a large class of stimuli that are intrinsically aversive (e.g., stress or pain). The latter signals a large class of stimuli that are intrinsically appetitive (e.g., pleasure or sweet). We experimented with this motivational system for two settings. The first is a visual recognition setting to investigate how such a system can learn through interactions with a teacher, who does not directly give answers, but only punishments and rewards. The second is a setting for wandering in the presence of a friend and a foe. Copyright © 2012 Elsevier Ltd. All rights reserved.

Finite Memory Walk and Its Application to Small-World Network

NASA Astrophysics Data System (ADS)

Oshima, Hiraku; Odagaki, Takashi

2012-07-01

In order to investigate the effects of cycles on the dynamical process on both regular lattices and complex networks, we introduce a finite memory walk (FMW) as an extension of the simple random walk (SRW), in which a walker is prohibited from moving to sites visited during m steps just before the current position. This walk interpolates the simple random walk (SRW), which has no memory (m = 0), and the self-avoiding walk (SAW), which has an infinite memory (m = ∞). We investigate the FMW on regular lattices and clarify the fundamental characteristics of the walk. We find that (1) the mean-square displacement (MSD) of the FMW shows a crossover from the SAW at a short time step to the SRW at a long time step, and the crossover time is approximately equivalent to the number of steps remembered, and that the MSD can be rescaled in terms of the time step and the size of memory; (2) the mean first-return time (MFRT) of the FMW changes significantly at the number of remembered steps that corresponds to the size of the smallest cycle in the regular lattice, where ``smallest'' indicates that the size of the cycle is the smallest in the network; (3) the relaxation time of the first-return time distribution (FRTD) decreases as the number of cycles increases. We also investigate the FMW on the Watts--Strogatz networks that can generate small-world networks, and show that the clustering coefficient of the Watts--Strogatz network is strongly related to the MFRT of the FMW that can remember two steps.

Prediction of adverse outcomes of acute coronary syndrome using intelligent fusion of triage information with HUMINT

NASA Astrophysics Data System (ADS)

McCullough, Claire L.; Novobilski, Andrew J.; Fesmire, Francis M.

2006-04-01

Faculty from the University of Tennessee at Chattanooga and the University of Tennessee College of Medicine, Chattanooga Unit, have used data mining techniques and neural networks to examine a set of fourteen features, data items, and HUMINT assessments for 2,148 emergency room patients with symptoms possibly indicative of Acute Coronary Syndrome. Specifically, the authors have generated Bayesian networks describing linkages and causality in the data, and have compared them with neural networks. The data includes objective information routinely collected during triage and the physician's initial case assessment, a HUMINT appraisal. Both the neural network and the Bayesian network were used to fuse the disparate types of information with the goal of forecasting thirty-day adverse patient outcome. This paper presents details of the methods of data fusion including both the data mining techniques and the neural network. Results are compared using Receiver Operating Characteristic curves describing the outcomes of both methods, both using only objective features and including the subjective physician's assessment. While preliminary, the results of this continuing study are significant both from the perspective of potential use of the intelligent fusion of biomedical informatics to aid the physician in prescribing treatment necessary to prevent serious adverse outcome from ACS and as a model of fusion of objective data with subjective HUMINT assessment. Possible future work includes extension of successfully demonstrated intelligent fusion methods to other medical applications, and use of decision level fusion to combine results from data mining and neural net approaches for even more accurate outcome prediction.

Towards Breaking the Histone Code – Bayesian Graphical Models for Histone Modifications

PubMed Central

Mitra, Riten; Müller, Peter; Liang, Shoudan; Xu, Yanxun; Ji, Yuan

2013-01-01

Background Histones are proteins that wrap DNA around in small spherical structures called nucleosomes. Histone modifications (HMs) refer to the post-translational modifications to the histone tails. At a particular genomic locus, each of these HMs can either be present or absent, and the combinatory patterns of the presence or absence of multiple HMs, or the ‘histone codes,’ are believed to co-regulate important biological processes. We aim to use raw data on HM markers at different genomic loci to (1) decode the complex biological network of HMs in a single region and (2) demonstrate how the HM networks differ in different regulatory regions. We suggest that these differences in network attributes form a significant link between histones and genomic functions. Methods and Results We develop a powerful graphical model under Bayesian paradigm. Posterior inference is fully probabilistic, allowing us to compute the probabilities of distinct dependence patterns of the HMs using graphs. Furthermore, our model-based framework allows for easy but important extensions for inference on differential networks under various conditions, such as the different annotations of the genomic locations (e.g., promoters versus insulators). We applied these models to ChIP-Seq data based on CD4+ T lymphocytes. The results confirmed many existing findings and provided a unified tool to generate various promising hypotheses. Differential network analyses revealed new insights on co-regulation of HMs of transcriptional activities in different genomic regions. Conclusions The use of Bayesian graphical models and borrowing strength across different conditions provide high power to infer histone networks and their differences. PMID:23748248

Holocene evolution of the Tonle Sap Lake: valley network infill and rates of sedimentation in Cambodia's Great Lake

NASA Astrophysics Data System (ADS)

Best, J.; Darby, S. E.; Langdon, P. G.; Hackney, C. R.; Leyland, J.; Parsons, D. R.; Aalto, R. E.; Marti, M.

2017-12-01

Tonle Sap Lake, the largest freshwater lake in SE Asia (c. 120km long and 35 km wide), is a vital ecosystem that provides 40-60% of the protein for the population of Cambodia. The lake is fed by flow from the Mekong River that causes the lake rise in level by c. 8m during monsoonal and cyclone-related floods, with drainage of the lake following the monsoon. Hydropower dam construction on the Mekong River has raised concerns as to the fragility of the Tonle Sap habitat due to any changing water levels and sedimentation rates within the lake. This paper details results of sub-bottom profiling surveys of Tonle Sap Lake in October 2014 that detailed the stratigraphy of the lake and assessed rates of infill. An Innomar Parametric Echo Sounder (PES) was used to obtain c. 250 km of sub-bottom profiles, with penetration up to 15m below the lake bed at a vertical resolution of c. 0.20m. These PES profiles were linked to cores from the north of the lake and previous literature. The PES profiles reveal a network of valleys, likely LGM, with relief up to c. 15-20m, that have been infilled by a suite of Holocene sediments. The valley surface is picked out as a strong reflector throughout the lake, and displays a series of valleys that are up to c. 15m deep and commonly 50-200m wide, although some of the largest valleys are 1.2km in width. Modelling of channel network incision during LGM conditions generates landscapes consistent with our field observations. The Tonle Sap valley network is infilled by sediments that show firstly fluvial and/or subaerial slope sedimentation, and then by extensive, parallel-bedded, lacustrine sedimentation. Lastly, the top c. 1m of sedimentation is marked by a distinct basal erosional surface that can be traced over much of the Tonle Sap Lake, and that is overlain by a series of parallel PES reflections. This upper sediment layer is interpreted to represent sedimentation in the Tonle Sap lake due to sediment suspension settling but after a period of widespread erosion that generated the extensive erosion surface. This paper will detail the characteristics and interpretation of the PES facies, their correlation to cores and estimates of sedimentation rates. Dating and PES profiles indicate that infill of the lake was complete by c. 6ka and that minimal sedimentation has occurred since then, likely due to reworking by wave resuspension.

Automated Item Generation with Recurrent Neural Networks.

PubMed

von Davier, Matthias

2018-03-12

Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven's progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.

A framework on the emergence and effectiveness of global health networks.

PubMed

Shiffman, Jeremy; Quissell, Kathryn; Schmitz, Hans Peter; Pelletier, David L; Smith, Stephanie L; Berlan, David; Gneiting, Uwe; Van Slyke, David; Mergel, Ines; Rodriguez, Mariela; Walt, Gill

2016-04-01

Since 1990 mortality and morbidity decline has been more extensive for some conditions prevalent in low- and middle-income countries than for others. One reason may be differences in the effectiveness of global health networks, which have proliferated in recent years. Some may be more capable than others in attracting attention to a condition, in generating funding, in developing interventions and in convincing national governments to adopt policies. This article introduces a supplement on the emergence and effectiveness of global health networks. The supplement examines networks concerned with six global health problems: tuberculosis (TB), pneumonia, tobacco use, alcohol harm, maternal mortality and newborn deaths. This article presents a conceptual framework delineating factors that may shape why networks crystallize more easily surrounding some issues than others, and once formed, why some are better able than others to shape policy and public health outcomes. All supplement papers draw on this framework. The framework consists of 10 factors in three categories: (1) features of the networks and actors that comprise them, including leadership, governance arrangements, network composition and framing strategies; (2) conditions in the global policy environment, including potential allies and opponents, funding availability and global expectations concerning which issues should be prioritized; (3) and characteristics of the issue, including severity, tractability and affected groups. The article also explains the design of the project, which is grounded in comparison of networks surrounding three matched issues: TB and pneumonia, tobacco use and alcohol harm, and maternal and newborn survival. Despite similar burden and issue characteristics, there has been considerably greater policy traction for the first in each pair. The supplement articles aim to explain the role of networks in shaping these differences, and collectively represent the first comparative effort to understand the emergence and effectiveness of global health networks. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2015; all rights reserved.

Psychological and behavioral acculturation in a social network of Mexican Americans in the United States and use of dental services.

PubMed

Maupome, G; McConnell, W R; Perry, B L; Marino, R; Wright, E R

2016-12-01

We used data from the TalaSurvey study to examine associations between dental health experiences, social network characteristics, and levels of behavioral and psychological acculturation in one location in the American Midwest. Starting in parishes and community organizations, we identified adults of Mexican origin living in Indianapolis, who were 1st- or 2nd-generation immigrants from Tala, Mexico. Using a social networks methodology and following extensive formative research, we created an egocentric social network survey and administered it via face-to-face interviews. We identified the peers (alters) in interviewees' (egos) personal networks. We asked egos about multiple oral health and dental care variables for self and for alters. Acculturation (psychological and behavioral) was measured with a validated tool. Through logistic and negative binomial regression, we examined the effects of acculturation and network composition on ego's dental insurance status, dental office visits, and the reason for most recent dental office visit. A total of 332 egos (mean age 36; 63% female) were interviewed: 90% were born in Mexico; 45% had completed elementary school or lower; and most had low income. Each ego named 3.9 (SD±1.9) alters in his/her personal network, for a total of 1299 alters (mean age 39; 61% female). Both behavioral acculturation and psychological acculturation were moderately associated with dental insurance coverage, and greater behavioral acculturation predicted more frequent dental care. More psychologically acculturated egos were more likely to seek preventive care. Further, egos with more highly educated networks sought care more frequently and for preventive purposes, net of ego's own education and acculturation. This study contextualizes acculturation of Mexican Americans within the personal networks in which oral health discussion takes place. The findings underscore the critical importance of acculturation and social network factors in shaping a subgroup of Latinos' orientation toward dental care. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Relative Panoramic Camera Position Estimation for Image-Based Virtual Reality Networks in Indoor Environments

NASA Astrophysics Data System (ADS)

Nakagawa, M.; Akano, K.; Kobayashi, T.; Sekiguchi, Y.

2017-09-01

Image-based virtual reality (VR) is a virtual space generated with panoramic images projected onto a primitive model. In imagebased VR, realistic VR scenes can be generated with lower rendering cost, and network data can be described as relationships among VR scenes. The camera network data are generated manually or by an automated procedure using camera position and rotation data. When panoramic images are acquired in indoor environments, network data should be generated without Global Navigation Satellite Systems (GNSS) positioning data. Thus, we focused on image-based VR generation using a panoramic camera in indoor environments. We propose a methodology to automate network data generation using panoramic images for an image-based VR space. We verified and evaluated our methodology through five experiments in indoor environments, including a corridor, elevator hall, room, and stairs. We confirmed that our methodology can automatically reconstruct network data using panoramic images for image-based VR in indoor environments without GNSS position data.

A Search Algorithm for Generating Alternative Process Plans in Flexible Manufacturing System

NASA Astrophysics Data System (ADS)

Tehrani, Hossein; Sugimura, Nobuhiro; Tanimizu, Yoshitaka; Iwamura, Koji

Capabilities and complexity of manufacturing systems are increasing and striving for an integrated manufacturing environment. Availability of alternative process plans is a key factor for integration of design, process planning and scheduling. This paper describes an algorithm for generation of alternative process plans by extending the existing framework of the process plan networks. A class diagram is introduced for generating process plans and process plan networks from the viewpoint of the integrated process planning and scheduling systems. An incomplete search algorithm is developed for generating and searching the process plan networks. The benefit of this algorithm is that the whole process plan network does not have to be generated before the search algorithm starts. This algorithm is applicable to large and enormous process plan networks and also to search wide areas of the network based on the user requirement. The algorithm can generate alternative process plans and to select a suitable one based on the objective functions.

Physical Configuration of the Next Generation Home Network

NASA Astrophysics Data System (ADS)

Terada, Shohei; Kakishima, Yu; Hanawa, Dai; Oguchi, Kimio

The number of broadband users is rapidly increasing worldwide. Japan already has over 10 million FTTH users. Another trend is the rapid digitalization of home electrical equipment e. g. digital cameras and hard disc recorders. These trends will encourage the emergence of the next generation home network. In this paper, we introduce the next generation home network image and describe the five domains into which home devices can be classified. We then clarify the optimum medium with which to configure the network given the requirements imposed by the home environment. Wiring cable lengths for three network topologies are calculated. The results gained from the next generation home network implemented on the first phase testbed are shown. Finally, our conclusions are given.

Social Networks Use, Loneliness and Academic Performance among University Students

ERIC Educational Resources Information Center

Stankovska, Gordana; Angelkovska, Slagana; Grncarovska, Svetlana Pandiloska

2016-01-01

The world is extensively changed by Social Networks Sites (SNSs) on the Internet. A large number of children and adolescents in the world have access to the internet and are exposed to the internet at a very early age. Most of them use the Social Networks Sites with the purpose of exchanging academic activities and developing a social network all…

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli.

PubMed

Antiqueira, Lucas; Janga, Sarath Chandra; Costa, Luciano da Fontoura

2012-11-01

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

NASA Astrophysics Data System (ADS)

Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto

2016-07-01

The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.

Cellular automata with object-oriented features for parallel molecular network modeling.

PubMed

Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

2005-06-01

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

Efficient Graph-Based Resource Allocation Scheme Using Maximal Independent Set for Randomly- Deployed Small Star Networks

PubMed Central

Zhou, Jian; Wang, Lusheng; Wang, Weidong; Zhou, Qingfeng

2017-01-01

In future scenarios of heterogeneous and dense networks, randomly-deployed small star networks (SSNs) become a key paradigm, whose system performance is restricted to inter-SSN interference and requires an efficient resource allocation scheme for interference coordination. Traditional resource allocation schemes do not specifically focus on this paradigm and are usually too time consuming in dense networks. In this article, a very efficient graph-based scheme is proposed, which applies the maximal independent set (MIS) concept in graph theory to help divide SSNs into almost interference-free groups. We first construct an interference graph for the system based on a derived distance threshold indicating for any pair of SSNs whether there is intolerable inter-SSN interference or not. Then, SSNs are divided into MISs, and the same resource can be repetitively used by all the SSNs in each MIS. Empirical parameters and equations are set in the scheme to guarantee high performance. Finally, extensive scenarios both dense and nondense are randomly generated and simulated to demonstrate the performance of our scheme, indicating that it outperforms the classical max K-cut-based scheme in terms of system capacity, utility and especially time cost. Its achieved system capacity, utility and fairness can be close to the near-optimal strategy obtained by a time-consuming simulated annealing search. PMID:29113109

Negative Correlations in Visual Cortical Networks

PubMed Central

Chelaru, Mircea I.; Dragoi, Valentin

2016-01-01

The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function. PMID:25217468

Improved Adjoint-Operator Learning For A Neural Network

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1995-01-01

Improved method of adjoint-operator learning reduces amount of computation and associated computational memory needed to make electronic neural network learn temporally varying pattern (e.g., to recognize moving object in image) in real time. Method extension of method described in "Adjoint-Operator Learning for a Neural Network" (NPO-18352).

Extension of mixture-of-experts networks for binary classification of hierarchical data.

PubMed

Ng, Shu-Kay; McLachlan, Geoffrey J

2007-09-01

For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be evaluated. This information can be taken into consideration for the assessment of treatment planning of the disease. The proposed extended ME network thus facilitates a more general approach to incorporate data hierarchy mechanism in network modeling.

NCC Simulation Model: Simulating the operations of the network control center, phase 2

NASA Technical Reports Server (NTRS)

Benjamin, Norman M.; Paul, Arthur S.; Gill, Tepper L.

1992-01-01

The simulation of the network control center (NCC) is in the second phase of development. This phase seeks to further develop the work performed in phase one. Phase one concentrated on the computer systems and interconnecting network. The focus of phase two will be the implementation of the network message dialogues and the resources controlled by the NCC. These resources are requested, initiated, monitored and analyzed via network messages. In the NCC network messages are presented in the form of packets that are routed across the network. These packets are generated, encoded, decoded and processed by the network host processors that generate and service the message traffic on the network that connects these hosts. As a result, the message traffic is used to characterize the work done by the NCC and the connected network. Phase one of the model development represented the NCC as a network of bi-directional single server queues and message generating sources. The generators represented the external segment processors. The served based queues represented the host processors. The NCC model consists of the internal and external processors which generate message traffic on the network that links these hosts. To fully realize the objective of phase two it is necessary to identify and model the processes in each internal processor. These processes live in the operating system of the internal host computers and handle tasks such as high speed message exchanging, ISN and NFE interface, event monitoring, network monitoring, and message logging. Inter process communication is achieved through the operating system facilities. The overall performance of the host is determined by its ability to service messages generated by both internal and external processors.

Monitoring and assessing the effects from Metro networks construction on the urban acoustic environment: The Athens Metro Line 3 Extension.

PubMed

Vogiatzis, Konstantinos; Zafiropoulou, Vassiliki; Mouzakis, Haralampos

2018-10-15

The Line 3 Extension from Aghia Marina to Piraeus constitutes one of the most significant construction projects in full development in Athens Greater area. For the management and abatement of the air borne noise generated from surface, and/or underground construction activities, relevant machinery operation, and trucks movements at open worksites and the tunnel, a continuous monthly noise and vibration monitoring program is enforced in order to assess any potential intrusion of the acoustic environment. On basis of measured 24 hour L eq noise levels, both L den and L night EU indices were assessed along with vibration velocity for every worksite and tunnel construction activity. The existing environmental noise background generated mainly from road traffic was assessed in order to evaluate potential effects on both air borne noise from construction activities. This comprehensive monitoring program aims to protect the inhabitants in the vicinity of worksites and the tunnel surrounding from construction noise and vibration processing and evaluating all necessary mitigation measures. Especially, for the protection of sensitive receptors, this program may serve as a tool ensuring a successful management of both noise and vibration levels emitted from open air construction activities and (Tunnel Boring Machine) TBM or hammer/pilling operation by implementing mitigation measures where necessary. Copyright © 2018 Elsevier B.V. All rights reserved.

The 2nd Generation Real Time Mission Monitor (RTMM) Development

NASA Technical Reports Server (NTRS)

Blakeslee, Richard; Goodman, Michael; Meyer, Paul; Hardin, Danny; Hall, John; He, Yubin; Regner, Kathryn; Conover, Helen; Smith, Tammy; Lu, Jessica;

NEVESIM: event-driven neural simulation framework with a Python interface.

PubMed

Pecevski, Dejan; Kappel, David; Jonke, Zeno

2014-01-01

NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.

Reticulate evolutionary history and extensive introgression in mosquito species revealed by phylogenetic network analysis

PubMed Central

Wen, Dingqiao; Yu, Yun; Hahn, Matthew W.; Nakhleh, Luay

2016-01-01

The role of hybridization and subsequent introgression has been demonstrated in an increasing number of species. Recently, Fontaine et al. (Science, 347, 2015, 1258524) conducted a phylogenomic analysis of six members of the Anopheles gambiae species complex. Their analysis revealed a reticulate evolutionary history and pointed to extensive introgression on all four autosomal arms. The study further highlighted the complex evolutionary signals that the co-occurrence of incomplete lineage sorting (ILS) and introgression can give rise to in phylogenomic analyses. While tree-based methodologies were used in the study, phylogenetic networks provide a more natural model to capture reticulate evolutionary histories. In this work, we reanalyse the Anopheles data using a recently devised framework that combines the multispecies coalescent with phylogenetic networks. This framework allows us to capture ILS and introgression simultaneously, and forms the basis for statistical methods for inferring reticulate evolutionary histories. The new analysis reveals a phylogenetic network with multiple hybridization events, some of which differ from those reported in the original study. To elucidate the extent and patterns of introgression across the genome, we devise a new method that quantifies the use of reticulation branches in the phylogenetic network by each genomic region. Applying the method to the mosquito data set reveals the evolutionary history of all the chromosomes. This study highlights the utility of ‘network thinking’ and the new insights it can uncover, in particular in phylogenomic analyses of large data sets with extensive gene tree incongruence. PMID:26808290

NEVESIM: event-driven neural simulation framework with a Python interface

PubMed Central

Pecevski, Dejan; Kappel, David; Jonke, Zeno

2014-01-01

NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies. PMID:25177291

Wild cricket social networks show stability across generations.

PubMed

Fisher, David N; Rodríguez-Muñoz, Rolando; Tregenza, Tom

2016-07-27

A central part of an animal's environment is its interactions with conspecifics. There has been growing interest in the potential to capture these interactions in the form of a social network. Such networks can then be used to examine how relationships among individuals affect ecological and evolutionary processes. However, in the context of selection and evolution, the utility of this approach relies on social network structures persisting across generations. This is an assumption that has been difficult to test because networks spanning multiple generations have not been available. We constructed social networks for six annual generations over a period of eight years for a wild population of the cricket Gryllus campestris. Through the use of exponential random graph models (ERGMs), we found that the networks in any given year were able to predict the structure of networks in other years for some network characteristics. The capacity of a network model of any given year to predict the networks of other years did not depend on how far apart those other years were in time. Instead, the capacity of a network model to predict the structure of a network in another year depended on the similarity in population size between those years. Our results indicate that cricket social network structure resists the turnover of individuals and is stable across generations. This would allow evolutionary processes that rely on network structure to take place. The influence of network size may indicate that scaling up findings on social behaviour from small populations to larger ones will be difficult. Our study also illustrates the utility of ERGMs for comparing networks, a task for which an effective approach has been elusive.

A random spatial network model based on elementary postulates

USGS Publications Warehouse

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

Land Use Change Impacts to Flows and Hydropower at the Southern Fringe of the Brazilian Amazon: A Regional, Empirical Study of Land-Water-Energy Nexus Dynamics

NASA Astrophysics Data System (ADS)

Levy, M. C.; Thompson, S. E.; Cohn, A.

2014-12-01

Land use/cover change (LUCC) has occurred extensively in the Brazilian Amazon rainforest-savanna transition. Agricultural development-driven LUCC at regional scales can alter surface energy budgets, evapotranspiration (ET) and rainfall; these hydroclimatic changes impact streamflows, and thus hydropower. To date, there is only limited empirical understanding of these complex land-water-energy nexus dynamics, yet understanding is important to developing countries where both agriculture and hydropower are expanding and intensifying. To observe these changes and their interconnections, we synthesize a novel combination of ground network, remotely sensed, and empirically modeled data for LUCC, rainfall, flows, and hydropower potential. We connect the extensive temporal and spatial trends in LUCC occurring from 2000-2012 (and thus observable in the satellite record) to long-term historical flow records and run-of-river hydropower generation potential estimates. Changes in hydrologic condition are observed in terms of dry and wet season moments, extremes, and flow duration curves. Run-of-river hydropower generation potential is modeled at basin gauge points using equation models parameterized with literature-based low-head turbine efficiencies, and simple algorithms establishing optimal head and capacity from elevation and flows, respectively. Regression analyses are used to demonstrate a preliminary causal analysis of LUCC impacts to flow and energy, and discuss extension of the analysis to ungauged basins. The results are transferable to tropical and transitional forest regions worldwide where simultaneous agricultural and hydropower development potentially compete for coupled components of regional water cycles, and where policy makers and planners require an understanding of LUCC impacts to hydroclimate-dependent industries and ecosystems.

RNA-Seq and Gene Network Analysis Uncover Activation of an ABA-Dependent Signalosome During the Cork Oak Root Response to Drought

PubMed Central

Magalhães, Alexandre P.; Verde, Nuno; Reis, Francisca; Martins, Inês; Costa, Daniela; Lino-Neto, Teresa; Castro, Pedro H.; Tavares, Rui M.; Azevedo, Herlânder

2016-01-01

Quercus suber (cork oak) is a West Mediterranean species of key economic interest, being extensively explored for its ability to generate cork. Like other Mediterranean plants, Q. suber is significantly threatened by climatic changes, imposing the need to quickly understand its physiological and molecular adaptability to drought stress imposition. In the present report, we uncovered the differential transcriptome of Q. suber roots exposed to long-term drought, using an RNA-Seq approach. 454-sequencing reads were used to de novo assemble a reference transcriptome, and mapping of reads allowed the identification of 546 differentially expressed unigenes. These were enriched in both effector genes (e.g., LEA, chaperones, transporters) as well as regulatory genes, including transcription factors (TFs) belonging to various different classes, and genes associated with protein turnover. To further extend functional characterization, we identified the orthologs of differentially expressed unigenes in the model species Arabidopsis thaliana, which then allowed us to perform in silico functional inference, including gene network analysis for protein function, protein subcellular localization and gene co-expression, and in silico enrichment analysis for TFs and cis-elements. Results indicated the existence of extensive transcriptional regulatory events, including activation of ABA-responsive genes and ABF-dependent signaling. We were then able to establish that a core ABA-signaling pathway involving PP2C-SnRK2-ABF components was induced in stressed Q. suber roots, identifying a key mechanism in this species’ response to drought. PMID:26793200

Membership generation using multilayer neural network

NASA Technical Reports Server (NTRS)

Kim, Jaeseok

1992-01-01

There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.

Scale-free networks which are highly assortative but not small world

NASA Astrophysics Data System (ADS)

Small, Michael; Xu, Xiaoke; Zhou, Jin; Zhang, Jie; Sun, Junfeng; Lu, Jun-An

2008-06-01

Uncorrelated scale-free networks are necessarily small world (and, in fact, smaller than small world). Nonetheless, for scale-free networks with correlated degree distribution this may not be the case. We describe a mechanism to generate highly assortative scale-free networks which are not small world. We show that it is possible to generate scale-free networks, with arbitrary degree exponent γ>1 , such that the average distance between nodes in the network is large. To achieve this, nodes are not added to the network with preferential attachment. Instead, we greedily optimize the assortativity of the network. The network generation scheme is physically motivated, and we show that the recently observed global network of Avian Influenza outbreaks arises through a mechanism similar to what we present here. Simulations show that this network exhibits very similar physical characteristics (very high assortativity, clustering, and path length).

Generating realistic environments for cyber operations development, testing, and training

NASA Astrophysics Data System (ADS)

Berk, Vincent H.; Gregorio-de Souza, Ian; Murphy, John P.

2012-06-01

Training eective cyber operatives requires realistic network environments that incorporate the structural and social complexities representative of the real world. Network trac generators facilitate repeatable experiments for the development, training and testing of cyber operations. However, current network trac generators, ranging from simple load testers to complex frameworks, fail to capture the realism inherent in actual environments. In order to improve the realism of network trac generated by these systems, it is necessary to quantitatively measure the level of realism in generated trac with respect to the environment being mimicked. We categorize realism measures into statistical, content, and behavioral measurements, and propose various metrics that can be applied at each level to indicate how eectively the generated trac mimics the real world.

SENTRE and TREND attenuator field installations

DOT National Transportation Integrated Search

1990-02-01

Arizona's canal network is extensive and necessitates the existence of many short bridges on the highway network. The necessity for maintaining access to adjacent canal roads dictates that any barrier installation intended to shield errant vehicles f...

Nature's Notebook and Extension: Engaging Citizen-Scientists and 4-H Youth to Observe a Changing Environment

ERIC Educational Resources Information Center

Posthumus, Erin E.; Barnett, LoriAnne; Crimmins, Theresa M.; Kish, George R.; Sheftall, Will; Stancioff, Esperanza; Warren, Peter

2013-01-01

Extension, with its access to long-term volunteers, has the unique ability to teach citizen scientists about the connection between climate variability and the resulting effects on plants, animals, and thus, humans. The USA National Phenology Network's Nature's Notebook on-line program provides a science learning tool for Extension's Master…

Network models provide insights into how oriens–lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations

PubMed Central

Ferguson, Katie A.; Huh, Carey Y. L.; Amilhon, Bénédicte; Manseau, Frédéric; Williams, Sylvain; Skinner, Frances K.

2015-01-01

Hippocampal theta is a 4–12 Hz rhythm associated with episodic memory, and although it has been studied extensively, the cellular mechanisms underlying its generation are unclear. The complex interactions between different interneuron types, such as those between oriens–lacunosum-moleculare (OLM) interneurons and bistratified cells (BiCs), make their contribution to network rhythms difficult to determine experimentally. We created network models that are tied to experimental work at both cellular and network levels to explore how these interneuron interactions affect the power of local oscillations. Our cellular models were constrained with properties from patch clamp recordings in the CA1 region of an intact hippocampus preparation in vitro. Our network models are composed of three different types of interneurons: parvalbumin-positive (PV+) basket and axo-axonic cells (BC/AACs), PV+ BiCs, and somatostatin-positive OLM cells. Also included is a spatially extended pyramidal cell model to allow for a simplified local field potential representation, as well as experimentally-constrained, theta frequency synaptic inputs to the interneurons. The network size, connectivity, and synaptic properties were constrained with experimental data. To determine how the interactions between OLM cells and BiCs could affect local theta power, we explored how the number of OLM-BiC connections and connection strength affected local theta power. We found that our models operate in regimes that could be distinguished by whether OLM cells minimally or strongly affected the power of network theta oscillations due to balances that, respectively, allow compensatory effects or not. Inactivation of OLM cells could result in no change or even an increase in theta power. We predict that the dis-inhibitory effect of OLM cells to BiCs to pyramidal cell interactions plays a critical role in the resulting power of network theta oscillations. Overall, our network models reveal a dynamic interplay between different classes of interneurons in influencing local theta power. PMID:26300744

Network Leadership's Balancing Act: Contrivance or Emergence?

ERIC Educational Resources Information Center

Kubiak, Chris; Bertram, Joan

2005-01-01

It is well understood that one learns much of what one knows through one's network of relationships and through shared discussion and activity. A logical extension of this idea is the growing prominence in the UK where formal school-to-school networking such as Education Action Zones and Excellence in Cities has been established. This article…

Experiments on Learning by Back Propagation.

ERIC Educational Resources Information Center

Plaut, David C.; And Others

This paper describes further research on a learning procedure for layered networks of deterministic, neuron-like units, described by Rumelhart et al. The units, the way they are connected, the learning procedure, and the extension to iterative networks are presented. In one experiment, a network learns a set of filters, enabling it to discriminate…

Request for Support: A Tool for Strengthening Network Capacity

ERIC Educational Resources Information Center

Bain, Jamie; Harden, Noelle; Heim, Stephanie

2017-01-01

A request for support (RFS) is a tool that is used to strengthen network capacity by prioritizing needs and optimizing learning opportunities. Within University of Minnesota Extension, we implemented an RFS process through an online survey designed to help leaders of food networks identify and rank learning and capacity-building needs and indicate…

Extensive site-directed mutagenesis reveals interconnected functional units in the alkaline phosphatase active site

PubMed Central

Sunden, Fanny; Peck, Ariana; Salzman, Julia; Ressl, Susanne; Herschlag, Daniel

2015-01-01

Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modes of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes. DOI: http://dx.doi.org/10.7554/eLife.06181.001 PMID:25902402

Extensive site-directed mutagenesis reveals interconnected functional units in the alkaline phosphatase active site

DOE PAGES

Sunden, Fanny; Peck, Ariana; Salzman, Julia; ...

2015-04-22

Enzymes enable life by accelerating reaction rates to biological timescales. Conventional studies have focused on identifying the residues that have a direct involvement in an enzymatic reaction, but these so-called ‘catalytic residues’ are embedded in extensive interaction networks. Although fundamental to our understanding of enzyme function, evolution, and engineering, the properties of these networks have yet to be quantitatively and systematically explored. We dissected an interaction network of five residues in the active site of Escherichia coli alkaline phosphatase. Analysis of the complex catalytic interdependence of specific residues identified three energetically independent but structurally interconnected functional units with distinct modesmore » of cooperativity. From an evolutionary perspective, this network is orders of magnitude more probable to arise than a fully cooperative network. From a functional perspective, new catalytic insights emerge. Further, such comprehensive energetic characterization will be necessary to benchmark the algorithms required to rationally engineer highly efficient enzymes.« less

Cerebral cartography and connectomics

PubMed Central

Sporns, Olaf

2015-01-01

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics. PMID:25823870

Improving Generation Y Volunteerism in Extension Programs

ERIC Educational Resources Information Center

Andrews, Kevin B.; Lockett, Landry L.

2013-01-01

Members of Generation Y have many positive attributes that make them attractive to Extension volunteer administrators as a potential source of labor. However, they think differently, have unique needs, require new management styles, and have less tolerance for unpleasant working conditions than previous generations. Additionally, they are engaged…

Chondroitin sulfate synthase-2 is necessary for chain extension of chondroitin sulfate but not critical for skeletal development.

PubMed

Ogawa, Hiroyasu; Hatano, Sonoko; Sugiura, Nobuo; Nagai, Naoko; Sato, Takashi; Shimizu, Katsuji; Kimata, Koji; Narimatsu, Hisashi; Watanabe, Hideto

2012-01-01

Chondroitin sulfate (CS) is a linear polysaccharide consisting of repeating disaccharide units of N-acetyl-D-galactosamine and D-glucuronic acid residues, modified with sulfated residues at various positions. Based on its structural diversity in chain length and sulfation patterns, CS provides specific biological functions in cell adhesion, morphogenesis, neural network formation, and cell division. To date, six glycosyltransferases are known to be involved in the biosynthesis of chondroitin saccharide chains, and a hetero-oligomer complex of chondroitin sulfate synthase-1 (CSS1)/chondroitin synthase-1 and chondroitin sulfate synthase-2 (CSS2)/chondroitin polymerizing factor is known to have the strongest polymerizing activity. Here, we generated and analyzed CSS2(-/-) mice. Although they were viable and fertile, exhibiting no overt morphological abnormalities or osteoarthritis, their cartilage contained CS chains with a shorter length and at a similar number to wild type. Further analysis using CSS2(-/-) chondrocyte culture systems, together with siRNA of CSS1, revealed the presence of two CS chain species in length, suggesting two steps of CS chain polymerization; i.e., elongation from the linkage region up to Mr ∼10,000, and further extension. There, CSS2 mainly participated in the extension, whereas CSS1 participated in both the extension and the initiation. Our study demonstrates the distinct function of CSS1 and CSS2, providing a clue in the elucidation of the mechanism of CS biosynthesis.

Capacity planning in a transitional economy: What issues? Which models?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mubayi, V.; Leigh, R.W.; Bright, R.N.

1996-03-01

This paper is devoted to an exploration of the important issues facing the Russian power generation system and its evolution in the foreseeable future and the kinds of modeling approaches that capture those issues. These issues include, for example, (1) trade-offs between investments in upgrading and refurbishment of existing thermal (fossil-fired) capacity and safety enhancements in existing nuclear capacity versus investment in new capacity, (2) trade-offs between investment in completing unfinished (under construction) projects based on their original design versus investment in new capacity with improved design, (3) incorporation of demand-side management options (investments in enhancing end-use efficiency, for example)more » within the planning framework, (4) consideration of the spatial dimensions of system planning including investments in upgrading electric transmission networks or fuel shipment networks and incorporating hydroelectric generation, (5) incorporation of environmental constraints and (6) assessment of uncertainty and evaluation of downside risk. Models for exploring these issues include low power shutdown (LPS) which are computationally very efficient, though approximate, and can be used to perform extensive sensitivity analyses to more complex models which can provide more detailed answers but are computationally cumbersome and can only deal with limited issues. The paper discusses which models can usefully treat a wide range of issues within the priorities facing decision makers in the Russian power sector and integrate the results with investment decisions in the wider economy.« less

Skin symptoms in four ectodermal dysplasia syndromes including two case reports of Rapp-Hodgkin-Syndrome.

PubMed

Knaudt, Björn; Volz, Thomas; Krug, Markus; Burgdorf, Walter; Röcken, Martin; Berneburg, Mark

2012-01-01

The skin, hair and nail changes in four distinct ectodermal dysplasia syndromes are compared and reviewed. These syndromes comprise Christ-Siemens-Touraine syndrome; ectrodactyly, ectodermal dysplasia and cleft lip/palate syndrome; ankyloblepharon-ectodermal defects-cleft lip/palate syndrome and Rapp-Hodgkin syndrome. A comprehensive overview of the dermatological signs and symptoms in these syndromes was generated from the database of the Ectodermal Dysplasia Network Germany, the clinical findings in the patients seen in our department and an extensive review of the literature. The findings included abnormalities of skin, sweating, hair and nails. These clinical findings are discussed in relation to the underlying molecular defects known to play a role in these four ectodermal dysplasia syndromes.

A Communication-Optimal Framework for Contracting Distributed Tensors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rajbhandari, Samyam; NIkam, Akshay; Lai, Pai-Wei

Tensor contractions are extremely compute intensive generalized matrix multiplication operations encountered in many computational science fields, such as quantum chemistry and nuclear physics. Unlike distributed matrix multiplication, which has been extensively studied, limited work has been done in understanding distributed tensor contractions. In this paper, we characterize distributed tensor contraction algorithms on torus networks. We develop a framework with three fundamental communication operators to generate communication-efficient contraction algorithms for arbitrary tensor contractions. We show that for a given amount of memory per processor, our framework is communication optimal for all tensor contractions. We demonstrate performance and scalability of our frameworkmore » on up to 262,144 cores of BG/Q supercomputer using five tensor contraction examples.« less

Drawing Road Networks with Mental Maps.

PubMed

Lin, Shih-Syun; Lin, Chao-Hung; Hu, Yan-Jhang; Lee, Tong-Yee

2014-09-01

Tourist and destination maps are thematic maps designed to represent specific themes in maps. The road network topologies in these maps are generally more important than the geometric accuracy of roads. A road network warping method is proposed to facilitate map generation and improve theme representation in maps. The basic idea is deforming a road network to meet a user-specified mental map while an optimization process is performed to propagate distortions originating from road network warping. To generate a map, the proposed method includes algorithms for estimating road significance and for deforming a road network according to various geometric and aesthetic constraints. The proposed method can produce an iconic mark of a theme from a road network and meet a user-specified mental map. Therefore, the resulting map can serve as a tourist or destination map that not only provides visual aids for route planning and navigation tasks, but also visually emphasizes the presentation of a theme in a map for the purpose of advertising. In the experiments, the demonstrations of map generations show that our method enables map generation systems to generate deformed tourist and destination maps efficiently.

Syntactic sequencing in Hebbian cell assemblies.

PubMed

Wennekers, Thomas; Palm, Günther

2009-12-01

Hebbian cell assemblies provide a theoretical framework for the modeling of cognitive processes that grounds them in the underlying physiological neural circuits. Recently we have presented an extension of cell assemblies by operational components which allows to model aspects of language, rules, and complex behaviour. In the present work we study the generation of syntactic sequences using operational cell assemblies timed by unspecific trigger signals. Syntactic patterns are implemented in terms of hetero-associative transition graphs in attractor networks which cause a directed flow of activity through the neural state space. We provide regimes for parameters that enable an unspecific excitatory control signal to switch reliably between attractors in accordance with the implemented syntactic rules. If several target attractors are possible in a given state, noise in the system in conjunction with a winner-takes-all mechanism can randomly choose a target. Disambiguation can also be guided by context signals or specific additional external signals. Given a permanently elevated level of external excitation the model can enter an autonomous mode, where it generates temporal grammatical patterns continuously.

Spinal Locomotor Circuits Develop Using Hierarchical Rules Based on Motorneuron Position and Identity.

PubMed

Hinckley, Christopher A; Alaynick, William A; Gallarda, Benjamin W; Hayashi, Marito; Hilde, Kathryn L; Driscoll, Shawn P; Dekker, Joseph D; Tucker, Haley O; Sharpee, Tatyana O; Pfaff, Samuel L

2015-09-02

The coordination of multi-muscle movements originates in the circuitry that regulates the firing patterns of spinal motorneurons. Sensory neurons rely on the musculotopic organization of motorneurons to establish orderly connections, prompting us to examine whether the intraspinal circuitry that coordinates motor activity likewise uses cell position as an internal wiring reference. We generated a motorneuron-specific GCaMP6f mouse line and employed two-photon imaging to monitor the activity of lumbar motorneurons. We show that the central pattern generator neural network coordinately drives rhythmic columnar-specific motorneuron bursts at distinct phases of the locomotor cycle. Using multiple genetic strategies to perturb the subtype identity and orderly position of motorneurons, we found that neurons retained their rhythmic activity-but cell position was decoupled from the normal phasing pattern underlying flexion and extension. These findings suggest a hierarchical basis of motor circuit formation that relies on increasingly stringent matching of neuronal identity and position. Copyright © 2015 Elsevier Inc. All rights reserved.

Genetic architecture of retinal and macular degenerative diseases: the promise and challenges of next-generation sequencing

PubMed Central

2013-01-01

Inherited retinal degenerative diseases (RDDs) display wide variation in their mode of inheritance, underlying genetic defects, age of onset, and phenotypic severity. Molecular mechanisms have not been delineated for many retinal diseases, and treatment options are limited. In most instances, genotype-phenotype correlations have not been elucidated because of extensive clinical and genetic heterogeneity. Next-generation sequencing (NGS) methods, including exome, genome, transcriptome and epigenome sequencing, provide novel avenues towards achieving comprehensive understanding of the genetic architecture of RDDs. Whole-exome sequencing (WES) has already revealed several new RDD genes, whereas RNA-Seq and ChIP-Seq analyses are expected to uncover novel aspects of gene regulation and biological networks that are involved in retinal development, aging and disease. In this review, we focus on the genetic characterization of retinal and macular degeneration using NGS technology and discuss the basic framework for further investigations. We also examine the challenges of NGS application in clinical diagnosis and management. PMID:24112618

Dynamics, morphogenesis and convergence of evolutionary quantum Prisoner's Dilemma games on networks

PubMed Central

Yong, Xi

2016-01-01

The authors proposed a quantum Prisoner's Dilemma (PD) game as a natural extension of the classic PD game to resolve the dilemma. Here, we establish a new Nash equilibrium principle of the game, propose the notion of convergence and discover the convergence and phase-transition phenomena of the evolutionary games on networks. We investigate the many-body extension of the game or evolutionary games in networks. For homogeneous networks, we show that entanglement guarantees a quick convergence of super cooperation, that there is a phase transition from the convergence of defection to the convergence of super cooperation, and that the threshold for the phase transitions is principally determined by the Nash equilibrium principle of the game, with an accompanying perturbation by the variations of structures of networks. For heterogeneous networks, we show that the equilibrium frequencies of super-cooperators are divergent, that entanglement guarantees emergence of super-cooperation and that there is a phase transition of the emergence with the threshold determined by the Nash equilibrium principle, accompanied by a perturbation by the variations of structures of networks. Our results explore systematically, for the first time, the dynamics, morphogenesis and convergence of evolutionary games in interacting and competing systems. PMID:27118882

Electricity generation and transmission planning in deregulated power markets

NASA Astrophysics Data System (ADS)

He, Yang

This dissertation addresses the long-term planning of power generation and transmission facilities in a deregulated power market. Three models with increasing complexities are developed, primarily for investment decisions in generation and transmission capacity. The models are presented in a two-stage decision context where generation and transmission capacity expansion decisions are made in the first stage, while power generation and transmission service fees are decided in the second stage. Uncertainties that exist in the second stage affect the capacity expansion decisions in the first stage. The first model assumes that the electric power market is not constrained by transmission capacity limit. The second model, which includes transmission constraints, considers the interactions between generation firms and the transmission network operator. The third model assumes that the generation and transmission sectors make capacity investment decisions separately. These models result in Nash-Cournot equilibrium among the unregulated generation firms, while the regulated transmission network operator supports the competition among generation firms. Several issues in the deregulated electric power market can be studied with these models such as market powers of generation firms and transmission network operator, uncertainties of the future market, and interactions between the generation and transmission sectors. Results deduced from the developed models include (a) regulated transmission network operator will not reserve transmission capacity to gain extra profits; instead, it will make capacity expansion decisions to support the competition in the generation sector; (b) generation firms will provide more power supplies when there is more demand; (c) in the presence of future uncertainties, the generation firms will add more generation capacity if the demand in the future power market is expected to be higher; and (d) the transmission capacity invested by the transmission network operator depends on the characteristic of the power market and the topology of the transmission network. Also, the second model, which considers interactions between generation and transmission sectors, yields higher social welfare in the electric power market, than the third model where generation firms and transmission network operator make investment decisions separately.

Novel mechanism of network protection against the new generation of cyber attacks

NASA Astrophysics Data System (ADS)

Milovanov, Alexander; Bukshpun, Leonid; Pradhan, Ranjit

2012-06-01

A new intelligent mechanism is presented to protect networks against the new generation of cyber attacks. This mechanism integrates TCP/UDP/IP protocol stack protection and attacker/intruder deception to eliminate existing TCP/UDP/IP protocol stack vulnerabilities. It allows to detect currently undetectable, highly distributed, low-frequency attacks such as distributed denial-of-service (DDoS) attacks, coordinated attacks, botnet, and stealth network reconnaissance. The mechanism also allows insulating attacker/intruder from the network and redirecting the attack to a simulated network acting as a decoy. As a result, network security personnel gain sufficient time to defend the network and collect the attack information. The presented approach can be incorporated into wireless or wired networks that require protection against known and the new generation of cyber attacks.

System and method for islanding detection and prevention in distributed generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhowmik, Shibashis; Mazhari, Iman; Parkhideh, Babak

Various examples are directed to systems and methods for detecting an islanding condition at an inverter configured to couple a distributed generation system to an electrical grid network. A controller may determine a command frequency and a command frequency variation. The controller may determine that the command frequency variation indicates a potential islanding condition and send to the inverter an instruction to disconnect the distributed generation system from the electrical grid network. When the distributed generation system is disconnected from the electrical grid network, the controller may determine whether the grid network is valid.

Turing instability in reaction-diffusion models on complex networks

NASA Astrophysics Data System (ADS)

Ide, Yusuke; Izuhara, Hirofumi; Machida, Takuya

2016-09-01

In this paper, the Turing instability in reaction-diffusion models defined on complex networks is studied. Here, we focus on three types of models which generate complex networks, i.e. the Erdős-Rényi, the Watts-Strogatz, and the threshold network models. From analysis of the Laplacian matrices of graphs generated by these models, we numerically reveal that stable and unstable regions of a homogeneous steady state on the parameter space of two diffusion coefficients completely differ, depending on the network architecture. In addition, we theoretically discuss the stable and unstable regions in the cases of regular enhanced ring lattices which include regular circles, and networks generated by the threshold network model when the number of vertices is large enough.

Network Traffic Generator for Low-rate Small Network Equipment Software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lanzisera, Steven

2013-05-28

Application that uses the Python low-level socket interface to pass network traffic between devices on the local side of a NAT router and the WAN side of the NAT router. This application is designed to generate traffic that complies with the Energy Star Small Network Equipment Test Method.

Initial deployment of the cardiogenic gene regulatory network in the basal chordate, Ciona intestinalis.

PubMed

Woznica, Arielle; Haeussler, Maximilian; Starobinska, Ella; Jemmett, Jessica; Li, Younan; Mount, David; Davidson, Brad

2012-08-01

The complex, partially redundant gene regulatory architecture underlying vertebrate heart formation has been difficult to characterize. Here, we dissect the primary cardiac gene regulatory network in the invertebrate chordate, Ciona intestinalis. The Ciona heart progenitor lineage is first specified by Fibroblast Growth Factor/Map Kinase (FGF/MapK) activation of the transcription factor Ets1/2 (Ets). Through microarray analysis of sorted heart progenitor cells, we identified the complete set of primary genes upregulated by FGF/Ets shortly after heart progenitor emergence. Combinatorial sequence analysis of these co-regulated genes generated a hypothetical regulatory code consisting of Ets binding sites associated with a specific co-motif, ATTA. Through extensive reporter analysis, we confirmed the functional importance of the ATTA co-motif in primary heart progenitor gene regulation. We then used the Ets/ATTA combination motif to successfully predict a number of additional heart progenitor gene regulatory elements, including an intronic element driving expression of the core conserved cardiac transcription factor, GATAa. This work significantly advances our understanding of the Ciona heart gene network. Furthermore, this work has begun to elucidate the precise regulatory architecture underlying the conserved, primary role of FGF/Ets in chordate heart lineage specification. Copyright © 2012 Elsevier Inc. All rights reserved.

Fiber Bragg grating sensor for fault detection in high voltage overhead transmission lines

NASA Astrophysics Data System (ADS)

Moghadas, Amin

2011-12-01

A fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by fiber Bragg grating (FBG) sensors. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signals. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG sensors and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

Quantitative Analysis of Cellular Metabolic Dissipative, Self-Organized Structures

PubMed Central

de la Fuente, Ildefonso Martínez

2010-01-01

One of the most important goals of the postgenomic era is understanding the metabolic dynamic processes and the functional structures generated by them. Extensive studies during the last three decades have shown that the dissipative self-organization of the functional enzymatic associations, the catalytic reactions produced during the metabolite channeling, the microcompartmentalization of these metabolic processes and the emergence of dissipative networks are the fundamental elements of the dynamical organization of cell metabolism. Here we present an overview of how mathematical models can be used to address the properties of dissipative metabolic structures at different organizational levels, both for individual enzymatic associations and for enzymatic networks. Recent analyses performed with dissipative metabolic networks have shown that unicellular organisms display a singular global enzymatic structure common to all living cellular organisms, which seems to be an intrinsic property of the functional metabolism as a whole. Mathematical models firmly based on experiments and their corresponding computational approaches are needed to fully grasp the molecular mechanisms of metabolic dynamical processes. They are necessary to enable the quantitative and qualitative analysis of the cellular catalytic reactions and also to help comprehend the conditions under which the structural dynamical phenomena and biological rhythms arise. Understanding the molecular mechanisms responsible for the metabolic dissipative structures is crucial for unraveling the dynamics of cellular life. PMID:20957111

Physically secured orthogonal frequency division multiplexing-passive optical network employing noise-based encryption and signal recovery process

NASA Astrophysics Data System (ADS)

Jin, Wei; Zhang, Chongfu; Yuan, Weicheng

2016-02-01

We propose a physically enhanced secure scheme for direct detection-orthogonal frequency division multiplexing-passive optical network (DD-OFDM-PON) and long reach coherent detection-orthogonal frequency division multiplexing-passive optical network (LRCO-OFDM-PON), by employing noise-based encryption and channel/phase estimation. The noise data generated by chaos mapping are used to substitute training sequences in preamble to realize channel estimation and frame synchronization, and also to be embedded on variable number of key-selected randomly spaced pilot subcarriers to implement phase estimation. Consequently, the information used for signal recovery is totally hidden as unpredictable noise information in OFDM frames to mask useful information and to prevent illegal users from correctly realizing OFDM demodulation, and thereby enhancing resistance to attackers. The levels of illegal-decryption complexity and implementation complexity are theoretically discussed. Through extensive simulations, the performances of the proposed channel/phase estimation and the security introduced by encrypted pilot carriers have been investigated in both DD-OFDM and LRCO-OFDM systems. In addition, in the proposed secure DD-OFDM/LRCO-OFDM PON models, both legal and illegal receiving scenarios have been considered. These results show that, by utilizing the proposed scheme, the resistance to attackers can be significantly enhanced in DD-OFDM-PON and LRCO-OFDM-PON systems without performance degradations.

Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range.

PubMed

He, Chenlong; Feng, Zuren; Ren, Zhigang

2018-02-03

For Wireless Sensor Networks (WSNs), the Voronoi partition of a region is a challenging problem owing to the limited sensing ability of each sensor and the distributed organization of the network. In this paper, an algorithm is proposed for each sensor having a limited sensing range to compute its limited Voronoi cell autonomously, so that the limited Voronoi partition of the entire WSN is generated in a distributed manner. Inspired by Graham's Scan (GS) algorithm used to compute the convex hull of a point set, the limited Voronoi cell of each sensor is obtained by sequentially scanning two consecutive bisectors between the sensor and its neighbors. The proposed algorithm called the Boundary Scan (BS) algorithm has a lower computational complexity than the existing Range-Constrained Voronoi Cell (RCVC) algorithm and reaches the lower bound of the computational complexity of the algorithms used to solve the problem of this kind. Moreover, it also improves the time efficiency of a key step in the Adjust-Sensing-Radius (ASR) algorithm used to compute the exact Voronoi cell. Extensive numerical simulations are performed to demonstrate the correctness and effectiveness of the BS algorithm. The distributed realization of the BS combined with a localization algorithm in WSNs is used to justify the WSN nature of the proposed algorithm.

Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range

PubMed Central

Feng, Zuren; Ren, Zhigang

2018-01-01

For Wireless Sensor Networks (WSNs), the Voronoi partition of a region is a challenging problem owing to the limited sensing ability of each sensor and the distributed organization of the network. In this paper, an algorithm is proposed for each sensor having a limited sensing range to compute its limited Voronoi cell autonomously, so that the limited Voronoi partition of the entire WSN is generated in a distributed manner. Inspired by Graham’s Scan (GS) algorithm used to compute the convex hull of a point set, the limited Voronoi cell of each sensor is obtained by sequentially scanning two consecutive bisectors between the sensor and its neighbors. The proposed algorithm called the Boundary Scan (BS) algorithm has a lower computational complexity than the existing Range-Constrained Voronoi Cell (RCVC) algorithm and reaches the lower bound of the computational complexity of the algorithms used to solve the problem of this kind. Moreover, it also improves the time efficiency of a key step in the Adjust-Sensing-Radius (ASR) algorithm used to compute the exact Voronoi cell. Extensive numerical simulations are performed to demonstrate the correctness and effectiveness of the BS algorithm. The distributed realization of the BS combined with a localization algorithm in WSNs is used to justify the WSN nature of the proposed algorithm. PMID:29401649

Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

PubMed

Grandjean, Bernard; Maier, Marc A

2017-02-01

Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

Building the Next Generation of Earth Scientists: the Deep Carbon Observatory Early Career Scientist Workshops

NASA Astrophysics Data System (ADS)

Pratt, K.; Fellowes, J.; Giovannelli, D.; Stagno, V.

2016-12-01

Building a network of collaborators and colleagues is a key professional development activity for early career scientists (ECS) dealing with a challenging job market. At large conferences, young scientists often focus on interacting with senior researchers, competing for a small number of positions in leading laboratories. However, building a strong, international network amongst their peers in related disciplines is often as valuable in the long run. The Deep Carbon Observatory (DCO) began funding a series of workshops in 2014 designed to connect early career researchers within its extensive network of multidisciplinary scientists. The workshops, by design, are by and for early career scientists, thus removing any element of competition and focusing on peer-to-peer networking, collaboration, and creativity. The successful workshops, organized by committees of early career deep carbon scientists, have nucleated a lively community of like-minded individuals from around the world. Indeed, the organizers themselves often benefit greatly from the leadership experience of pulling together an international workshop on budget and on deadline. We have found that a combination of presentations from all participants in classroom sessions, professional development training such as communication and data management, and field-based relationship building and networking is a recipe for success. Small groups within the DCO ECS network have formed; publishing papers together, forging new research directions, and planning novel and ambitious field campaigns. Many DCO ECS also have come together to convene sessions at major international conferences, including the AGU Fall Meeting. Most of all, there is a broad sense of camaraderie and accessibility within the DCO ECS Community, providing the foundation for a career in the new, international, and interdisciplinary field of deep carbon science.

"Nickel Nanoflowers" with Surface-Attached Fluoropolymer Networks by C,H Insertion for the Generation of Metallic Superhydrophobic Surfaces.

PubMed

Hönes, Roland; Rühe, Jürgen

2018-05-08

Metallic superhydrophobic surfaces (SHSs) combine the attractive properties of metals, such as ductility, hardness, and conductivity, with the favorable wetting properties of nanostructured surfaces. Moreover, they promise additional benefits with respect to corrosion protection. For the modification of the intrinsically polar and hydrophilic surfaces of metals, a new method has been developed to deposit a long-term stable, highly hydrophobic coating, using nanostructured Ni surfaces as an example. Such substrates were chosen because the deposition of a thin Ni layer is a common choice for enhancing corrosion resistance of other metals. As the hydrophobic coating, we propose a thin film of an extremely hydrophobic fluoropolymer network. To form this network, a thin layer of a fluoropolymer precursor is deposited on the Ni substrate which includes a comonomer that is capable of C,H insertion cross-linking (CHic). Upon UV irradiation or heating, the cross-linker units become activated and the thin glassy film of the precursor is transformed into a polymer network that coats the surface conformally and permanently, as shown by extensive extraction experiments. To achieve an even higher stability, the same precursor film can also be transformed into a chemically surface-attached network by depositing a self-assembled monolayer of an alkane phosphonic acid on the Ni before coating with the precursor. During cross-linking, by the same chemical process, the growing polymer network will simultaneously attach to the alkane phosphonic acid layer at the surface of the metal. This strategy has been used to turn fractal Ni "nanoflower" surfaces grown by anisotropic electroplating into SHSs. The wetting characteristics of the obtained nanostructured metallic surfaces are studied. Additionally, the corrosion protection effect and the significant mechanical durability are demonstrated.

In silico experiment system for testing hypothesis on gene functions using three condition specific biological networks.

PubMed

Lee, Chai-Jin; Kang, Dongwon; Lee, Sangseon; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

2018-05-25

Determining functions of a gene requires time consuming, expensive biological experiments. Scientists can speed up this experimental process if the literature information and biological networks can be adequately provided. In this paper, we present a web-based information system that can perform in silico experiments of computationally testing hypothesis on the function of a gene. A hypothesis that is specified in English by the user is converted to genes using a literature and knowledge mining system called BEST. Condition-specific TF, miRNA and PPI (protein-protein interaction) networks are automatically generated by projecting gene and miRNA expression data to template networks. Then, an in silico experiment is to test how well the target genes are connected from the knockout gene through the condition-specific networks. The test result visualizes path from the knockout gene to the target genes in the three networks. Statistical and information-theoretic scores are provided on the resulting web page to help scientists either accept or reject the hypothesis being tested. Our web-based system was extensively tested using three data sets, such as E2f1, Lrrk2, and Dicer1 knockout data sets. We were able to re-produce gene functions reported in the original research papers. In addition, we comprehensively tested with all disease names in MalaCards as hypothesis to show the effectiveness of our system. Our in silico experiment system can be very useful in suggesting biological mechanisms which can be further tested in vivo or in vitro. http://biohealth.snu.ac.kr/software/insilico/. Copyright © 2018 Elsevier Inc. All rights reserved.

Epidemic spreading with activity-driven awareness diffusion on multiplex network.

PubMed

Guo, Quantong; Lei, Yanjun; Jiang, Xin; Ma, Yifang; Huo, Guanying; Zheng, Zhiming

2016-04-01

There has been growing interest in exploring the interplay between epidemic spreading with human response, since it is natural for people to take various measures when they become aware of epidemics. As a proper way to describe the multiple connections among people in reality, multiplex network, a set of nodes interacting through multiple sets of edges, has attracted much attention. In this paper, to explore the coupled dynamical processes, a multiplex network with two layers is built. Specifically, the information spreading layer is a time varying network generated by the activity driven model, while the contagion layer is a static network. We extend the microscopic Markov chain approach to derive the epidemic threshold of the model. Compared with extensive Monte Carlo simulations, the method shows high accuracy for the prediction of the epidemic threshold. Besides, taking different spreading models of awareness into consideration, we explored the interplay between epidemic spreading with awareness spreading. The results show that the awareness spreading can not only enhance the epidemic threshold but also reduce the prevalence of epidemics. When the spreading of awareness is defined as susceptible-infected-susceptible model, there exists a critical value where the dynamical process on the awareness layer can control the onset of epidemics; while if it is a threshold model, the epidemic threshold emerges an abrupt transition with the local awareness ratio α approximating 0.5. Moreover, we also find that temporal changes in the topology hinder the spread of awareness which directly affect the epidemic threshold, especially when the awareness layer is threshold model. Given that the threshold model is a widely used model for social contagion, this is an important and meaningful result. Our results could also lead to interesting future research about the different time-scales of structural changes in multiplex networks.

Epidemic spreading with activity-driven awareness diffusion on multiplex network

NASA Astrophysics Data System (ADS)

Guo, Quantong; Lei, Yanjun; Jiang, Xin; Ma, Yifang; Huo, Guanying; Zheng, Zhiming

2016-04-01

There has been growing interest in exploring the interplay between epidemic spreading with human response, since it is natural for people to take various measures when they become aware of epidemics. As a proper way to describe the multiple connections among people in reality, multiplex network, a set of nodes interacting through multiple sets of edges, has attracted much attention. In this paper, to explore the coupled dynamical processes, a multiplex network with two layers is built. Specifically, the information spreading layer is a time varying network generated by the activity driven model, while the contagion layer is a static network. We extend the microscopic Markov chain approach to derive the epidemic threshold of the model. Compared with extensive Monte Carlo simulations, the method shows high accuracy for the prediction of the epidemic threshold. Besides, taking different spreading models of awareness into consideration, we explored the interplay between epidemic spreading with awareness spreading. The results show that the awareness spreading can not only enhance the epidemic threshold but also reduce the prevalence of epidemics. When the spreading of awareness is defined as susceptible-infected-susceptible model, there exists a critical value where the dynamical process on the awareness layer can control the onset of epidemics; while if it is a threshold model, the epidemic threshold emerges an abrupt transition with the local awareness ratio α approximating 0.5. Moreover, we also find that temporal changes in the topology hinder the spread of awareness which directly affect the epidemic threshold, especially when the awareness layer is threshold model. Given that the threshold model is a widely used model for social contagion, this is an important and meaningful result. Our results could also lead to interesting future research about the different time-scales of structural changes in multiplex networks.

Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study | Office of Cancer Genomics

Cancer.gov

Motivation: Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging.

Extension of a System Level Tool for Component Level Analysis

NASA Technical Reports Server (NTRS)

Majumdar, Alok; Schallhorn, Paul

2002-01-01

This paper presents an extension of a numerical algorithm for network flow analysis code to perform multi-dimensional flow calculation. The one dimensional momentum equation in network flow analysis code has been extended to include momentum transport due to shear stress and transverse component of velocity. Both laminar and turbulent flows are considered. Turbulence is represented by Prandtl's mixing length hypothesis. Three classical examples (Poiseuille flow, Couette flow and shear driven flow in a rectangular cavity) are presented as benchmark for the verification of the numerical scheme.

Extension of a System Level Tool for Component Level Analysis

NASA Technical Reports Server (NTRS)

Majumdar, Alok; Schallhorn, Paul; McConnaughey, Paul K. (Technical Monitor)

2001-01-01

This paper presents an extension of a numerical algorithm for network flow analysis code to perform multi-dimensional flow calculation. The one dimensional momentum equation in network flow analysis code has been extended to include momentum transport due to shear stress and transverse component of velocity. Both laminar and turbulent flows are considered. Turbulence is represented by Prandtl's mixing length hypothesis. Three classical examples (Poiseuille flow, Couette flow, and shear driven flow in a rectangular cavity) are presented as benchmark for the verification of the numerical scheme.

Distribution System Reliability Analysis for Smart Grid Applications

NASA Astrophysics Data System (ADS)

Aljohani, Tawfiq Masad

Reliability of power systems is a key aspect in modern power system planning, design, and operation. The ascendance of the smart grid concept has provided high hopes of developing an intelligent network that is capable of being a self-healing grid, offering the ability to overcome the interruption problems that face the utility and cost it tens of millions in repair and loss. To address its reliability concerns, the power utilities and interested parties have spent extensive amount of time and effort to analyze and study the reliability of the generation and transmission sectors of the power grid. Only recently has attention shifted to be focused on improving the reliability of the distribution network, the connection joint between the power providers and the consumers where most of the electricity problems occur. In this work, we will examine the effect of the smart grid applications in improving the reliability of the power distribution networks. The test system used in conducting this thesis is the IEEE 34 node test feeder, released in 2003 by the Distribution System Analysis Subcommittee of the IEEE Power Engineering Society. The objective is to analyze the feeder for the optimal placement of the automatic switching devices and quantify their proper installation based on the performance of the distribution system. The measures will be the changes in the reliability system indices including SAIDI, SAIFI, and EUE. The goal is to design and simulate the effect of the installation of the Distributed Generators (DGs) on the utility's distribution system and measure the potential improvement of its reliability. The software used in this work is DISREL, which is intelligent power distribution software that is developed by General Reliability Co.

A low-cost mobile adaptive tracking system for chronic pulmonary patients in home environment.

PubMed

Işik, Ali Hakan; Güler, Inan; Sener, Melahat Uzel

2013-01-01

The main objective of this study is presenting a real-time mobile adaptive tracking system for patients diagnosed with diseases such as asthma or chronic obstructive pulmonary disease and application results at home. The main role of the system is to support and track chronic pulmonary patients in real time who are comfortable in their home environment. It is not intended to replace the doctor, regular treatment, and diagnosis. In this study, the Java 2 micro edition-based system is integrated with portable spirometry, smartphone, extensible markup language-based Web services, Web server, and Web pages for visualizing pulmonary function test results. The Bluetooth(®) (Bluetooth SIG, Kirkland, WA) virtual serial port protocol is used to obtain the test results from spirometry. General packet radio service, wireless local area network, or third-generation-based wireless networks are used to send the test results from a smartphone to the remote database. The system provides real-time classification of test results with the back propagation artificial neural network algorithm on a mobile smartphone. It also provides the generation of appropriate short message service-based notification and sending of all data to the Web server. In this study, the test results of 486 patients, obtained from Atatürk Chest Diseases and Thoracic Surgery Training and Research Hospital in Ankara, Turkey, are used as the training and test set in the algorithm. The algorithm has 98.7% accuracy, 97.83% specificity, 97.63% sensitivity, and 0.946 correlation values. The results show that the system is cheap (900 Euros) and reliable. The developed real-time system provides improvement in classification accuracy and facilitates tracking of chronic pulmonary patients.

Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data

PubMed Central

Ahmed, Moiz; Mehmood, Nadeem; Mehmood, Amir; Rizwan, Kashif

2017-01-01

Objectives Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. Methods We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. Results To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. Conclusions In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios. PMID:28875049

IBiSA_Tools: A Computational Toolkit for Ion-Binding State Analysis in Molecular Dynamics Trajectories of Ion Channels.

PubMed

Kasahara, Kota; Kinoshita, Kengo

2016-01-01

Ion conduction mechanisms of ion channels are a long-standing conundrum. Although the molecular dynamics (MD) method has been extensively used to simulate ion conduction dynamics at the atomic level, analysis and interpretation of MD results are not straightforward due to complexity of the dynamics. In our previous reports, we proposed an analytical method called ion-binding state analysis to scrutinize and summarize ion conduction mechanisms by taking advantage of a variety of analytical protocols, e.g., the complex network analysis, sequence alignment, and hierarchical clustering. This approach effectively revealed the ion conduction mechanisms and their dependence on the conditions, i.e., ion concentration and membrane voltage. Here, we present an easy-to-use computational toolkit for ion-binding state analysis, called IBiSA_tools. This toolkit consists of a C++ program and a series of Python and R scripts. From the trajectory file of MD simulations and a structure file, users can generate several images and statistics of ion conduction processes. A complex network named ion-binding state graph is generated in a standard graph format (graph modeling language; GML), which can be visualized by standard network analyzers such as Cytoscape. As a tutorial, a trajectory of a 50 ns MD simulation of the Kv1.2 channel is also distributed with the toolkit. Users can trace the entire process of ion-binding state analysis step by step. The novel method for analysis of ion conduction mechanisms of ion channels can be easily used by means of IBiSA_tools. This software is distributed under an open source license at the following URL: http://www.ritsumei.ac.jp/~ktkshr/ibisa_tools/.

An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

PubMed

Amozegar, M; Khorasani, K

2016-04-01

In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

PubMed Central

Brorsson, C.; Hansen, N. T.; Lage, K.; Bergholdt, R.; Brunak, S.; Pociot, F.

2009-01-01

Aim To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1 genes. Methods We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein–protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation. Results A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in β-cell development and diabetic complications. Conclusions The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D. PMID:19143816

Complex monitoring and alert network for electromagnetic, infrasound, acoustic seismotectonic phenomena

NASA Astrophysics Data System (ADS)

-Emilian Toader, Victorin; Moldovan, Iren-Adelina; Constantin, Ionescu

2014-05-01

The Romanian seismicity recorded in 2013 three important events: the largest seismic "silence", the shortest sequence of two earthquakes greater than 4.8R in less than 14 days after the "Romanian National Institute for Earth Physics" (NIEP) developed a digital network, and a very high crustal activity in Galati area. We analyze the variations of the telluric currents and local magnetic field, variations of the atmospheric electrostatic field, infrasound, temperature, humidity, wind speed and direction, atmospheric pressure, variations in the earth crust with inclinometers and animal behavior. The general effect is the first high seismic energy discontinuity that could be a precursor factor. Since 1977 Romania did not register any important earthquake that would generate a sense of fear among the population. In parallel with the seismic network NIEP developed a magneto-telluric, bioseismic, VLF and acoustic network. A large frequency spectrum is covered for mechanical vibration, magnetic and electric field with ground and air sensors. Special software was designed for acquisition, analysis and real time alert using internet direct connection, web page, email and SMS. Many examples show the sensitivity of telluric current, infrasound, acoustic records (from air-ground), and the effect of tectonic stress on the magnetic field or ground deformation. The next update of the multidisciplinary monitoring network will include measurement of ionization, radon emission, sky color, solar radiation and extension of infrasound and VL/LF equipment. NOAA Space Weather satellites transmit solar activity magnetic field data, X ray flux, electron, and proton flux information useful for complex analysis.

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc05720a Click here for additional data file.

PubMed Central

Smith, J. S.

2017-01-01

Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set. PMID:28507695

Design, Implementation, and Evaluation of a Virtual Shared Memory System in a Multi-Transputer Network.

DTIC Science & Technology

1987-12-01

Synchronization and Data Passing Mechanism ........ 50 4. System Shut Down .................................................................. 51 5...high performance, fault tolerance, and extensibility. These features are attained by synchronizing and coordinating the dis- tributed multicomputer... synchronizing all processors in the network. In a multitransputer network, processes that communicate with each other do so synchronously . This makes

Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

NASA Technical Reports Server (NTRS)

Soloway, Donald I.; Bialasiewicz, Jan T.

1992-01-01

A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

The development of computer networks: First results from a microeconomic model

NASA Astrophysics Data System (ADS)

Maier, Gunther; Kaufmann, Alexander

Computer networks like the Internet are gaining importance in social and economic life. The accelerating pace of the adoption of network technologies for business purposes is a rather recent phenomenon. Many applications are still in the early, sometimes even experimental, phase. Nevertheless, it seems to be certain that networks will change the socioeconomic structures we know today. This is the background for our special interest in the development of networks, in the role of spatial factors influencing the formation of networks, and consequences of networks on spatial structures, and in the role of externalities. This paper discusses a simple economic model - based on a microeconomic calculus - that incorporates the main factors that generate the growth of computer networks. The paper provides analytic results about the generation of computer networks. The paper discusses (1) under what conditions economic factors will initiate the process of network formation, (2) the relationship between individual and social evaluation, and (3) the efficiency of a network that is generated based on economic mechanisms.

Stochastic Geometric Network Models for Groups of Functional and Structural Connectomes

PubMed Central

Friedman, Eric J.; Landsberg, Adam S.; Owen, Julia P.; Li, Yi-Ou; Mukherjee, Pratik

2014-01-01

Structural and functional connectomes are emerging as important instruments in the study of normal brain function and in the development of new biomarkers for a variety of brain disorders. In contrast to single-network studies that presently dominate the (non-connectome) network literature, connectome analyses typically examine groups of empirical networks and then compare these against standard (stochastic) network models. Current practice in connectome studies is to employ stochastic network models derived from social science and engineering contexts as the basis for the comparison. However, these are not necessarily best suited for the analysis of connectomes, which often contain groups of very closely related networks, such as occurs with a set of controls or a set of patients with a specific disorder. This paper studies important extensions of standard stochastic models that make them better adapted for analysis of connectomes, and develops new statistical fitting methodologies that account for inter-subject variations. The extensions explicitly incorporate geometric information about a network based on distances and inter/intra hemispherical asymmetries (to supplement ordinary degree-distribution information), and utilize a stochastic choice of networks' density levels (for fixed threshold networks) to better capture the variance in average connectivity among subjects. The new statistical tools introduced here allow one to compare groups of networks by matching both their average characteristics and the variations among them. A notable finding is that connectomes have high “smallworldness” beyond that arising from geometric and degree considerations alone. PMID:25067815

Multi-Domain SDN Survivability for Agricultural Wireless Sensor Networks.

PubMed

Huang, Tao; Yan, Siyu; Yang, Fan; Liu, Jiang

2016-11-06

Wireless sensor networks (WSNs) have been widely applied in agriculture field; meanwhile, the advent of multi-domain software-defined networks (SDNs) have improved the wireless resource utilization rate and strengthened network management. In recent times, multi-domain SDNs have been applied to agricultural sensor networks, namely multi-domain software-defined wireless sensor networks (SDWSNs). However, when the SDNs controlling agriculture networks suddenly become unavailable, whether intra-domain or inter-domain, sensor network communication is abnormal because of the loss of control. Moreover, there are controller and switch info-updating problems even if the controller becomes available again. To resolve these problems, this paper proposes a new approach based on an Open vSwitch extension for multi-domain SDWSNs, which can enhance agriculture network survivability and stability. We achieved this by designing a connection-state mechanism, a communication mechanism on both L2 and L3, and an info-updating mechanism based on Open vSwitch. The experimental results show that, whether it is agricultural inter-domain or intra-domain during the controller failure period, the sensor switches can enter failure recovery mode as soon as possible so that the sensor network keeps a stable throughput, a short failure recovery time below 300 ms, and low packet loss. Further, the domain can smoothly control the domain network again once the controller becomes available. This approach based on an Open vSwitch extension can enhance the survivability and stability of multi-domain SDWSNs in precision agriculture.

Multi-Domain SDN Survivability for Agricultural Wireless Sensor Networks

PubMed Central

Huang, Tao; Yan, Siyu; Yang, Fan; Liu, Jiang

2016-01-01

Wireless sensor networks (WSNs) have been widely applied in agriculture field; meanwhile, the advent of multi-domain software-defined networks (SDNs) have improved the wireless resource utilization rate and strengthened network management. In recent times, multi-domain SDNs have been applied to agricultural sensor networks, namely multi-domain software-defined wireless sensor networks (SDWSNs). However, when the SDNs controlling agriculture networks suddenly become unavailable, whether intra-domain or inter-domain, sensor network communication is abnormal because of the loss of control. Moreover, there are controller and switch info-updating problems even if the controller becomes available again. To resolve these problems, this paper proposes a new approach based on an Open vSwitch extension for multi-domain SDWSNs, which can enhance agriculture network survivability and stability. We achieved this by designing a connection-state mechanism, a communication mechanism on both L2 and L3, and an info-updating mechanism based on Open vSwitch. The experimental results show that, whether it is agricultural inter-domain or intra-domain during the controller failure period, the sensor switches can enter failure recovery mode as soon as possible so that the sensor network keeps a stable throughput, a short failure recovery time below 300 ms, and low packet loss. Further, the domain can smoothly control the domain network again once the controller becomes available. This approach based on an Open vSwitch extension can enhance the survivability and stability of multi-domain SDWSNs in precision agriculture. PMID:27827971

Limit of a nonpreferential attachment multitype network model

NASA Astrophysics Data System (ADS)

Shang, Yilun

2017-02-01

Here, we deal with a model of multitype network with nonpreferential attachment growth. The connection between two nodes depends asymmetrically on their types, reflecting the implication of time order in temporal networks. Based upon graph limit theory, we analytically determined the limit of the network model characterized by a kernel, in the sense that the number of copies of any fixed subgraph converges when network size tends to infinity. The results are confirmed by extensive simulations. Our work thus provides a theoretical framework for quantitatively understanding grown temporal complex networks as a whole.

Technologies for unattended network operations

NASA Technical Reports Server (NTRS)

Jaworski, Allan; Odubiyi, Jide; Holdridge, Mark; Zuzek, John

1991-01-01

The necessary network management functions for a telecommunications, navigation and information management (TNIM) system in the framework of an extension of the ISO model for communications network management are described. Various technologies that could substantially reduce the need for TNIM network management, automate manpower intensive functions, and deal with synchronization and control at interplanetary distances are presented. Specific technologies addressed include the use of the ISO Common Management Interface Protocol, distributed artificial intelligence for network synchronization and fault management, and fault-tolerant systems engineering.

26 CFR 26.6081-1 - Automatic extension of time for filing generation-skipping transfer tax returns.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 26 Internal Revenue 14 2011-04-01 2010-04-01 true Automatic extension of time for filing generation-skipping transfer tax returns. 26.6081-1 Section 26.6081-1 Internal Revenue INTERNAL REVENUE SERVICE, DEPARTMENT OF THE TREASURY (CONTINUED) ESTATE AND GIFT TAXES GENERATION-SKIPPING TRANSFER TAX...

26 CFR 26.6081-1 - Automatic extension of time for filing generation-skipping transfer tax returns.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 14 2010-04-01 2010-04-01 false Automatic extension of time for filing generation-skipping transfer tax returns. 26.6081-1 Section 26.6081-1 Internal Revenue INTERNAL REVENUE SERVICE, DEPARTMENT OF THE TREASURY (CONTINUED) ESTATE AND GIFT TAXES GENERATION-SKIPPING TRANSFER TAX...

26 CFR 26.6081-1 - Automatic extension of time for filing generation-skipping transfer tax returns.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 26 Internal Revenue 14 2013-04-01 2013-04-01 false Automatic extension of time for filing generation-skipping transfer tax returns. 26.6081-1 Section 26.6081-1 Internal Revenue INTERNAL REVENUE SERVICE, DEPARTMENT OF THE TREASURY (CONTINUED) ESTATE AND GIFT TAXES GENERATION-SKIPPING TRANSFER TAX...

26 CFR 26.6081-1 - Automatic extension of time for filing generation-skipping transfer tax returns.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 26 Internal Revenue 14 2014-04-01 2013-04-01 true Automatic extension of time for filing generation-skipping transfer tax returns. 26.6081-1 Section 26.6081-1 Internal Revenue INTERNAL REVENUE SERVICE, DEPARTMENT OF THE TREASURY (CONTINUED) ESTATE AND GIFT TAXES GENERATION-SKIPPING TRANSFER TAX...

26 CFR 26.6081-1 - Automatic extension of time for filing generation-skipping transfer tax returns.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 26 Internal Revenue 14 2012-04-01 2012-04-01 false Automatic extension of time for filing generation-skipping transfer tax returns. 26.6081-1 Section 26.6081-1 Internal Revenue INTERNAL REVENUE SERVICE, DEPARTMENT OF THE TREASURY (CONTINUED) ESTATE AND GIFT TAXES GENERATION-SKIPPING TRANSFER TAX...

Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy

PubMed Central

Khambhati, Ankit N.; Davis, Kathryn A.; Oommen, Brian S.; Chen, Stephanie H.; Lucas, Timothy H.; Litt, Brian; Bassett, Danielle S.

2015-01-01

The epileptic network is characterized by pathologic, seizure-generating ‘foci’ embedded in a web of structural and functional connections. Clinically, seizure foci are considered optimal targets for surgery. However, poor surgical outcome suggests a complex relationship between foci and the surrounding network that drives seizure dynamics. We developed a novel technique to objectively track seizure states from dynamic functional networks constructed from intracranial recordings. Each dynamical state captures unique patterns of network connections that indicate synchronized and desynchronized hubs of neural populations. Our approach suggests that seizures are generated when synchronous relationships near foci work in tandem with rapidly changing desynchronous relationships from the surrounding epileptic network. As seizures progress, topographical and geometrical changes in network connectivity strengthen and tighten synchronous connectivity near foci—a mechanism that may aid seizure termination. Collectively, our observations implicate distributed cortical structures in seizure generation, propagation and termination, and may have practical significance in determining which circuits to modulate with implantable devices. PMID:26680762

Membrane Assembly during the Infection Cycle of the Giant Mimivirus

PubMed Central

Mutsafi, Yael; Shimoni, Eyal; Shimon, Amir; Minsky, Abraham

2013-01-01

Although extensively studied, the structure, cellular origin and assembly mechanism of internal membranes during viral infection remain unclear. By combining diverse imaging techniques, including the novel Scanning-Transmission Electron Microscopy tomography, we elucidate the structural stages of membrane biogenesis during the assembly of the giant DNA virus Mimivirus. We show that this elaborate multistage process occurs at a well-defined zone localized at the periphery of large viral factories that are generated in the host cytoplasm. Membrane biogenesis is initiated by fusion of multiple vesicles, ∼70 nm in diameter, that apparently derive from the host ER network and enable continuous supply of lipid components to the membrane-assembly zone. The resulting multivesicular bodies subsequently rupture to form large open single-layered membrane sheets from which viral membranes are generated. Membrane generation is accompanied by the assembly of icosahedral viral capsids in a process involving the hypothetical major capsid protein L425 that acts as a scaffolding protein. The assembly model proposed here reveals how multiple Mimivirus progeny can be continuously and efficiently generated and underscores the similarity between the infection cycles of Mimivirus and Vaccinia virus. Moreover, the membrane biogenesis process indicated by our findings provides new insights into the pathways that might mediate assembly of internal viral membranes in general. PMID:23737745

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

PubMed Central

Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

2013-01-01

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production. PMID:23555215

Connecting to create: expertise in musical improvisation is associated with increased functional connectivity between premotor and prefrontal areas.

PubMed

Pinho, Ana Luísa; de Manzano, Örjan; Fransson, Peter; Eriksson, Helene; Ullén, Fredrik

2014-04-30

Musicians have been used extensively to study neural correlates of long-term practice, but no studies have investigated the specific effects of training musical creativity. Here, we used human functional MRI to measure brain activity during improvisation in a sample of 39 professional pianists with varying backgrounds in classical and jazz piano playing. We found total hours of improvisation experience to be negatively associated with activity in frontoparietal executive cortical areas. In contrast, improvisation training was positively associated with functional connectivity of the bilateral dorsolateral prefrontal cortices, dorsal premotor cortices, and presupplementary areas. The effects were significant when controlling for hours of classical piano practice and age. These results indicate that even neural mechanisms involved in creative behaviors, which require a flexible online generation of novel and meaningful output, can be automated by training. Second, improvisational musical training can influence functional brain properties at a network level. We show that the greater functional connectivity seen in experienced improvisers may reflect a more efficient exchange of information within associative networks of importance for musical creativity.

Connecting to Create: Expertise in Musical Improvisation Is Associated with Increased Functional Connectivity between Premotor and Prefrontal Areas

PubMed Central

Pinho, Ana Luísa; de Manzano, Örjan; Fransson, Peter; Eriksson, Helene

2014-01-01

Musicians have been used extensively to study neural correlates of long-term practice, but no studies have investigated the specific effects of training musical creativity. Here, we used human functional MRI to measure brain activity during improvisation in a sample of 39 professional pianists with varying backgrounds in classical and jazz piano playing. We found total hours of improvisation experience to be negatively associated with activity in frontoparietal executive cortical areas. In contrast, improvisation training was positively associated with functional connectivity of the bilateral dorsolateral prefrontal cortices, dorsal premotor cortices, and presupplementary areas. The effects were significant when controlling for hours of classical piano practice and age. These results indicate that even neural mechanisms involved in creative behaviors, which require a flexible online generation of novel and meaningful output, can be automated by training. Second, improvisational musical training can influence functional brain properties at a network level. We show that the greater functional connectivity seen in experienced improvisers may reflect a more efficient exchange of information within associative networks of importance for musical creativity. PMID:24790186

New partnerships in widowhood in Spain: Realities and desires.

PubMed

Ayuso, Luis

2018-04-27

Widowhood has traditionally been associated with the end of the family cycle; however, social and generational transformations in Spain are providing a new context for the development of new partnerships in widowhood. This study analyzes widowed persons who have found new partners and those who would be willing to do so, focusing on their characteristics and motives and related sociodemographic factors. Research is based on a sample of 306 widows and widowers in Spain taken from the survey Social Networks and Well-Being. The results reveal the importance of sociodemographic factors for both those who wish to have a partner as well as for those who have one. For the former, elements associated with quality of life are very important, while among those with a new partner, the key is not having an extensive family support network. The principal motives for looking for a new relationship are related to enjoying life more and not feeling alone; while those who reject a new relationship do so because of the belief that their lost partner is irreplaceable.

Neural Networks for the Classification of Building Use from Street-View Imagery

NASA Astrophysics Data System (ADS)

Laupheimer, D.; Tutzauer, P.; Haala, N.; Spicker, M.

2018-05-01

Within this paper we propose an end-to-end approach for classifying terrestrial images of building facades into five different utility classes (commercial, hybrid, residential, specialUse, underConstruction) by using Convolutional Neural Networks (CNNs). For our examples we use images provided by Google Street View. These images are automatically linked to a coarse city model, including the outlines of the buildings as well as their respective use classes. By these means an extensive dataset is available for training and evaluation of our Deep Learning pipeline. The paper describes the implemented end-to-end approach for classifying street-level images of building facades and discusses our experiments with various CNNs. In addition to the classification results, so-called Class Activation Maps (CAMs) are evaluated. These maps give further insights into decisive facade parts that are learned as features during the training process. Furthermore, they can be used for the generation of abstract presentations which facilitate the comprehension of semantic image content. The abstract representations are a result of the stippling method, an importance-based image rendering.

A Secure-Enhanced Data Aggregation Based on ECC in Wireless Sensor Networks

PubMed Central

Zhou, Qiang; Yang, Geng; He, Liwen

2014-01-01

Data aggregation is an important technique for reducing the energy consumption of sensor nodes in wireless sensor networks (WSNs). However, compromised aggregators may forge false values as the aggregated results of their child nodes in order to conduct stealthy attacks or steal other nodes' privacy. This paper proposes a Secure-Enhanced Data Aggregation based on Elliptic Curve Cryptography (SEDA-ECC). The design of SEDA-ECC is based on the principles of privacy homomorphic encryption (PH) and divide-and-conquer. An aggregation tree disjoint method is first adopted to divide the tree into three subtrees of similar sizes, and a PH-based aggregation is performed in each subtree to generate an aggregated subtree result. Then the forged result can be identified by the base station (BS) by comparing the aggregated count value. Finally, the aggregated result can be calculated by the BS according to the remaining results that have not been forged. Extensive analysis and simulations show that SEDA-ECC can achieve the highest security level on the aggregated result with appropriate energy consumption compared with other asymmetric schemes. PMID:24732099

WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings

PubMed Central

Ghayvat, Hemant; Mukhopadhyay, Subhas; Gui, Xiang; Suryadevara, Nagender

2015-01-01

Our research approach is to design and develop reliable, efficient, flexible, economical, real-time and realistic wellness sensor networks for smart home systems. The heterogeneous sensor and actuator nodes based on wireless networking technologies are deployed into the home environment. These nodes generate real-time data related to the object usage and movement inside the home, to forecast the wellness of an individual. Here, wellness stands for how efficiently someone stays fit in the home environment and performs his or her daily routine in order to live a long and healthy life. We initiate the research with the development of the smart home approach and implement it in different home conditions (different houses) to monitor the activity of an inhabitant for wellness detection. Additionally, our research extends the smart home system to smart buildings and models the design issues related to the smart building environment; these design issues are linked with system performance and reliability. This research paper also discusses and illustrates the possible mitigation to handle the ISM band interference and attenuation losses without compromising optimum system performance. PMID:25946630

Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.

PubMed

Wu, Yonghui; Jiang, Min; Lei, Jianbo; Xu, Hua

2015-01-01

Rapid growth in electronic health records (EHRs) use has led to an unprecedented expansion of available clinical data in electronic formats. However, much of the important healthcare information is locked in the narrative documents. Therefore Natural Language Processing (NLP) technologies, e.g., Named Entity Recognition that identifies boundaries and types of entities, has been extensively studied to unlock important clinical information in free text. In this study, we investigated a novel deep learning method to recognize clinical entities in Chinese clinical documents using the minimal feature engineering approach. We developed a deep neural network (DNN) to generate word embeddings from a large unlabeled corpus through unsupervised learning and another DNN for the NER task. The experiment results showed that the DNN with word embeddings trained from the large unlabeled corpus outperformed the state-of-the-art CRF's model in the minimal feature engineering setting, achieving the highest F1-score of 0.9280. Further analysis showed that word embeddings derived through unsupervised learning from large unlabeled corpus remarkably improved the DNN with randomized embedding, denoting the usefulness of unsupervised feature learning.

Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins.

PubMed

Hu, Pingzhao; Janga, Sarath Chandra; Babu, Mohan; Díaz-Mejía, J Javier; Butland, Gareth; Yang, Wenhong; Pogoutse, Oxana; Guo, Xinghua; Phanse, Sadhna; Wong, Peter; Chandran, Shamanta; Christopoulos, Constantine; Nazarians-Armavil, Anaies; Nasseri, Negin Karimi; Musso, Gabriel; Ali, Mehrab; Nazemof, Nazila; Eroukova, Veronika; Golshani, Ashkan; Paccanaro, Alberto; Greenblatt, Jack F; Moreno-Hagelsieb, Gabriel; Emili, Andrew

2009-04-28

One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans' biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a "systems-wide" functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins.

Effective preemptive scheduling scheme for optical burst-switched networks with cascaded wavelength conversion consideration

NASA Astrophysics Data System (ADS)

Gao, Xingbo

2010-03-01

We introduce a new preemptive scheduling technique for next-generation optical burst switching (OBS) networks considering the impact of cascaded wavelength conversions. It has been shown that when optical bursts are transmitted all optically from source to destination, each wavelength conversion performed along the lightpath may cause certain signal-to-noise deterioration. If the distortion of the signal quality becomes significant enough, the receiver would not be able to recover the original data. Accordingly, subject to this practical impediment, we improve a recently proposed fair channel scheduling algorithm to deal with the fairness problem and aim at burst loss reduction simultaneously in OBS environments. In our scheme, the dynamic priority associated with each burst is based on a constraint threshold and the number of already conducted wavelength conversions among other factors for this burst. When contention occurs, a new arriving superior burst may preempt another scheduled one according to their priorities. Extensive simulation results have shown that the proposed scheme further improves fairness and achieves burst loss reduction as well.

WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings.

PubMed

Ghayvat, Hemant; Mukhopadhyay, Subhas; Gui, Xiang; Suryadevara, Nagender

2015-05-04

Our research approach is to design and develop reliable, efficient, flexible, economical, real-time and realistic wellness sensor networks for smart home systems. The heterogeneous sensor and actuator nodes based on wireless networking technologies are deployed into the home environment. These nodes generate real-time data related to the object usage and movement inside the home, to forecast the wellness of an individual. Here, wellness stands for how efficiently someone stays fit in the home environment and performs his or her daily routine in order to live a long and healthy life. We initiate the research with the development of the smart home approach and implement it in different home conditions (different houses) to monitor the activity of an inhabitant for wellness detection. Additionally, our research extends the smart home system to smart buildings and models the design issues related to the smart building environment; these design issues are linked with system performance and reliability. This research paper also discusses and illustrates the possible mitigation to handle the ISM band interference and attenuation losses without compromising optimum system performance.

Machine Learning and Quantum Mechanics

NASA Astrophysics Data System (ADS)

Chapline, George

The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

Software-defined network abstractions and configuration interfaces for building programmable quantum networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasari, Venkat; Sadlier, Ronald J; Geerhart, Mr. Billy

Well-defined and stable quantum networks are essential to realize functional quantum applications. Quantum networks are complex and must use both quantum and classical channels to support quantum applications like QKD, teleportation, and superdense coding. In particular, the no-cloning theorem prevents the reliable copying of quantum signals such that the quantum and classical channels must be highly coordinated using robust and extensible methods. We develop new network abstractions and interfaces for building programmable quantum networks. Our approach leverages new OpenFlow data structures and table type patterns to build programmable quantum networks and to support quantum applications.

Cisco Networking Academy: Next-Generation Assessments and Their Implications for K-12 Education

ERIC Educational Resources Information Center

Liu, Meredith

2014-01-01

To illuminate the possibilities for next-generation assessments in K-12 schools, this case study profiles the Cisco Networking Academy, which creates comprehensive online training curriculum to teach networking skills. Since 1997, the Cisco Networking Academy has served more than five million high school and college students and now delivers…

SNAP: A computer program for generating symbolic network functions

NASA Technical Reports Server (NTRS)

Lin, P. M.; Alderson, G. E.

1970-01-01

The computer program SNAP (symbolic network analysis program) generates symbolic network functions for networks containing R, L, and C type elements and all four types of controlled sources. The program is efficient with respect to program storage and execution time. A discussion of the basic algorithms is presented, together with user's and programmer's guides.

A Statistical Method to Distinguish Functional Brain Networks

PubMed Central

Fujita, André; Vidal, Maciel C.; Takahashi, Daniel Y.

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001). PMID:28261045

A Statistical Method to Distinguish Functional Brain Networks.

PubMed

Fujita, André; Vidal, Maciel C; Takahashi, Daniel Y

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism ( p < 0.001).

Generating global network structures by triad types

PubMed Central

Ferligoj, Anuška; Žiberna, Aleš

2018-01-01

This paper addresses the question of whether one can generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical, and transitivity), considering only different types of triads. Two methods are used to generate networks: (i) the newly proposed method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm implemented in the ergm package in R. Most of the selected blockmodel types can be generated by considering all types of triads. The selection of only a subset of triads can improve the generated networks’ blockmodel structure. Yet, in the case of a hierarchical blockmodel without complete blocks on the diagonal, additional local structures are needed to achieve the desired global structure of generated networks. This shows that blockmodels can emerge based only on local processes that do not take attributes into account. PMID:29847563

Interdependence and dynamics of essential services in an extensive risk context: a case study in Montserrat, West Indies

NASA Astrophysics Data System (ADS)

Sword-Daniels, V. L.; Rossetto, T.; Wilson, T. M.; Sargeant, S.

2015-05-01

The essential services that support urban living are complex and interdependent, and their disruption in disasters directly affects society. Yet there are few empirical studies to inform our understanding of the vulnerabilities and resilience of complex infrastructure systems in disasters. This research takes a systems thinking approach to explore the dynamic behaviour of a network of essential services, in the presence and absence of volcanic ashfall hazards in Montserrat, West Indies. Adopting a case study methodology and qualitative methods to gather empirical data, we centre the study on the healthcare system and its interconnected network of essential services. We identify different types of relationship between sectors and develop a new interdependence classification system for analysis. Relationships are further categorised by hazard conditions, for use in extensive risk contexts. During heightened volcanic activity, relationships between systems transform in both number and type: connections increase across the network by 41%, and adapt to increase cooperation and information sharing. Interconnections add capacities to the network, increasing the resilience of prioritised sectors. This in-depth and context-specific approach provides a new methodology for studying the dynamics of infrastructure interdependence in an extensive risk context, and can be adapted for use in other hazard contexts.

Interdependence and dynamics of essential services in an extensive risk context: a case study in Montserrat, West Indies

NASA Astrophysics Data System (ADS)

Sword-Daniels, V. L.; Rossetto, T.; Wilson, T. M.; Sargeant, S.

2015-02-01

The essential services that support urban living are complex and interdependent, and their disruption in disasters directly affects society. Yet there are few empirical studies to inform our understanding of the vulnerabilities and resilience of complex infrastructure systems in disasters. This research takes a systems thinking approach to explore the dynamic behaviour of a network of essential services, in the presence and absence of volcanic ashfall hazards in Montserrat, West Indies. Adopting a case study methodology and qualitative methods to gather empirical data we centre the study on the healthcare system and its interconnected network of essential services. We identify different types of relationship between sectors and develop a new interdependence classification system for analysis. Relationships are further categorised by hazard condition, for use in extensive risk contexts. During heightened volcanic activity, relationships between systems transform in both number and type: connections increase across the network by 41%, and adapt to increase cooperation and information sharing. Interconnections add capacities to the network, increasing the resilience of prioritised sectors. This in-depth and context-specific approach provides a new methodology for studying the dynamics of infrastructure interdependence in an extensive risk context, and can be adapted for use in other hazard contexts.

Landslide Susceptibility Index Determination Using Aritificial Neural Network

NASA Astrophysics Data System (ADS)

Kawabata, D.; Bandibas, J.; Urai, M.

2004-12-01

The occurrence of landslide is the result of the interaction of complex and diverse environmental factors. The geomorphic features, rock types and geologic structure are especially important base factors of the landslide occurrence. Generating landslide susceptibility index by defining the relationship between landslide occurrence and that base factors using conventional mathematical and statistical methods is very difficult and inaccurate. This study focuses on generating landslide susceptibility index using artificial neural networks in Southern Japanese Alps. The training data are geomorphic (e.g. altitude, slope and aspect) and geologic parameters (e.g. rock type, distance from geologic boundary and geologic dip-strike angle) and landslides. Artificial neural network structure and training scheme are formulated to generate the index. Data from areas with and without landslide occurrences are used to train the network. The network is trained to output 1 when the input data are from areas with landslides and 0 when no landslide occurred. The trained network generates an output ranging from 0 to 1 reflecting the possibility of landslide occurrence based on the inputted data. Output values nearer to 1 means higher possibility of landslide occurrence. The artificial neural network model is incorporated into the GIS software to generate a landslide susceptibility map.

Modular GIS Framework for National Scale Hydrologic and Hydraulic Modeling Support

NASA Astrophysics Data System (ADS)

Djokic, D.; Noman, N.; Kopp, S.

2015-12-01

Geographic information systems (GIS) have been extensively used for pre- and post-processing of hydrologic and hydraulic models at multiple scales. An extensible GIS-based framework was developed for characterization of drainage systems (stream networks, catchments, floodplain characteristics) and model integration. The framework is implemented as a set of free, open source, Python tools and builds on core ArcGIS functionality and uses geoprocessing capabilities to ensure extensibility. Utilization of COTS GIS core capabilities allows immediate use of model results in a variety of existing online applications and integration with other data sources and applications.The poster presents the use of this framework to downscale global hydrologic models to local hydraulic scale and post process the hydraulic modeling results and generate floodplains at any local resolution. Flow forecasts from ECMWF or WRF-Hydro are downscaled and combined with other ancillary data for input into the RAPID flood routing model. RAPID model results (stream flow along each reach) are ingested into a GIS-based scale dependent stream network database for efficient flow utilization and visualization over space and time. Once the flows are known at localized reaches, the tools can be used to derive the floodplain depth and extent for each time step in the forecast at any available local resolution. If existing rating curves are available they can be used to relate the flow to the depth of flooding, or synthetic rating curves can be derived using the tools in the toolkit and some ancillary data/assumptions. The results can be published as time-enabled spatial services to be consumed by web applications that use floodplain information as an input. Some of the existing online presentation templates can be easily combined with available online demographic and infrastructure data to present the impact of the potential floods on the local community through simple, end user products. This framework has been successfully used in both the data rich environments as well as in locales with minimum available spatial and hydrographic data.

CUFID-query: accurate network querying through random walk based network flow estimation.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

Cellular and network properties of the subiculum in the pilocarpine model of temporal lobe epilepsy.

PubMed

Knopp, Andreas; Kivi, Anatol; Wozny, Christian; Heinemann, Uwe; Behr, Joachim

2005-03-21

The subiculum was recently shown to be crucially involved in the generation of interictal activity in human temporal lobe epilepsy. Using the pilocarpine model of epilepsy, this study examines the anatomical substrates for network hyperexcitability recorded in the subiculum. Regular- and burst-spiking subicular pyramidal cells were stained with fluorescence dyes and reconstructed to analyze seizure-induced alterations of the dendritic and axonal system. In control animals burst-spiking cells outnumbered regular-spiking cells by about two to one. Regular- and burst-spiking cells were characterized by extensive axonal branching and autapse-like contacts, suggesting a high intrinsic connectivity. In addition, subicular axons projecting to CA1 indicate a CA1-subiculum-CA1 circuit. In the subiculum of pilocarpine-treated rats we found an enhanced network excitability characterized by spontaneous rhythmic activity, polysynaptic responses, and all-or-none evoked bursts of action potentials. In pilocarpine-treated rats the subiculum showed cell loss of about 30%. The ratio of regular- and burst-spiking cells was practically inverse as compared to control preparations. A reduced arborization and spine density in the proximal part of the apical dendrites suggests a partial deafferentiation from CA1. In pilocarpine-treated rats no increased axonal outgrowth of pyramidal cells was observed. Hence, axonal sprouting of subicular pyramidal cells is not mandatory for the development of the pathological events. We suggest that pilocarpine-induced seizures cause an unmasking or strengthening of synaptic contacts within the recurrent subicular network. Copyright 2005 Wiley-Liss, Inc.

Prediction of Pig Trade Movements in Different European Production Systems Using Exponential Random Graph Models.

PubMed

Relun, Anne; Grosbois, Vladimir; Alexandrov, Tsviatko; Sánchez-Vizcaíno, Jose M; Waret-Szkuta, Agnes; Molia, Sophie; Etter, Eric Marcel Charles; Martínez-López, Beatriz

2017-01-01

In most European countries, data regarding movements of live animals are routinely collected and can greatly aid predictive epidemic modeling. However, the use of complete movements' dataset to conduct policy-relevant predictions has been so far limited by the massive amount of data that have to be processed (e.g., in intensive commercial systems) or the restricted availability of timely and updated records on animal movements (e.g., in areas where small-scale or extensive production is predominant). The aim of this study was to use exponential random graph models (ERGMs) to reproduce, understand, and predict pig trade networks in different European production systems. Three trade networks were built by aggregating movements of pig batches among premises (farms and trade operators) over 2011 in Bulgaria, Extremadura (Spain), and Côtes-d'Armor (France), where small-scale, extensive, and intensive pig production are predominant, respectively. Three ERGMs were fitted to each network with various demographic and geographic attributes of the nodes as well as six internal network configurations. Several statistical and graphical diagnostic methods were applied to assess the goodness of fit of the models. For all systems, both exogenous (attribute-based) and endogenous (network-based) processes appeared to govern the structure of pig trade network, and neither alone were capable of capturing all aspects of the network structure. Geographic mixing patterns strongly structured pig trade organization in the small-scale production system, whereas belonging to the same company or keeping pigs in the same housing system appeared to be key drivers of pig trade, in intensive and extensive production systems, respectively. Heterogeneous mixing between types of production also explained a part of network structure, whichever production system considered. Limited information is thus needed to capture most of the global structure of pig trade networks. Such findings will be useful to simplify trade networks analysis and better inform European policy makers on risk-based and more cost-effective prevention and control against swine diseases such as African swine fever, classical swine fever, or porcine reproductive and respiratory syndrome.

Investigation of a generator system for generating electrical power, to supply directly to the public network, using a windmill

NASA Technical Reports Server (NTRS)

Tromp, C.

1979-01-01

A windpowered generator system is described which uses a windmill to convert mechanical energy to electrical energy for a three phase (network) voltage of constant amplitude and frequency. The generator system controls the windmill by the number of revolutions so that the power drawn from the wind for a given wind velocity is maximum. A generator revolution which is proportional to wind velocity is achieved. The stator of the generator is linked directly to the network and a feed converter at the rotor takes care of constant voltage and frequency at the stator.

Toward the automated generation of genome-scale metabolic networks in the SEED.

PubMed

DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

2007-04-26

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the stage for the automated generation of substantially complete metabolic networks for over 400 complete genome sequences currently in the SEED. With each genome that is processed using our tools, the database of common components grows to cover more of the diversity of metabolic pathways. This increases the likelihood that components of reaction networks for subsequently processed genomes can be retrieved from the database, rather than assembled and verified manually.

Form and function in gene regulatory networks: the structure of network motifs determines fundamental properties of their dynamical state space.

PubMed

Ahnert, S E; Fink, T M A

2016-07-01

Network motifs have been studied extensively over the past decade, and certain motifs, such as the feed-forward loop, play an important role in regulatory networks. Recent studies have used Boolean network motifs to explore the link between form and function in gene regulatory networks and have found that the structure of a motif does not strongly determine its function, if this is defined in terms of the gene expression patterns the motif can produce. Here, we offer a different, higher-level definition of the 'function' of a motif, in terms of two fundamental properties of its dynamical state space as a Boolean network. One is the basin entropy, which is a complexity measure of the dynamics of Boolean networks. The other is the diversity of cyclic attractor lengths that a given motif can produce. Using these two measures, we examine all 104 topologically distinct three-node motifs and show that the structural properties of a motif, such as the presence of feedback loops and feed-forward loops, predict fundamental characteristics of its dynamical state space, which in turn determine aspects of its functional versatility. We also show that these higher-level properties have a direct bearing on real regulatory networks, as both basin entropy and cycle length diversity show a close correspondence with the prevalence, in neural and genetic regulatory networks, of the 13 connected motifs without self-interactions that have been studied extensively in the literature. © 2016 The Authors.

Beyond Getting in and Fitting in: An Examination of Social Networks and Professionally Relevant Social Capital among Latina/o University Students

ERIC Educational Resources Information Center

Rios-Aguilar, Cecilia; Deil-Amen, Regina

2012-01-01

Social network analyses, combined with qualitative analyses, are examined to understand key components of the college trajectories of 261 Latina/o students. Their social network ties reveal variation in extensity and the relevance. Most ties facilitate social capital relevant to getting into college, fewer engage social capital relevant to…

Protocols for Detection and Removal of Wormholes for Secure Routing and Neighborhood Creation in Wireless Ad Hoc Networks

ERIC Educational Resources Information Center

Hayajneh, Thaier Saleh

2009-01-01

Wireless ad hoc networks are suitable and sometimes the only solution for several applications. Many applications, particularly those in military and critical civilian domains (such as battlefield surveillance and emergency rescue) require that ad hoc networks be secure and stable. In fact, security is one of the main barriers to the extensive use…

Cerebral cartography and connectomics.

PubMed

Sporns, Olaf

2015-05-19

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Interaction Networks: Generating High Level Hints Based on Network Community Clustering

ERIC Educational Resources Information Center

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

Network Sampling and Classification:An Investigation of Network Model Representations

PubMed Central

Airoldi, Edoardo M.; Bai, Xue; Carley, Kathleen M.

2011-01-01

Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in recent literature. Most of these algorithms are based on simple intuitions that associate the full features of connectivity patterns with specific values of only one or two network metrics. Substantive conclusions are crucially dependent on this association holding true. However, the extent to which this simple intuition holds true is not yet known. In this paper, we examine the association between the connectivity patterns that a network sampling algorithm aims to generate and the connectivity patterns of the generated networks, measured by an existing set of popular network metrics. We find that different network sampling algorithms can yield networks with similar connectivity patterns. We also find that the alternative algorithms for the same connectivity pattern can yield networks with different connectivity patterns. We argue that conclusions based on simulated network studies must focus on the full features of the connectivity patterns of a network instead of on the limited set of network metrics for a specific network type. This fact has important implications for network data analysis: for instance, implications related to the way significance is currently assessed. PMID:21666773

The Deep Space Network. An instrument for radio navigation of deep space probes

NASA Technical Reports Server (NTRS)

Renzetti, N. A.; Jordan, J. F.; Berman, A. L.; Wackley, J. A.; Yunck, T. P.

1982-01-01

The Deep Space Network (DSN) network configurations used to generate the navigation observables and the basic process of deep space spacecraft navigation, from data generation through flight path determination and correction are described. Special emphasis is placed on the DSN Systems which generate the navigation data: the DSN Tracking and VLBI Systems. In addition, auxiliary navigational support functions are described.

Bayesian WLS/GLS regression for regional skewness analysis for regions with large crest stage gage networks

USGS Publications Warehouse

Veilleux, Andrea G.; Stedinger, Jery R.; Eash, David A.

2012-01-01

This paper summarizes methodological advances in regional log-space skewness analyses that support flood-frequency analysis with the log Pearson Type III (LP3) distribution. A Bayesian Weighted Least Squares/Generalized Least Squares (B-WLS/B-GLS) methodology that relates observed skewness coefficient estimators to basin characteristics in conjunction with diagnostic statistics represents an extension of the previously developed B-GLS methodology. B-WLS/B-GLS has been shown to be effective in two California studies. B-WLS/B-GLS uses B-WLS to generate stable estimators of model parameters and B-GLS to estimate the precision of those B-WLS regression parameters, as well as the precision of the model. The study described here employs this methodology to develop a regional skewness model for the State of Iowa. To provide cost effective peak-flow data for smaller drainage basins in Iowa, the U.S. Geological Survey operates a large network of crest stage gages (CSGs) that only record flow values above an identified recording threshold (thus producing a censored data record). CSGs are different from continuous-record gages, which record almost all flow values and have been used in previous B-GLS and B-WLS/B-GLS regional skewness studies. The complexity of analyzing a large CSG network is addressed by using the B-WLS/B-GLS framework along with the Expected Moments Algorithm (EMA). Because EMA allows for the censoring of low outliers, as well as the use of estimated interval discharges for missing, censored, and historic data, it complicates the calculations of effective record length (and effective concurrent record length) used to describe the precision of sample estimators because the peak discharges are no longer solely represented by single values. Thus new record length calculations were developed. The regional skewness analysis for the State of Iowa illustrates the value of the new B-WLS/BGLS methodology with these new extensions.

Inter-generational Contact From a Network Perspective

PubMed Central

Marcum, Christopher Steven; Koehly, Laura M.

2015-01-01

Pathways for resource—or other—exchanges within families have long been known to be dependent on the structure of relations between generations (Silverstein, 2011; Fuller-Thomson et al., 1997; Agree et al., 2005; Treas and Marcum, 2011). Much life course research has theorized models of inter-generational exchange— including, the ‘sandwich generation’ (Miller, 1981) and the ‘skipped generation’ pathways (Chalfie, 1994)—but there is little work relating these theories to relevant network mechanisms such as liaison brokerage (Gould and Fernandez, 1989) and other triadic configurations (Davis and Leinhardt, 1972; Wasserman and Faust, 1994). To address this, a survey of models of resource allocation between members of inter-generational households from a network perspective is introduced in this paper. Exemplary data come from health discussion networks among Mexican-origin multi-generational households. PMID:26047986

An Amorphous Network Model for Capillary Flow and Dispersion in a Partially Saturated Porous Medium

NASA Astrophysics Data System (ADS)

Simmons, C. S.; Rockhold, M. L.

2013-12-01

Network models of capillary flow are commonly used to represent conduction of fluids at pore scales. Typically, a flow system is described by a regular geometric lattice of interconnected tubes. Tubes constitute the pore throats, while connection junctions (nodes) are pore bodies. Such conceptualization of the geometry, however, is questionable for the pore scale, where irregularity clearly prevails, although prior published models using a regular lattice have demonstrated successful descriptions of the flow in the bulk medium. Here a network is allowed to be amorphous, and is not subject to any particular lattice structure. Few network flow models have treated partially saturated or even multiphase conditions. The research trend is toward using capillary tubes with triangular or square cross sections that have corners and always retain some fluid by capillarity when drained. In contrast, this model uses only circular capillaries, whose filled state is controlled by a capillary pressure rule for the junctions. The rule determines which capillary participate in the flow under an imposed matric potential gradient during steady flow conditions. Poiseuille's Law and Laplace equation are used to describe flow and water retention in the capillary units of the model. A modified conjugate gradient solution for steady flow that tracks which capillary in an amorphous network contribute to fluid conduction was devised for partially saturated conditions. The model thus retains the features of classical capillary models for determining hydraulic flow properties under unsaturated conditions based on distribution of non-interacting tubes, but now accounts for flow exchange at junctions. Continuity of the flow balance at every junction is solved simultaneously. The effective water retention relationship and unsaturated permeability are evaluated for an extensive enough network to represent a small bulk sample of porous medium. The model is applied for both a hypothetically randomly generate network and for a directly measured porous medium structure, by means of xray-CT scan. A randomly generated network has the benefit of providing ensemble averages for sample replicates of a medium's properties, whereas network structure measurements are expected to be more predictive. Dispersion of solute in a network flow is calculate by using particle tracking to determine the travel time breakthrough between inflow and outflow boundaries. The travel time distribution can exhibit substantial skewness that reflects both network velocity variability and mixing dilution at junctions. When local diffusion is not included, and transport is strictly advective, then the skew breakthrough is not due to mobile-immobile flow region behavior. The approach of dispersivity to its asymptotic value with sample size is examined, and may be only an indicator of particular stochastic flow variation. It is not proven that a simplified network flow model can accurately predict the hydraulic properties of a sufficiently large-size medium sample, but such a model can at least demonstrate macroscopic flow resulting from the interaction of physical processes at pore scales.

Structure constrained by metadata in networks of chess players.

PubMed

Almeira, Nahuel; Schaigorodsky, Ana L; Perotti, Juan I; Billoni, Orlando V

2017-11-09

Chess is an emblematic sport that stands out because of its age, popularity and complexity. It has served to study human behavior from the perspective of a wide number of disciplines, from cognitive skills such as memory and learning, to aspects like innovation and decision-making. Given that an extensive documentation of chess games played throughout history is available, it is possible to perform detailed and statistically significant studies about this sport. Here we use one of the most extensive chess databases in the world to construct two networks of chess players. One of the networks includes games that were played over-the-board and the other contains games played on the Internet. We study the main topological characteristics of the networks, such as degree distribution and correlations, transitivity and community structure. We complement the structural analysis by incorporating players' level of play as node metadata. Although both networks are topologically different, we show that in both cases players gather in communities according to their expertise and that an emergent rich-club structure, composed by the top-rated players, is also present.

Strain accumulation in southern California, 1973-1980.

USGS Publications Warehouse

Savage, J.C.; Prescott, W.H.; Lisowski, M.; King, N.E.

1981-01-01

Frequent surveys of seven trilateration networks in southern California over the interval 1973-1980 suggest that a regional increment in strain may have occurred in 1978-1979. Prior to 1978 and after late 1979 the strain accumulation has been predominantly a uniaxial north-south compression. This secular trend was interrupted sometime in 1978-1979 by an increment in both north-south and east-west extension in five of the seven networks. The onset of this change appears to have occurred first in the networks farthest south. The changes occurred without any unusual seismicity within the networks, but the overall seismicity in southern California was unusually low prior to and has been unusually high since the occurrence. The average principal strain rates for the seven networks in the 1973-1980 interval are 0.17 mu strain/yr north- south contraction and 0.08 mu strain/yr east-west extension. Although the observed increment in strain could be related to unidentified systematic error in the measuring system, a careful review of the measurements and comparisons with three other measuring systems reveal no appreciable cumulative systematic error. -Authors

Free space optics: a viable last-mile alternative

NASA Astrophysics Data System (ADS)

Willebrand, Heinz A.; Clark, Gerald R.

2001-10-01

This paper explores Free Space Optics (FSO) as an access technology in the last mile of metropolitan area networks (MANs). These networks are based in part on fiber-optic telecommunications infrastructure, including network architectures of Synchronous Optical Network (commonly referred to as SONET), the North American standard for synchronous data transmission; and Synchronous Digital Hierarchy (commonly referred to as SDH), the international standard and equivalent of SONET. Several converging forces have moved FSO beyond a niche technology for use only in local area networks (LANs) as a bridge connecting two facilities. FSO now allows service providers to cost effectively provide optical bandwidth for access networks and accelerate the extension of metro optical networks bridging what has been termed by industry experts as the optical dead zone. The optical dead zone refers to both the slowdown in capital investment in the short-term future and the actual connectivity gap that exists today between core metro optical networks and the access optical networks. Service providers have built extensive core and minimal metro networks but have not yet provided optical bandwidth to the access market largely due to the non-compelling economics to bridge the dead zone with fiber. Historically, such infrastructure build-out slowdowns have been blamed on a combination of economics, time-to-market constraints and limited technology options. However, new technology developments and market acceptance of FSO give service providers a new cost-effective alternative to provide high-bandwidth services with optical bandwidth in the access networks. Merrill Lynch predicts FSO will grow into a $2 billion market by 2005. The drivers for this market are a mere 5%- 6% penetration of fiber to business buildings; cost effective solution versus RF or fiber; and significant capacity which can only be matched by a physical fiber link, Merrill Lynch reports. This paper will describe FSO technology, its capabilities and its limitations. The paper will investigate how FSO technology has evolved to its current stage for deployment in MANs, LANs, wireless backhaul and metropolitan network extensions - applications that fall within the category of last mile. The paper will address the market, drivers and the adoption of FSO, plus provide a projection of future FSO technology, based on today's product roadmaps. The paper concludes with a summary of findings and recommendations.

Enzymes and other agents that enhance cell wall extensibility

NASA Technical Reports Server (NTRS)

Cosgrove, D. J.

1999-01-01

Polysaccharides and proteins are secreted to the inner surface of the growing cell wall, where they assemble into a network that is mechanically strong, yet remains extensible until the cells cease growth. This review focuses on the agents that directly or indirectly enhance the extensibility properties of growing walls. The properties of expansins, endoglucanases, and xyloglucan transglycosylases are reviewed and their postulated roles in modulating wall extensibility are evaluated. A summary model for wall extension is presented, in which expansin is a primary agent of wall extension, whereas endoglucanases, xyloglucan endotransglycosylase, and other enzymes that alter wall structure act secondarily to modulate expansin action.

QoS for Real Time Applications over Next Generation Data Networks

NASA Technical Reports Server (NTRS)

Ivancic, William; Atiquzzaman, Mohammed; Bai, Haowei; Su, Hongjun; Chitri, Jyotsna; Ahamed, Faruque

2001-01-01

Viewgraphs on Qualtity of Service (QOS) for real time applications over next generation data networks are presented. The progress to date include: Task 1: QoS in Integrated Services over DiffServ networks (UD); Task 2: Interconnecting ATN with the next generation Internet (UD); Task 3: QoS in DiffServ over ATM (UD); Task 4: Improving Explicit Congestion Notification with the Mark-Front Strategy (OSU); Task 5: Multiplexing VBR over VBR (OSU); and Task 6: Achieving QoS for TCP traffic in Satellite Networks with Differentiated Services (OSU).

Validating Farmers' Indigenous Social Networks for Local Seed Supply in Central Rift Valley of Ethiopia.

ERIC Educational Resources Information Center

Seboka, B.; Deressa, A.

2000-01-01

Indigenous social networks of Ethiopian farmers participate in seed exchange based on mutual interdependence and trust. A government-imposed extension program must validate the role of local seed systems in developing a national seed industry. (SK)

Visualizing Transmedia Networks: Links, Paths and Peripheries

ERIC Educational Resources Information Center

Ruppel, Marc Nathaniel

2012-01-01

'Visualizing Transmedia Networks: Links, Paths and Peripheries' examines the increasingly complex rhetorical intersections between narrative and media ("old" and "new") in the creation of transmedia fictions, loosely defined as multisensory and multimodal stories told extensively across a diverse media set. In order…

Social Network Structures among Groundnut Farmers

ERIC Educational Resources Information Center

Thuo, Mary; Bell, Alexandra A.; Bravo-Ureta, Boris E.; Okello, David K.; Okoko, Evelyn Nasambu; Kidula, Nelson L.; Deom, C. Michael; Puppala, Naveen

2013-01-01

Purpose: Groundnut farmers in East Africa have experienced declines in production despite research and extension efforts to increase productivity. This study examined how social network structures related to acquisition of information about new seed varieties and productivity among groundnut farmers in Uganda and Kenya.…

Modeling Renewable Penertration Using a Network Economic Model

NASA Astrophysics Data System (ADS)

Lamont, A.

2001-03-01

This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

Using Reputation Systems and Non-Deterministic Routing to Secure Wireless Sensor Networks

PubMed Central

Moya, José M.; Vallejo, Juan Carlos; Fraga, David; Araujo, Álvaro; Villanueva, Daniel; de Goyeneche, Juan-Mariano

2009-01-01

Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios. PMID:22412345

Smart pitch control strategy for wind generation system using doubly fed induction generator

NASA Astrophysics Data System (ADS)

Raza, Syed Ahmed

A smart pitch control strategy for a variable speed doubly fed wind generation system is presented in this thesis. A complete dynamic model of DFIG system is developed. The model consists of the generator, wind turbine, aerodynamic and the converter system. The strategy proposed includes the use of adaptive neural network to generate optimized controller gains for pitch control. This involves the generation of controller parameters of pitch controller making use of differential evolution intelligent technique. Training of the back propagation neural network has been carried out for the development of an adaptive neural network. This tunes the weights of the network according to the system states in a variable wind speed environment. Four cases have been taken to test the pitch controller which includes step and sinusoidal changes in wind speeds. The step change is composed of both step up and step down changes in wind speeds. The last case makes use of scaled wind data collected from the wind turbine installed at King Fahd University beach front. Simulation studies show that the differential evolution based adaptive neural network is capable of generating the appropriate control to deliver the maximum possible aerodynamic power available from wind to the generator in an efficient manner by minimizing the transients.

The Human Thalamus Is an Integrative Hub for Functional Brain Networks

PubMed Central

Bertolero, Maxwell A.

2017-01-01

The thalamus is globally connected with distributed cortical regions, yet the functional significance of this extensive thalamocortical connectivity remains largely unknown. By performing graph-theoretic analyses on thalamocortical functional connectivity data collected from human participants, we found that most thalamic subdivisions display network properties that are capable of integrating multimodal information across diverse cortical functional networks. From a meta-analysis of a large dataset of functional brain-imaging experiments, we further found that the thalamus is involved in multiple cognitive functions. Finally, we found that focal thalamic lesions in humans have widespread distal effects, disrupting the modular organization of cortical functional networks. This converging evidence suggests that the human thalamus is a critical hub region that could integrate diverse information being processed throughout the cerebral cortex as well as maintain the modular structure of cortical functional networks. SIGNIFICANCE STATEMENT The thalamus is traditionally viewed as a passive relay station of information from sensory organs or subcortical structures to the cortex. However, the thalamus has extensive connections with the entire cerebral cortex, which can also serve to integrate information processing between cortical regions. In this study, we demonstrate that multiple thalamic subdivisions display network properties that are capable of integrating information across multiple functional brain networks. Moreover, the thalamus is engaged by tasks requiring multiple cognitive functions. These findings support the idea that the thalamus is involved in integrating information across cortical networks. PMID:28450543

Actin protofilament orientation in deformation of the erythrocyte membrane skeleton.

PubMed Central

Picart, C; Dalhaimer, P; Discher, D E

2000-01-01

The red cell's spectrin-actin network is known to sustain local states of shear, dilation, and condensation, and yet the short actin filaments are found to maintain membrane-tangent and near-random azimuthal orientations. When calibrated with polarization results for single actin filaments, imaging of micropipette-deformed red cell ghosts has allowed an assessment of actin orientations and possible reorientations in the network. At the hemispherical cap of the aspirated projection, where the network can be dilated severalfold, filaments have the same membrane-tangent orientation as on a relatively unstrained portion of membrane. Likewise, over the length of the network projection pulled into the micropipette, where the network is strongly sheared in axial extension and circumferential contraction, actin maintains its tangent orientation and is only very weakly aligned with network extension. Similar results are found for the integral membrane protein Band 3. Allowing for thermal fluctuations, we deduce a bound for the effective coupling constant, alpha, between network shear and azimuthal orientation of the protofilament. The finding that alpha must be about an order of magnitude or more below its tight-coupling value illustrates how nanostructural kinematics can decouple from more macroscopic responses. Monte Carlo simulations of spectrin-actin networks at approximately 10-nm resolution further support this conclusion and substantiate an image of protofilaments as elements of a high-temperature spin glass. PMID:11106606

Creating Turbulent Flow Realizations with Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

King, Ryan; Graf, Peter; Chertkov, Michael

2017-11-01

Generating valid inflow conditions is a crucial, yet computationally expensive, step in unsteady turbulent flow simulations. We demonstrate a new technique for rapid generation of turbulent inflow realizations that leverages recent advances in machine learning for image generation using a deep convolutional generative adversarial network (DCGAN). The DCGAN is an unsupervised machine learning technique consisting of two competing neural networks that are trained against each other using backpropagation. One network, the generator, tries to produce samples from the true distribution of states, while the discriminator tries to distinguish between true and synthetic samples. We present results from a fully-trained DCGAN that is able to rapidly draw random samples from the full distribution of possible inflow states without needing to solve the Navier-Stokes equations, eliminating the costly process of spinning up inflow turbulence. This suggests a new paradigm in physics informed machine learning where the turbulence physics can be encoded in either the discriminator or generator. Finally, we also propose additional applications such as feature identification and subgrid scale modeling.

Path planning on cellular nonlinear network using active wave computing technique

NASA Astrophysics Data System (ADS)

Yeniçeri, Ramazan; Yalçın, Müstak E.

2009-05-01

This paper introduces a simple algorithm to solve robot path finding problem using active wave computing techniques. A two-dimensional Cellular Neural/Nonlinear Network (CNN), consist of relaxation oscillators, has been used to generate active waves and to process the visual information. The network, which has been implemented on a Field Programmable Gate Array (FPGA) chip, has the feature of being programmed, controlled and observed by a host computer. The arena of the robot is modelled as the medium of the active waves on the network. Active waves are employed to cover the whole medium with their own dynamics, by starting from an initial point. The proposed algorithm is achieved by observing the motion of the wave-front of the active waves. Host program first loads the arena model onto the active wave generator network and command to start the generation. Then periodically pulls the network image from the generator hardware to analyze evolution of the active waves. When the algorithm is completed, vectorial data image is generated. The path from any of the pixel on this image to the active wave generating pixel is drawn by the vectors on this image. The robot arena may be a complicated labyrinth or may have a simple geometry. But, the arena surface always must be flat. Our Autowave Generator CNN implementation which is settled on the Xilinx University Program Virtex-II Pro Development System is operated by a MATLAB program running on the host computer. As the active wave generator hardware has 16, 384 neurons, an arena with 128 × 128 pixels can be modeled and solved by the algorithm. The system also has a monitor and network image is depicted on the monitor simultaneously.

Interictal 18FDG PET findings in temporal lobe epilepsy with déjà vu.

PubMed

Adachi, N; Koutroumanidis, M; Elwes, R D; Polkey, C E; Binnie, C D; Reynolds, E H; Barrington, S F; Maisey, M N; Panayiotopoulos, C P

1999-01-01

The authors studied the functional anatomy of the déjà vu (DV) experience in nonlesional temporal lobe epilepsy (TLE), using interictal fluorine-18 fluorodeoxyglucose PET in 14 patients with and 17 patients without DV. Several clinical conditions, such as age at PET study, side of ictal onset zone, and dominance for language, were no different between the two groups. The patients with DV showed significant relative reductions in glucose metabolism in the mesial temporal structures and the parietal cortex. The findings demonstrate that ictal DV is of no lateralizing value. They further suggest that temporal lobe dysfunction is necessary but not sufficient for the generation of DV. Extensive association cortical areas may be involved as part of the network that integrates this distinct experience.

Processing the Viking lander camera data

NASA Technical Reports Server (NTRS)

Levinthal, E. C.; Tucker, R.; Green, W.; Jones, K. L.

1977-01-01

Over 1000 camera events were returned from the two Viking landers during the Primary Mission. A system was devised for processing camera data as they were received, in real time, from the Deep Space Network. This system provided a flexible choice of parameters for three computer-enhanced versions of the data for display or hard-copy generation. Software systems allowed all but 0.3% of the imagery scan lines received on earth to be placed correctly in the camera data record. A second-order processing system was developed which allowed extensive interactive image processing including computer-assisted photogrammetry, a variety of geometric and photometric transformations, mosaicking, and color balancing using six different filtered images of a common scene. These results have been completely cataloged and documented to produce an Experiment Data Record.

Using Deep Learning to Analyze the Voices of Stars.

NASA Astrophysics Data System (ADS)

Boudreaux, Thomas Macaulay

2018-01-01

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and compare the performance of different deep learning algorithms, including Artifical Neural Netoworks, and Convolutional Neural Networks, in classifing these synthetic data sets as either pulsators, or not observed to vary stars.

Fibronectin forms the most extensible biological fibers displaying switchable force-exposed cryptic binding sites.

PubMed

Klotzsch, Enrico; Smith, Michael L; Kubow, Kristopher E; Muntwyler, Simon; Little, William C; Beyeler, Felix; Gourdon, Delphine; Nelson, Bradley J; Vogel, Viola

2009-10-27

Rather than maximizing toughness, as needed for silk and muscle titin fibers to withstand external impact, the much softer extracellular matrix fibers made from fibronectin (Fn) can be stretched by cell generated forces and display extraordinary extensibility. We show that Fn fibers can be extended more than 8-fold (>700% strain) before 50% of the fibers break. The Young's modulus of single fibers, given by the highly nonlinear slope of the stress-strain curve, changes orders of magnitude, up to MPa. Although many other materials plastically deform before they rupture, evidence is provided that the reversible breakage of force-bearing backbone hydrogen bonds enables the large strain. When tension is released, the nano-sized Fn domains first contract in the crowded environment of fibers within seconds into random coil conformations (molten globule states), before the force-bearing hydrogen bond networks that stabilize the domain's secondary structures are reestablished within minutes (double exponential). The exposure of cryptic binding sites on Fn type III modules increases steeply upon stretching. Thus fiber extension steadily up-regulates fiber rigidity and cryptic epitope exposure, both of which are known to differentially alter cell behavior. Finally, since stress-strain relationships cannot directly be measured in native extracellular matrix (ECM), the stress-strain curves were correlated with stretch-induced alterations of intramolecular fluorescence resonance energy transfer (FRET) obtained from trace amounts of Fn probes (mechanical strain sensors) that can be incorporated into native ECM. Physiological implications of the extraordinary extensibility of Fn fibers and contraction kinetics are discussed.

Learning Orthographic Structure With Sequential Generative Neural Networks.

PubMed

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

A neural-network approach to robotic control

NASA Technical Reports Server (NTRS)

Graham, D. P. W.; Deleuterio, G. M. T.

1993-01-01

An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

Software-defined network abstractions and configuration interfaces for building programmable quantum networks

NASA Astrophysics Data System (ADS)

Dasari, Venkat R.; Sadlier, Ronald J.; Geerhart, Billy E.; Snow, Nikolai A.; Williams, Brian P.; Humble, Travis S.

2017-05-01

Well-defined and stable quantum networks are essential to realize functional quantum communication applications. Quantum networks are complex and must use both quantum and classical channels to support quantum applications like QKD, teleportation, and superdense coding. In particular, the no-cloning theorem prevents the reliable copying of quantum signals such that the quantum and classical channels must be highly coordinated using robust and extensible methods. In this paper, we describe new network abstractions and interfaces for building programmable quantum networks. Our approach leverages new OpenFlow data structures and table type patterns to build programmable quantum networks and to support quantum applications.

Tweaked residual convolutional network for face alignment

NASA Astrophysics Data System (ADS)

Du, Wenchao; Li, Ke; Zhao, Qijun; Zhang, Yi; Chen, Hu

2017-08-01

We propose a novel Tweaked Residual Convolutional Network approach for face alignment with two-level convolutional networks architecture. Specifically, the first-level Tweaked Convolutional Network (TCN) module predicts the landmark quickly but accurately enough as a preliminary, by taking low-resolution version of the detected face holistically as the input. The following Residual Convolutional Networks (RCN) module progressively refines the landmark by taking as input the local patch extracted around the predicted landmark, particularly, which allows the Convolutional Neural Network (CNN) to extract local shape-indexed features to fine tune landmark position. Extensive evaluations show that the proposed Tweaked Residual Convolutional Network approach outperforms existing methods.

Analysis and Application of Microgrids

NASA Astrophysics Data System (ADS)

Yue, Lu

New trends of generating electricity locally and utilizing non-conventional or renewable energy sources have attracted increasing interests due to the gradual depletion of conventional fossil fuel energy sources. The new type of power generation is called Distributed Generation (DG) and the energy sources utilized by Distributed Generation are termed Distributed Energy Sources (DERs). With DGs embedded in the distribution networks, they evolve from passive distribution networks to active distribution networks enabling bidirectional power flows in the networks. Further incorporating flexible and intelligent controllers and employing future technologies, active distribution networks will turn to a Microgrid. A Microgrid is a small-scale, low voltage Combined with Heat and Power (CHP) supply network designed to supply electrical and heat loads for a small community. To further implement Microgrids, a sophisticated Microgrid Management System must be integrated. However, due to the fact that a Microgrid has multiple DERs integrated and is likely to be deregulated, the ability to perform real-time OPF and economic dispatch with fast speed advanced communication network is necessary. In this thesis, first, problems such as, power system modelling, power flow solving and power system optimization, are studied. Then, Distributed Generation and Microgrid are studied and reviewed, including a comprehensive review over current distributed generation technologies and Microgrid Management Systems, etc. Finally, a computer-based AC optimization method which minimizes the total transmission loss and generation cost of a Microgrid is proposed and a wireless communication scheme based on synchronized Code Division Multiple Access (sCDMA) is proposed. The algorithm is tested with a 6-bus power system and a 9-bus power system.

Quality of Service for Real-Time Applications Over Next Generation Data Networks

NASA Technical Reports Server (NTRS)

Ivancic, William; Atiquzzaman, Mohammed; Bai, Haowei; Su, Hongjun; Jain, Raj; Duresi, Arjan; Goyal, Mukyl; Bharani, Venkata; Liu, Chunlei; Kota, Sastri

2001-01-01

This project, which started on January 1, 2000, was funded by NASA Glenn Research Center for duration of one year. The deliverables of the project included the following tasks: Study of QoS mapping between the edge and core networks envisioned in the Next Generation networks will provide us with the QoS guarantees that can be obtained from next generation networks. Buffer management techniques to provide strict guarantees to real-time end-to-end applications through preferential treatment to packets belonging to real-time applications. In particular, use of ECN to help reduce the loss on high bandwidth-delay product satellite networks needs to be studied. Effect of Prioritized Packet Discard to increase goodput of the network and reduce the buffering requirements in the ATM switches. Provision of new IP circuit emulation services over Satellite IP backbones using MPLS will be studied. Determine the architecture and requirements for internetworking ATN and the Next Generation Internet for real-time applications.

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses.

PubMed

Mostafa, Hesham; Cauwenberghs, Gert

2018-06-01

Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

NETWORK DESIGN FOR OZONE MONITORING

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 from air pollution. A major cr...

Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level

NASA Astrophysics Data System (ADS)

Thakar, Juilee; Albert, Réka

The following sections are included: * Introduction * Boolean Network Concepts and History * Extensions of the Classical Boolean Framework * Boolean Inference Methods and Examples in Biology * Dynamic Boolean Models: Examples in Plant Biology, Developmental Biology and Immunology * Conclusions * References

Grower Communication Networks: Information Sources for Organic Farmers

ERIC Educational Resources Information Center

Crawford, Chelsi; Grossman, Julie; Warren, Sarah T.; Cubbage, Fred

2015-01-01

This article reports on a study to determine which information sources organic growers use to inform farming practices by conducting in-depth semi-structured interviews with 23 organic farmers across 17 North Carolina counties. Effective information sources included: networking, agricultural organizations, universities, conferences, Extension, Web…

Modeling stream network-scale variation in coho salmon overwinter survival and smolt size

EPA Science Inventory

We used multiple regression and hierarchical mixed-effects models to examine spatial patterns of overwinter survival and size at smolting in juvenile coho salmon Oncorhynchus kisutch in relation to habitat attributes across an extensive stream network in southwestern Oregon over ...

Synchronised integrated online e-health profiles.

PubMed

Liang, Jian; Iannella, Renato; Sahama, Tony

2011-01-01

Web-based social networking applications have become increasingly important in recent years. The current applications in the healthcare sphere can support the health management, but to date there is no patient-controlled integrator. This paper proposes a platform called Multiple Profile Manager (MPM) that enables a user to create and manage an integrated profile that can be shared across numerous social network sites. Moreover, it is able to facilitate the collection of personal healthcare data, which makes a contribution to the development of public health informatics. Here we want to illustrate how patients and physicians can be benefited from enabling the platform for online social network sites. The MPM simplifies the management of patients' profiles and allows health professionals to obtain a more complete picture of the patients' background so that they can provide better health care. To do so, we demonstrate a prototype of the platform and describe its protocol specification, which is an XMPP (Extensible Messaging and Presence Protocol) [1] extension, for sharing and synchronising profile data (vCard²) between different social networks.

The High-Resolution Structure of Activated Opsin Reveals a Conserved Solvent Network in the Transmembrane Region Essential for Activation.

PubMed

Blankenship, Elise; Vahedi-Faridi, Ardeschir; Lodowski, David T

2015-12-01

Rhodopsin, a light-activated G protein coupled receptor (GPCR), has been the subject of numerous biochemical and structural investigations, serving as a model receptor for GPCRs and their activation. We present the 2.3-Å resolution structure of native source rhodopsin stabilized in a conformation competent for G protein binding. An extensive water-mediated hydrogen bond network linking the chromophore binding site to the site of G protein binding is observed, providing connections to conserved motifs essential for GPCR activation. Comparison of this extensive solvent-mediated hydrogen-bonding network with the positions of ordered solvent in earlier crystallographic structures of rhodopsin photointermediates reveals both static structural and dynamic functional water-protein interactions present during the activation process. When considered along with observations that solvent occupies similar positions in the structures of other GPCRs, these analyses strongly support an integral role for this dynamic ordered water network in both rhodopsin and GPCR activation. Copyright © 2015 Elsevier Ltd. All rights reserved.