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Sample records for network information theory

  1. Information theory perspective on network robustness

    NASA Astrophysics Data System (ADS)

    Schieber, Tiago A.; Carpi, Laura; Frery, Alejandro C.; Rosso, Osvaldo A.; Pardalos, Panos M.; Ravetti, Martín G.

    2016-01-01

    A crucial challenge in network theory is the study of the robustness of a network when facing a sequence of failures. In this work, we propose a dynamical definition of network robustness based on Information Theory, that considers measurements of the structural changes caused by failures of the network's components. Failures are defined here as a temporal process defined in a sequence. Robustness is then evaluated by measuring dissimilarities between topologies after each time step of the sequence, providing a dynamical information about the topological damage. We thoroughly analyze the efficiency of the method in capturing small perturbations by considering different probability distributions on networks. In particular, we find that distributions based on distances are more consistent in capturing network structural deviations, as better reflect the consequences of the failures. Theoretical examples and real networks are used to study the performance of this methodology.

  2. Information theory and the ethylene genetic network.

    PubMed

    González-García, José S; Díaz, José

    2011-10-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  3. Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

    PubMed

    Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-03-01

    "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. PMID:26701126

  4. Entropy measures for networks: toward an information theory of complex topologies.

    PubMed

    Anand, Kartik; Bianconi, Ginestra

    2009-10-01

    The quantification of the complexity of networks is, today, a fundamental problem in the physics of complex systems. A possible roadmap to solve the problem is via extending key concepts of information theory to networks. In this Rapid Communication we propose how to define the Shannon entropy of a network ensemble and how it relates to the Gibbs and von Neumann entropies of network ensembles. The quantities we introduce here will play a crucial role for the formulation of null models of networks through maximum-entropy arguments and will contribute to inference problems emerging in the field of complex networks. PMID:19905379

  5. Information theory and signal transduction systems: from molecular information processing to network inference.

    PubMed

    Mc Mahon, Siobhan S; Sim, Aaron; Filippi, Sarah; Johnson, Robert; Liepe, Juliane; Smith, Dominic; Stumpf, Michael P H

    2014-11-01

    Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design. PMID:24953199

  6. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    PubMed

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. PMID:26691180

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

    SciTech Connect

    Yoon, Hong-Jun; Tourassi, Georgia

    2014-01-01

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

  8. Optimal design of hydrometric monitoring networks with dynamic components based on Information Theory

    NASA Astrophysics Data System (ADS)

    Alfonso, Leonardo; Chacon, Juan; Solomatine, Dimitri

    2016-04-01

    The EC-FP7 WeSenseIt project proposes the development of a Citizen Observatory of Water, aiming at enhancing environmental monitoring and forecasting with the help of citizens equipped with low-cost sensors and personal devices such as smartphones and smart umbrellas. In this regard, Citizen Observatories may complement the limited data availability in terms of spatial and temporal density, which is of interest, among other areas, to improve hydraulic and hydrological models. At this point, the following question arises: how can citizens, who are part of a citizen observatory, be optimally guided so that the data they collect and send is useful to improve modelling and water management? This research proposes a new methodology to identify the optimal location and timing of potential observations coming from moving sensors of hydrological variables. The methodology is based on Information Theory, which has been widely used in hydrometric monitoring design [1-4]. In particular, the concepts of Joint Entropy, as a measure of the amount of information that is contained in a set of random variables, which, in our case, correspond to the time series of hydrological variables captured at given locations in a catchment. The methodology presented is a step forward in the state of the art because it solves the multiobjective optimisation problem of getting simultaneously the minimum number of informative and non-redundant sensors needed for a given time, so that the best configuration of monitoring sites is found at every particular moment in time. To this end, the existing algorithms have been improved to make them efficient. The method is applied to cases in The Netherlands, UK and Italy and proves to have a great potential to complement the existing in-situ monitoring networks. [1] Alfonso, L., A. Lobbrecht, and R. Price (2010a), Information theory-based approach for location of monitoring water level gauges in polders, Water Resour. Res., 46(3), W03528 [2] Alfonso, L., A

  9. Efficiency of a seismic network from the viewpoint of mathematical information theory: application to the Andalucia (Spain) seismic network

    NASA Astrophysics Data System (ADS)

    Lana, X.; Martínez, M. D.; Miguel, F. De

    1990-05-01

    Concepts of information theory applied to data from the Andalucia (Spain) seismic network permit the discussion of whether data are correctly used in determining epicentres. The elementary definition of Shannon's information allows a discussion of the coverage of the epicentres in terms of azimuths and distances to the seismic stations. The contributions of all the stations of the network to the coverage is also investigated. Data were obtained from 13 seismic stations and 765 epicentral determinations corresponding to the seismic activity of the years 1983-84 and some months of 1985 in the Central Belies area (Southern Spain). Information theory concepts were applied to the data after a distribution of epicentres according to their mo values and hypocentral depths. The obtained results show a better treatment of very shallow earthquakes, especially of those with values of mb less than or equal to 2.5. No significant different coverages were obtained for the sets of earthquakes classified according to their hypocentral depths and mb values.

  10. Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory

    PubMed Central

    2013-01-01

    Background Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks. Typically, such mathematical models involve networks of coupled jump stochastic processes with a large number of parameters that need to be suitably calibrated against experimental data. In this direction, the parameter sensitivity analysis of reaction networks is an essential mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, existing sensitivity analysis approaches such as variants of the finite difference method can have an overwhelming computational cost in models with a high-dimensional parameter space. Results We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters. The proposed approach is based on Information Theory methods and relies on the quantification of information loss due to parameter perturbations between time-series distributions. For this reason, we need to work on path-space, i.e., the set consisting of all stochastic trajectories, hence the proposed approach is referred to as “pathwise”. The pathwise sensitivity analysis method is realized by employing the rigorously-derived Relative Entropy Rate, which is directly computable from the propensity functions. A key aspect of the method is that an associated pathwise Fisher Information Matrix (FIM) is defined, which in turn constitutes a gradient-free approach to quantifying parameter sensitivities. The structure of the FIM turns out to be block-diagonal, revealing hidden parameter dependencies and sensitivities in reaction networks. Conclusions As a gradient-free method, the proposed sensitivity analysis provides a significant advantage when dealing with complex stochastic systems with a large number of parameters. In addition, the knowledge of the structure of the

  11. Blogs and Social Network Sites as Activity Systems: Exploring Adult Informal Learning Process through Activity Theory Framework

    ERIC Educational Resources Information Center

    Heo, Gyeong Mi; Lee, Romee

    2013-01-01

    This paper uses an Activity Theory framework to explore adult user activities and informal learning processes as reflected in their blogs and social network sites (SNS). Using the assumption that a web-based space is an activity system in which learning occurs, typical features of the components were investigated and each activity system then…

  12. Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

    PubMed Central

    Sayyed-Ahmad, Abdallah; Tuncay, Kagan; Ortoleva, Peter J

    2007-01-01

    Background Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion Multiplex time series data can be used for the construction of the network of

  13. Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare

    PubMed Central

    2010-01-01

    Background Actor-Network Theory (ANT) is an increasingly influential, but still deeply contested, approach to understand humans and their interactions with inanimate objects. We argue that health services research, and in particular evaluations of complex IT systems in health service organisations, may benefit from being informed by Actor-Network Theory perspectives. Discussion Despite some limitations, an Actor-Network Theory-based approach is conceptually useful in helping to appreciate the complexity of reality (including the complexity of organisations) and the active role of technology in this context. This can prove helpful in understanding how social effects are generated as a result of associations between different actors in a network. Of central importance in this respect is that Actor-Network Theory provides a lens through which to view the role of technology in shaping social processes. Attention to this shaping role can contribute to a more holistic appreciation of the complexity of technology introduction in healthcare settings. It can also prove practically useful in providing a theoretically informed approach to sampling (by drawing on informants that are related to the technology in question) and analysis (by providing a conceptual tool and vocabulary that can form the basis for interpretations). We draw on existing empirical work in this area and our ongoing work investigating the integration of electronic health record systems introduced as part of England's National Programme for Information Technology to illustrate salient points. Summary Actor-Network Theory needs to be used pragmatically with an appreciation of its shortcomings. Our experiences suggest it can be helpful in investigating technology implementations in healthcare settings. PMID:21040575

  14. Intervening in Partner Violence against Women: A Grounded Theory Exploration of Informal Network Members' Experiences

    ERIC Educational Resources Information Center

    Latta, Rachel E.; Goodman, Lisa A.

    2011-01-01

    A large body of cross-sectional and longitudinal research demonstrates the important contribution of informal social networks to the well-being and safety of female survivors of intimate partner violence (IPV). Most survivors turn to family and friends before, during, and after their involvement with formal services; and many rely solely on…

  15. Application of neural networks and information theory to the identification of E. coli transcriptional promoters

    SciTech Connect

    Abremski, K. . Experimental Station); Sirotkin, K. ); Lapedes, A. )

    1991-01-01

    The Humane Genome Project has as its eventual goal the determination of the entire DNA sequence of man, which comprises approximately 3 billion base pairs. An important aspect of this project will be the analysis of the sequence to locate regions of biological importance. New computer methods will be needed to automate and facilitate this task. In this paper, we have investigated use of neural networks for the recognition of functional patterns in biological sequences. The prediction of Escherichia coli transcriptional promoters was chosen as a model system for these studies. Two approaches were employed. In the fist method, a mutual information analysis of promoter and nonpromoter sequences was carried out to demonstrate the informative base positions that help to distinguish promoter sequences from non-promoter sequences. These base positions were than used to train a Perceptron to predict new promoter sequences. In the second method, the experimental knowledge of promoters was used to indicate the important base positions in the sequence. These base positions were used to train a back propagation network with hidden units which represented regions of sequence conservation found in promoters. With both types of networks, prediction of new promoter sequences was greater than 96.9%. 12 refs., 1 fig., 4 tabs.

  16. A finite-size ergodic theory of stable chaos for quantifying information processing in balanced state networks of spiking neurons

    NASA Astrophysics Data System (ADS)

    Puelma Touzel, Maximilian; Monteforte, Michael; Wolf, Fred

    2015-03-01

    The stability of a dynamics constrains its ability to process information, a notion intended to be captured by the ergodic theory of chaos and one likely to be important for neuroscience. Asynchronous, irregular network activity can be produced by models in which excitatory and inhibitory inputs are balanced. For negative and sharply pulsed interactions, these networks turn out to be stable. The coexistence of aperiodic activity and stability is called stable chaos. This stability to perturbations only exists up to some finite average strength beyond which they are unstable. This finite-size instability produces entropy not captured by conventional ergodic theory. We derive and use the probability of divergence as a function of perturbation strength to give an expression for a finite-sized analogue of the Kolmolgorov-Sinai (KS) entropy that scales with the perturbation strength, and thus deviates from the conventional KS entropy value of 0. This work provides a foundation for understanding the information processing capacity of networks in the fast synapse, fast action potential onset, and inhibition-dominated regime.

  17. Optimal Observation Network Design for Model Discrimination using Information Theory and Bayesian Model Averaging

    NASA Astrophysics Data System (ADS)

    Pham, H. V.; Tsai, F. T. C.

    2014-12-01

    Groundwater systems are complex and subject to multiple interpretations and conceptualizations due to a lack of sufficient information. As a result, multiple conceptual models are often developed and their mean predictions are preferably used to avoid biased predictions from using a single conceptual model. Yet considering too many conceptual models may lead to high prediction uncertainty and may lose the purpose of model development. In order to reduce the number of models, an optimal observation network design is proposed based on maximizing the Kullback-Leibler (KL) information to discriminate competing models. The KL discrimination function derived by Box and Hill [1967] for one additional observation datum at a time is expanded to account for multiple independent spatiotemporal observations. The Bayesian model averaging (BMA) method is used to incorporate existing data and quantify future observation uncertainty arising from conceptual and parametric uncertainties in the discrimination function. To consider the future observation uncertainty, the Monte Carlo realizations of BMA predicted future observations are used to calculate the mean and variance of posterior model probabilities of the competing models. The goal of the optimal observation network design is to find the number and location of observation wells and sampling rounds such that the highest posterior model probability of a model is larger than a desired probability criterion (e.g., 95%). The optimal observation network design is implemented to a groundwater study in the Baton Rouge area, Louisiana to collect new groundwater heads from USGS wells. The considered sources of uncertainty that create multiple groundwater models are the geological architecture, the boundary condition, and the fault permeability architecture. All possible design solutions are enumerated using high performance computing systems. Results show that total model variance (the sum of within-model variance and between

  18. Congenital Heart Information Network

    MedlinePlus

    ... heart defects. Important Notice The Congenital Heart Information Network website is temporarily out of service. Please join ... and Uwe Baemayr for The Congenital Heart Information Network Exempt organization under Section 501(c)3. Copyright © ...

  19. Geographic Theories of Educational Development: Innovation Diffusion Within Informal Interpersonal Networks.

    ERIC Educational Resources Information Center

    Berry, Brian J. L.

    An examination of geographic theories of social change clarifies how and why Torsten Haagerstrand's ideas have revolutionized geographic thinking, particularly regarding educational change and development, and provides the background for analyzing his models in detail. Haagerstrand developed the first formal geoqraphic model of diffusion…

  20. Improving access to health information for older migrants by using grounded theory and social network analysis to understand their information behaviour and digital technology use.

    PubMed

    Goodall, K T; Newman, L A; Ward, P R

    2014-11-01

    Migrant well-being can be strongly influenced by the migration experience and subsequent degree of mainstream language acquisition. There is little research on how older Culturally And Linguistically Diverse (CALD) migrants who have 'aged in place' find health information, and the role which digital technology plays in this. Although the research for this paper was not focused on cancer, we draw out implications for providing cancer-related information to this group. We interviewed 54 participants (14 men and 40 women) aged 63-94 years, who were born in Italy or Greece, and who migrated to Australia mostly as young adults after World War II. Constructivist grounded theory and social network analysis were used for data analysis. Participants identified doctors, adult children, local television, spouse, local newspaper and radio as the most important information sources. They did not generally use computers, the Internet or mobile phones to access information. Literacy in their birth language, and the degree of proficiency in understanding and using English, influenced the range of information sources accessed and the means used. The ways in which older CALD migrants seek and access information has important implications for how professionals and policymakers deliver relevant information to them about cancer prevention, screening, support and treatment, particularly as information and resources are moved online as part of e-health. PMID:25250535

  1. Constructor theory of information

    PubMed Central

    Deutsch, David; Marletto, Chiara

    2015-01-01

    We propose a theory of information expressed solely in terms of which transformations of physical systems are possible and which are impossible—i.e. in constructor-theoretic terms. It includes conjectured, exact laws of physics expressing the regularities that allow information to be physically instantiated. Although these laws are directly about information, independently of the details of particular physical instantiations, information is not regarded as an a priori mathematical or logical concept, but as something whose nature and properties are determined by the laws of physics alone. This theory solves a problem at the foundations of existing information theory, namely that information and distinguishability are each defined in terms of the other. It also explains the relationship between classical and quantum information, and reveals the single, constructor-theoretic property underlying the most distinctive phenomena associated with the latter, including the lack of in-principle distinguishability of some states, the impossibility of cloning, the existence of pairs of variables that cannot simultaneously have sharp values, the fact that measurement processes can be both deterministic and unpredictable, the irreducible perturbation caused by measurement, and locally inaccessible information (as in entangled systems). PMID:25663803

  2. Rural Information Network.

    ERIC Educational Resources Information Center

    National Public Telecomputing Network, Cleveland, OH.

    This report describes the National Public Telecomputing Network's (NPTN) development of free, public-access, community computer systems throughout the United States. It also provides information on how to initiate a "Free-Net" through the Rural Information Network. Free-Nets are multi-user systems with some of the power and sophistication of…

  3. PESTICIDE INFORMATION NETWORK

    EPA Science Inventory

    The Pesticide Information Network (PIN) is an interactive database containing information about pesticides. PIN is a free service offered by the USEPAs Office of Pesticide Programs which provides contacts on pesticide issues, has a bulletin board network for public and private us...

  4. Network theory for inhomogeneous thermoelectrics

    NASA Astrophysics Data System (ADS)

    Angst, Sebastian; Wolf, Dietrich E.

    2016-04-01

    The Onsager–de Groot–Callen transport theory, implemented as a network model, is used to simulate the transient Harman method, which is widely used experimentally to determine all thermoelectric transport coefficients in a single measurement setup. It is shown that this method systematically overestimates the Seebeck coefficient for samples composed of two different materials. As a consequence, the figure of merit is also overestimated, if the thermal coupling of the measurement setup to the environment is weak. For a mixture of metal and semiconductor particles near metal percolation the figure of merit obtained by the Harman method is more than 100% too large. For a correct interpretation of the experimental data, information on composition and microstructure of the sample are indispensable.

  5. Graphical Model Theory for Wireless Sensor Networks

    SciTech Connect

    Davis, William B.

    2002-12-08

    Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.

  6. Network Security Validation Using Game Theory

    NASA Astrophysics Data System (ADS)

    Papadopoulou, Vicky; Gregoriades, Andreas

    Non-functional requirements (NFR) such as network security recently gained widespread attention in distributed information systems. Despite their importance however, there is no systematic approach to validate these requirements given the complexity and uncertainty characterizing modern networks. Traditionally, network security requirements specification has been the results of a reactive process. This however, limited the immunity property of the distributed systems that depended on these networks. Security requirements specification need a proactive approach. Networks' infrastructure is constantly under attack by hackers and malicious software that aim to break into computers. To combat these threats, network designers need sophisticated security validation techniques that will guarantee the minimum level of security for their future networks. This paper presents a game-theoretic approach to security requirements validation. An introduction to game theory is presented along with an example that demonstrates the application of the approach.

  7. Psychology and social networks: a dynamic network theory perspective.

    PubMed

    Westaby, James D; Pfaff, Danielle L; Redding, Nicholas

    2014-04-01

    Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved). PMID:24750076

  8. State energy information networks

    SciTech Connect

    Tatar, J.; Ettinger, G.; Wrabel, M.

    1984-06-01

    In November 1983, Argonne National Laboratory (ANL) initiated a study under the sponsorship of the US Department of Energy (DOE) State Programs Branch to examine state energy information networks. Goal was to help DOE decide how best to allocate resources to assist states in acquiring information related to state energy programs and policies.

  9. An information theory account of cognitive control

    PubMed Central

    Fan, Jin

    2014-01-01

    Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory. PMID:25228875

  10. Potential Theory for Directed Networks

    PubMed Central

    Zhang, Qian-Ming; Lü, Linyuan; Wang, Wen-Qiang; Zhou, Tao

    2013-01-01

    Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds to a decrease of a unit potential and subgraphs with definable potential values for all nodes are preferred. Combining the potential theory with the clustering and homophily mechanisms, it is deduced that the Bi-fan structure consisting of 4 nodes and 4 directed links is the most favored local structure in directed networks. Our hypothesis receives strongly positive supports from extensive experiments on 15 directed networks drawn from disparate fields, as indicated by the most accurate and robust performance of Bi-fan predictor within the link prediction framework. In summary, our main contribution is twofold: (i) We propose a new mechanism for the local organization of directed networks; (ii) We design the corresponding link prediction algorithm, which can not only testify our hypothesis, but also find out direct applications in missing link prediction and friendship recommendation. PMID:23408979

  11. Translating the Interconnections between Ecological and Hydrological Processes in a Small Watershed into Process Networks using Information Theory

    NASA Astrophysics Data System (ADS)

    Kim, J.; Woo, N. C.; Kim, S.; Yun, J.; Kim, S.; Kang, M.; Cho, C. H.; Chun, J. H.

    2014-12-01

    We demonstrate how field measurements can inform the selection of model frameworks in small watershed applications. Based on the assumption that ecohydrological systems are open and complex, we employ the process network analysis to identify the system state and the subsystems architecture with changing environment conditions. Ecohydrological and biogeochemical processes in a watershed can be viewed as a network of processes of a wide range of scales involving various feedback loops and time delay. Using the KoFlux tower-based measurements of energy, water and CO2 flux time series along with those representing the soil-plant-atmospheric continuum; we evaluated statistical measures of characterizing the organization of the information flows in the system. We used Shannon's information entropy and calculated the mutual information and transfer entropy, following Ruddell and Kumar (2009). Transfer entropy can measure the relative strength and time scale of couplings between the variables. In this analysis, we selected 15 variables associated with ecohydrological processes, which are groundwater table height, water temperature, specific conductivity, soil moisture contents at three depths, ecosystem respiration, gross primary productivity, sensible heat flux, latent heat flux, precipitation, air temperature, vapor pressure deficit, atmospheric pressure, and solar radiation. The data-driven nature of this investigation may shed a light on reconciling model parsimony with equifinality in small watershed applications. (Acknowledgment: This work and the data used in the study were funded by the Korea Meteorological Administration Research and Development Program under Grant Weather Information Service Engine (WISE) project,153-3100-3133-302-350 and Grant CATER 2014-3030, respectively. The KoFlux site was supported by the Long-term Ecological Study and Monitoring of Forest Ecosystem Project of Korea Forest Research Institute.)

  12. WATERSHED INFORMATION NETWORK

    EPA Science Inventory

    Resource Purpose:The Watershed Information Network is a set of about 30 web pages that are organized by topic. These pages access existing databases like the American Heritage Rivers Services database and Surf Your Watershed. WIN in itself has no data or data sets.
    L...

  13. Quantum Theory is an Information Theory

    NASA Astrophysics Data System (ADS)

    D'Ariano, Giacomo M.; Perinotti, Paolo

    2016-03-01

    In this paper we review the general framework of operational probabilistic theories (OPT), along with the six axioms from which quantum theory can be derived. We argue that the OPT framework along with a relaxed version of five of the axioms, define a general information theory. We close the paper with considerations about the role of the observer in an OPT, and the interpretation of the von Neumann postulate and the Schrödinger-cat paradox.

  14. Inquiry Calculus and Information Theory

    NASA Astrophysics Data System (ADS)

    Center, Julian L.

    2009-12-01

    We consider the relationship between information theory and a calculus of inquiries. We show how an inquiry calculus can be constructed using lattice theory, and how the inquiry calculus relates to information theory. The key idea is to identify both inquiries and variables with partitions of the state space. We also show an approach to extending information theory that deals with the problem of negative entropies on questions that do not correspond to partitions.

  15. Information cascade on networks

    NASA Astrophysics Data System (ADS)

    Hisakado, Masato; Mori, Shintaro

    2016-05-01

    In this paper, we discuss a voting model by considering three different kinds of networks: a random graph, the Barabási-Albert (BA) model, and a fitness model. A voting model represents the way in which public perceptions are conveyed to voters. Our voting model is constructed by using two types of voters-herders and independents-and two candidates. Independents conduct voting based on their fundamental values; on the other hand, herders base their voting on the number of previous votes. Hence, herders vote for the majority candidates and obtain information relating to previous votes from their networks. We discuss the difference between the phases on which the networks depend. Two kinds of phase transitions, an information cascade transition and a super-normal transition, were identified. The first of these is a transition between a state in which most voters make the correct choices and a state in which most of them are wrong. The second is a transition of convergence speed. The information cascade transition prevails when herder effects are stronger than the super-normal transition. In the BA and fitness models, the critical point of the information cascade transition is the same as that of the random network model. However, the critical point of the super-normal transition disappears when these two models are used. In conclusion, the influence of networks is shown to only affect the convergence speed and not the information cascade transition. We are therefore able to conclude that the influence of hubs on voters' perceptions is limited.

  16. Computer and information networks.

    PubMed

    Greenberger, M; Aronofsky, J; McKenney, J L; Massy, W F

    1973-10-01

    The most basic conclusion coming out of the EDUCOM seminars is that computer networking must be acknowledged as an important new mode for obtaining information and computation (15). It is a real alternative that needs to be given serious attention in current planning and decision-making. Yet the fact is that many institutions are not taking account of networks when they confer on whether or how to replace their main computer. Articulation of the possibilities of computer networks goes back to the early 1960's and before, and working networks have been in evidence for several years now, both commercially and in universities. What is new, however, is the unmistakable recognition-bordering on a sense of the inevitable-that networks are finally practical and here to stay. The visionary and promotional phases of computer networks are over. It is time for hard-nosed comparative analysis (16). Another conclusion of the seminars has to do with the factors that hinder the fuller development of networking. The major problems to be overcome in applying networks to research and education are political, organizational, and economic in nature rather than technological. This is not to say that the hardware and software problems of linking computers and information systems are completely solved, but they are not the big bottlenecks at present. Research and educational institutions must find ways to organize themselves as well as their computers to work together for greater resource sharing. The coming of age of networks takes on special significance as a result of widespread dissatisfactions expressed with the present computing situation. There is a feeling that the current mode of autonomous, self-sufficient operation in the provision of computing and information services is frequently wasteful, deficient, and unresponsive to users' needs because of duplication of effort from one installation to another, incompatibilities, and inadequate documentation, program support, and user

  17. Information theoretic description of networks

    NASA Astrophysics Data System (ADS)

    Wilhelm, Thomas; Hollunder, Jens

    2007-11-01

    We present a new information theoretic approach for network characterizations. It is developed to describe the general type of networks with n nodes and L directed and weighted links, i.e., it also works for the simpler undirected and unweighted networks. The new information theoretic measures for network characterizations are based on a transmitter-receiver analogy of effluxes and influxes. Based on these measures, we classify networks as either complex or non-complex and as either democracy or dictatorship networks. Directed networks, in particular, are furthermore classified as either information spreading and information collecting networks. The complexity classification is based on the information theoretic network complexity measure medium articulation (MA). It is proven that special networks with a medium number of links ( L∼n1.5) show the theoretical maximum complexity MA=(log n)2/2. A network is complex if its MA is larger than the average MA of appropriately randomized networks: MA>MAr. A network is of the democracy type if its redundancy Rnetwork. In democracy networks all nodes are, on average, of similar importance, whereas in dictatorship networks some nodes play distinguished roles in network functioning. In other words, democracy networks are characterized by cycling of information (or mass, or energy), while in dictatorship networks there is a straight through-flow from sources to sinks. The classification of directed networks into information spreading and information collecting networks is based on the conditional entropies of the considered networks ( H(A/B)=uncertainty of sender node if receiver node is known, H(B/A)=uncertainty of receiver node if sender node is known): if H(A/B)>H(B/A), it is an information collecting network, otherwise an information spreading network. Finally, different real networks (directed and undirected, weighted and unweighted) are classified according to our general scheme.

  18. Informational derivation of quantum theory

    NASA Astrophysics Data System (ADS)

    Chiribella, Giulio; D'Ariano, Giacomo Mauro; Perinotti, Paolo

    2011-07-01

    We derive quantum theory from purely informational principles. Five elementary axioms—causality, perfect distinguishability, ideal compression, local distinguishability, and pure conditioning—define a broad class of theories of information processing that can be regarded as standard. One postulate—purification—singles out quantum theory within this class.

  19. Interdisciplinary and physics challenges of network theory

    NASA Astrophysics Data System (ADS)

    Bianconi, Ginestra

    2015-09-01

    Network theory has unveiled the underlying structure of complex systems such as the Internet or the biological networks in the cell. It has identified universal properties of complex networks, and the interplay between their structure and dynamics. After almost twenty years of the field, new challenges lie ahead. These challenges concern the multilayer structure of most of the networks, the formulation of a network geometry and topology, and the development of a quantum theory of networks. Making progress on these aspects of network theory can open new venues to address interdisciplinary and physics challenges including progress on brain dynamics, new insights into quantum technologies, and quantum gravity.

  20. Reconstructing input for artificial neural networks based on embedding theory and mutual information to simulate soil pore water salinity in tidal floodplain

    NASA Astrophysics Data System (ADS)

    Zheng, Fawen; Wan, Yongshan; Song, Keunyea; Sun, Detong; Hedgepeth, Marion

    2016-01-01

    Soil pore water salinity plays an important role in the distribution of vegetation and biogeochemical processes in coastal floodplain ecosystems. In this study, artificial neural networks (ANNs) were applied to simulate the pore water salinity of a tidal floodplain in Florida. We present an approach based on embedding theory with mutual information to reconstruct ANN model input time series from one system state variable. Mutual information between system output and input was computed and the local minimum mutual information points were used to determine a time lag vector for time series embedding and reconstruction, with which the mutual information weighted average method was developed to compute the components of reconstructed time series. The optimal embedding dimension was obtained by optimizing model performance. The method was applied to simulate soil pore water salinity dynamics at 12 probe locations in the tidal floodplain influenced by saltwater intrusion using 4 years (2005-2008) data, in which adjacent river water salinity was used to reconstruct model input. The simulated electrical conductivity of the pore water showed close agreement with field observations (RMSE and ), suggesting the reconstructed input by the proposed approach provided adequate input information for ANN modeling. Multiple linear regression model, partial mutual information algorithm for input variable selection, k-NN algorithm, and simple time delay embedding were also used to further verify the merit of the proposed approach.

  1. Scholarly Information Network

    NASA Astrophysics Data System (ADS)

    Ginsparg, Paul

    I review the background and some recent trends of a particular scholarly information network, arXiv.org, and discuss some of its implications for new scholarly publication models. If we were to start from scratch today to design a quality-controlled archive and distribution system for scientific and technical information, it could take a very different form from what has evolved in the past decade from pre-existing print infrastructure. Near-term advances in automated classification systems, authoring tools, and document formats will facilitate efficient datamining and long-term archival stability, and I discuss how these could provide not only more efficient means of accessing and navigating the information, but also more cost-effective means of authentication and quality control. Finally, I illustrate the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature. An emerging field of research can be identified as part of an existing corpus, permitting the implementation of a more coherent community structure for its network of practitioners.

  2. Network Information System

    Energy Science and Technology Software Center (ESTSC)

    1996-05-01

    The Network Information System (NWIS) was initially implemented in May 1996 as a system in which computing devices could be recorded so that unique names could be generated for each device. Since then the system has grown to be an enterprise wide information system which is integrated with other systems to provide the seamless flow of data through the enterprise. The system Iracks data for two main entities: people and computing devices. The following aremore » the type of functions performed by NWIS for these two entities: People Provides source information to the enterprise person data repository for select contractors and visitors Generates and tracks unique usernames and Unix user IDs for every individual granted cyber access Tracks accounts for centrally managed computing resources, and monitors and controls the reauthorization of the accounts in accordance with the DOE mandated interval Computing Devices Generates unique names for all computing devices registered in the system Tracks the following information for each computing device: manufacturer, make, model, Sandia property number, vendor serial number, operating system and operating system version, owner, device location, amount of memory, amount of disk space, and level of support provided for the machine Tracks the hardware address for network cards Tracks the P address registered to computing devices along with the canonical and alias names for each address Updates the Dynamic Domain Name Service (DDNS) for canonical and alias names Creates the configuration files for DHCP to control the DHCP ranges and allow access to only properly registered computers Tracks and monitors classified security plans for stand-alone computers Tracks the configuration requirements used to setup the machine Tracks the roles people have on machines (system administrator, administrative access, user, etc...) Allows systems administrators to track changes made on the machine (both hardware and software) Generates an

  3. Geoscience Information Network

    NASA Astrophysics Data System (ADS)

    Allison, M. L.; Gundersen, L. C.

    2007-12-01

    Geological surveys in the USA have an estimated 2,000-3,000 databases that represent one of the largest, long- term information resources on the geology of the United States and collectively constitute a national geoscience data "backbone" for research and applications. An NSF-supported workshop in February, 2007, among representatives of the Association of American State Geologists (AASG) and the USGS, recommended that "the nation's geological surveys develop a national geoscience information framework that is distributed, interoperable, uses open source standards and common protocols, respects and acknowledges data ownership, fosters communities of practice to grow, and develops new web services and clients." The AASG and USGS have formally endorsed the workshop recommendations and formed a joint Steering Committee to pursue design and implementation of the Geoscience Information Network (GIN). GIN is taking a modular approach in assembling the network: 1. Agreement on open-source standards and common protocols through the use of Open Geospatial Consortium (OGC) standards. 2. A data exchange model utilizing the geoscience mark-up language GeoSciML, an OGC GML-based application. 3. A prototype data discovery tool (National Digital Catalogue - NDC) developing under the National Geological and Geophysical Data Preservation Program run by the USGS. 4. Data integration tools developed or planned by a number of independent projects. A broader NSF-sponsored workshop in March 2007 examined what direction the geoinformatics community in the US should take towards developing a National Geoinformatics System. The final report stated that, "It was clear that developing such a system should involve a partnership between academia, government, and industry that should be closely connected to the efforts of the U. S. Geological Survey and the state geological surveys..." The GIN is collaborating with 1-G Europe, a coalition of 27 European geological surveys in the One

  4. Informed Grounded Theory

    ERIC Educational Resources Information Center

    Thornberg, Robert

    2012-01-01

    There is a widespread idea that in grounded theory (GT) research, the researcher has to delay the literature review until the end of the analysis to avoid contamination--a dictum that might turn educational researchers away from GT. Nevertheless, in this article the author (a) problematizes the dictum of delaying a literature review in classic…

  5. Extracting information from multiplex networks.

    PubMed

    Iacovacci, Jacopo; Bianconi, Ginestra

    2016-06-01

    Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ̃(S) for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science. PMID:27368796

  6. Extracting information from multiplex networks

    NASA Astrophysics Data System (ADS)

    Iacovacci, Jacopo; Bianconi, Ginestra

    2016-06-01

    Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ ˜ S for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.

  7. Publications of the Jet Propulsion Laboratory, January through December 1974. [deep space network, Apollo project, information theory, and space exploration

    NASA Technical Reports Server (NTRS)

    1975-01-01

    Formalized technical reporting is described and indexed, which resulted from scientific and engineering work performed, or managed, by the Jet Propulsion Laboratory. The five classes of publications included are technical reports, technical memorandums, articles from the bimonthly Deep Space Network Progress Report, special publications, and articles published in the open literature. The publications are indexed by author, subject, and publication type and number.

  8. Information Networking in Population Education.

    ERIC Educational Resources Information Center

    United Nations Educational, Scientific, and Cultural Organization, Bangkok (Thailand). Regional Office for Education in Asia and the Pacific.

    The rapidly increasing body of knowledge in population education has created the need for systematic and effective information services. Information networking entails sharing resources so that the information needs of all network participants are met. The goals of this manual are to: (1) instill in population education specialists a more…

  9. Information Networking and Economic Development.

    ERIC Educational Resources Information Center

    McGinn, Howard

    1987-01-01

    Infrastructures and competitiveness are considered in the context of library networking, library service, and economic development in the state of North Carolina. The North Carolina Information Network, a network developed and maintained by the state library and responsive to the needs of the nonlibrary community, is described. (MES)

  10. Higher category theory as a paradigm for network applications

    NASA Astrophysics Data System (ADS)

    Bonick, James R.

    2006-05-01

    The importance of network science to the present and future military is unquestioned. Networks of some type pervade every aspect of military operations-a situation that is shared by civilian society. However, several aspects of militarily oriented network science must be considered unique or given significantly greater emphasis than their civilian counterparts. Military, especially battlespace, networks must be mobile and robust. They must utilize diverse sensors moving in and out of the network. They must be able to survive various modes of attack and the destruction of large segments of their structure. Nodes often must pass on classifications made locally while other nodes must serve as combined sensor/classifiers or information coordinators. They must be capable of forming fluidly and in an ad hoc manner. In this paper, it will be shown how category theory, higher category theory, and topos theory provide just the model required by military network science. Category theory is a well-developed mathematical field that views mathematical structures abstractly, often revealing previously unnoticed correspondences. It has been used in database and software modeling, and in sensor and data fusion. It provides an advantage over other modeling formalisms both in its generality and in its extensive theory. Higher category theory extends the insights of category theory into higher dimensions, enhancing robustness. Topos theory was developed, in part, through the application of category theory to logic, but it also has geometric aspects. The motivation behind including topos theory in network science is the idea that a mathematical theory fundamental to geometry and logic should be applicable to the study of systems of spatially distributed information and analysis flow. The structures presented in this paper will have profound and far-reaching applications to military networks.

  11. Social Network Theory and Educational Change

    ERIC Educational Resources Information Center

    Daly, Alan J., Ed.

    2010-01-01

    "Social Network Theory and Educational Change" offers a provocative and fascinating exploration of how social networks in schools can impede or facilitate the work of education reform. Drawing on the work of leading scholars, the book comprises a series of studies examining networks among teachers and school leaders, contrasting formal and…

  12. Beyond mean field theory: statistical field theory for neural networks

    PubMed Central

    Buice, Michael A; Chow, Carson C

    2014-01-01

    Mean field theories have been a stalwart for studying the dynamics of networks of coupled neurons. They are convenient because they are relatively simple and possible to analyze. However, classical mean field theory neglects the effects of fluctuations and correlations due to single neuron effects. Here, we consider various possible approaches for going beyond mean field theory and incorporating correlation effects. Statistical field theory methods, in particular the Doi–Peliti–Janssen formalism, are particularly useful in this regard. PMID:25243014

  13. Information Horizons in Complex Networks

    NASA Astrophysics Data System (ADS)

    Sneppen, Kim

    2005-03-01

    We investigate how the structure constrain specific communication in social-, man-made and biological networks. We find that human networks of governance and collaboration are predictable on teat-a-teat level, reflecting well defined pathways, but globally inefficient (1). In contrast, the Internet tends to have better overall communication abilities, more alternative pathways, and is therefore more robust. Between these extremes are the molecular network of living organisms. Further, for most real world networks we find that communication ability is favored by topology on small distances, but disfavored at larger distances (2,3,4). We discuss the topological implications in terms of modularity and the positioning of hubs in the networks (5,6). Finally we introduce some simple models which demonstarte how communication may shape the structure of in particular man made networks (7,8). 1) K. Sneppen, A. Trusina, M. Rosvall (2004). Hide and seek on complex networks [cond-mat/0407055] 2) M. Rosvall, A. Trusina, P. Minnhagen and K. Sneppen (2004). Networks and Cities: An Information Perspective [cond-mat/0407054]. In PRL. 3) A. Trusina, M. Rosvall, K. Sneppen (2004). Information Horizons in Networks. [cond-mat/0412064] 4) M. Rosvall, P. Minnhagen, K. Sneppen (2004). Navigating Networks with Limited Information. [cond-mat/0412051] 5) S. Maslov and K. Sneppen (2002). Specificity and stability in topology of protein networks Science 296, 910-913 [cond-mat/0205380]. 6) A. Trusina, S. Maslov, P. Minnhagen, K. Sneppen Hierarchy Measures in Complex Networks. Phys. Rev. Lett. 92, 178702 [cond-mat/0308339]. 7) M. Rosvall and K. Sneppen (2003). Modeling Dynamics of Information Networks. Phys. Rev. Lett. 91, 178701 [cond-mat/0308399]. 8) B-J. Kim, A. Trusina, P. Minnhagen, K. Sneppen (2003). Self Organized Scale-Free Networks from Merging and Regeneration. nlin.AO/0403006. In European Journal of Physics.

  14. Dempster-Shafer theory and connections to information theory

    NASA Astrophysics Data System (ADS)

    Peri, Joseph S. J.

    2013-05-01

    The Dempster-Shafer theory is founded on probability theory. The entire machinery of probability theory, and that of measure theory, is at one's disposal for the understanding and the extension of the Dempster-Shafer theory. It is well known that information theory is also founded on probability theory. Claude Shannon developed, in the 1940's, the basic concepts of the theory and demonstrated their utility in communications and coding. Shannonian information theory is not, however, the only type of information theory. In the 1960's and 1970's, further developments in this field were made by French and Italian mathematicians. They developed information theory axiomatically, and discovered not only the Wiener- Shannon composition law, but also the hyperbolic law and the Inf-law. The objective of this paper is to demonstrate the mathematical connections between the Dempster Shafer theory and the various types of information theory. A simple engineering example will be used to demonstrate the utility of the concepts.

  15. Networking Theories by Iterative Unpacking

    ERIC Educational Resources Information Center

    Koichu, Boris

    2014-01-01

    An iterative unpacking strategy consists of sequencing empirically-based theoretical developments so that at each step of theorizing one theory serves as an overarching conceptual framework, in which another theory, either existing or emerging, is embedded in order to elaborate on the chosen element(s) of the overarching theory. The strategy is…

  16. Unification of quantum information theory

    NASA Astrophysics Data System (ADS)

    Abeyesinghe, Anura

    We present the unification of many previously disparate results in noisy quantum Shannon theory and the unification of all of noiseless quantum Shannon theory. More specifically we deal here with bipartite, unidirectional, and memoryless quantum Shannon theory. We find all the optimal protocols and quantify the relationship between the resources used, both for the one-shot and for the ensemble case, for what is arguably the most fundamental task in quantum information theory: sharing entangled states between a sender and a receiver. We find that all of these protocols are derived from our one-shot superdense coding protocol and relate nicely to each other. We then move on to noisy quantum information theory and give a simple, direct proof of the "mother" protocol, or rather her generalization to the Fully Quantum Slepian-Wolf protocol (FQSW). FQSW simultaneously accomplishes two goals: quantum communication-assisted entanglement distillation, and state transfer from the sender to the receiver. As a result, in addition to her other "children," the mother protocol generates the state merging primitive of Horodecki, Oppenheim, and Winter as well as a new class of distributed compression protocols for correlated quantum sources, which are optimal for sources described by separable density operators. Moreover, the mother protocol described here is easily transformed into the so-called "father" protocol, demonstrating that the division of single-sender/single-receiver protocols into two families was unnecessary: all protocols in the family are children of the mother.

  17. Building a Unified Information Network.

    ERIC Educational Resources Information Center

    Avram, Henriette D.

    1988-01-01

    Discusses cooperative efforts between research organizations and libraries to create a national information network. Topics discussed include the Linked System Project (LSP); technical processing versus reference and research functions; Open Systems Interconnection (OSI) Reference Model; the National Science Foundation Network (NSFNET); and…

  18. World-Wide Information Networks.

    ERIC Educational Resources Information Center

    Samuelson, Kjell A. H. W.

    The future paths of research and development towards world-wide, automated information networks in full operation are examined. From international networked planning and projects under way it appears that exploratory as well as normative approaches have been taken. To some extent adequate technolgical facilities have already come into existence…

  19. Analysis of surface-water data network in Kansas for effectiveness in providing regional streamflow information; with a section on theory and application of generalized least squares

    USGS Publications Warehouse

    Medina, K.D.; Tasker, Gary D.

    1987-01-01

    This report documents the results of an analysis of the surface-water data network in Kansas for its effectiveness in providing regional streamflow information. The network was analyzed using generalized least squares regression. The correlation and time-sampling error of the streamflow characteristic are considered in the generalized least squares method. Unregulated medium-, low-, and high-flow characteristics were selected to be representative of the regional information that can be obtained from streamflow-gaging-station records for use in evaluating the effectiveness of continuing the present network stations, discontinuing some stations, and (or) adding new stations. The analysis used streamflow records for all currently operated stations that were not affected by regulation and for discontinued stations for which unregulated flow characteristics, as well as physical and climatic characteristics, were available. The State was divided into three network areas, western, northeastern, and southeastern Kansas, and analysis was made for the three streamflow characteristics in each area, using three planning horizons. The analysis showed that the maximum reduction of sampling mean-square error for each cost level could be obtained by adding new stations and discontinuing some current network stations. Large reductions in sampling mean-square error for low-flow information could be achieved in all three network areas, the reduction in western Kansas being the most dramatic. The addition of new stations would be most beneficial for mean-flow information in western Kansas. The reduction of sampling mean-square error for high-flow information would benefit most from the addition of new stations in western Kansas. Southeastern Kansas showed the smallest error reduction in high-flow information. A comparison among all three network areas indicated that funding resources could be most effectively used by discontinuing more stations in northeastern and southeastern Kansas

  20. Weight-Control Information Network

    MedlinePlus

    ... Research Training & Career Development Grant programs for students, postdocs, and faculty Research at NIDDK Labs, faculty, and ... full list of resources . Alternate Language URL Weight-control Information Network (WIN) Page Content The Weight-control ...

  1. Network Information Management Subsystem

    NASA Technical Reports Server (NTRS)

    Chatburn, C. C.

    1985-01-01

    The Deep Space Network is implementing a distributed data base management system in which the data are shared among several applications and the host machines are not totally dedicated to a particular application. Since the data and resources are to be shared, the equipment must be operated carefully so that the resources are shared equitably. The current status of the project is discussed and policies, roles, and guidelines are recommended for the organizations involved in the project.

  2. Workplace Learning in Informal Networks

    ERIC Educational Resources Information Center

    Milligan, Colin; Littlejohn, Allison; Margaryan, Anoush

    2014-01-01

    Learning does not stop when an individual leaves formal education, but becomes increasingly informal, and deeply embedded within other activities such as work. This article describes the challenges of informal learning in knowledge intensive industries, highlighting the important role of personal learning networks. The article argues that…

  3. Information communication on complex networks

    NASA Astrophysics Data System (ADS)

    Igarashi, Akito; Kawamoto, Hiroki; Maruyama, Takahiro; Morioka, Atsushi; Naganuma, Yuki

    2013-02-01

    Since communication networks such as the Internet, which is regarded as a complex network, have recently become a huge scale and a lot of data pass through them, the improvement of packet routing strategies for transport is one of the most significant themes in the study of computer networks. It is especially important to find routing strategies which can bear as many traffic as possible without congestion in complex networks. First, using neural networks, we introduce a strategy for packet routing on complex networks, where path lengths and queue lengths in nodes are taken into account within a framework of statistical physics. Secondly, instead of using shortest paths, we propose efficient paths which avoid hubs, nodes with a great many degrees, on scale-free networks with a weight of each node. We improve the heuristic algorithm proposed by Danila et. al. which optimizes step by step routing properties on congestion by using the information of betweenness, the probability of paths passing through a node in all optimal paths which are defined according to a rule, and mitigates the congestion. We confirm the new heuristic algorithm which balances traffic on networks by achieving minimization of the maximum betweenness in much smaller number of iteration steps. Finally, We model virus spreading and data transfer on peer-to-peer (P2P) networks. Using mean-field approximation, we obtain an analytical formulation and emulate virus spreading on the network and compare the results with those of simulation. Moreover, we investigate the mitigation of information traffic congestion in the P2P networks.

  4. Queuing theory models for computer networks

    NASA Technical Reports Server (NTRS)

    Galant, David C.

    1989-01-01

    A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.

  5. Modern temporal network theory: a colloquium

    NASA Astrophysics Data System (ADS)

    Holme, Petter

    2015-09-01

    The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.

  6. Recoverability in quantum information theory

    NASA Astrophysics Data System (ADS)

    Wilde, Mark

    The fact that the quantum relative entropy is non-increasing with respect to quantum physical evolutions lies at the core of many optimality theorems in quantum information theory and has applications in other areas of physics. In this work, we establish improvements of this entropy inequality in the form of physically meaningful remainder terms. One of the main results can be summarized informally as follows: if the decrease in quantum relative entropy between two quantum states after a quantum physical evolution is relatively small, then it is possible to perform a recovery operation, such that one can perfectly recover one state while approximately recovering the other. This can be interpreted as quantifying how well one can reverse a quantum physical evolution. Our proof method is elementary, relying on the method of complex interpolation, basic linear algebra, and the recently introduced Renyi generalization of a relative entropy difference. The theorem has a number of applications in quantum information theory, which have to do with providing physically meaningful improvements to many known entropy inequalities. This is based on arXiv:1505.04661, now accepted for publication in Proceedings of the Royal Society A. I acknowledge support from startup funds from the Department of Physics and Astronomy at LSU, the NSF under Award No. CCF-1350397, and the DARPA Quiness Program through US Army Research Office award W31P4Q-12-1-0019.

  7. Information Theory, Inference and Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Mackay, David J. C.

    2003-10-01

    Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

  8. Whether information network supplements friendship network

    NASA Astrophysics Data System (ADS)

    Miao, Lili; Zhang, Qian-Ming; Nie, Da-Cheng; Cai, Shi-Min

    2015-02-01

    Homophily is a significant mechanism for link prediction in complex network, of which principle describes that people with similar profiles or experiences tend to tie with each other. In a multi-relationship network, friendship among people has been utilized to reinforce similarity of taste for recommendation system whose basic idea is similar to homophily, yet how the taste inversely affects friendship prediction is little discussed. This paper contributes to address the issue by analyzing two benchmark data sets both including user's behavioral information of taste and friendship based on the principle of homophily. It can be found that the creation of friendship tightly associates with personal taste. Especially, the behavioral information of taste involving with popular objects is much more effective to improve the performance of friendship prediction. However, this result seems to be contradictory to the finding in Zhang et al. (2013) that the behavior information of taste involving with popular objects is redundant in recommendation system. We thus discuss this inconformity to comprehensively understand the correlation between them.

  9. Towards a predictive theory for genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Tkacik, Gasper

    When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.

  10. Weight-Control Information Network (WIN)

    MedlinePlus

    ... Feature: Reducing Childhood Obesity The Weight-control Information Network (WIN) Past Issues / Spring - Summer 2010 Table of ... here are tips from the Weight-control Information Network (WIN), an information service of the National Institute ...

  11. Actor-Network Theory of Cosmopolitan Education

    ERIC Educational Resources Information Center

    Saito, Hiro

    2010-01-01

    In the past, philosophers discussed cosmopolitanism as a normative ideal of allegiance to humanity as a whole. A debate among social theorists, however, has examined cosmopolitanism as an incipient empirical phenomenon: an orientation of openness to foreign others and cultures. This paper introduces actor-network theory to elaborate the…

  12. Urban traffic-network performance: flow theory and simulation experiments

    SciTech Connect

    Williams, J.C.

    1986-01-01

    Performance models for urban street networks were developed to describe the response of a traffic network to given travel-demand levels. The three basic traffic flow variables, speed, flow, and concentration, are defined at the network level, and three model systems are proposed. Each system consists of a series of interrelated, consistent functions between the three basic traffic-flow variables as well as the fraction of stopped vehicles in the network. These models are subsequently compared with the results of microscopic simulation of a small test network. The sensitivity of one of the model systems to a variety of network features was also explored. Three categories of features were considered, with the specific features tested listed in parentheses: network topology (block length and street width), traffic control (traffic signal coordination), and traffic characteristics (level of inter-vehicular interaction). Finally, a fundamental issue concerning the estimation of two network-level parameters (from a nonlinear relation in the two-fluid theory) was examined. The principal concern was that of comparability of these parameters when estimated with information from a single vehicle (or small group of vehicles), as done in conjunction with previous field studies, and when estimated with network-level information (i.e., all the vehicles), as is possible with simulation.

  13. Polymer networks and gels: Simulation and theory

    NASA Astrophysics Data System (ADS)

    Kenkare, Nirupama Ramamurthy

    1998-12-01

    network pressure is treated as the sum of liquid-like and elastic components. The liquid-like component is obtained by extending the Generalized Flory-Dimer theory to networks, and the elastic component is obtained by treating the network as a set of interpenetrated tree-like structures and using a ideal chain-spring analogy to calculate the free energy. The theoretical predictions for network pressure are in very good agreement with simulation data. Our simulation results for the network chain properties show that the chain end-to-end vectors scale affinely with macroscopic deformation at large densities, but show a weaker-than-affine scaling at low densities. A combined discontinuous molecular dynamics and Monte Carlo simulation technique is used to study the swelling of trifunctional networks of chain lengths 20 and 35 in an athermal solvent. The swelling simulations are conducted under conditions of constant pressure and chemical potential. The gel packing fraction and solvent fraction at swelling equilibrium were found to increase with pressure as expected. We present a simple, analytical theory for gel swelling, grounded in our previous theoretical work for solvent-free networks. The predictions of this theory for the gel properties at swelling equilibrium show remarkably good agreement with simulation results.

  14. Research on invulnerability of equipment support information network

    NASA Astrophysics Data System (ADS)

    Sun, Xiao; Liu, Bin; Zhong, Qigen; Cao, Zhiyi

    2013-03-01

    In this paper, the entity composition of equipment support information network is studied, and the network abstract model is built. The influence factors of the invulnerability of equipment support information network are analyzed, and the invulnerability capabilities under random attack are analyzed. According to the centrality theory, the materiality evaluation centralities of the nodes are given, and the invulnerability capabilities under selective attack are analyzed. Finally, the reasons that restrict the invulnerability of equipment support information network are summarized, and the modified principles and methods are given.

  15. Information Network Model Query Processing

    NASA Astrophysics Data System (ADS)

    Song, Xiaopu

    Information Networking Model (INM) [31] is a novel database model for real world objects and relationships management. It naturally and directly supports various kinds of static and dynamic relationships between objects. In INM, objects are networked through various natural and complex relationships. INM Query Language (INM-QL) [30] is designed to explore such information network, retrieve information about schema, instance, their attributes, relationships, and context-dependent information, and process query results in the user specified form. INM database management system has been implemented using Berkeley DB, and it supports INM-QL. This thesis is mainly focused on the implementation of the subsystem that is able to effectively and efficiently process INM-QL. The subsystem provides a lexical and syntactical analyzer of INM-QL, and it is able to choose appropriate evaluation strategies and index mechanism to process queries in INM-QL without the user's intervention. It also uses intermediate result structure to hold intermediate query result and other helping structures to reduce complexity of query processing.

  16. Nonequilibrium landscape theory of neural networks

    PubMed Central

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-01-01

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

  17. Bridging genetic networks and queueing theory

    NASA Astrophysics Data System (ADS)

    Arazi, Arnon; Ben-Jacob, Eshel; Yechiali, Uri

    2004-02-01

    One of the main challenges facing biology today is the understanding of the joint action of genes, proteins and RNA molecules, interwoven in intricate interdependencies commonly known as genetic networks. To this end, several mathematical approaches have been introduced to date. In addition to developing the analytical tools required for this task anew, one can utilize knowledge found in existing disciplines, specializing in the representation and analysis of systems featuring similar aspects. We suggest queueing theory as a possible source of such knowledge. This discipline, which focuses on the study of workloads forming in a variety of scenarios, offers an assortment of tools allowing for the derivation of the statistical properties of the inspected systems. We argue that a proper adaptation of modeling techniques and analytical methods used in queueing theory can contribute to the study of genetic regulatory networks. This is demonstrated by presenting a queueing-inspired model of a genetic network of arbitrary size and structure, for which the probability distribution function is derived. This model is further applied to the description of the lac operon regulation mechanism. In addition, we discuss the possible benefits stemming for queueing theory from the interdisciplinary dialogue with molecular biology-in particular, the incorporation of various dynamical behaviours into queueing networks.

  18. Information Theory in Biology after 18 Years

    ERIC Educational Resources Information Center

    Johnson, Horton A.

    1970-01-01

    Reviews applications of information theory to biology, concluding that they have not proved very useful. Suggests modifications and extensions to increase the biological relevance of the theory, and speculates about applications in quantifying cell proliferation, chemical homeostasis and aging. (EB)

  19. Information Security and Privacy in Network Environments.

    ERIC Educational Resources Information Center

    Congress of the U.S., Washington, DC. Office of Technology Assessment.

    The use of information networks for business and government is expanding enormously. Government use of networks features prominently in plans to make government more efficient, effective, and responsive. But the transformation brought about by the networking also raises new concerns for the security and privacy of networked information. This…

  20. A novel approach to characterize information radiation in complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoyang; Wang, Ying; Zhu, Lin; Li, Chao

    2016-06-01

    The traditional research of information dissemination is mostly based on the virus spreading model that the information is being spread by probability, which does not match very well to the reality, because the information that we receive is always more or less than what was sent. In order to quantitatively describe variations in the amount of information during the spreading process, this article proposes a safety information radiation model on the basis of communication theory, combining with relevant theories of complex networks. This model comprehensively considers the various influence factors when safety information radiates in the network, and introduces some concepts from the communication theory perspective, such as the radiation gain function, receiving gain function, information retaining capacity and information second reception capacity, to describe the safety information radiation process between nodes and dynamically investigate the states of network nodes. On a micro level, this article analyzes the influence of various initial conditions and parameters on safety information radiation through the new model simulation. The simulation reveals that this novel approach can reflect the variation of safety information quantity of each node in the complex network, and the scale-free network has better "radiation explosive power", while the small-world network has better "radiation staying power". The results also show that it is efficient to improve the overall performance of network security by selecting nodes with high degrees as the information source, refining and simplifying the information, increasing the information second reception capacity and decreasing the noises. In a word, this article lays the foundation for further research on the interactions of information and energy between internal components within complex systems.

  1. Information transport in multiplex networks

    NASA Astrophysics Data System (ADS)

    Pu, Cunlai; Li, Siyuan; Yang, Xianxia; Yang, Jian; Wang, Kai

    2016-04-01

    In this paper, we study information transport in multiplex networks comprised of two coupled subnetworks. The upper subnetwork, called the logical layer, employs the shortest paths protocol to determine the logical paths for packets transmission, while the lower subnetwork acts as the physical layer, in which packets are delivered by the biased random walk mechanism characterized with a parameter α. Through simulation, we obtain the optimal α corresponding to the maximum network lifetime and the maximum number of the arrival packets. Assortative coupling is better than random coupling and disassortative coupling, since it achieves better transmission performance. Generally, the more homogeneous the lower subnetwork is, the better the transmission performance, which is the opposite for the upper subnetwork. Finally, we propose an attack centrality for nodes based on the topological information of both subnetworks, and investigate the transmission performance under targeted attacks. Our work aids in understanding the spread and robustness issues of multiplex networks and provides some clues about the design of more efficient and robust routing architectures in communication systems.

  2. Post Disaster Governance, Complexity and Network Theory

    PubMed Central

    Lassa, Jonatan A.

    2015-01-01

    This research aims to understand the organizational network typology of large­-scale disaster intervention in developing countries and to understand the complexity of post-­disaster intervention, through the use of network theory based on empirical data from post-­tsunami reconstruction in Aceh, Indonesia, during 2005/­2007. The findings suggest that the ‘ degrees of separation’ (or network diameter) between any two organizations in the field is 5, thus reflecting ‘small­ world’ realities and therefore making no significant difference with the real human networks, as found in previous experiments. There are also significant loops in the network reflecting the fact that some actors tend to not cooperate, which challenges post­ disaster coordination. The findings show the landscape of humanitarian actors is not randomly distributed. Many actors were connected to each other through certain hubs, while hundreds of actors make ‘scattered’ single ‘principal-­client’ links. The paper concludes that by understanding the distribution of degree, centrality, ‘degrees of separation’ and visualization of the network, authorities can improve their understanding of the realities of coordination, from macro to micro scales. PMID:26236562

  3. Network theory and its applications in economic systems

    NASA Astrophysics Data System (ADS)

    Huang, Xuqing

    This dissertation covers the two major parts of my Ph.D. research: i) developing theoretical framework of complex networks; and ii) applying complex networks models to quantitatively analyze economics systems. In part I, we focus on developing theories of interdependent networks, which includes two chapters: 1) We develop a mathematical framework to study the percolation of interdependent networks under targeted-attack and find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold pc = 0, coupled SF networks are significantly more vulnerable with pc significantly larger than zero. 2) We analytically demonstrates that clustering, which quantifies the propensity for two neighbors of the same vertex to also be neighbors of each other, significantly increases the vulnerability of the system. In part II, we apply the complex networks models to study economics systems, which also includes two chapters: 1) We study the US corporate governance network, in which nodes representing directors and links between two directors representing their service on common company boards, and propose a quantitative measure of information and influence transformation in the network. Thus we are able to identify the most influential directors in the network. 2) We propose a bipartite networks model to simulate the risk propagation process among commercial banks during financial crisis. With empirical bank's balance sheet data in 2007 as input to the model, we find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation during the financial crisis between 2008 and 2011. The results suggest that complex networks model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the

  4. Theory of correlations in stochastic neural networks

    NASA Astrophysics Data System (ADS)

    Ginzburg, Iris; Sompolinsky, Haim

    1994-10-01

    One of the main experimental tools in probing the interactions between neurons has been the measurement of the correlations in their activity. In general, however, the interpretation of the observed correlations is difficult since the correlation between a pair of neurons is influenced not only by the direct interaction between them but also by the dynamic state of the entire network to which they belong. Thus a comparison between the observed correlations and the predictions from specific model networks is needed. In this paper we develop a theory of neuronal correlation functions in large networks comprising several highly connected subpopulations and obeying stochastic dynamic rules. When the networks are in asynchronous states, the cross correlations are relatively weak, i.e., their amplitude relative to that of the autocorrelations is of order of 1/N, N being the size of the interacting populations. Using the weakness of the cross correlations, general equations that express the matrix of cross correlations in terms of the mean neuronal activities and the effective interaction matrix are presented. The effective interactions are the synaptic efficacies multiplied by the gain of the postsynaptic neurons. The time-delayed cross-correlation matrix can be expressed as a sum of exponentially decaying modes that correspond to the (nonorthogonal) eigenvectors of the effective interaction matrix. The theory is extended to networks with random connectivity, such as randomly dilute networks. This allows for a comparison between the contribution from the internal common input and that from the direct interactions to the correlations of monosynaptically coupled pairs. A closely related quantity is the linear response of the neurons to external time-dependent perturbations. We derive the form of the dynamic linear response function of neurons in the above architecture in terms of the eigenmodes of the effective interaction matrix. The behavior of the correlations and the

  5. The transfer and transformation of collective network information in gene-matched networks

    PubMed Central

    Kitsukawa, Takashi; Yagi, Takeshi

    2015-01-01

    Networks, such as the human society network, social and professional networks, and biological system networks, contain vast amounts of information. Information signals in networks are distributed over nodes and transmitted through intricately wired links, making the transfer and transformation of such information difficult to follow. Here we introduce a novel method for describing network information and its transfer using a model network, the Gene-matched network (GMN), in which nodes (neurons) possess attributes (genes). In the GMN, nodes are connected according to their expression of common genes. Because neurons have multiple genes, the GMN is cluster-rich. We show that, in the GMN, information transfer and transformation were controlled systematically, according to the activity level of the network. Furthermore, information transfer and transformation could be traced numerically with a vector using genes expressed in the activated neurons, the active-gene array, which was used to assess the relative activity among overlapping neuronal groups. Interestingly, this coding style closely resembles the cell-assembly neural coding theory. The method introduced here could be applied to many real-world networks, since many systems, including human society and various biological systems, can be represented as a network of this type. PMID:26450411

  6. The transfer and transformation of collective network information in gene-matched networks.

    PubMed

    Kitsukawa, Takashi; Yagi, Takeshi

    2015-01-01

    Networks, such as the human society network, social and professional networks, and biological system networks, contain vast amounts of information. Information signals in networks are distributed over nodes and transmitted through intricately wired links, making the transfer and transformation of such information difficult to follow. Here we introduce a novel method for describing network information and its transfer using a model network, the Gene-matched network (GMN), in which nodes (neurons) possess attributes (genes). In the GMN, nodes are connected according to their expression of common genes. Because neurons have multiple genes, the GMN is cluster-rich. We show that, in the GMN, information transfer and transformation were controlled systematically, according to the activity level of the network. Furthermore, information transfer and transformation could be traced numerically with a vector using genes expressed in the activated neurons, the active-gene array, which was used to assess the relative activity among overlapping neuronal groups. Interestingly, this coding style closely resembles the cell-assembly neural coding theory. The method introduced here could be applied to many real-world networks, since many systems, including human society and various biological systems, can be represented as a network of this type. PMID:26450411

  7. Local Area Networks for Information Retrieval.

    ERIC Educational Resources Information Center

    Kibirige, Harry M.

    This examination of the use of local area networks (LANs) by libraries summarizes the findings of a nationwide survey of 600 libraries and information centers and 200 microcomputer networking system manufacturers and vendors, which was conducted to determine the relevance of currently available networking systems for library and information center…

  8. Computer-Based Information Networks: Selected Examples.

    ERIC Educational Resources Information Center

    Hardesty, Larry

    The history, purpose, and operation of six computer-based information networks are described in general and nontechnical terms. In the introduction the many definitions of an information network are explored. Ohio College Library Center's network (OCLC) is the first example. OCLC began in 1963, and since early 1973 has been extending its services…

  9. Pathways, Networks, and Systems: Theory and Experiments

    SciTech Connect

    Joseph H. Nadeau; John D. Lambris

    2004-10-30

    The international conference provided a unique opportunity for theoreticians and experimenters to exchange ideas, strategies, problems, challenges, language and opportunities in both formal and informal settings. This dialog is an important step towards developing a deep and effective integration of theory and experiments in studies of systems biology in humans and model organisms.

  10. Dempster-Shafer theory and connections to Choquet's theory of capacities and information theory

    NASA Astrophysics Data System (ADS)

    Peri, Joseph S. J.

    2014-06-01

    The axiomatic development of information theory, during the 1960's, led to the discovery of various composition laws. The Wiener-Shannon law is well understood, but the Inf law holds particular interest because it creates a connection with the Dempster-Shafer theory. Proceeding along these lines, in a previous paper, I demonstrated the connection between the Dempster-Shafer theory and Information theory. In 1954, Gustave Choquet developed the theory of capacities in connection with potential theory. The basic concepts of capacity theory arise from electrostatics, but a capacity is a generalization of the concept of measure in Analysis. It is well known that Belief and Plausibility in the Dempster-Shafer theory are Choquet capacities. However, it is not well known that the inverse of an information measure is a Choquet capacity. The objective of this paper is to demonstrate the connections among the Dempster- Shafer theory, Information theory and Choquet's theory of capacities.

  11. Dynamic information routing in complex networks

    PubMed Central

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-01-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

  12. Dynamic information routing in complex networks.

    PubMed

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-01-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

  13. Dynamic information routing in complex networks

    NASA Astrophysics Data System (ADS)

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-04-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.

  14. Automata network theories in immunology: their utility and their underdetermination.

    PubMed

    Atlan, H

    1989-01-01

    Small networks of threshold automata are used to model complex interactions between populations of regulatory cells (helpers and suppressors, antigen specific and anti-idiotypic) which participate in the immune response. The models, being discrete and semiquantitative, are well adapted to the situation of incomplete information often encountered in vivo. However, the dynamics of many different network structures usually end up in the same attractor set. Thus, many different theories are equivalent in their explicative power for the same facts. This property, known as underdetermination of the theories by the facts, is given a quantitative estimate. It appears that such an underdetermination, as a kind of irreducible complexity, can be expected in many in vivo biological processes, even when the number of interacting and functionally coupled elements is relatively small. PMID:2924021

  15. Maximizing information exchange between complex networks

    NASA Astrophysics Data System (ADS)

    West, Bruce J.; Geneston, Elvis L.; Grigolini, Paolo

    2008-10-01

    Science is not merely the smooth progressive interaction of hypothesis, experiment and theory, although it sometimes has that form. More realistically the scientific study of any given complex phenomenon generates a number of explanations, from a variety of perspectives, that eventually requires synthesis to achieve a deep level of insight and understanding. One such synthesis has created the field of out-of-equilibrium statistical physics as applied to the understanding of complex dynamic networks. Over the past forty years the concept of complexity has undergone a metamorphosis. Complexity was originally seen as a consequence of memory in individual particle trajectories, in full agreement with a Hamiltonian picture of microscopic dynamics and, in principle, macroscopic dynamics could be derived from the microscopic Hamiltonian picture. The main difficulty in deriving macroscopic dynamics from microscopic dynamics is the need to take into account the actions of a very large number of components. The existence of events such as abrupt jumps, considered by the conventional continuous time random walk approach to describing complexity was never perceived as conflicting with the Hamiltonian view. Herein we review many of the reasons why this traditional Hamiltonian view of complexity is unsatisfactory. We show that as a result of technological advances, which make the observation of single elementary events possible, the definition of complexity has shifted from the conventional memory concept towards the action of non-Poisson renewal events. We show that the observation of crucial processes, such as the intermittent fluorescence of blinking quantum dots as well as the brain’s response to music, as monitored by a set of electrodes attached to the scalp, has forced investigators to go beyond the traditional concept of complexity and to establish closer contact with the nascent field of complex networks. Complex networks form one of the most challenging areas of

  16. Towards understanding the behavior of physical systems using information theory

    NASA Astrophysics Data System (ADS)

    Quax, Rick; Apolloni, Andrea; Sloot, Peter M. A.

    2013-09-01

    One of the goals of complex network analysis is to identify the most influential nodes, i.e., the nodes that dictate the dynamics of other nodes. In the case of autonomous systems or transportation networks, highly connected hubs play a preeminent role in diffusing the flow of information and viruses; in contrast, in language evolution most linguistic norms come from the peripheral nodes who have only few contacts. Clearly a topological analysis of the interactions alone is not sufficient to identify the nodes that drive the state of the network. Here we show how information theory can be used to quantify how the dynamics of individual nodes propagate through a system. We interpret the state of a node as a storage of information about the state of other nodes, which is quantified in terms of Shannon information. This information is transferred through interactions and lost due to noise, and we calculate how far it can travel through a network. We apply this concept to a model of opinion formation in a complex social network to calculate the impact of each node by measuring how long its opinion is remembered by the network. Counter-intuitively we find that the dynamics of opinions are not determined by the hubs or peripheral nodes, but rather by nodes with an intermediate connectivity.

  17. Link Prediction in Complex Networks: A Mutual Information Perspective

    PubMed Central

    Tan, Fei; Xia, Yongxiang; Zhu, Boyao

    2014-01-01

    Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity. PMID:25207920

  18. Stoichiometric network theory for nonequilibrium biochemical systems.

    PubMed

    Qian, Hong; Beard, Daniel A; Liang, Shou-dan

    2003-02-01

    We introduce the basic concepts and develop a theory for nonequilibrium steady-state biochemical systems applicable to analyzing large-scale complex isothermal reaction networks. In terms of the stoichiometric matrix, we demonstrate both Kirchhoff's flux law sigma(l)J(l)=0 over a biochemical species, and potential law sigma(l) mu(l)=0 over a reaction loop. They reflect mass and energy conservation, respectively. For each reaction, its steady-state flux J can be decomposed into forward and backward one-way fluxes J = J+ - J-, with chemical potential difference deltamu = RT ln(J-/J+). The product -Jdeltamu gives the isothermal heat dissipation rate, which is necessarily non-negative according to the second law of thermodynamics. The stoichiometric network theory (SNT) embodies all of the relevant fundamental physics. Knowing J and deltamu of a biochemical reaction, a conductance can be computed which directly reflects the level of gene expression for the particular enzyme. For sufficiently small flux a linear relationship between J and deltamu can be established as the linear flux-force relation in irreversible thermodynamics, analogous to Ohm's law in electrical circuits. PMID:12542691

  19. A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

    NASA Technical Reports Server (NTRS)

    Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher

    2005-01-01

    This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

  20. A security architecture for health information networks.

    PubMed

    Kailar, Rajashekar; Muralidhar, Vinod

    2007-01-01

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today's healthcare enterprise. Recent work on 'nationwide health information network' architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately. PMID:18693862

  1. Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks.

    PubMed

    Garofalo, Matteo; Nieus, Thierry; Massobrio, Paolo; Martinoia, Sergio

    2009-01-01

    Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these "connectivity methods" on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings. PMID:19652720

  2. Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory.

    PubMed

    Greenhalgh, Trisha; Stones, Rob

    2010-05-01

    The UK National Health Service is grappling with various large and controversial IT programmes. We sought to develop a sharper theoretical perspective on the question "What happens - at macro-, meso- and micro-level - when government tries to modernise a health service with the help of big IT?" Using examples from data fragments at the micro-level of clinical work, we considered how structuration theory and actor-network theory (ANT) might be combined to inform empirical investigation. Giddens (1984) argued that social structures and human agency are recursively linked and co-evolve. ANT studies the relationships that link people and technologies in dynamic networks. It considers how discourses become inscribed in data structures and decision models of software, making certain network relations irreversible. Stones' (2005) strong structuration theory (SST) is a refinement of Giddens' work, systematically concerned with empirical research. It views human agents as linked in dynamic networks of position-practices. A quadripartite approcach considers [a] external social structures (conditions for action); [b] internal social structures (agents' capabilities and what they 'know' about the social world); [c] active agency and actions and [d] outcomes as they feed back on the position-practice network. In contrast to early structuration theory and ANT, SST insists on disciplined conceptual methodology and linking this with empirical evidence. In this paper, we adapt SST for the study of technology programmes, integrating elements from material interactionism and ANT. We argue, for example, that the position-practice network can be a socio-technical one in which technologies in conjunction with humans can be studied as 'actants'. Human agents, with their complex socio-cultural frames, are required to instantiate technology in social practices. Structurally relevant properties inscribed and embedded in technological artefacts constrain and enable human agency. The fortunes

  3. Tsallis information dimension of complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Qi; Luo, Chuanhai; Li, Meizhu; Deng, Yong; Mahadevan, Sankaran

    2015-02-01

    The fractal and self-similarity properties are revealed in many complex networks. The information dimension is a useful method to describe the fractal and self-similarity properties of the complex networks. In order to show the influence of different parts in the complex networks to the information dimension, we have proposed a new information dimension based on the Tsallis entropy namely the Tsallis information dimension. The proposed information dimension is changed according to the scale which is described by the nonextensivity parameter q, and it is inverse with the nonextensivity parameter q. The existing information dimension is a special case of the Tsallis information dimension when q = 1. The Tsallis information dimension is a generalized information dimension of the complex networks.

  4. Modeling information flow in biological networks

    NASA Astrophysics Data System (ADS)

    Kim, Yoo-Ah; Przytycki, Jozef H.; Wuchty, Stefan; Przytycka, Teresa M.

    2011-06-01

    Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method.

  5. Information jet: Handling noisy big data from weakly disconnected network

    NASA Astrophysics Data System (ADS)

    Aurongzeb, Deeder

    Sudden aggregation (information jet) of large amount of data is ubiquitous around connected social networks, driven by sudden interacting and non-interacting events, network security threat attacks, online sales channel etc. Clustering of information jet based on time series analysis and graph theory is not new but little work is done to connect them with particle jet statistics. We show pre-clustering based on context can element soft network or network of information which is critical to minimize time to calculate results from noisy big data. We show difference between, stochastic gradient boosting and time series-graph clustering. For disconnected higher dimensional information jet, we use Kallenberg representation theorem (Kallenberg, 2005, arXiv:1401.1137) to identify and eliminate jet similarities from dense or sparse graph.

  6. Information theory and the earth's density distribution

    NASA Technical Reports Server (NTRS)

    Rubincam, D. P.

    1978-01-01

    The present paper argues for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia leads to a density distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.

  7. Information Theory and the Earth's Density Distribution

    NASA Technical Reports Server (NTRS)

    Rubincam, D. P.

    1979-01-01

    An argument for using the information theory approach as an inference technique in solid earth geophysics. A spherically symmetric density distribution is derived as an example of the method. A simple model of the earth plus knowledge of its mass and moment of inertia lead to a density distribution which was surprisingly close to the optimum distribution. Future directions for the information theory approach in solid earth geophysics as well as its strengths and weaknesses are discussed.

  8. Predicting Information Flows in Network Traffic.

    ERIC Educational Resources Information Center

    Hinich, Melvin J.; Molyneux, Robert E.

    2003-01-01

    Discusses information flow in networks and predicting network traffic and describes a study that uses time series analysis on a day's worth of Internet log data. Examines nonlinearity and traffic invariants, and suggests that prediction of network traffic may not be possible with current techniques. (Author/LRW)

  9. Biological impacts and context of network theory

    SciTech Connect

    Almaas, E

    2007-01-05

    Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World-Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory-, signal transduction-, protein interaction- and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.

  10. The Social Side of Information Networking.

    ERIC Educational Resources Information Center

    Katz, James E.

    1997-01-01

    Explores the social issues, including manners, security, crime (fraud), and social control associated with information networking, with emphasis on the Internet. Also addresses the influence of cellular phones, the Internet and other information technologies on society. (GR)

  11. Basing quantum theory on information processing

    NASA Astrophysics Data System (ADS)

    Barnum, Howard

    2008-03-01

    I consider information-based derivations of the quantum formalism, in a framework encompassing quantum and classical theory and a broad spectrum of theories serving as foils to them. The most ambitious hope for such a derivation is a role analogous to Einstein's development of the dynamics and kinetics of macroscopic bodies, and later of their gravitational interactions, on the basis of simple principles with clear operational meanings and experimental consequences. Short of this, it could still provide a principled understanding of the features of quantum mechanics that account for its greater-than-classical information-processing power, helping guide the search for new quantum algorithms and protocols. I summarize the convex operational framework for theories, and discuss information-processing in theories therein. Results include the fact that information that can be obtained without disturbance is inherently classical, generalized no-cloning and no-broadcasting theorems, exponentially secure bit commitment in all non-classical theories without entanglement, properties of theories that allow teleportation, and properties of theories that allow ``remote steering'' of ensembles using entanglement. Joint work with collaborators including Jonathan Barrett, Matthew Leifer, Alexander Wilce, Oscar Dahlsten, and Ben Toner.

  12. The application of information theory to biochemical signaling systems

    PubMed Central

    Rhee, Alex; Cheong, Raymond; Levchenko, Andre

    2012-01-01

    Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell’s signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon’s information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling. PMID:22872091

  13. Impact of imperfect information on network attack

    NASA Astrophysics Data System (ADS)

    Melchionna, Andrew; Caloca, Jesus; Squires, Shane; Antonsen, Thomas M.; Ott, Edward; Girvan, Michelle

    2015-03-01

    This paper explores the effectiveness of network attack when the attacker has imperfect information about the network. For Erdős-Rényi networks, we observe that dynamical importance and betweenness centrality-based attacks are surprisingly robust to the presence of a moderate amount of imperfect information and are more effective compared with simpler degree-based attacks even at moderate levels of network information error. In contrast, for scale-free networks the effectiveness of attack is much less degraded by a moderate level of information error. Furthermore, in the Erdős-Rényi case the effectiveness of network attack is much more degraded by missing links as compared with the same number of false links.

  14. Impact of imperfect information on network attack.

    PubMed

    Melchionna, Andrew; Caloca, Jesus; Squires, Shane; Antonsen, Thomas M; Ott, Edward; Girvan, Michelle

    2015-03-01

    This paper explores the effectiveness of network attack when the attacker has imperfect information about the network. For Erdős-Rényi networks, we observe that dynamical importance and betweenness centrality-based attacks are surprisingly robust to the presence of a moderate amount of imperfect information and are more effective compared with simpler degree-based attacks even at moderate levels of network information error. In contrast, for scale-free networks the effectiveness of attack is much less degraded by a moderate level of information error. Furthermore, in the Erdős-Rényi case the effectiveness of network attack is much more degraded by missing links as compared with the same number of false links. PMID:25871157

  15. Conditioned reinforcement and information theory reconsidered.

    PubMed

    Shahan, Timothy A; Cunningham, Paul

    2015-03-01

    The idea that stimuli might function as conditioned reinforcers because of the information they convey about primary reinforcers has a long history in the study of learning. However, formal application of information theory to conditioned reinforcement has been largely abandoned in modern theorizing because of its failures with respect to observing behavior. In this paper we show how recent advances in the application of information theory to Pavlovian conditioning offer a novel approach to conditioned reinforcement. The critical feature of this approach is that calculations of information are based on reductions of uncertainty about expected time to primary reinforcement signaled by a conditioned reinforcer. Using this approach, we show that previous failures of information theory with observing behavior can be remedied, and that the resulting framework produces predictions similar to Delay Reduction Theory in both observing-response and concurrent-chains procedures. We suggest that the similarity of these predictions might offer an analytically grounded reason for why Delay Reduction Theory has been a successful theory of conditioned reinforcement. Finally, we suggest that the approach provides a formal basis for the assertion that conditioned reinforcement results from Pavlovian conditioning and may provide an integrative approach encompassing both domains. PMID:25766452

  16. Information Services in the International Network Marketplace.

    ERIC Educational Resources Information Center

    Hepworth, Mark E.

    1987-01-01

    Examines the internationalism of the network marketplace through case studies of the London Stock Exchange and I. P. Sharp Associates, a Canadian computer service bureau. Discussion focuses on the importance of transnational computer networks to the production of information services and marketplace expansion, and global information policy issues.…

  17. Informational Benefits via Knowledge Networks among Farmers

    ERIC Educational Resources Information Center

    Sligo, F. X.; Massey, Claire; Lewis, Kate

    2005-01-01

    Purpose: This research aimed to obtain insights into how farmers on small and medium-sized farms perceived the benefits of the information they receive from their interpersonal networks and other sources. Design/methodology/approach: Farmers' information environments were explored using socio-spatial knowledge networks, diaries and in-depth…

  18. Information Processing Theory and Conceptual Development.

    ERIC Educational Resources Information Center

    Schroder, H. M.

    An educational program based upon information processing theory has been developed at Southern Illinois University. The integrating theme was the development of conceptual ability for coping with social and personal problems. It utilized student information search and concept formation as foundations for discussion and judgment and was organized…

  19. Using actor network theory to understand network centric healthcare operations.

    PubMed

    Wickramasinghe, Nilmini; Bali, Rajeev K; Tatnall, Arthur

    2007-01-01

    The adoption and diffusion of e-health and the application of ICT in healthcare is being heralded as the panacea with both European and US governments making e-health a priority on their agendas. In this context, a model of networkcentric healthcare operations has been proffered as the best way to maximise the benefits of ICT use in healthcare. We suggest that, before we can move forward and realise such a state, it is vital to examine the critical issues, likely barriers and facilitators and, most importantly, the critical success factors. To do this however, we need an appropriate cognitive lens through which we can capture all the complexities of healthcare dynamics. In this paper we suggest why Actor Network Theory (ANT) should be this lens. PMID:18048305

  20. Connectivism and Information Literacy: Moving from Learning Theory to Pedagogical Practice

    ERIC Educational Resources Information Center

    Transue, Beth M.

    2013-01-01

    Connectivism is an emerging learning theory positing that knowledge comprises networked relationships and that learning comprises the ability to successfully navigate through these networks. Successful pedagogical strategies involve the instructor helping students to identify, navigate, and evaluate information from their learning networks. Many…

  1. A game theory model of urban public traffic networks

    NASA Astrophysics Data System (ADS)

    Su, B. B.; Chang, H.; Chen, Y.-Z.; He, D. R.

    2007-06-01

    We have studied urban public traffic networks from the viewpoint of complex networks and game theory. Firstly, we have empirically investigated an urban public traffic network in Beijing in 2003, and obtained its statistical properties. Then a simplified game theory model is proposed for simulating the evolution of the traffic network. The basic idea is that three network manipulators, passengers, an urban public traffic company, and a government traffic management agency, play games in a network evolution process. Each manipulator tries to build the traffic lines to magnify its “benefit”. Simulation results show a good qualitative agreement with the empirical results.

  2. Noise enhances information transfer in hierarchical networks.

    PubMed

    Czaplicka, Agnieszka; Holyst, Janusz A; Sloot, Peter M A

    2013-01-01

    We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. PMID:23390574

  3. Noise enhances information transfer in hierarchical networks

    PubMed Central

    Czaplicka, Agnieszka; Holyst, Janusz A.; Sloot, Peter M. A.

    2013-01-01

    We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. PMID:23390574

  4. Reasonable fermionic quantum information theories require relativity

    NASA Astrophysics Data System (ADS)

    Friis, Nicolai

    2016-03-01

    We show that any quantum information theory based on anticommuting operators must be supplemented by a superselection rule deeply rooted in relativity to establish a reasonable notion of entanglement. While quantum information may be encoded in the fermionic Fock space, the unrestricted theory has a peculiar feature: the marginals of bipartite pure states need not have identical entropies, which leads to an ambiguous definition of entanglement. We solve this problem, by proving that it is removed by relativity, i.e., by the parity superselection rule that arises from Lorentz invariance via the spin-statistics connection. Our results hence unveil a fundamental conceptual inseparability of quantum information and the causal structure of relativistic field theory.

  5. How Might Better Network Theories Support School Leadership Research?

    ERIC Educational Resources Information Center

    Hadfield, Mark; Jopling, Michael

    2012-01-01

    This article explores how recent research in education has applied different aspects of "network" theory to the study of school leadership. Constructs from different network theories are often used because of their perceived potential to clarify two perennial issues in leadership research. The first is the relative importance of formal and…

  6. Hierarchical social networks and information flow

    NASA Astrophysics Data System (ADS)

    López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.

    2002-12-01

    Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.

  7. Nonlinear adaptive networks: A little theory, a few applications

    SciTech Connect

    Jones, R.D.; Qian, S.; Barnes, C.W.; Bisset, K.R.; Bruce, G.M.; Lee, K.; Lee, L.A.; Mead, W.C.; O'Rourke, M.K.; Thode, L.E. ); Lee, Y.C.; Flake, G.W. Maryland Univ., College Park, MD ); Poli, I.J. Bologna Univ. )

    1990-01-01

    We present the theory of nonlinear adaptive networks and discuss a few applications. In particular, we review the theory of feedforward backpropagation networks. We than present the theory of the Connectionist Normalized Linear Spline network in both its feedforward and iterated modes. Also, we briefly discuss the theory of stochastic cellular automata. We then discuss applications to chaotic time series tidal prediction in Venice Lagoon, sonar transient detection, control of nonlinear processes, balancing a double inverted pendulum and design advice for free electron lasers. 26 refs., 23 figs.

  8. An anti-attack model based on complex network theory in P2P networks

    NASA Astrophysics Data System (ADS)

    Peng, Hao; Lu, Songnian; Zhao, Dandan; Zhang, Aixin; Li, Jianhua

    2012-04-01

    Complex network theory is a useful way to study many real systems. In this paper, an anti-attack model based on complex network theory is introduced. The mechanism of this model is based on a dynamic compensation process and a reverse percolation process in P2P networks. The main purpose of the paper is: (i) a dynamic compensation process can turn an attacked P2P network into a power-law (PL) network with exponential cutoff; (ii) a local healing process can restore the maximum degree of peers in an attacked P2P network to a normal level; (iii) a restoring process based on reverse percolation theory connects the fragmentary peers of an attacked P2P network together into a giant connected component. In this way, the model based on complex network theory can be effectively utilized for anti-attack and protection purposes in P2P networks.

  9. The Embedded Self: A Social Networks Approach to Identity Theory

    ERIC Educational Resources Information Center

    Walker, Mark H.; Lynn, Freda B.

    2013-01-01

    Despite the fact that key sociological theories of self and identity view the self as fundamentally rooted in networks of interpersonal relationships, empirical research investigating how personal network structure influences the self is conspicuously lacking. To address this gap, we examine links between network structure and role identity…

  10. Network anomaly detection system with optimized DS evidence theory.

    PubMed

    Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

    2014-01-01

    Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

  11. The Teen Health Information Network (THINK).

    ERIC Educational Resources Information Center

    Kuzel, Judith; Erickson, Su

    1995-01-01

    Discusses the Teen Health Information Network (THINK), a grant-funded partnership of Aurora, Illinois, public libraries, schools, and community agencies to provide materials, information, and programming on issues related to teen health. Seven appendixes provide detailed information on survey results, collection evaluation and development,…

  12. Equity trees and graphs via information theory

    NASA Astrophysics Data System (ADS)

    Harré, M.; Bossomaier, T.

    2010-01-01

    We investigate the similarities and differences between two measures of the relationship between equities traded in financial markets. Our measures are the correlation coefficients and the mutual information. In the context of financial markets correlation coefficients are well established whereas mutual information has not previously been as well studied despite its theoretically appealing properties. We show that asset trees which are derived from either the correlation coefficients or the mutual information have a mixture of both similarities and differences at the individual equity level and at the macroscopic level. We then extend our consideration from trees to graphs using the "genus 0" condition recently introduced in order to study the networks of equities.

  13. A Network for Physics Information.

    ERIC Educational Resources Information Center

    Koch, H. William; Herschman, Arthur

    The American Institute of Physics is working toward the development of a national information system for physics, whose objective is the organization of the flow of physics information from the producers to the users. The complete physics information system has several constituent subsystems, among which are: one for the management of the flow of…

  14. Engaging Theories and Models to Inform Practice

    ERIC Educational Resources Information Center

    Kraus, Amanda

    2012-01-01

    Helping students prepare for the complex transition to life after graduation is an important responsibility shared by those in student affairs and others in higher education. This chapter explores theories and models that can inform student affairs practitioners and faculty in preparing students for life after college. The focus is on roles,…

  15. BOULDER AREA SUSTAINABILITY INFORMATION NETWORK (BASIN)

    EPA Science Inventory

    The primary goal of the Boulder Area Sustainability Information Network (BASIN) is to help citizens make meaningful connections between environmental data and their day-to-day activities and facilitate involvement in public policy development. Objectives include:

      ...

    • Pain: A Distributed Brain Information Network?

      PubMed Central

      Mano, Hiroaki; Seymour, Ben

      2015-01-01

      Understanding how pain is processed in the brain has been an enduring puzzle, because there doesn't appear to be a single “pain cortex” that directly codes the subjective perception of pain. An emerging concept is that, instead, pain might emerge from the coordinated activity of an integrated brain network. In support of this view, Woo and colleagues present evidence that distinct brain networks support the subjective changes in pain that result from nociceptive input and self-directed cognitive modulation. This evidence for the sensitivity of distinct neural subsystems to different aspects of pain opens up the way to more formal computational network theories of pain. PMID:25562782

    • 78 FR 17418 - Rural Health Information Technology Network Development Grant

      Federal Register 2010, 2011, 2012, 2013, 2014

      2013-03-21

      ... HUMAN SERVICES Health Resources and Services Administration Rural Health Information Technology Network... award under the Rural Health Information Technology Network Development Grant (RHITND) to Grace... relinquishing its fiduciary responsibilities for the Rural Health Information Technology Network...

    • Origin of cells and network information

      PubMed Central

      Tanabe, Shihori

      2015-01-01

      All cells are derived from one cell, and the origin of different cell types is a subject of curiosity. Cells construct life through appropriately timed networks at each stage of development. Communication among cells and intracellular signaling are essential for cell differentiation and for life processes. Cellular molecular networks establish cell diversity and life. The investigation of the regulation of each gene in the genome within the cellular network is therefore of interest. Stem cells produce various cells that are suitable for specific purposes. The dynamics of the information in the cellular network changes as the status of cells is altered. The components of each cell are subject to investigation. PMID:25914760

    • A Preliminary Theory of Dark Network Resilience

      ERIC Educational Resources Information Center

      Bakker, Rene M.; Raab, Jorg; Milward, H. Brinton

      2012-01-01

      A crucial contemporary policy question for governments across the globe is how to cope with international crime and terrorist networks. Many such "dark" networks--that is, networks that operate covertly and illegally--display a remarkable level of resilience when faced with shocks and attacks. Based on an in-depth study of three cases (MK, the…

    • Reinforce Networking Theory with OPNET Simulation

      ERIC Educational Resources Information Center

      Guo, Jinhua; Xiang, Weidong; Wang, Shengquan

      2007-01-01

      As networking systems have become more complex and expensive, hands-on experiments based on networking simulation have become essential for teaching the key computer networking topics to students. The simulation approach is the most cost effective and highly useful because it provides a virtual environment for an assortment of desirable features…

  1. Insights into the organization of biochemical regulatory networks using graph theory analyses.

    PubMed

    Ma'ayan, Avi

    2009-02-27

    Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks. PMID:18940806

  2. Network Anomaly Detection System with Optimized DS Evidence Theory

    PubMed Central

    Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

    2014-01-01

    Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

  3. An information integration theory of consciousness

    PubMed Central

    Tononi, Giulio

    2004-01-01

    Background Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition? Presentation of the hypothesis This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex. Testing the hypothesis The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As

  4. Information theory in living systems, methods, applications, and challenges.

    PubMed

    Gatenby, Robert A; Frieden, B Roy

    2007-02-01

    Living systems are distinguished in nature by their ability to maintain stable, ordered states far from equilibrium. This is despite constant buffeting by thermodynamic forces that, if unopposed, will inevitably increase disorder. Cells maintain a steep transmembrane entropy gradient by continuous application of information that permits cellular components to carry out highly specific tasks that import energy and export entropy. Thus, the study of information storage, flow and utilization is critical for understanding first principles that govern the dynamics of life. Initial biological applications of information theory (IT) used Shannon's methods to measure the information content in strings of monomers such as genes, RNA, and proteins. Recent work has used bioinformatic and dynamical systems to provide remarkable insights into the topology and dynamics of intracellular information networks. Novel applications of Fisher-, Shannon-, and Kullback-Leibler informations are promoting increased understanding of the mechanisms by which genetic information is converted to work and order. Insights into evolution may be gained by analysis of the the fitness contributions from specific segments of genetic information as well as the optimization process in which the fitness are constrained by the substrate cost for its storage and utilization. Recent IT applications have recognized the possible role of nontraditional information storage structures including lipids and ion gradients as well as information transmission by molecular flux across cell membranes. Many fascinating challenges remain, including defining the intercellular information dynamics of multicellular organisms and the role of disordered information storage and flow in disease. PMID:17083004

  5. Mean-field theory of echo state networks

    NASA Astrophysics Data System (ADS)

    Massar, Marc; Massar, Serge

    2013-04-01

    Dynamical systems driven by strong external signals are ubiquitous in nature and engineering. Here we study “echo state networks,” networks of a large number of randomly connected nodes, which represent a simple model of a neural network, and have important applications in machine learning. We develop a mean-field theory of echo state networks. The dynamics of the network is captured by the evolution law, similar to a logistic map, for a single collective variable. When the network is driven by many independent external signals, this collective variable reaches a steady state. But when the network is driven by a single external signal, the collective variable is non stationary but can be characterized by its time averaged distribution. The predictions of the mean-field theory, including the value of the largest Lyapunov exponent, are compared with the numerical integration of the equations of motion.

  6. Mean-field theory of echo state networks.

    PubMed

    Massar, Marc; Massar, Serge

    2013-04-01

    Dynamical systems driven by strong external signals are ubiquitous in nature and engineering. Here we study "echo state networks," networks of a large number of randomly connected nodes, which represent a simple model of a neural network, and have important applications in machine learning. We develop a mean-field theory of echo state networks. The dynamics of the network is captured by the evolution law, similar to a logistic map, for a single collective variable. When the network is driven by many independent external signals, this collective variable reaches a steady state. But when the network is driven by a single external signal, the collective variable is non stationary but can be characterized by its time averaged distribution. The predictions of the mean-field theory, including the value of the largest Lyapunov exponent, are compared with the numerical integration of the equations of motion. PMID:23679475

  7. The theory of pattern formation on directed networks

    NASA Astrophysics Data System (ADS)

    Asllani, Malbor; Challenger, Joseph D.; Pavone, Francesco Saverio; Sacconi, Leonardo; Fanelli, Duccio

    2014-07-01

    Dynamical processes on networks have generated widespread interest in recent years. The theory of pattern formation in reaction-diffusion systems defined on symmetric networks has often been investigated, due to its applications in a wide range of disciplines. Here we extend the theory to the case of directed networks, which are found in a number of different fields, such as neuroscience, computer networks and traffic systems. Owing to the structure of the network Laplacian, the dispersion relation has both real and imaginary parts, at variance with the case for a symmetric, undirected network. The homogeneous fixed point can become unstable due to the topology of the network, resulting in a new class of instabilities, which cannot be induced on undirected graphs. Results from a linear stability analysis allow the instability region to be analytically traced. Numerical simulations show travelling waves, or quasi-stationary patterns, depending on the characteristics of the underlying graph.

  8. Child Rights Information Network Newsletter, 1996.

    ERIC Educational Resources Information Center

    Purbrick, Becky, Ed.

    1996-01-01

    These two newsletter issues communicate activities of the newly formed Child Rights Information Network (CRIN) and report on emerging information resources and activities concerning children and child rights. The January 1996 issue describes the history of CRIN, provides updates on the activities of projects linked to CRIN, and summarizes…

  9. Searching LOGIN, the Local Government Information Network.

    ERIC Educational Resources Information Center

    Jack, Robert F.

    1984-01-01

    Describes a computer-based information retrieval and electronic messaging system produced by Control Data Corporation now being used by government agencies and other organizations. Background of Local Government Information Network (LOGIN), database structure, types of LOGIN units, searching LOGIN (intersect, display, and list commands), and how…

  10. SCIENTIFIC AND TECHNICAL INFORMATION NETWORK (STN INTERNATIONAL)

    EPA Science Inventory

    STN International (the Scientific and Technical Information Network) offers both a fee based online search service that provides accurate, up-to-date, specific information from over 200 scientific, technical, business, and patent databases, and also fee based WWW access to select...

  11. Ohio Valley Community Health Information Network.

    ERIC Educational Resources Information Center

    Guard, Roger; And Others

    The Ohio Valley Community Health Information Network (OVCHIN) works to determine the efficacy of delivering health information to residents of rural southern Ohio and the urban and suburban Cincinnati area. OVCHIN is a community-based, consumer-defined demonstration grant program funded by the National Telecommunications and Information…

  12. Distributing Executive Information Systems through Networks.

    ERIC Educational Resources Information Center

    Penrod, James I.; And Others

    1993-01-01

    Many colleges and universities will soon adopt distributed systems for executive information and decision support. Distribution of shared information through computer networks will improve decision-making processes dramatically on campuses. Critical success factors include administrative support, favorable organizational climate, ease of use,…

  13. Protecting Personal Information on Social Networking Sites

    ERIC Educational Resources Information Center

    Gallant, David T.

    2011-01-01

    Almost everyone uses social networking sites like Facebook, MySpace, and LinkedIn. Since Facebook is the most popular site in the history of the Internet, this article will focus on how one can protect his/her personal information and how that extends to protecting the private information of others.

  14. Information transfer in community structured multiplex networks

    NASA Astrophysics Data System (ADS)

    Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

    2015-08-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  15. An integrated multimedia medical information network system.

    PubMed

    Yamamoto, K; Makino, J; Sasagawa, N; Nagira, M

    1998-01-01

    An integrated multimedia medical information network system at Shimane Medical university has been developed to organize medical information generated from each section and provide information services useful for education, research and clinical practice. The report describes the outline of our system. It is designed to serve as a distributed database for electronic medical records and images. We are developing the MML engine that is to be linked to the world wide web (WWW) network system. To the users, this system will present an integrated multimedia representation of the patient records, providing access to both the image and text-based data required for an effective clinical decision making and medical education. PMID:10384445

  16. Clinical information systems for integrated healthcare networks.

    PubMed Central

    Teich, J. M.

    1998-01-01

    In the 1990's, a large number of hospitals and medical practices have merged to form integrated healthcare networks (IHN's). The nature of an IHN creates new demands for information management, and also imposes new constraints on information systems for the network. Important tradeoffs must be made between homogeneity and flexibility, central and distributed governance, and access and confidentiality. This paper describes key components of clinical information systems for IHN's, and examines important design decisions that affect the value of such systems. Images Figure 1 PMID:9929178

  17. The network perspective: an integration of attachment and family systems theories.

    PubMed

    Kozlowska, Kasia; Hanney, Lesley

    2002-01-01

    In this article we discuss the network paradigm as a useful base from which to integrate attachment and family systems theories. The network perspective refers to the application of general systems theory to living systems, and provides a framework that conceptualizes the dyadic and family systems as simultaneously distinct and interconnected. Network thinking requires that the clinician holds multiple perspectives in mind, considers each system level as both a part and a whole, and shifts the focus of attention between levels as required. Key epistemological issues that have hindered the integration of the theories are discussed. These include inconsistencies within attachment theory itself and confusion surrounding the theoretical conceptualizations of the relationship between attachment and family systems theories. Detailed information about attachment categories is provided using the Dynamic Maturational model. Case vignettes illustrating work with young children and their families explore the clinical implications of integrating attachment data into family therapy practice. PMID:12395561

  18. Common cold outbreaks: A network theory approach

    NASA Astrophysics Data System (ADS)

    Vishkaie, Faranak Rajabi; Bakouie, Fatemeh; Gharibzadeh, Shahriar

    2014-11-01

    In this study, at first we evaluated the network structure in social encounters by which respiratory diseases can spread. We considered common-cold and recorded a sample of human population and actual encounters between them. Our results show that the database structure presents a great value of clustering. In the second step, we evaluated dynamics of disease spread with SIR model by assigning a function to each node of the structural network. The rate of disease spread in networks was observed to be inversely correlated with characteristic path length. Therefore, the shortcuts have a significant role in increasing spread rate. We conclude that the dynamics of social encounters' network stands between the random and the lattice in network spectrum. Although in this study we considered the period of common-cold disease for network dynamics, it seems that similar approaches may be useful for other airborne diseases such as SARS.

  19. Comparing cosmic web classifiers using information theory

    NASA Astrophysics Data System (ADS)

    Leclercq, Florent; Lavaux, Guilhem; Jasche, Jens; Wandelt, Benjamin

    2016-08-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  20. The decoupling approach to quantum information theory

    NASA Astrophysics Data System (ADS)

    Dupuis, Frédéric

    2010-04-01

    Quantum information theory studies the fundamental limits that physical laws impose on information processing tasks such as data compression and data transmission on noisy channels. This thesis presents general techniques that allow one to solve many fundamental problems of quantum information theory in a unified framework. The central theorem of this thesis proves the existence of a protocol that transmits quantum data that is partially known to the receiver through a single use of an arbitrary noisy quantum channel. In addition to the intrinsic interest of this problem, this theorem has as immediate corollaries several central theorems of quantum information theory. The following chapters use this theorem to prove the existence of new protocols for two other types of quantum channels, namely quantum broadcast channels and quantum channels with side information at the transmitter. These protocols also involve sending quantum information partially known by the receiver with a single use of the channel, and have as corollaries entanglement-assisted and unassisted asymptotic coding theorems. The entanglement-assisted asymptotic versions can, in both cases, be considered as quantum versions of the best coding theorems known for the classical versions of these problems. The last chapter deals with a purely quantum phenomenon called locking. We demonstrate that it is possible to encode a classical message into a quantum state such that, by removing a subsystem of logarithmic size with respect to its total size, no measurement can have significant correlations with the message. The message is therefore "locked" by a logarithmic-size key. This thesis presents the first locking protocol for which the success criterion is that the trace distance between the joint distribution of the message and the measurement result and the product of their marginals be sufficiently small.

  1. Information theory of open fragmenting systems

    SciTech Connect

    Gulminelli, F.; Juillet, O.; Ison, M. J.; Dorso, C. O.

    2007-02-12

    An information theory description of finite systems explicitly evolving in time is presented. We impose a MaxEnt variational principle on the Shannon entropy at a given time while the constraints are set at a former time. The resulting density matrix contains explicit time odd components in the form of collective flows. As a specific application we consider the dynamics of the expansion in connection with heavy ion experiments. Lattice gas and classical molecular dynamics simulations are shown.

  2. Information theory and solar energy collection

    SciTech Connect

    Patera, R.P.; Robertson, H.S.

    1980-07-15

    Information theory is applied to the problem of solar radiation collection. We find that the optimum solar concentrator corresponds to a perfect imaging system, i.e., one that images the entire sky on the absorber with no aberrations. For a nonisotropic distribution of radiation at the collector aperture, many thermally separated absorber segments are necessary at the absorber for optimum performance. The heat transfer fluid is first passed through the warm segments and then passed sequentially through the progressively hotter segments.

  3. Propagation of confidential information on scale-free networks

    NASA Astrophysics Data System (ADS)

    Kosmidis, Kosmas; Bunde, Armin

    2007-03-01

    We use Monte Carlo simulations and arguments from percolation theory in order to determine how “confidential” information propagates or localizes on a scale-free network. The basic assumption of our models is that this type of information propagates through the subnetwork of “best friends” which constitute a persons “circle of trust”. We find that there is a sharp percolation transition between a phase where “confidential” information localizes and a phase where “confidential” information propagates. This transition is controlled by the number of best friends m0 that a person is willing to have, and occurs for m0 values higher than intuitively expected from the “small world” property of random networks.

  4. Information processing in convex operational theories

    SciTech Connect

    Barnum, Howard Nelch; Wilce, Alexander G

    2008-01-01

    In order to understand the source and extent of the greater-than-classical information processing power of quantum systems, one wants to characterize both classical and quantum mechanics as points in a broader space of possible theories. One approach to doing this, pioneered by Abramsky and Coecke, is to abstract the essential categorical features of classical and quantum mechanics that support various information-theoretic constraints and possibilities, e.g., the impossibility of cloning in the latter, and the possibility of teleportation in both. Another approach, pursued by the authors and various collaborators, is to begin with a very conservative, and in a sense very concrete, generalization of classical probability theory--which is still sufficient to encompass quantum theory--and to ask which 'quantum' informational phenomena can be reproduced in this much looser setting. In this paper, we review the progress to date in this second programme, and offer some suggestions as to how to link it with the categorical semantics for quantum processes developed by Abramsky and Coecke.

  5. Information spreading on dynamic social networks

    NASA Astrophysics Data System (ADS)

    Liu, Chuang; Zhang, Zi-Ke

    2014-04-01

    Nowadays, information spreading on social networks has triggered an explosive attention in various disciplines. Most of previous works in this area mainly focus on discussing the effects of spreading probability or immunization strategy on static networks. However, in real systems, the peer-to-peer network structure changes constantly according to frequently social activities of users. In order to capture this dynamical property and study its impact on information spreading, in this paper, a link rewiring strategy based on the Fermi function is introduced. In the present model, the informed individuals tend to break old links and reconnect to their second-order friends with more uninformed neighbors. Simulation results on the susceptible-infected-recovered (SIR) model with fixed recovery time T=1 indicate that the information would spread more faster and broader with the proposed rewiring strategy. Extensive analyses of the information cascade size distribution show that the spreading process of the initial steps plays a very important role, that is to say, the information will spread out if it is still survival at the beginning time. The proposed model may shed some light on the in-depth understanding of information spreading on dynamical social networks.

  6. Weight-Control Information Network

    MedlinePlus

    ... Griffin Rodgers, Director of the NIDDK Clinical Trials Current research studies and how you can volunteer Community Outreach and Health Fairs Science-based information and tips for planning an outreach effort or community event For Health Care Professionals Patient and provider resources ...

  7. Fisheries Information Network in Indonesia.

    ERIC Educational Resources Information Center

    Balachandran, Sarojini

    During the early 1980s the Indonesian government made a policy decision to develop fisheries as an important sector of the national economy. In doing so, it recognized the need for the collection and dissemination of fisheries research information not only for the scientists themselves, but also for the ultimate transfer of technology through…

  8. Computerized and Networked Government Information.

    ERIC Educational Resources Information Center

    Stratford, Jean Slemmons; Stratford, Juri

    1998-01-01

    The United States has taken only piecemeal steps to ensure privacy of personal information. This article examines the U.S. relating to privacy and data protection. It defines privacy and discusses international agreements relating to privacy, federal data protection laws, and narrowly applicable laws. (AEF)

  9. Networks in financial markets based on the mutual information rate

    NASA Astrophysics Data System (ADS)

    Fiedor, Paweł

    2014-05-01

    In the last few years there have been many efforts in econophysics studying how network theory can facilitate understanding of complex financial markets. These efforts consist mainly of the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financial markets are that they are complex systems and thus behave in a nonlinear manner, which is confirmed by numerous studies, making the use of correlations which are inherently dealing with linear dependencies only baffling. In this paper we introduce a way to incorporate nonlinear dynamics and dependencies into hierarchical networks to study financial markets using mutual information and its dynamical extension: the mutual information rate. We show that this approach leads to different results than the correlation-based approach used in most studies, on the basis of 91 companies listed on the New York Stock Exchange 100 between 2003 and 2013, using minimal spanning trees and planar maximally filtered graphs.

  10. An Attractor Network in the Hippocampus: Theory and Neurophysiology

    ERIC Educational Resources Information Center

    Rolls, Edmund T.

    2007-01-01

    A quantitative computational theory of the operation of the CA3 system as an attractor or autoassociation network is described. Based on the proposal that CA3-CA3 autoassociative networks are important for episodic or event memory in which space is a component (place in rodents and spatial view in primates), it has been shown behaviorally that the…

  11. Network Strength Theory of Storage and Retrieval Dynamics

    ERIC Educational Resources Information Center

    Wickelgren, Wayne A.

    1976-01-01

    The notion of strength is defined in several alternative ways for chains of associations connected in series and in parallel. Network strength theory is extended to handle retrieval dynamics for a network of associations, in a manner that permits various degrees of serial versus parallel manner that permits various degrees of serial versus…

  12. Information filtering in complex weighted networks

    NASA Astrophysics Data System (ADS)

    Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

    2011-04-01

    Many systems in nature, society, and technology can be described as networks, where the vertices are the system’s elements, and edges between vertices indicate the interactions between the corresponding elements. Edges may be weighted if the interaction strength is measurable. However, the full network information is often redundant because tools and techniques from network analysis do not work or become very inefficient if the network is too dense, and some weights may just reflect measurement errors and need to be be discarded. Moreover, since weight distributions in many complex weighted networks are broad, most of the weight is concentrated among a small fraction of all edges. It is then crucial to properly detect relevant edges. Simple thresholding would leave only the largest weights, disrupting the multiscale structure of the system, which is at the basis of the structure of complex networks and ought to be kept. In this paper we propose a weight-filtering technique based on a global null model [Global Statistical Significance (GloSS) filter], keeping both the weight distribution and the full topological structure of the network. The method correctly quantifies the statistical significance of weights assigned independently to the edges from a given distribution. Applications to real networks reveal that the GloSS filter is indeed able to identify relevant connections between vertices.

  13. Groups, information theory, and Einstein's likelihood principle

    NASA Astrophysics Data System (ADS)

    Sicuro, Gabriele; Tempesta, Piergiulio

    2016-04-01

    We propose a unifying picture where the notion of generalized entropy is related to information theory by means of a group-theoretical approach. The group structure comes from the requirement that an entropy be well defined with respect to the composition of independent systems, in the context of a recently proposed generalization of the Shannon-Khinchin axioms. We associate to each member of a large class of entropies a generalized information measure, satisfying the additivity property on a set of independent systems as a consequence of the underlying group law. At the same time, we also show that Einstein's likelihood function naturally emerges as a byproduct of our informational interpretation of (generally nonadditive) entropies. These results confirm the adequacy of composable entropies both in physical and social science contexts.

  14. Groups, information theory, and Einstein's likelihood principle.

    PubMed

    Sicuro, Gabriele; Tempesta, Piergiulio

    2016-04-01

    We propose a unifying picture where the notion of generalized entropy is related to information theory by means of a group-theoretical approach. The group structure comes from the requirement that an entropy be well defined with respect to the composition of independent systems, in the context of a recently proposed generalization of the Shannon-Khinchin axioms. We associate to each member of a large class of entropies a generalized information measure, satisfying the additivity property on a set of independent systems as a consequence of the underlying group law. At the same time, we also show that Einstein's likelihood function naturally emerges as a byproduct of our informational interpretation of (generally nonadditive) entropies. These results confirm the adequacy of composable entropies both in physical and social science contexts. PMID:27176234

  15. Possibilistic systems within a general information theory

    SciTech Connect

    Joslyn, C.

    1999-06-01

    The author surveys possibilistic systems theory and place it in the context of Imprecise Probabilities and General Information Theory (GIT). In particular, he argues that possibilistic systems hold a distinct position within a broadly conceived, synthetic GIT. The focus is on systems and applications which are semantically grounded by empirical measurement methods (statistical counting), rather than epistemic or subjective knowledge elicitation or assessment methods. Regarding fuzzy measures as special provisions, and evidence measures (belief and plausibility measures) as special fuzzy measures, thereby he can measure imprecise probabilities directly and empirically from set-valued frequencies (random set measurement). More specifically, measurements of random intervals yield empirical fuzzy intervals. In the random set (Dempster-Shafer) context, probability and possibility measures stand as special plausibility measures in that their distributionality (decomposability) maps directly to an aggregable structure of the focal classes of their random sets. Further, possibility measures share with imprecise probabilities the ability to better handle open world problems where the universe of discourse is not specified in advance. In addition to empirically grounded measurement methods, possibility theory also provides another crucial component of a full systems theory, namely prediction methods in the form of finite (Markov) processes which are also strictly analogous to the probabilistic forms.

  16. Minimum energy information fusion in sensor networks

    SciTech Connect

    Chapline, G

    1999-05-11

    In this paper we consider how to organize the sharing of information in a distributed network of sensors and data processors so as to provide explanations for sensor readings with minimal expenditure of energy. We point out that the Minimum Description Length principle provides an approach to information fusion that is more naturally suited to energy minimization than traditional Bayesian approaches. In addition we show that for networks consisting of a large number of identical sensors Kohonen self-organization provides an exact solution to the problem of combing the sensor outputs into minimal description length explanations.

  17. Curriculum Theory Network [CTN 4: Winter 1969-70].

    ERIC Educational Resources Information Center

    Herbert, John, Ed.

    This issue of the journal of the "Curriculum Theory Network" contains four major articles on aspects of the curriculum. Karplus, using Science Curriculum Improvement Study as an example, presents three guidelines for developing elementary school science curricula: separate "experience" and "concept" goals; use developmental and learning theories;…

  18. Social Capital Theory: Implications for Women's Networking and Learning

    ERIC Educational Resources Information Center

    Alfred, Mary V.

    2009-01-01

    This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.

  19. Information theory, novelty and hippocampal responses: unpredicted or unpredictable?

    PubMed

    Strange, Bryan A; Duggins, Andrew; Penny, William; Dolan, Raymond J; Friston, Karl J

    2005-04-01

    Shannon's information theory provides a principled framework for the quantitative analysis of brain responses during the encoding and representation of event streams. In particular, entropy measures the expected uncertainty of events in a given context. This contextual uncertainty or unpredictability may, itself, be important for balancing [bottom-up] sensory information and [top-down] prior expectations during perceptual synthesis. Using event-related functional magnetic resonance imaging (fMRI), we found that the anterior hippocampus is sensitive to the entropy of a visual stimulus stream. In contrast, activity in an extensive bilateral cortico-thalamic network was dictated by the surprise or information associated with each particular stimulus. In short, we show that the probabilistic structure or context in which events occur is an important predictor of hippocampal activity. PMID:15896570

  20. Directedness of Information Flow in Mobile Phone Communication Networks

    PubMed Central

    Peruani, Fernando; Tabourier, Lionel

    2011-01-01

    Without having direct access to the information that is being exchanged, traces of information flow can be obtained by looking at temporal sequences of user interactions. These sequences can be represented as causality trees whose statistics result from a complex interplay between the topology of the underlying (social) network and the time correlations among the communications. Here, we study causality trees in mobile-phone data, which can be represented as a dynamical directed network. This representation of the data reveals the existence of super-spreaders and super-receivers. We show that the tree statistics, respectively the information spreading process, are extremely sensitive to the in-out degree correlation exhibited by the users. We also learn that a given information, e.g., a rumor, would require users to retransmit it for more than 30 hours in order to cover a macroscopic fraction of the system. Our analysis indicates that topological node-node correlations of the underlying social network, while allowing the existence of information loops, they also promote information spreading. Temporal correlations, and therefore causality effects, are only visible as local phenomena and during short time scales. Consequently, the very idea that there is (intentional) information spreading beyond a small vecinity is called into question. These results are obtained through a combination of theory and data analysis techniques. PMID:22216128

  1. Astrophysical data analysis with information field theory

    SciTech Connect

    Enßlin, Torsten

    2014-12-05

    Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.

  2. BOOK REVIEW: Theory of Neural Information Processing Systems

    NASA Astrophysics Data System (ADS)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  3. Information filtering on coupled social networks.

    PubMed

    Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

    2014-01-01

    In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

  4. Information transfer network of global market indices

    NASA Astrophysics Data System (ADS)

    Kim, Yup; Kim, Jinho; Yook, Soon-Hyung

    2015-07-01

    We study the topological properties of the information transfer networks (ITN) of the global financial market indices for six different periods. ITN is a directed weighted network, in which the direction and weight are determined by the transfer entropy between market indices. By applying the threshold method, it is found that ITN undergoes a crossover from the complete graph to a small-world (SW) network. SW regime of ITN for a global crisis is found to be much more enhanced than that for ordinary periods. Furthermore, when ITN is in SW regime, the average clustering coefficient is found to be synchronized with average volatility of markets. We also compare the results with the topological properties of correlation networks.

  5. Information Filtering on Coupled Social Networks

    PubMed Central

    Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

    2014-01-01

    In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

  6. Does information theory explain biological evolution?

    NASA Astrophysics Data System (ADS)

    Battail, G.

    1997-11-01

    It is suggested that Dawkins' model of evolution needs error-correction coding in the genome replication process. Nested coding is moreover assumed. Consequences of these hypotheses are drawn using fundamental results of information theory. Genome replication is dealt with independently of phenotype encoding, which pertains to semantics. The proposed hypotheses enable explaining facts of genetics and evolution, including the existence of redundant DNA (the introns), the observed correlation between the rate of mutations on the one hand, the genome length and the redundancy rate on the other hand, the discreteness of species and the trend of eukaryotes evolution towards complexity.

  7. Optimal Network Modularity for Information Diffusion

    NASA Astrophysics Data System (ADS)

    Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol

    2014-08-01

    We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.

  8. Networked Information Resources. SPEC Kit 253.

    ERIC Educational Resources Information Center

    Bleiler, Richard, Comp.; Plum, Terry, Comp.

    1999-01-01

    This SPEC Kit, published six times per year, examines how Association of Research Libraries (ARL) libraries have structured themselves to identify networked information resources in the market, to evaluate them for purchase, to make purchasing decisions, to publicize them, and to assess their continued utility. In the summer of 1999, the survey…

  9. Information spread in networks: Games, optimal control, and stabilization

    NASA Astrophysics Data System (ADS)

    Khanafer, Ali

    This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack

  10. Towards the understanding of network information processing in biology

    NASA Astrophysics Data System (ADS)

    Singh, Vijay

    Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

  11. Form, Function, and Information Processing in Stochastic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Wiggins, Chris

    2009-03-01

    The ability of a biological network to transduce signals, e.g., from chemical information about the abundance of small molecules into regulatory information about the rate of mRNA expression, is thwarted by numerous sources of noise. A great amount has been learned and conjectured in the last decade about the extent to which the form of a network --- specified by the connectivity and sign of regulation --- constrains or guides the networks function --- the particular noisy input-output relation(s) the network is capable of executing. In parallel, a great amount of research has sought to elucidate the role of inescapable or 'intrinsic' noise arising from the finite copy number of the participating molecules, which sets physical limits on information processing in small cells. I'll discuss how information theory may help illuminate these topics by providing a framework for quantifying function which does not rely on specifying the particular task to be performed a priori, as well as by providing a measure for the extent to which form follows function. En route I hope to show how stochastic chemical kinetics, modeled by the (linear) master equation describing the probability of copy counts for all reactants, benefits from the same spectral approaches fundamental to solving the (linear) diffusion equation.

  12. An information theory of image gathering

    NASA Technical Reports Server (NTRS)

    Fales, Carl L.; Huck, Friedrich O.

    1991-01-01

    Shannon's mathematical theory of communication is extended to image gathering. Expressions are obtained for the total information that is received with a single image-gathering channel and with parallel channels. It is concluded that the aliased signal components carry information even though these components interfere with the within-passband components in conventional image gathering and restoration, thereby degrading the fidelity and visual quality of the restored image. An examination of the expression for minimum mean-square-error, or Wiener-matrix, restoration from parallel image-gathering channels reveals a method for unscrambling the within-passband and aliased signal components to restore spatial frequencies beyond the sampling passband out to the spatial frequency response cutoff of the optical aperture.

  13. 76 FR 67750 - Homeland Security Information Network Advisory Committee

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-02

    ... SECURITY Homeland Security Information Network Advisory Committee AGENCY: Department of Homeland Security... Applicants for Appointment to Homeland Security Information Network Advisory Committee. SUMMARY: The Secretary of Homeland Security has determined that the renewal of the Homeland Security Information...

  14. Realizing Wisdom Theory in Complex Learning Networks

    ERIC Educational Resources Information Center

    Kok, Ayse

    2009-01-01

    The word "wisdom" is rarely seen in contemporary technology and learning discourse. This conceptual paper aims to provide some clear principles that answer the question: How can we establish wisdom in complex learning networks? By considering the nature of contemporary calls for wisdom the paper provides a metatheoretial framework to evaluate the…

  15. Unravelling the Social Network: Theory and Research

    ERIC Educational Resources Information Center

    Merchant, Guy

    2012-01-01

    Despite the widespread popularity of social networking sites (SNSs) amongst children and young people in compulsory education, relatively little scholarly work has explored the fundamental issues at stake. This paper makes an original contribution to the field by locating the study of this online activity within the broader terrain of social…

  16. Dynamics of interacting information waves in networks.

    PubMed

    Mirshahvalad, A; Esquivel, A V; Lizana, L; Rosvall, M

    2014-01-01

    To better understand the inner workings of information spreading, network researchers often use simple models to capture the spreading dynamics. But most models only highlight the effect of local interactions on the global spreading of a single information wave, and ignore the effects of interactions between multiple waves. Here we take into account the effect of multiple interacting waves by using an agent-based model in which the interaction between information waves is based on their novelty. We analyzed the global effects of such interactions and found that information that actually reaches nodes reaches them faster. This effect is caused by selection between information waves: lagging waves die out and only leading waves survive. As a result, and in contrast to models with noninteracting information dynamics, the access to information decays with the distance from the source. Moreover, when we analyzed the model on various synthetic and real spatial road networks, we found that the decay rate also depends on the path redundancy and the effective dimension of the system. In general, the decay of the information wave frequency as a function of distance from the source follows a power-law distribution with an exponent between -0.2 for a two-dimensional system with high path redundancy and -0.5 for a tree-like system with no path redundancy. We found that the real spatial networks provide an infrastructure for information spreading that lies in between these two extremes. Finally, to better understand the mechanics behind the scaling results, we provide analytical calculations of the scaling for a one-dimensional system. PMID:24580283

  17. Information diffusion in structured online social networks

    NASA Astrophysics Data System (ADS)

    Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

    2015-05-01

    Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

  18. Informal Theory: The Ignored Link in Theory-to-Practice

    ERIC Educational Resources Information Center

    Love, Patrick

    2012-01-01

    Applying theory to practice in student affairs is dominated by the assumption that formal theory is directly applied to practice. Among the problems with this assumption is that many practitioners believe they must choose between their lived experiences and formal theory, and that graduate students are taught that their experience "does not…

  19. Improving information filtering via network manipulation

    NASA Astrophysics Data System (ADS)

    Zhang, Fuguo; Zeng, An

    2012-12-01

    The recommender system is a very promising way to address the problem of overabundant information for online users. Although the information filtering for the online commercial systems has received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e., low recommendation accuracy for the small-degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improves the recommendation performance. Specifically, it not only improves the recommendations accuracy (especially for the small-degree items), but also helps the recommender systems generate more diverse and novel recommendations.

  20. Self-assembly of information in networks

    NASA Astrophysics Data System (ADS)

    Rosvall, M.; Sneppen, K.

    2006-06-01

    We model self-assembly of information in networks to investigate necessary conditions for building a global perception of a system by local communication. Our approach is to let agents chat in a model system to self-organize distant communication pathways. We demonstrate that simple local rules allow agents to build a perception of the system, that is robust to dynamical changes and mistakes. We find that messages are most effectively forwarded in the presence of hubs, while transmission in hub-free networks is more robust against misinformation and failures.

  1. Spectral renormalization group theory on nonspatial networks

    NASA Astrophysics Data System (ADS)

    Tuncer, Asli; Erzan, Ayse

    We recently proposed a ``spectral renormalization group'' scheme, for non-spatial networks with no metric defined on them. We implemented the spectral renormalization group on two deterministic non-spatial networks without translational invariance, namely the Cayley tree and diamond lattice . The thermodynamic critical exponents for the Gaussian model are only functions of the spectral dimension, d ~. The Gaussian fixed point is stable with respect to a Ψ4 perturbation up to second order on these lattices with d ~ = 2 , the lower critical dimension for the Ising universality class. This is expected for the Cayley tree, but for the diamond lattice it is an indication that the perturbation expansion up to second order breaks down at d ~ = 2 , as it does for the Wilson scheme on the square lattice. On generalized diamond lattices, with 2 < d ~ < 4 , we find non-Gaussian fixed points with non-trivial exponents. For d ~ > 4 , the critical behavior is once again mean field.

  2. Extended Network Generalized Entanglement Theory: therapeutic mechanisms, empirical predictions, and investigations.

    PubMed

    Hyland, Michael E

    2003-12-01

    Extended Network Generalized Entanglement Theory (Entanglement Theory for short) combines two earlier theories based on complexity theory and quantum mechanics. The theory's assumptions are: the body is a complex, self-organizing system (the extended network) that self-organizes so as to achieve genetically defined patterns (where patterns include morphologic as well as lifestyle patterns). These pattern-specifying genes require feedback that is provided by generalized quantum entanglement. Additionally, generalized entanglement has evolved as a form of communication between people (and animals) and can be used in healing. Entanglement Theory suggests that several processes are involved in complementary and alternative medicine (CAM). Direct subtle therapy creates network change either through lifestyle management, some manual therapies, and psychologically mediated effects of therapy. Indirect subtle therapy is a process of entanglement with other people or physical entities (e.g., remedies, healing sites). Both types of subtle therapy create two kinds of information within the network--either that the network is more disregulated than it is and the network then compensates for this error, or as a guide for network change leading to healing. Most CAM therapies involve a combination of indirect and direct therapies, making empirical evaluation complex. Empirical predictions from this theory are contrasted with those from two other possible mechanisms of healing: (1) psychologic processes and (2) mechanisms involving electromagnetic influence between people (biofield/energy medicine). Topics for empirical study include a hyperfast communication system, the phenomenology of entanglement, predictors of outcome in naturally occurring clinical settings, and the importance of therapist and patient characteristics to outcome. PMID:14736363

  3. A unified data representation theory for network visualization, ordering and coarse-graining

    NASA Astrophysics Data System (ADS)

    Kovács, István A.; Mizsei, Réka; Csermely, Péter

    2015-09-01

    Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.

  4. A unified data representation theory for network visualization, ordering and coarse-graining

    PubMed Central

    Kovács, István A.; Mizsei, Réka; Csermely, Péter

    2015-01-01

    Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form. PMID:26348923

  5. Mapping information flow in sensorimotor networks.

    PubMed

    Lungarella, Max; Sporns, Olaf

    2006-10-27

    Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a) is spatially and temporally specific; (b) can be affected by learning, and (c) can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural) information processing, and illuminating the role of various system components on the generation of behavior. PMID:17069456

  6. Mean Field Theory for Nonequilibrium Network Reconstruction

    NASA Astrophysics Data System (ADS)

    Roudi, Yasser; Hertz, John

    2011-01-01

    There has been recent progress on inferring the structure of interactions in complex networks when they are in stationary states satisfying detailed balance, but little has been done for nonequilibrium systems. Here we introduce an approach to this problem, considering, as an example, the question of recovering the interactions in an asymmetrically coupled, synchronously updated Sherrington-Kirkpatrick model. We derive an exact iterative inversion algorithm and develop efficient approximations based on dynamical mean-field and Thouless-Anderson-Palmer equations that express the interactions in terms of equal-time and one-time-step-delayed correlation functions.

  7. Boundary Depth Information Using Hopfield Neural Network

    NASA Astrophysics Data System (ADS)

    Xu, Sheng; Wang, Ruisheng

    2016-06-01

    Depth information is widely used for representation, reconstruction and modeling of 3D scene. Generally two kinds of methods can obtain the depth information. One is to use the distance cues from the depth camera, but the results heavily depend on the device, and the accuracy is degraded greatly when the distance from the object is increased. The other one uses the binocular cues from the matching to obtain the depth information. It is more and more mature and convenient to collect the depth information of different scenes by stereo matching methods. In the objective function, the data term is to ensure that the difference between the matched pixels is small, and the smoothness term is to smooth the neighbors with different disparities. Nonetheless, the smoothness term blurs the boundary depth information of the object which becomes the bottleneck of the stereo matching. This paper proposes a novel energy function for the boundary to keep the discontinuities and uses the Hopfield neural network to solve the optimization. We first extract the region of interest areas which are the boundary pixels in original images. Then, we develop the boundary energy function to calculate the matching cost. At last, we solve the optimization globally by the Hopfield neural network. The Middlebury stereo benchmark is used to test the proposed method, and results show that our boundary depth information is more accurate than other state-of-the-art methods and can be used to optimize the results of other stereo matching methods.

  8. Intra- Versus Intersex Aggression: Testing Theories of Sex Differences Using Aggression Networks.

    PubMed

    Wölfer, Ralf; Hewstone, Miles

    2015-08-01

    Two theories offer competing explanations of sex differences in aggressive behavior: sexual-selection theory and social-role theory. While each theory has specific strengths and limitations depending on the victim's sex, research hardly differentiates between intrasex and intersex aggression. In the present study, 11,307 students (mean age = 14.96 years; 50% girls, 50% boys) from 597 school classes provided social-network data (aggression and friendship networks) as well as physical (body mass index) and psychosocial (gender and masculinity norms) information. Aggression networks were used to disentangle intra- and intersex aggression, whereas their class-aggregated sex differences were analyzed using contextual predictors derived from sexual-selection and social-role theories. As expected, results revealed that sexual-selection theory predicted male-biased sex differences in intrasex aggression, whereas social-role theory predicted male-biased sex differences in intersex aggression. Findings suggest the value of explaining sex differences separately for intra- and intersex aggression with a dual-theory framework covering both evolutionary and normative components. PMID:26158924

  9. Complex network theory, streamflow, and hydrometric monitoring system design

    NASA Astrophysics Data System (ADS)

    Halverson, M. J.; Fleming, S. W.

    2015-07-01

    Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia (BC) and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and, more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, have a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the observed degree distribution did not clearly indicate a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A network theoretic community detection algorithm identified separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Furthermore, betweenness analyses suggest a handful of key stations which serve as bridges between communities and might be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.

  10. Social Network Theory in Engineering Education

    NASA Astrophysics Data System (ADS)

    Simon, Peter A.

    Collaborative groups are important both in the learning environment of engineering education and, in the real world, the business of engineering design. Selecting appropriate individuals to form an effective group and monitoring a group's progress are important aspects of successful task performance. This exploratory study looked at using the concepts of cognitive social structures, structural balance, and centrality from social network analysis as well as the measures of emotional intelligence. The concepts were used to analyze potential team members to examine if an individual's ability to perceive emotion in others and the self and to use, understand, and manage those emotions are a factor in a group's performance. The students from a capstone design course in computer engineering were used as volunteer subjects. They were formed into groups and assigned a design exercise to determine whether and which of the above-mentioned tools would be effective in both selecting teams and predicting the quality of the resultant design. The results were inconclusive with the exception of an individual's ability to accurately perceive emotions. The instruments that were successful were the Self-Monitoring scale and the accuracy scores derived from cognitive social structures and Level IV of network levels of analysis.

  11. Wireless network traffic modeling based on extreme value theory

    NASA Astrophysics Data System (ADS)

    Liu, Chunfeng; Shu, Yantai; Yang, Oliver W. W.; Liu, Jiakun; Dong, Linfang

    2006-10-01

    In this paper, Extreme Value Theory (EVT) is presented to analyze wireless network traffic. The role of EVT is to allow the development of procedures that are scientifically and statistically rational to estimate the extreme behavior of random processes. There are two primary methods for studying extremes: the Block Maximum (BM) method and the Points Over Threshold (POT) method. By taking limited traffic data that is greater than the threshold value, our experiment and analysis show the wireless network traffic model obtained with the EVT fits well with that of empirical distribution of traffic, thus illustrating that EVT has a good application foreground in the analysis of wireless network traffic.

  12. Using information networks for competitive advantage.

    PubMed

    Rothenberg, R L

    1995-01-01

    Although the healthcare "information superhighway" has received considerable attention, the use of information technology to create a sustainable competitive advantage is not new to other industries. Economic survival in the new world of managed care may depend on a healthcare delivery system's ability to use network-based communications technologies to differentiate itself in the market, especially through cost savings and demonstration of desirable outcomes. The adaptability of these technologies can help position healthcare organizations to break the paradigms of the past and thrive in a market environment that stresses coordination, efficiency, and quality in various settings. PMID:10146130

  13. Percolation theory applied to measures of fragmentation in social networks

    NASA Astrophysics Data System (ADS)

    Chen, Yiping; Paul, Gerald; Cohen, Reuven; Havlin, Shlomo; Borgatti, Stephen P.; Liljeros, Fredrik; Stanley, H. Eugene

    2007-04-01

    We apply percolation theory to a recently proposed measure of fragmentation F for social networks. The measure F is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removing a fraction q of nodes and the total number of pairs in the original fully connected network. We compare F with the traditional measure used in percolation theory, P∞ , the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods from percolation, we study Erdős-Rényi and scale-free networks under various types of node removal strategies. The removal strategies are random removal, high degree removal, and high betweenness centrality removal. We find that for a network obtained after removal (all strategies) of a fraction q of nodes above percolation threshold, P∞≈(1-F)1/2 . For fixed P∞ and close to percolation threshold (q=qc) , we show that 1-F better reflects the actual fragmentation. Close to qc , for a given P∞ , 1-F has a broad distribution and it is thus possible to improve the fragmentation of the network. We also study and compare the fragmentation measure F and the percolation measure P∞ for a real social network of workplaces linked by the households of the employees and find similar results.

  14. Percolation theory applied to measures of fragmentation in social networks.

    PubMed

    Chen, Yiping; Paul, Gerald; Cohen, Reuven; Havlin, Shlomo; Borgatti, Stephen P; Liljeros, Fredrik; Stanley, H Eugene

    2007-04-01

    We apply percolation theory to a recently proposed measure of fragmentation F for social networks. The measure F is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removing a fraction q of nodes and the total number of pairs in the original fully connected network. We compare F with the traditional measure used in percolation theory, P(infinity), the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods from percolation, we study Erdos-Rényi and scale-free networks under various types of node removal strategies. The removal strategies are random removal, high degree removal, and high betweenness centrality removal. We find that for a network obtained after removal (all strategies) of a fraction q of nodes above percolation threshold, P(infinity) approximately (1-F)1/2. For fixed P(infinity) and close to percolation threshold (q=qc), we show that 1-F better reflects the actual fragmentation. Close to qc, for a given P(infinity), 1-F has a broad distribution and it is thus possible to improve the fragmentation of the network. We also study and compare the fragmentation measure F and the percolation measure P(infinity) for a real social network of workplaces linked by the households of the employees and find similar results. PMID:17500961

  15. Theory of interface: category theory, directed networks and evolution of biological networks.

    PubMed

    Haruna, Taichi

    2013-11-01

    Biological networks have two modes. The first mode is static: a network is a passage on which something flows. The second mode is dynamic: a network is a pattern constructed by gluing functions of entities constituting the network. In this paper, first we discuss that these two modes can be associated with the category theoretic duality (adjunction) and derive a natural network structure (a path notion) for each mode by appealing to the category theoretic universality. The path notion corresponding to the static mode is just the usual directed path. The path notion for the dynamic mode is called lateral path which is the alternating path considered on the set of arcs. Their general functionalities in a network are transport and coherence, respectively. Second, we introduce a betweenness centrality of arcs for each mode and see how the two modes are embedded in various real biological network data. We find that there is a trade-off relationship between the two centralities: if the value of one is large then the value of the other is small. This can be seen as a kind of division of labor in a network into transport on the network and coherence of the network. Finally, we propose an optimization model of networks based on a quality function involving intensities of the two modes in order to see how networks with the above trade-off relationship can emerge through evolution. We show that the trade-off relationship can be observed in the evolved networks only when the dynamic mode is dominant in the quality function by numerical simulations. We also show that the evolved networks have features qualitatively similar to real biological networks by standard complex network analysis. PMID:24012823

  16. Evaluating Action Learning: A Critical Realist Complex Network Theory Approach

    ERIC Educational Resources Information Center

    Burgoyne, John G.

    2010-01-01

    This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…

  17. Theory VI. Computational Materials Sciences Network (CMSN)

    SciTech Connect

    Zhang, Z Y

    2008-06-25

    The Computational Materials Sciences Network (CMSN) is a virtual center consisting of scientists interested in working together, across organizational and disciplinary boundaries, to formulate and pursue projects that reflect challenging and relevant computational research in the materials sciences. The projects appropriate for this center involve those problems best pursued through broad cooperative efforts, rather than those key problems best tackled by single investigator groups. CMSN operates similarly to the DOE Center of Excellence for the Synthesis and Processing of Advanced Materials, coordinated by George Samara at Sandia. As in the Synthesis and Processing Center, the intent of the modest funding for CMSN is to foster partnering and collective activities. All CMSN proposals undergo external peer review and are judged foremost on the quality and timeliness of the science and also on criteria relevant to the objective of the center, especially concerning a strategy for partnering. More details about CMSN can be found on the CMSN webpages at: http://cmpweb.ameslab.gov/ccms/CMSN-homepage.html.

  18. Optimal learning paths in information networks.

    PubMed

    Rodi, G C; Loreto, V; Servedio, V D P; Tria, F

    2015-01-01

    Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances. PMID:26030508

  19. Optimal Learning Paths in Information Networks

    PubMed Central

    Rodi, G. C.; Loreto, V.; Servedio, V. D. P.; Tria, F.

    2015-01-01

    Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances. PMID:26030508

  20. Theory-independent limits on correlations from generalized Bayesian networks

    NASA Astrophysics Data System (ADS)

    Henson, Joe; Lal, Raymond; Pusey, Matthew F.

    2014-11-01

    Bayesian networks provide a powerful tool for reasoning about probabilistic causation, used in many areas of science. They are, however, intrinsically classical. In particular, Bayesian networks naturally yield the Bell inequalities. Inspired by this connection, we generalize the formalism of classical Bayesian networks in order to investigate non-classical correlations in arbitrary causal structures. Our framework of ‘generalized Bayesian networks’ replaces latent variables with the resources of any generalized probabilistic theory, most importantly quantum theory, but also, for example, Popescu-Rohrlich boxes. We obtain three main sets of results. Firstly, we prove that all of the observable conditional independences required by the classical theory also hold in our generalization; to obtain this, we extend the classical d-separation theorem to our setting. Secondly, we find that the theory-independent constraints on probabilities can go beyond these conditional independences. For example we find that no probabilistic theory predicts perfect correlation between three parties using only bipartite common causes. Finally, we begin a classification of those causal structures, such as the Bell scenario, that may yield a separation between classical, quantum and general-probabilistic correlations.

  1. Theory of rumour spreading in complex social networks

    NASA Astrophysics Data System (ADS)

    Nekovee, M.; Moreno, Y.; Bianconi, G.; Marsili, M.

    2007-01-01

    We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.

  2. Percolation theory and fragmentation measures in social networks

    NASA Astrophysics Data System (ADS)

    Chen, Yiping; Paul, Gerald; Cohen, Reuven; Havlin, Shlomo; Borgatti, Stephen P.; Liljeros, Fredrik; Eugene Stanley, H.

    2007-05-01

    We study the statistical properties of a recently proposed social networks measure of fragmentation F after removal of a fraction q of nodes or links from the network. The measure F is defined as the ratio of the number of pairs of nodes that are not connected in the fragmented network to the total number of pairs in the original fully connected network. We compare this measure with the one traditionally used in percolation theory, P∞, the fraction of nodes in the largest cluster relative to the total number of nodes. Using both analytical and numerical methods, we study Erdős-Rényi (ER) and scale-free (SF) networks under various node removal strategies. We find that for a network obtained after removal of a fraction q of nodes above criticality, P∞≈(1-F). For fixed P∞ and close to criticality, we show that 1-F better reflects the actual fragmentation. For a given P∞, 1-F has a broad distribution and thus one can improve significantly the fragmentation of the network. We also study and compare the fragmentation measure F and the percolation measure P∞ for a real national social network of workplaces linked by the households of the employees and find similar results.

  3. Critical Theory and Information Studies: A Marcusean Infusion

    ERIC Educational Resources Information Center

    Pyati, Ajit K.

    2006-01-01

    In the field of library and information science, also known as information studies, critical theory is often not included in debates about the discipline's theoretical foundations. This paper argues that the critical theory of Herbert Marcuse, in particular, has a significant contribution to make to the field of information studies. Marcuse's…

  4. Client-Controlled Case Information: A General System Theory Perspective

    ERIC Educational Resources Information Center

    Fitch, Dale

    2004-01-01

    The author proposes a model for client control of case information via the World Wide Web built on principles of general system theory. It incorporates the client into the design, resulting in an information structure that differs from traditional human services information-sharing practices. Referencing general system theory, the concepts of…

  5. The problem of applying information theory to efficient image transmission.

    NASA Technical Reports Server (NTRS)

    Sakrison, D. J.

    1973-01-01

    The main ideas of Shannon's (1948, 1960) theory of source encoding with a fidelity constraint, more commonly known as rate distortion theory, are summarized. The theory was specifically intended to provide a theoretical basis for efficient transmission of information such as images. What the theory has to contribute to the problem is demonstrated. Difficulties that impeded application of the theory to image transmission, and current efforts to solve these difficulties are discussed.

  6. Deep Space Network information system architecture study

    NASA Technical Reports Server (NTRS)

    Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

    1992-01-01

    The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

  7. Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust.

    PubMed

    Wang, Xin; Wang, Ying; Sun, Hongbin

    2016-01-01

    In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework. PMID:27034651

  8. Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

    PubMed Central

    Wang, Xin; Wang, Ying; Sun, Hongbin

    2016-01-01

    In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework. PMID:27034651

  9. Profile: the Philippine Population Information Network.

    PubMed

    1991-06-01

    The profile of Philippine Population Information Network (POPIN) is described in this article as having changed management structure from the Population Center Foundation to the Government's Population Commission, Information Management and Research Division (IMRD) in 1989. This restructuring resulted in the transfer in 1990 of the Department of Social Welfare and Development to the Office of the President. POPIN also serves Asia/Pacific POPIN. POPCOM makes policy and coordinates and monitors population activities. POPIN's goal is to improve the flow and utilization of population information nationwide. The National Population Library was moved in 1989 to the POPCOM Central Office Building and became the Philippine Information Center. The collection includes 6000 books, 400 research reports, and 4000 other documents (brochures, reprints, conference materials, and so on); 42 video tapes about the Philippine population program and a cassette player are available. In 1989, 14 regional centers were set up in POPCOM regional offices and designated Regional Population Information Centers. There are also school-based information centers operating as satellite information centers. The Regional and school-based centers serve the purpose of providing technical information through collection development, cataloguing, classification, storage and retrieval, and circulation. The target users are policy makers, government and private research agencies, researchers, and faculty and students. Publications developed and produced by the Center include the 3rd Supplement of the Union Catalogue of Population Literature, the 1987-88 Annotated Bibliography of Philippine Population Literature (PPL), the forthcoming 1989-90 edition of the Annotated Bibliography of PPL, and a biyearly newsletter, POPINEWS. Microcomputers have been acquired for the Regional Centers, with the idea of computerizing POPIN. Computer upgrading is also being done within the IMRD to provide POPLINE CD

  10. Utilizing general information theories for uncertainty quantification

    SciTech Connect

    Booker, J. M.

    2002-01-01

    Uncertainties enter into a complex problem from many sources: variability, errors, and lack of knowledge. A fundamental question arises in how to characterize the various kinds of uncertainty and then combine within a problem such as the verification and validation of a structural dynamics computer model, reliability of a dynamic system, or a complex decision problem. Because uncertainties are of different types (e.g., random noise, numerical error, vagueness of classification), it is difficult to quantify all of them within the constructs of a single mathematical theory, such as probability theory. Because different kinds of uncertainty occur within a complex modeling problem, linkages between these mathematical theories are necessary. A brief overview of some of these theories and their constituents under the label of Generalized lnforrnation Theory (GIT) is presented, and a brief decision example illustrates the importance of linking at least two such theories.

  11. Chemical reaction network approaches to Biochemical Systems Theory.

    PubMed

    Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R

    2015-11-01

    This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed. PMID:26363083

  12. Essential elements of online information networks on invasive alien species

    USGS Publications Warehouse

    Simpson, A.; Sellers, E.; Grosse, A.; Xie, Y.

    2006-01-01

    In order to be effective, information must be placed in the proper context and organized in a manner that is logical and (preferably) standardized. Recently, invasive alien species (IAS) scientists have begun to create online networks to share their information concerning IAS prevention and control. At a special networking session at the Beijing International Symposium on Biological Invasions, an online Eastern Asia-North American IAS Information Network (EA-NA Network) was proposed. To prepare for the development of this network, and to provide models for other regional collaborations, we compare four examples of global, regional, and national online IAS information networks: the Global Invasive Species Information Network, the Invasives Information Network of the Inter-American Biodiversity Information Network, the Chinese Species Information System, and the Invasive Species Information Node of the US National Biological Information Infrastructure. We conclude that IAS networks require a common goal, dedicated leaders, effective communication, and broad endorsement, in order to obtain sustainable, long-term funding and long-term stability. They need to start small, use the experience of other networks, partner with others, and showcase benefits. Global integration and synergy among invasive species networks will succeed with contributions from both the top-down and the bottom-up. ?? 2006 Springer.

  13. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    USGS Publications Warehouse

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  14. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    NASA Astrophysics Data System (ADS)

    Chang, Hsien-cheng; Kopaska-Merkel, David C.; Chen, Hui-Chuan; Durrans, S. Rocky

    2000-06-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorical data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%.

  15. An Information Processing Theory of Learning and Forgetting.

    ERIC Educational Resources Information Center

    Andre, Thomas

    A theory of learning and forgetting is proposed which uses an information processing (IP) model. The IP model views learning as a process of storing, retrieving, and outputing information from a permanent memory. The concept of information pattern is important to the IP model because the pattern of information determines how the information will…

  16. Modeling and dynamical topology properties of VANET based on complex networks theory

    NASA Astrophysics Data System (ADS)

    Zhang, Hong; Li, Jie

    2015-01-01

    Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What's more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a

  17. Modeling and dynamical topology properties of VANET based on complex networks theory

    SciTech Connect

    Zhang, Hong; Li, Jie

    2015-01-15

    Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What’s more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a

  18. Identifying influential nodes in weighted networks based on evidence theory

    NASA Astrophysics Data System (ADS)

    Wei, Daijun; Deng, Xinyang; Zhang, Xiaoge; Deng, Yong; Mahadevan, Sankaran

    2013-05-01

    The design of an effective ranking method to identify influential nodes is an important problem in the study of complex networks. In this paper, a new centrality measure is proposed based on the Dempster-Shafer evidence theory. The proposed measure trades off between the degree and strength of every node in a weighted network. The influences of both the degree and the strength of each node are represented by basic probability assignment (BPA). The proposed centrality measure is determined by the combination of these BPAs. Numerical examples are used to illustrate the effectiveness of the proposed method.

  19. Phase response theory extended to nonoscillatory network components

    NASA Astrophysics Data System (ADS)

    Sieling, Fred H.; Archila, Santiago; Hooper, Ryan; Canavier, Carmen C.; Prinz, Astrid A.

    2012-05-01

    New tools for analysis of oscillatory networks using phase response theory (PRT) under the assumption of pulsatile coupling have been developed steadily since the 1980s, but none have yet allowed for analysis of mixed systems containing nonoscillatory elements. This caveat has excluded the application of PRT to most real systems, which are often mixed. We show that a recently developed tool, the functional phase resetting curve (fPRC), provides a serendipitous benefit: it allows incorporation of nonoscillatory elements into systems of oscillators where PRT can be applied. We validate this method in a model system of neural oscillators and a biological system, the pyloric network of crustacean decapods.

  20. Lewis Information Network (LINK): Background and overview

    NASA Technical Reports Server (NTRS)

    Schulte, Roger R.

    1987-01-01

    The NASA Lewis Research Center supports many research facilities with many isolated buildings, including wind tunnels, test cells, and research laboratories. These facilities are all located on a 350 acre campus adjacent to the Cleveland Hopkins Airport. The function of NASA-Lewis is to do basic and applied research in all areas of aeronautics, fluid mechanics, materials and structures, space propulsion, and energy systems. These functions require a great variety of remote high speed, high volume data communications for computing and interactive graphic capabilities. In addition, new requirements for local distribution of intercenter video teleconferencing and data communications via satellite have developed. To address these and future communications requirements for the next 15 yrs, a project team was organized to design and implement a new high speed communication system that would handle both data and video information in a common lab-wide Local Area Network. The project team selected cable television broadband coaxial cable technology as the communications medium and first installation of in-ground cable began in the summer of 1980. The Lewis Information Network (LINK) became operational in August 1982 and has become the backbone of all data communications and video.

  1. Surfactant self-assembly in oppositely charged polymer networks. Theory.

    PubMed

    Hansson, Per

    2009-10-01

    The interaction of ionic surfactants with polyion networks of opposite charge in an aqueous environment is analyzed theoretically by applying a recent theory of surfactant ion-polyion complex salts (J. Colloid. Int. Sci. 2009, 332, 183). The theory takes into account attractive and repulsive polyion-mediated interactions between the micelles, the deformation of the polymer network, the mixing of micelles, polyion chains, and simple ions with water, and the hydrophobic free energy at the micelle surface. The theory is used to calculate binding isotherms, swelling isotherms, surfactant aggregation numbers, compositions of complexes,and phase structure under various conditions. Factors controlling the gel volume transition and conditions for core/shell phase coexistence are investigated in detail, as well as the influence of salt. In particular, the interplay between electrostatic and elastic interactions is highlighted. Results from theory are compared with experimental data reported in the literature. The agreement is found to be semiquantitative or qualitative. The theory explains both the discrete volume transition observed in systems where the surfactant is in excess over the polyion and the core/shell phase coexistence in systems where the polyion is in excess. PMID:19728696

  2. On the genesis of the idiotypic network theory.

    PubMed

    Civello, Andrea

    2013-01-01

    The idiotypic network theory (INT) was conceived by the Danish immunologist Niels Kaj Jerne in 1973/1974. It proposes an overall view of the immune system as a network of lymphocytes and antibodies. The paper tries to offer a reconstruction of the genesis of the theory, now generally discarded and of mostly historical interest, first of all, by taking into account the context in which Jerne's theoretical proposal was advanced. It is argued the theory challenged, in a sense, the supremacy of the clonal selection theory (CST), this being regarded as the predominant paradigm in the immunological scenario. As CST found shortcomings in explaining certain phenomena, anomalies, one could view INT as a competing paradigm claiming to be able to make sense of such phenomena in its own conceptual framework. After a summary outline of the historical background and some relevant terminological elucidations, a narrative of the various phases of elaboration of the theory is proposed, up to its official public presentation. PMID:23207664

  3. Information spread in networks: Games, optimal control, and stabilization

    NASA Astrophysics Data System (ADS)

    Khanafer, Ali

    This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack

  4. USING INFORMATION THEORY TO DEFINE A SUSTAINABILITY INDEX

    EPA Science Inventory

    Information theory has many applications in Ecology and Environmental science, such as a biodiversity indicator, as a measure of evolution, a measure of distance from thermodynamic equilibrium, and as a measure of system organization. Fisher Information, in particular, provides a...

  5. Information Theory Density Matrix for a Simple Quantum System.

    ERIC Educational Resources Information Center

    Titus, William J.

    1979-01-01

    Derives the density matrix that best describes, according to information theory, a one-dimensional single particle quantum system when the only information available is the values for the linear and quadratic position-momentum moments. (Author/GA)

  6. Quantum theory of Ur-objects as a theory of information

    NASA Astrophysics Data System (ADS)

    Lyre, Holger

    1995-08-01

    The quantum theory of ur-objects proposed by C. F. von Weizsäcker has to be interpreted as a quantum theory of information. Ur-objects, or urs, are thought to be the simplest objects in quantum theory. Thus an ur is represented by a two-dimensional Hilbert space with the universal symmetry group SU(2), and can only be characterized as one bit of potential information. In this sense it is not a spatial but an information atom. The physical structure of the ur theory is reviewed, and the philosophical consequences of its interpretation as an information theory are demonstrated by means of some important concepts of physics such as time, space, entropy, energy, and matter, which in ur theory appear to be directly connected with information as “the” fundamental substance. This hopefully will help to provide a new understanding of the concept of information.

  7. Optimizing online social networks for information propagation.

    PubMed

    Chen, Duan-Bing; Wang, Guan-Nan; Zeng, An; Fu, Yan; Zhang, Yi-Cheng

    2014-01-01

    Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved. PMID:24816894

  8. Optimizing Online Social Networks for Information Propagation

    PubMed Central

    Chen, Duan-Bing; Wang, Guan-Nan; Zeng, An; Fu, Yan; Zhang, Yi-Cheng

    2014-01-01

    Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved. PMID:24816894

  9. Modelling mechanical characteristics of microbial biofilms by network theory

    PubMed Central

    Ehret, Alexander E.; Böl, Markus

    2013-01-01

    In this contribution, we present a constitutive model to describe the mechanical behaviour of microbial biofilms based on classical approaches in the continuum theory of polymer networks. Although the model is particularly developed for the well-studied biofilms formed by mucoid Pseudomonas aeruginosa strains, it could easily be adapted to other biofilms. The basic assumption behind the model is that the network of extracellular polymeric substances can be described as a superposition of worm-like chain networks, each connected by transient junctions of a certain lifetime. Several models that were applied to biofilms previously are included in the presented approach as special cases, and for small shear strains, the governing equations are those of four parallel Maxwell elements. Rheological data given in the literature are very adequately captured by the proposed model, and the simulated response for a series of compression tests at large strains is in good qualitative agreement with reported experimental behaviour. PMID:23034354

  10. Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network.

    PubMed

    Ding, Rui; Ujang, Norsidah; Hamid, Hussain Bin; Wu, Jianjun

    2015-01-01

    Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks. PMID:26448645

  11. Strengthening Prevention Program Theories and Evaluations: Contributions from Social Network Analysis

    PubMed Central

    Gest, Scott D.; Osgood, D. Wayne; Feinberg, Mark; Bierman, Karen L.; Moody, James

    2011-01-01

    A majority of school-based prevention programs target the modification of setting-level social dynamics, either explicitly (e.g., by changing schools’ organizational, cultural or instructional systems that influence children’s relationships), or implicitly (e.g., by altering behavioral norms designed to influence children’s social affiliations and interactions). Yet, in outcome analyses of these programs, the rich and complicated set of peer network dynamics is often reduced to an aggregation of individual characteristics or assessed with methods that do not account for the interdependencies of network data. In this paper, we present concepts and analytic methods from the field of social network analysis and illustrate their great value to prevention science – both as a source of tools for refining program theories and as methods that enable more sophisticated and focused tests of intervention effects. An additional goal is to inform discussions of the broader implications of social network analysis for public health efforts. PMID:21728069

  12. Strengthening prevention program theories and evaluations: contributions from social network analysis.

    PubMed

    Gest, Scott D; Osgood, D Wayne; Feinberg, Mark E; Bierman, Karen L; Moody, James

    2011-12-01

    A majority of school-based prevention programs target the modification of setting-level social dynamics, either explicitly (e.g., by changing schools' organizational, cultural or instructional systems that influence children's relationships), or implicitly (e.g., by altering behavioral norms designed to influence children's social affiliations and interactions). Yet, in outcome analyses of these programs, the rich and complicated set of peer network dynamics is often reduced to an aggregation of individual characteristics or assessed with methods that do not account for the interdependencies of network data. In this paper, we present concepts and analytic methods from the field of social network analysis and illustrate their great value to prevention science--both as a source of tools for refining program theories and as methods that enable more sophisticated and focused tests of intervention effects. An additional goal is to inform discussions of the broader implications of social network analysis for public health efforts. PMID:21728069

  13. Actor-network theory: a tool to support ethical analysis of commercial genetic testing.

    PubMed

    Williams-Jones, Bryn; Graham, Janice E

    2003-12-01

    Social, ethical and policy analysis of the issues arising from gene patenting and commercial genetic testing is enhanced by the application of science and technology studies, and Actor-Network Theory (ANT) in particular. We suggest the potential for transferring ANT's flexible nature to an applied heuristic methodology for gathering empirical information and for analysing the complex networks involved in the development of genetic technologies. Three concepts are explored in this paper--actor-networks, translation, and drift--and applied to the case of Myriad Genetics and their commercial BRACAnalysis genetic susceptibility test for hereditary breast cancer. Treating this test as an active participant in socio-technical networks clarifies the extent to which it interacts with, shapes and is shaped by people, other technologies, and institutions. Such an understanding enables more sophisticated and nuanced technology assessment, academic analysis, as well as public debate about the social, ethical and policy implications of the commercialization of new genetic technologies. PMID:15115034

  14. Tensor Networks for Lattice Gauge Theories with Continuous Groups

    NASA Astrophysics Data System (ADS)

    Tagliacozzo, L.; Celi, A.; Lewenstein, M.

    2014-10-01

    We discuss how to formulate lattice gauge theories in the tensor-network language. In this way, we obtain both a consistent-truncation scheme of the Kogut-Susskind lattice gauge theories and a tensor-network variational ansatz for gauge-invariant states that can be used in actual numerical computations. Our construction is also applied to the simplest realization of the quantum link models or gauge magnets and provides a clear way to understand their microscopic relation with the Kogut-Susskind lattice gauge theories. We also introduce a new set of gauge-invariant operators that modify continuously Rokhsar-Kivelson wave functions and can be used to extend the phase diagrams of known models. As an example, we characterize the transition between the deconfined phase of the Z2 lattice gauge theory and the Rokhsar-Kivelson point of the U (1 ) gauge magnet in 2D in terms of entanglement entropy. The topological entropy serves as an order parameter for the transition but not the Schmidt gap.

  15. Deep Space Network information system architecture study

    NASA Technical Reports Server (NTRS)

    Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

    1992-01-01

    The purpose of this article is to describe an architecture for the DSN information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990's. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies--i.e., computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

  16. VIOLIN: vaccine investigation and online information network.

    PubMed

    Xiang, Zuoshuang; Todd, Thomas; Ku, Kim P; Kovacic, Bethany L; Larson, Charles B; Chen, Fang; Hodges, Andrew P; Tian, Yuying; Olenzek, Elizabeth A; Zhao, Boyang; Colby, Lesley A; Rush, Howard G; Gilsdorf, Janet R; Jourdian, George W; He, Yongqun

    2008-01-01

    Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org. PMID:18025042

  17. VIOLIN: vaccine investigation and online information network

    PubMed Central

    Xiang, Zuoshuang; Todd, Thomas; Ku, Kim P.; Kovacic, Bethany L.; Larson, Charles B.; Chen, Fang; Hodges, Andrew P.; Tian, Yuying; Olenzek, Elizabeth A.; Zhao, Boyang; Colby, Lesley A.; Rush, Howard G.; Gilsdorf, Janet R.; Jourdian, George W.; He, Yongqun

    2008-01-01

    Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org PMID:18025042

  18. Game Theory for Wireless Sensor Networks: A Survey

    PubMed Central

    Shi, Hai-Yan; Wang, Wan-Liang; Kwok, Ngai-Ming; Chen, Sheng-Yong

    2012-01-01

    Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field. PMID:23012533

  19. Training Records And Information Network UNIX Version

    Energy Science and Technology Software Center (ESTSC)

    1996-12-01

    TRAIN-UNIX is used to track training requirements, qualifications, training completion and schedule training, classrooms and instructors. TRAIN-UNIX is a requirements-based system. When the identified training requirements for specific jobs are entered into the system, the employees manager or responsible training person assigns jobs to an employee. TRAIN-UNIX will then assemble an Individual Training Plan (ITP) with all courses required. ITP''s can also be modified to add any special training directed or identified by management, bestmore » business practices, procedures, etc. TRAIN-UNIX also schedules and tracks conferences, seminars, and required reading. TRAIN-UNIX is a secure database system on a server accessible via the network. Access to the user functions (scheduling, data entry, ITP modification etc.) within TRAIN-UNIX are granted by function, as needed, by the system administrator. An additional level of security allows those who access TRAIN-UNIX to only add, modify or view information for the organizations to which they belong. TRAIN-UNIX scheduling function allows network access to scheduling of students. As a function of the scheduling process, TRAIN-UNIX checks to insure that the student is a valid employee, not double booked, and the instructor and classroom are not double booked. TRAIN-UNIX will report pending lapse of courses or qualifications. This ability to know the lapse of training along with built in training requesting function allows the training deliverers to forecast training needs.« less

  20. Networked Information Resources and Services: Next Steps.

    ERIC Educational Resources Information Center

    Peters, Paul Evans

    1992-01-01

    Argues that advanced networks, particularly BITNET, NSFNET (National Science Foundation Network), Internet, and NREN (National Research and Education Network) can simplify global networking as well as library functions and interfaces; provide easier and more cost-effective connection of the research and education communities; and transport very…

  1. An information-theoretic model for link prediction in complex networks

    NASA Astrophysics Data System (ADS)

    Zhu, Boyao; Xia, Yongxiang

    2015-09-01

    Various structural features of networks have been applied to develop link prediction methods. However, because different features highlight different aspects of network structural properties, it is very difficult to benefit from all of the features that might be available. In this paper, we investigate the role of network topology in predicting missing links from the perspective of information theory. In this way, the contributions of different structural features to link prediction are measured in terms of their values of information. Then, an information-theoretic model is proposed that is applicable to multiple structural features. Furthermore, we design a novel link prediction index, called Neighbor Set Information (NSI), based on the information-theoretic model. According to our experimental results, the NSI index performs well in real-world networks, compared with other typical proximity indices.

  2. An information-theoretic model for link prediction in complex networks

    PubMed Central

    Zhu, Boyao; Xia, Yongxiang

    2015-01-01

    Various structural features of networks have been applied to develop link prediction methods. However, because different features highlight different aspects of network structural properties, it is very difficult to benefit from all of the features that might be available. In this paper, we investigate the role of network topology in predicting missing links from the perspective of information theory. In this way, the contributions of different structural features to link prediction are measured in terms of their values of information. Then, an information-theoretic model is proposed that is applicable to multiple structural features. Furthermore, we design a novel link prediction index, called Neighbor Set Information (NSI), based on the information-theoretic model. According to our experimental results, the NSI index performs well in real-world networks, compared with other typical proximity indices. PMID:26335758

  3. A complex network peer-to-peer system for geographic information services discovery

    NASA Astrophysics Data System (ADS)

    Shen, Shengyu; Wu, Huayi

    2008-12-01

    With the rapid development and application of Internet technology, Geographic Information System has stepped into a new age with its main form as Geographic Information Services. Although there are so many Geographic Information Services available on the Internet now, they are still in very low rate of application. To facilitate the discovery, some proposals for Geographic Information Services infrastructures focus on centralized service registry (UDDI, Universal Description, Discovery and Integration ) for cataloguing their geospatial functions and characteristics. Centralized systems introduce single points of failure, hotspots in the network and expose vulnerability to malicious attacks. In order to solve the problem above, this paper proposes A Complex Network Peer-to-Peer Approach for Geospatial Web Services Discovery. Based on complex network theory, a Peer-to-Peer network has been established, and it takes the charge of each peer's communication and management, and an EBRIM registry centre has been inserted into each peer for the registry and query of Geographic Information Services.

  4. Improving clustering by imposing network information

    PubMed Central

    Gerber, Susanne; Horenko, Illia

    2015-01-01

    Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface. PMID:26601225

  5. Business information query expansion through semantic network

    NASA Astrophysics Data System (ADS)

    Gong, Zhiguo; Muyeba, Maybin; Guo, Jingzhi

    2010-02-01

    In this article, we propose a method for business information query expansions. In our approach, hypernym/hyponymy and synonym relations in WordNet are used as the basic expansion rules. Then we use WordNet Lexical Chains and WordNet semantic similarity to assign terms in the same query into different groups with respect to their semantic similarities. For each group, we expand the highest terms in the WordNet hierarchies with hypernym and synonym, the lowest terms with hyponym and synonym and all other terms with only synonym. In this way, the contradictory caused by full expansion can be well controlled. Furthermore, we use collection-related term semantic network to further improve the expansion performance. And our experiment reveals that our solution for query expansion can improve the query performance dramatically.

  6. Distributed Inference in Tree Networks Using Coding Theory

    NASA Astrophysics Data System (ADS)

    Kailkhura, Bhavya; Vempaty, Aditya; Varshney, Pramod K.

    2015-07-01

    In this paper, we consider the problem of distributed inference in tree based networks. In the framework considered in this paper, distributed nodes make a 1-bit local decision regarding a phenomenon before sending it to the fusion center (FC) via intermediate nodes. We propose the use of coding theory based techniques to solve this distributed inference problem in such structures. Data is progressively compressed as it moves towards the FC. The FC makes the global inference after receiving data from intermediate nodes. Data fusion at nodes as well as at the FC is implemented via error correcting codes. In this context, we analyze the performance for a given code matrix and also design the optimal code matrices at every level of the tree. We address the problems of distributed classification and distributed estimation separately and develop schemes to perform these tasks in tree networks. The proposed schemes are of practical significance due to their simple structure. We study the asymptotic inference performance of our schemes for two different classes of tree networks: fixed height tree networks, and fixed degree tree networks. We show that the proposed schemes are asymptotically optimal under certain conditions.

  7. Network theory and spatial rainfall connections: An interpretation

    NASA Astrophysics Data System (ADS)

    Jha, Sanjeev Kumar; Zhao, Honghan; Woldemeskel, Fitsum M.; Sivakumar, Bellie

    2015-08-01

    Adequate knowledge of spatial connections in rainfall is important for reliable modeling of catchment processes and water management. This study applies the ideas of network theory to examine and interpret the spatial connections in rainfall in Australian conditions. As case studies, monthly rainfall data across a network of raingages from two vastly different areas are studied: (1) Western Australia - data over a period of 67 years (1937-2003) from 57 raingages; and (2) Sydney catchment - data over a period of 114 years (1890-2003) from 47 monitoring stations. The spatial rainfall connections in the two networks are examined using clustering coefficient (CC), a popular network connectivity measure. The clustering coefficient measures the local density and quantifies the network's tendency to cluster. Different values of rainfall correlation threshold (CT) are used to measure the strength of connections in rainfall between different stations and, hence, to calculate CC. The clustering coefficient values are interpreted in terms of topographic factors (latitude, longitude, and elevation) and rainfall properties (mean, standard deviation, and coefficient of variation).

  8. New scaling relation for information transfer in biological networks.

    PubMed

    Kim, Hyunju; Davies, Paul; Walker, Sara Imari

    2015-12-01

    We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781-4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös-Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883

  9. A neural network theory of proportional analogy-making.

    PubMed

    Jani, N G; Levine, D S

    2000-03-01

    A neural network model that can simulate the learning of some simple proportional analogies is presented. These analogies include, for example, (a) red-square:red-circle :: yellow-square:?, (b) apple:red :: banana: ?, (c) a:b :: c:?. Underlying the development of this network is a theory for how the brain learns the nature of association between pairs of concepts. Traditional Hebbian learning of associations is necessary for this process but not sufficient. This is because it simply says, for example, that the concepts "apple" and "red" have been associated, but says nothing about the nature of this relationship. The types of context-dependent interlevel connections in the network suggest a semilocal type of learning that in some manner involves association among more than two nodes or neurons at once. Such connections have been called synaptic triads, and related to potential cell responses in the prefrontal cortex. Some additional types of connections are suggested by the problem of modeling analogies. These types of connections have not yet been verified by brain imaging, but the work herein suggests that they may occur and, possibly, be made and broken quickly in the course of working memory encoding. These working memory connections are referred to as differential, delayed and anti-Hebbian connections. In these connections, one can learn transitions such as "keep red the same"; "change red to yellow"; "turn off red"; "turn on yellow," and so forth. Also, included in the network is a kind of weight transport so that, for example, red to red can be transported to a different instance of color, such as yellow to yellow. The network instantiation developed here, based on common connectionist building blocks such as associative learning, competition, and adaptive resonance, along with additional principles suggested by analogy data, is a step toward a theory of interactions among several brain areas to develop and learn meaningful relationships between concepts

  10. Quantum Information Processing with Modular Networks

    NASA Astrophysics Data System (ADS)

    Crocker, Clayton; Inlek, Ismail V.; Hucul, David; Sosnova, Ksenia; Vittorini, Grahame; Monroe, Chris

    2015-05-01

    Trapped atomic ions are qubit standards for the production of entangled states in quantum information science and metrology applications. Trapped ions can exhibit very long coherence times, external fields can drive strong local interactions via phonons, and remote qubits can be entangled via photons. Transferring quantum information across spatially separated ion trap modules for a scalable quantum network architecture relies on the juxtaposition of both phononic and photonic buses. We report the successful combination of these protocols within and between two ion trap modules on a unit structure of this architecture where the remote entanglement generation rate exceeds the experimentally measured decoherence rate. Additionally, we report an experimental implementation of a technique to maintain phase coherence between spatially and temporally distributed quantum gate operations, a crucial prerequisite for scalability. Finally, we discuss our progress towards addressing the issue of uncontrolled cross-talk between photonic qubits and memory qubits by implementing a second ion species, Barium, to generate the photonic link. This work is supported by the ARO with funding from the IARPA MQCO program, the DARPA Quiness Program, the ARO MURI on Hybrid Quantum Circuits, the AFOSR MURI on Quantum Transduction, and the NSF Physics Frontier Center at JQI.

  11. Electronic Information and Applications in Musicology and Music Theory.

    ERIC Educational Resources Information Center

    Duggan, Mary Kay

    1992-01-01

    Describes electronic publishing and information resources in the field of music. Topics addressed include bibliographic citations of books, journal articles, scores, and sound recordings; bibliographic utilities; computer network resources; electronic music applications; tutorial and laboratory projects; interactive multimedia publications; and…

  12. Public Management Information Systems: Theory and Prescription.

    ERIC Educational Resources Information Center

    Bozeman, Barry; Bretschneider, Stuart

    1986-01-01

    The existing theoretical framework for research in management information systems (MIS) is criticized for its lack of attention to the external environment of organizations, and a new framework is developed which better accommodates MIS in public organizations: public management information systems. Four models of publicness that reflect external…

  13. Analysis and improvement of vehicle information sharing networks

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  14. A theory of information with special application to search problems.

    PubMed

    Wilbur, W J; Neuwald, A F

    2000-01-01

    Classical information theory concerns itself with communication through a noisy channel and how much one can infer about the channel input from a knowledge of the channel output. Because the channel is noisy the input and output are only related statistically and the rate of information transmission is a statistical concept with little meaning for the individual symbol used in transmission. Here we develop a more intuitive notion of information that is concerned with asking the right questions--that is, with finding those questions whose answer conveys the most information. We call this confirmatory information. In the first part of the paper we develop the general theory, show how it relates to classical information theory, and how in the special case of search problems it allows us to quantify the efficacy of information transmission regarding individual events. That is, confirmatory information measures how well a search for items having certain observable properties retrieves items having some unobserved property of interest. Thus confirmatory information facilitates a useful analysis of search problems and contrasts with classical information theory, which quantifies the efficiency of information transmission but is indifferent to the nature of the particular information being transmitted. The last part of the paper presents several examples where confirmatory information is used to quantify protein structural properties in a search setting. PMID:10642878

  15. How mirror-touch informs theories of synesthesia.

    PubMed

    Meier, Beat; Lunke, Katrin; Rothen, Nicolas

    2015-01-01

    Ward and Banissy provide an excellent overview of the state of mirror-touch research in order to advance this field. They present a comparison of two prominent theoretical approaches for understanding mirror-touch phenomena. According to the threshold theory, the phenomena arise as a result of a hyperactive mirror neuron system. According to the Self-Other Theory, they are due to disturbances in the ability to distinguish the self from others. Here, we explore how these two theories can inform theories of synesthesia more generally. We conclude that both theories are not suited as general models of synesthesia. PMID:26118388

  16. Can computational goals inform theories of vision?

    PubMed

    Anderson, Barton L

    2015-04-01

    One of the most lasting contributions of Marr's posthumous book is his articulation of the different "levels of analysis" that are needed to understand vision. Although a variety of work has examined how these different levels are related, there is comparatively little examination of the assumptions on which his proposed levels rest, or the plausibility of the approach Marr articulated given those assumptions. Marr placed particular significance on computational level theory, which specifies the "goal" of a computation, its appropriateness for solving a particular problem, and the logic by which it can be carried out. The structure of computational level theory is inherently teleological: What the brain does is described in terms of its purpose. I argue that computational level theory, and the reverse-engineering approach it inspires, requires understanding the historical trajectory that gave rise to functional capacities that can be meaningfully attributed with some sense of purpose or goal, that is, a reconstruction of the fitness function on which natural selection acted in shaping our visual abilities. I argue that this reconstruction is required to distinguish abilities shaped by natural selection-"natural tasks" -from evolutionary "by-products" (spandrels, co-optations, and exaptations), rather than merely demonstrating that computational goals can be embedded in a Bayesian model that renders a particular behavior or process rational. PMID:25772207

  17. Organizational Application of Social Networking Information Technologies

    ERIC Educational Resources Information Center

    Reppert, Jeffrey R.

    2012-01-01

    The focus of this qualitative research study using the Delphi method is to provide a framework for leaders to develop their own social networks. By exploring concerns in four areas, leaders may be able to better plan, implement, and manage social networking systems in organizations. The areas addressed are: (a) social networking using…

  18. Information Processing Theory and Learning Disabilities: An Overview.

    ERIC Educational Resources Information Center

    Swanson, H. Lee

    1987-01-01

    The article provides an overview of a special topical issue on information processing as it relates to learning disabilities. Components of information processing theory are described, a model of information processing is presented, and subsequent articles in the special issue are summarized. (JW)

  19. Bridging disparate symptoms of schizophrenia: a triple network dysfunction theory.

    PubMed

    Nekovarova, Tereza; Fajnerova, Iveta; Horacek, Jiri; Spaniel, Filip

    2014-01-01

    Schizophrenia is a complex neuropsychiatric disorder with variable symptomatology, traditionally divided into positive and negative symptoms, and cognitive deficits. However, the etiology of this disorder has yet to be fully understood. Recent findings suggest that alteration of the basic sense of self-awareness may be an essential distortion of schizophrenia spectrum disorders. In addition, extensive research of social and mentalizing abilities has stressed the role of distortion of social skills in schizophrenia.This article aims to propose and support a concept of a triple brain network model of the dysfunctional switching between default mode and central executive network (CEN) related to the aberrant activity of the salience network. This model could represent a unitary mechanism of a wide array of symptom domains present in schizophrenia including the deficit of self (self-awareness and self-representation) and theory of mind (ToM) dysfunctions along with the traditional positive, negative and cognitive domains. We review previous studies which document the dysfunctions of self and ToM in schizophrenia together with neuroimaging data that support the triple brain network model as a common neuronal substrate of this dysfunction. PMID:24910597

  20. Bridging disparate symptoms of schizophrenia: a triple network dysfunction theory

    PubMed Central

    Nekovarova, Tereza; Fajnerova, Iveta; Horacek, Jiri; Spaniel, Filip

    2014-01-01

    Schizophrenia is a complex neuropsychiatric disorder with variable symptomatology, traditionally divided into positive and negative symptoms, and cognitive deficits. However, the etiology of this disorder has yet to be fully understood. Recent findings suggest that alteration of the basic sense of self-awareness may be an essential distortion of schizophrenia spectrum disorders. In addition, extensive research of social and mentalizing abilities has stressed the role of distortion of social skills in schizophrenia.This article aims to propose and support a concept of a triple brain network model of the dysfunctional switching between default mode and central executive network (CEN) related to the aberrant activity of the salience network. This model could represent a unitary mechanism of a wide array of symptom domains present in schizophrenia including the deficit of self (self-awareness and self-representation) and theory of mind (ToM) dysfunctions along with the traditional positive, negative and cognitive domains. We review previous studies which document the dysfunctions of self and ToM in schizophrenia together with neuroimaging data that support the triple brain network model as a common neuronal substrate of this dysfunction. PMID:24910597

  1. Modeling network traffic with the extreme value theory

    NASA Astrophysics Data System (ADS)

    Liu, Jiakun; Shu, Yantai; Yang, Oliver W. W.; Gao, Deyun

    2003-08-01

    This paper introduced the Extreme Value Theory (EVT) for analysis of network traffic. The role of EVT is to allow the development of procedures that are scientifically and statistically rational to estimate the extreme behavior of random processes. In this paper, we propose an EVT_based procedure to fit a model to the traffic trace. We have performed some simulation experiments on real-traffic traces such as video data to study the feasibility of our proposed method. Our experiments showed that the EVT method can be applied to statistical analysis of real traffic. Furthermore, since only the data greater than the threshold are processed, the computation overhead is reduced greatly. It indicates that EVT method could be applied to real time network control.

  2. Mean field theory for scale-free random networks

    NASA Astrophysics Data System (ADS)

    Barabási, Albert-László; Albert, Réka; Jeong, Hawoong

    1999-10-01

    Random networks with complex topology are common in Nature, describing systems as diverse as the world wide web or social and business networks. Recently, it has been demonstrated that most large networks for which topological information is available display scale-free features. Here we study the scaling properties of the recently introduced scale-free model, that can account for the observed power-law distribution of the connectivities. We develop a mean-field method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the scaling exponents. The mean-field method can be used to address the properties of two variants of the scale-free model, that do not display power-law scaling.

  3. Advances on tensor network theory: symmetries, fermions, entanglement, and holography

    NASA Astrophysics Data System (ADS)

    Orús, Román

    2014-11-01

    This is a short review on selected theory developments on tensor network (TN) states for strongly correlated systems. Specifically, we briefly review the effect of symmetries in TN states, fermionic TNs, the calculation of entanglement Hamiltonians from projected entangled pair states (PEPS), and the relation between the multi-scale entanglement renormalization ansatz (MERA) and the AdS/CFT or gauge/gravity duality. We stress the role played by entanglement in the emergence of several physical properties and objects through the TN language. Some recent results along these lines are also discussed.

  4. Stochastical modeling for Viral Disease: Statistical Mechanics and Network Theory

    NASA Astrophysics Data System (ADS)

    Zhou, Hao; Deem, Michael

    2007-04-01

    Theoretical methods of statistical mechanics are developed and applied to study the immunological response against viral disease, such as dengue. We use this theory to show how the immune response to four different dengue serotypes may be sculpted. It is the ability of avian influenza, to change and to mix, that has given rise to the fear of a new human flu pandemic. Here we propose to utilize a scale free network based stochastic model to investigate the mitigation strategies and analyze the risk.

  5. PREFACE: Complex Networks: from Biology to Information Technology

    NASA Astrophysics Data System (ADS)

    Barrat, A.; Boccaletti, S.; Caldarelli, G.; Chessa, A.; Latora, V.; Motter, A. E.

    2008-06-01

    The field of complex networks is one of the most active areas in contemporary statistical physics. Ten years after seminal work initiated the modern study of networks, interest in the field is in fact still growing, as indicated by the ever increasing number of publications in network science. The reason for such a resounding success is most likely the simplicity and broad significance of the approach that, through graph theory, allows researchers to address a variety of different complex systems within a common framework. This special issue comprises a selection of contributions presented at the workshop 'Complex Networks: from Biology to Information Technology' held in July 2007 in Pula (Cagliari), Italy as a satellite of the general conference STATPHYS23. The contributions cover a wide range of problems that are currently among the most important questions in the area of complex networks and that are likely to stimulate future research. The issue is organised into four sections. The first two sections describe 'methods' to study the structure and the dynamics of complex networks, respectively. After this methodological part, the issue proceeds with a section on applications to biological systems. The issue closes with a section concentrating on applications to the study of social and technological networks. The first section, entitled Methods: The Structure, consists of six contributions focused on the characterisation and analysis of structural properties of complex networks: The paper Motif-based communities in complex networks by Arenas et al is a study of the occurrence of characteristic small subgraphs in complex networks. These subgraphs, known as motifs, are used to define general classes of nodes and their communities by extending the mathematical expression of the Newman-Girvan modularity. The same line of research, aimed at characterising network structure through the analysis of particular subgraphs, is explored by Bianconi and Gulbahce in Algorithm

  6. A Danger-Theory-Based Immune Network Optimization Algorithm

    PubMed Central

    Li, Tao; Xiao, Xin; Shi, Yuanquan

    2013-01-01

    Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times. PMID:23483853

  7. Dretske's Semantic Information Theory and Metatheories in Library and Information Science.

    ERIC Educational Resources Information Center

    Bonnevie, Ellen

    2001-01-01

    Presents the semantic information theory, formulated by the philosopher Fred Dretske, as a contribution to the discussion of metatheories and their practical implications in library and information science. Highlights include mathematical communication theory; digitization, perception, cognition, and concept formation; information and knowledge;…

  8. Discovery of Information Diffusion Process in Social Networks

    NASA Astrophysics Data System (ADS)

    Kim, Kwanho; Jung, Jae-Yoon; Park, Jonghun

    Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

  9. Information theories for time-dependent harmonic oscillator

    SciTech Connect

    Choi, Jeong Ryeol; Kim, Min-Soo; Kim, Daeyeoul; Maamache, Mustapha; Menouar, Salah; Nahm, In Hyun

    2011-06-15

    Highlights: > Information theories for the general time-dependent harmonic oscillator based on invariant operator method. > Time dependence of entropies and entropic uncertainty relation. > Characteristics of Shannon information and Fisher information. > Application of information theories to particular systems that have time-dependent behavior. - Abstract: Information theories for the general time-dependent harmonic oscillator are described on the basis of invariant operator method. We obtained entropic uncertainty relation of the system and discussed whether it is always larger than or equal to the physically allowed minimum value. Shannon information and Fisher information are derived by means of density operator that satisfies Liouville-von Neumann equation and their characteristics are investigated. Shannon information is independent of time, but Fisher information is explicitly dependent on time as the time functions of the Hamiltonian vary. We can regard that the Fisher information is a local measure since its time behavior is largely affected by local arrangements of the density, whilst the Shannon information plays the role of a global measure of the spreading of density. To promote the understanding, our theory is applied to special systems, the so-called quantum oscillator with time-dependent frequency and strongly pulsating mass system.

  10. Reactivity Network: Secondary Sources for Inorganic Reactivity Information.

    ERIC Educational Resources Information Center

    Mellon, E. K.

    1989-01-01

    Provides an eclectic annotated bibliography of secondary sources for inorganic reactivity information of interest to reactivity network review authors and to anyone seeking information about simple inorganic reactions in order to develop experiments and demonstrations. Gives 119 sources. (MVL)

  11. Query Networks for Medical Information Retrieval-Assigning Probabilistic Relationships

    PubMed Central

    Cousins, Steve B.; Silverstein, Jonathan C.; Frisse, Mark E.

    1990-01-01

    Query networks are specializations of Belief networks used in information retrieval. We hypothesize that query networks can be incorporated into medical information systems in at least two ways: First, the relative values of nodes in the query networks can be used to initiate searches based on query term-weights. Second, query models can incorporate reader feedback and can become simple task-specific user models. If large query networks are to be useful, one must find means to assign reasonable “default” values to those nodes and edges which are not explicitly defined by some other means. This paper presents preliminary data assessing the suitability of various default heuristic query network edge assignment functions. Early evidence suggests that query networks using default assignment functions exhibit behavior consistent with that expected from an information retrieval aid.

  12. Extracting spatial information from networks with low-order eigenvectors

    NASA Astrophysics Data System (ADS)

    Cucuringu, Mihai; Blondel, Vincent D.; Van Dooren, Paul

    2013-03-01

    We consider the problem of inferring meaningful spatial information in networks from incomplete information on the connection intensity between the nodes of the network. We consider two spatially distributed networks: a population migration flow network within the US, and a network of mobile phone calls between cities in Belgium. For both networks we use the eigenvectors of the Laplacian matrix constructed from the link intensities to obtain informative visualizations and capture natural geographical subdivisions. We observe that some low-order eigenvectors localize very well and seem to reveal small geographically cohesive regions that match remarkably well with political and administrative boundaries. We discuss possible explanations for this observation by describing diffusion maps and localized eigenfunctions. In addition, we discuss a possible connection with the weighted graph cut problem, and provide numerical evidence supporting the idea that lower-order eigenvectors point out local cuts in the network. However, we do not provide a formal and rigorous justification for our observations.

  13. Optimal multi-community network modularity for information diffusion

    NASA Astrophysics Data System (ADS)

    Wu, Jiaocan; Du, Ruping; Zheng, Yingying; Liu, Dong

    2016-02-01

    Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

  14. Response to Patrick Love's "Informal Theory": A Rejoinder

    ERIC Educational Resources Information Center

    Evans, Nancy J.; Guido, Florence M.

    2012-01-01

    This rejoinder to Patrick Love's article, "Informal Theory: The Ignored Link in Theory-to-Practice," which appears earlier in this issue of the "Journal of College Student Development", was written at the invitation of the Editor. In the critique, we point out the weaknesses of many of Love's arguments and propositions. We provide an alternative…

  15. Information Processing Theories and the Education of the Gifted.

    ERIC Educational Resources Information Center

    Rawl, Ruth K.; O'Tuel, Frances S.

    1983-01-01

    The basic assumptions of information processing theories in cognitive psychology are reviewed, and the application of this approach to problem solving in gifted education is considered. Specific implications are cited on problem selection and instruction giving. (CL)

  16. Influence Function Learning in Information Diffusion Networks

    PubMed Central

    Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

    2015-01-01

    Can we learn the influence of a set of people in a social network from cascades of information diffusion? This question is often addressed by a two-stage approach: first learn a diffusion model, and then calculate the influence based on the learned model. Thus, the success of this approach relies heavily on the correctness of the diffusion model which is hard to verify for real world data. In this paper, we exploit the insight that the influence functions in many diffusion models are coverage functions, and propose a novel parameterization of such functions using a convex combination of random basis functions. Moreover, we propose an efficient maximum likelihood based algorithm to learn such functions directly from cascade data, and hence bypass the need to specify a particular diffusion model in advance. We provide both theoretical and empirical analysis for our approach, showing that the proposed approach can provably learn the influence function with low sample complexity, be robust to the unknown diffusion models, and significantly outperform existing approaches in both synthetic and real world data. PMID:25973445

  17. TOWARDS A SUSTAINABILITY INDEX USING INFORMATION THEORY

    EPA Science Inventory

    We explore the use of Fisher Information as a basis for an index of sustainability. Sustainability of an ecosystem refers to the robustness of a preferred dynamic regime to human and natural disturbances. Ecosystems under perturbations of varying regularity and intensity can ei...

  18. Biogeography revisited with network theory: retracing the history of hydrothermal vent communities.

    PubMed

    Moalic, Yann; Desbruyères, Daniel; Duarte, Carlos M; Rozenfeld, Alejandro F; Bachraty, Charleyne; Arnaud-Haond, Sophie

    2012-01-01

    Defining biogeographic provinces to understand the history and evolution of communities associated with a given kind of ecosystem is challenging and usually requires a priori assumptions to be made. We applied network theory, a holistic and exploratory method, to the most complete database of faunal distribution available on oceanic hydrothermal vents, environments which support fragmented and unstable ecosystems, to infer the processes driving their worldwide biogeography. Besides the identification of robust provinces, the network topology allowed us to identify preferential pathways that had hitherto been overlooked. These pathways are consistent with the previously proposed hypothesis of a role of plate tectonics in the biogeographical history of hydrothermal vent communities. A possible ancestral position of the Western Pacific is also suggested for the first time. Finally, this work provides an innovative example of the potential of network tools to unravel the biogeographic history of faunal assemblages and to supply comprehensive information for the conservation and management of biodiversity. PMID:21856628

  19. Blending Formal and Informal Learning Networks for Online Learning

    ERIC Educational Resources Information Center

    Czerkawski, Betül C.

    2016-01-01

    With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

  20. The Role of the Australian Open Learning Information Network.

    ERIC Educational Resources Information Center

    Bishop, Robin; And Others

    Three documents are presented which describe the Australian Open Learning Information Network (AOLIN)--a national, independent, and self-supporting network of educational researchers with a common interest in the use of information technology for open and distance education--and discuss two evaluative studies undertaken by the organization. The…

  1. Instructional Technology: The Information Superhighway, the Internet, Interactive Video Networks.

    ERIC Educational Resources Information Center

    Odell, Kerry S.; And Others

    1994-01-01

    Includes "It Boggles the Mind" (Odell); "Merging Your Classroom onto the Information Superhighway" (Murphy); "The World's Largest Computer Network" (Fleck); "The Information Highway in Iowa" (Miller); "Interactive Video Networks in Secondary Schools" (Swan et al.); and "Upgrade to Humancentric Technology" (Berry). (JOW)

  2. Quantum Information Theory for Quantum Communication

    NASA Astrophysics Data System (ADS)

    Koashi, Masato

    This chapter gives a concise description of the fundamental concepts of quantum information and quantum communication, which is pertinent to the discussions in the subsequent chapters. Beginning with the basic set of rules that dictate quantum mechanics, the chapter explains the most general ways to describe quantum states, measurements, and state transformations. Convenient mathematical tools are also presented to provide an intuitive picture of a qubit, which is the simplest unit of quantum information. The chapter then elaborates on the distinction between quantum communication and classical communication, with emphasis on the role of quantum entanglement as a communication resource. Quantum teleportation and dense coding are then explained in the context of optimal resource conversions among quantum channels, classical channels, and entanglement.

  3. Quantifying networks complexity from information geometry viewpoint

    SciTech Connect

    Felice, Domenico Mancini, Stefano; Pettini, Marco

    2014-04-15

    We consider a Gaussian statistical model whose parameter space is given by the variances of random variables. Underlying this model we identify networks by interpreting random variables as sitting on vertices and their correlations as weighted edges among vertices. We then associate to the parameter space a statistical manifold endowed with a Riemannian metric structure (that of Fisher-Rao). Going on, in analogy with the microcanonical definition of entropy in Statistical Mechanics, we introduce an entropic measure of networks complexity. We prove that it is invariant under networks isomorphism. Above all, considering networks as simplicial complexes, we evaluate this entropy on simplexes and find that it monotonically increases with their dimension.

  4. Transportation and dynamic networks: Models, theory, and applications to supply chains, electric power, and financial networks

    NASA Astrophysics Data System (ADS)

    Liu, Zugang

    Network systems, including transportation and logistic systems, electric power generation and distribution networks as well as financial networks, provide the critical infrastructure for the functioning of our societies and economies. The understanding of the dynamic behavior of such systems is also crucial to national security and prosperity. The identification of new connections between distinct network systems is the inspiration for the research in this dissertation. In particular, I answer two questions raised by Beckmann, McGuire, and Winsten (1956) and Copeland (1952) over half a century ago, which are, respectively, how are electric power flows related to transportation flows and does money flow like water or electricity? In addition, in this dissertation, I achieve the following: (1) I establish the relationships between transportation networks and three other classes of complex network systems: supply chain networks, electric power generation and transmission networks, and financial networks with intermediation. The establishment of such connections provides novel theoretical insights as well as new pricing mechanisms, and efficient computational methods. (2) I develop new modeling frameworks based on evolutionary variational inequality theory that capture the dynamics of such network systems in terms of the time-varying flows and incurred costs, prices, and, where applicable, profits. This dissertation studies the dynamics of such network systems by addressing both internal competition and/or cooperation, and external changes, such as varying costs and demands. (3) I focus, in depth, on electric power supply chains. By exploiting the relationships between transportation networks and electric power supply chains, I develop a large-scale network model that integrates electric power supply chains and fuel supply markets. The model captures both the economic transactions as well as the physical transmission constraints. The model is then applied to the New

  5. Integrated information theory: from consciousness to its physical substrate.

    PubMed

    Tononi, Giulio; Boly, Melanie; Massimini, Marcello; Koch, Christof

    2016-07-01

    In this Opinion article, we discuss how integrated information theory accounts for several aspects of the relationship between consciousness and the brain. Integrated information theory starts from the essential properties of phenomenal experience, from which it derives the requirements for the physical substrate of consciousness. It argues that the physical substrate of consciousness must be a maximum of intrinsic cause-effect power and provides a means to determine, in principle, the quality and quantity of experience. The theory leads to some counterintuitive predictions and can be used to develop new tools for assessing consciousness in non-communicative patients. PMID:27225071

  6. A quantitative approach to measure road network information based on edge diversity

    NASA Astrophysics Data System (ADS)

    Wu, Xun; Zhang, Hong; Lan, Tian; Cao, Weiwei; He, Jing

    2015-12-01

    The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.

  7. Southeastern Library Network (SOLINET) General Information.

    ERIC Educational Resources Information Center

    Southeastern Library Network, Inc., Atlanta, GA.

    The computer and telecommunications services of the Southeastern Library Network (SOLINET) are specified in this introduction to the Network. Since members of SOLINET may participate in the services of the Ohio College Library Center (OCLC), a description of these services is included. Membership requirements, fees and dues, and charges to SOLINET…

  8. Weather information network including graphical display

    NASA Technical Reports Server (NTRS)

    Leger, Daniel R. (Inventor); Burdon, David (Inventor); Son, Robert S. (Inventor); Martin, Kevin D. (Inventor); Harrison, John (Inventor); Hughes, Keith R. (Inventor)

    2006-01-01

    An apparatus for providing weather information onboard an aircraft includes a processor unit and a graphical user interface. The processor unit processes weather information after it is received onboard the aircraft from a ground-based source, and the graphical user interface provides a graphical presentation of the weather information to a user onboard the aircraft. Preferably, the graphical user interface includes one or more user-selectable options for graphically displaying at least one of convection information, turbulence information, icing information, weather satellite information, SIGMET information, significant weather prognosis information, and winds aloft information.

  9. A proposed concept for a crustal dynamics information management network

    NASA Technical Reports Server (NTRS)

    Lohman, G. M.; Renfrow, J. T.

    1980-01-01

    The findings of a requirements and feasibility analysis of the present and potential producers, users, and repositories of space-derived geodetic information are summarized. A proposed concept is presented for a crustal dynamics information management network that would apply state of the art concepts of information management technology to meet the expanding needs of the producers, users, and archivists of this geodetic information.

  10. Network selection, Information filtering and Scalable computation

    NASA Astrophysics Data System (ADS)

    Ye, Changqing

    This dissertation explores two application scenarios of sparsity pursuit method on large scale data sets. The first scenario is classification and regression in analyzing high dimensional structured data, where predictors corresponds to nodes of a given directed graph. This arises in, for instance, identification of disease genes for the Parkinson's diseases from a network of candidate genes. In such a situation, directed graph describes dependencies among the genes, where direction of edges represent certain causal effects. Key to high-dimensional structured classification and regression is how to utilize dependencies among predictors as specified by directions of the graph. In this dissertation, we develop a novel method that fully takes into account such dependencies formulated through certain nonlinear constraints. We apply the proposed method to two applications, feature selection in large margin binary classification and in linear regression. We implement the proposed method through difference convex programming for the cost function and constraints. Finally, theoretical and numerical analyses suggest that the proposed method achieves the desired objectives. An application to disease gene identification is presented. The second application scenario is personalized information filtering which extracts the information specifically relevant to a user, predicting his/her preference over a large number of items, based on the opinions of users who think alike or its content. This problem is cast into the framework of regression and classification, where we introduce novel partial latent models to integrate additional user-specific and content-specific predictors, for higher predictive accuracy. In particular, we factorize a user-over-item preference matrix into a product of two matrices, each representing a user's preference and an item preference by users. Then we propose a likelihood method to seek a sparsest latent factorization, from a class of over

  11. Information Diffusion in Facebook-Like Social Networks Under Information Overload

    NASA Astrophysics Data System (ADS)

    Li, Pei; Xing, Kai; Wang, Dapeng; Zhang, Xin; Wang, Hui

    2013-07-01

    Research on social networks has received remarkable attention, since many people use social networks to broadcast information and stay connected with their friends. However, due to the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account, and models the process of information diffusion under information overload. The term view scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated is proposed to characterize the information diffusion efficiency. Through theoretical analysis, we find that factors such as network structure and view scope number have no impact on the information diffusion efficiency, which is a surprising result. To verify the results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly.

  12. Conceptions and Practice of Information Literacy in Academic Libraries: Espoused Theories and Theories-in-Use

    ERIC Educational Resources Information Center

    Kerr, Paulette A.

    2010-01-01

    This research was conducted to investigate the relationships between conceptions and practice of information literacy in academic libraries. To create a structure for the investigation, the research adopted the framework of Argyris and Schon (1974) in which professional practice is examined via theories of action, namely espoused theories and…

  13. The Case of Mandy: Applying Holland's Theory and Cognitive Information Processing Theory.

    ERIC Educational Resources Information Center

    Reardon, Robert C.; Wright, Laura K.

    1999-01-01

    Discusses the application of Holland's theory and cognitive information processing theory to the case of a college student who was deciding about a major and a future career. The outcome of the student's case, her personal reactions, and practical implications, are discussed. (Author/GCP)

  14. Morphological brain network assessed using graph theory and network filtration in deaf adults.

    PubMed

    Kim, Eunkyung; Kang, Hyejin; Lee, Hyekyoung; Lee, Hyo-Jeong; Suh, Myung-Whan; Song, Jae-Jin; Oh, Seung-Ha; Lee, Dong Soo

    2014-09-01

    Prolonged deprivation of auditory input can change brain networks in pre- and postlingual deaf adults by brain-wide reorganization. To investigate morphological changes in these brains voxel-based morphometry, voxel-wise correlation with the primary auditory cortex, and whole brain network analyses using morphological covariance were performed in eight prelingual deaf, eleven postlingual deaf, and eleven hearing adults. Network characteristics based on graph theory and network filtration based on persistent homology were examined. Gray matter density in the primary auditor cortex was preserved in prelingual deafness, while it tended to decrease in postlingual deafness. Unlike postlingual, prelingual deafness showed increased bilateral temporal connectivity of the primary auditory cortex compared to the hearing adults. Of the graph theory-based characteristics, clustering coefficient, betweenness centrality, and nodal efficiency all increased in prelingual deafness, while all the parameters of postlingual deafness were similar to the hearing adults. Patterns of connected components changing during network filtration were different between prelingual deafness and hearing adults according to the barcode, dendrogram, and single linkage matrix representations, while these were the same in postlingual deafness. Nodes in fronto-limbic and left temporal components were closely coupled, and nodes in the temporo-parietal component were loosely coupled, in prelingual deafness. Patterns of connected components changing in postlingual deafness were the same as hearing adults. We propose that the preserved density of auditory cortex associated with increased connectivity in prelingual deafness, and closer coupling between certain brain areas, represent distinctive reorganization of auditory and related cortices compared with hearing or postlingual deaf adults. The differential network reorganization in the prelingual deaf adults could be related to the absence of auditory speech

  15. Quantum metrology from an information theory perspective

    SciTech Connect

    Boixo, Sergio; Datta, Animesh; Davis, Matthew J.; Flammia, Steven T.; Shaji, Anil; Tacla, Alexandre B.; Caves, Carlton M.

    2009-04-13

    Questions about quantum limits on measurement precision were once viewed from the perspective of how to reduce or avoid the effects of quantum noise. With the advent of quantum information science came a paradigm shift to proving rigorous bounds on measurement precision. These bounds have been interpreted as saying, first, that the best achievable sensitivity scales as 1/n, where n is the number of particles one has available for a measurement and, second, that the only way to achieve this Heisenberg-limited sensitivity is to use quantum entanglement. We review these results and show that using quadratic couplings of n particles to a parameter to be estimated, one can achieve sensitivities that scale as 1/n{sup 2} if one uses entanglement, but even in the absence of any entanglement at any time during the measurement protocol, one can achieve a super-Heisenberg scaling of 1/n{sup 3/2}.

  16. From information theory to quantitative description of steric effects.

    PubMed

    Alipour, Mojtaba; Safari, Zahra

    2016-07-21

    Immense efforts have been made in the literature to apply the information theory descriptors for investigating the electronic structure theory of various systems. In the present study, the information theoretic quantities, such as Fisher information, Shannon entropy, Onicescu information energy, and Ghosh-Berkowitz-Parr entropy, have been used to present a quantitative description for one of the most widely used concepts in chemistry, namely the steric effects. Taking the experimental steric scales for the different compounds as benchmark sets, there are reasonable linear relationships between the experimental scales of the steric effects and theoretical values of steric energies calculated from information theory functionals. Perusing the results obtained from the information theoretic quantities with the two representations of electron density and shape function, the Shannon entropy has the best performance for the purpose. On the one hand, the usefulness of considering the contributions of functional groups steric energies and geometries, and on the other hand, dissecting the effects of both global and local information measures simultaneously have also been explored. Furthermore, the utility of the information functionals for the description of steric effects in several chemical transformations, such as electrophilic and nucleophilic reactions and host-guest chemistry, has been analyzed. The functionals of information theory correlate remarkably with the stability of systems and experimental scales. Overall, these findings show that the information theoretic quantities can be introduced as quantitative measures of steric effects and provide further evidences of the quality of information theory toward helping theoreticians and experimentalists to interpret different problems in real systems. PMID:27321125

  17. ASK For Information Retrieval: Part I. Background and Theory.

    ERIC Educational Resources Information Center

    Belkin, N. J.; And Others

    1982-01-01

    Discusses background and theory underlying a design study for an interactive information retrieval system funded by the British Library Research and Development Department which will determine structural representations of anomalous states of knowledge (ASKs) underlying information needs. References are cited. (EJS)

  18. An Information-Related Systems Theory of Counseling

    ERIC Educational Resources Information Center

    Wilkinson, Melvin

    1973-01-01

    The author suggests that a systems theory of counseling should have an adequate theoretical foundation in the way various types of systems handle information. Using the works of W. R. Ashby as a basis, the paper is a beginning attempt to establish this foundation. It describes how information can be used to maintain homeostasis and protect key…

  19. Courtrooms, Politics, and Morality: Information Integration Theory is Theoretically Integrative.

    ERIC Educational Resources Information Center

    Kaplan, Martin F.; And Others

    The application of information integration concepts to judicial, political and moral judgments is discussed. The authors describe how the information integration theory can provide a unitary treatment of judgement on both conception and quantitative levels. The decision-making processes involved in the courtroom setting, political choice, and…

  20. Year 7 Students, Information Literacy, and Transfer: A Grounded Theory

    ERIC Educational Resources Information Center

    Herring, James E.

    2011-01-01

    This study examined the views of year 7 students, teacher librarians, and teachers in three state secondary schools in rural New South Wales, Australia, on information literacy and transfer. The aims of the study included the development of a grounded theory in relation to information literacy and transfer in these schools. The study's perspective…

  1. Cooperation and information replication in wireless networks.

    PubMed

    Poularakis, Konstantinos; Tassiulas, Leandros

    2016-03-01

    A significant portion of today's network traffic is due to recurring downloads of a few popular contents. It has been observed that replicating the latter in caches installed at network edges-close to users-can drastically reduce network bandwidth usage and improve content access delay. Such caching architectures are gaining increasing interest in recent years as a way of dealing with the explosive traffic growth, fuelled further by the downward slope in storage space price. In this work, we provide an overview of caching with a particular emphasis on emerging network architectures that enable caching at the radio access network. In this context, novel challenges arise due to the broadcast nature of the wireless medium, which allows simultaneously serving multiple users tuned into a multicast stream, and the mobility of the users who may be frequently handed off from one cell tower to another. Existing results indicate that caching at the wireless edge has a great potential in removing bottlenecks on the wired backbone networks. Taking into consideration the schedule of multicast service and mobility profiles is crucial to extract maximum benefit in network performance. PMID:26809574

  2. A Theory of Self-Learning in a Networked Human and IT Environment: Implications for Education Reforms.

    ERIC Educational Resources Information Center

    Mok, Magdalena Mo Ching; Cheng, Yin Cheong

    2001-01-01

    Discusses a theoretical model that can be used to deepen understanding of the nature and process of self-learning, and to facilitate students in becoming highly motivated and effective self-learners with the support of a networked human and information technology (IT) environment. Explores how the theory's implications can contribute to…

  3. An integration of integrated information theory with fundamental physics

    PubMed Central

    Barrett, Adam B.

    2014-01-01

    To truly eliminate Cartesian ghosts from the science of consciousness, we must describe consciousness as an aspect of the physical. Integrated Information Theory states that consciousness arises from intrinsic information generated by dynamical systems; however existing formulations of this theory are not applicable to standard models of fundamental physical entities. Modern physics has shown that fields are fundamental entities, and in particular that the electromagnetic field is fundamental. Here I hypothesize that consciousness arises from information intrinsic to fundamental fields. This hypothesis unites fundamental physics with what we know empirically about the neuroscience underlying consciousness, and it bypasses the need to consider quantum effects. PMID:24550877

  4. Optimizing protein representations with information theory.

    PubMed

    Mintseris, Julian; Weng, Zhiping

    2004-01-01

    The problem of describing a protein representation by breaking up the amino acids atoms into functionally similar atom groups has been addressed by many researchers in the past 25 years. They have used a variety of physical, chemical and biological criteria of varying degrees of rigor to essentially impose our understanding of protein structures onto various atom-typing schemes used in studies of protein folding, protein-protein and protein-ligand interactions, and others. Here, instead, we have chosen to rely primarily on the data and use information-theoretic techniques to dissect it. We show that we can obtain an optimized protein representation for a given alphabet size from protein monomers or protein interface datasets that are in agreement with general concepts of protein energetics. Closer inspection of the atom partitions led to interesting observations pointing to the greater importance of the hydrophobic interactions in protein monomers compared to interfaces and, conversely, greater importance of polar/charged interaction in protein interfaces. Comparing the atom partitions from the two datasets we show that the two are strikingly similar at alphabet size of five, proving that despite some differences, the general energetic concepts are very similar for folding and binding. Implications for further structural studies are discussed. PMID:15712119

  5. Analysing collaboration among HIV agencies through combining network theory and relational coordination.

    PubMed

    Khosla, Nidhi; Marsteller, Jill Ann; Hsu, Yea Jen; Elliott, David L

    2016-02-01

    Agencies with different foci (e.g. nutrition, social, medical, housing) serve people living with HIV (PLHIV). Serving needs of PLHIV comprehensively requires a high degree of coordination among agencies which often benefits from more frequent communication. We combined Social Network theory and Relational Coordination theory to study coordination among HIV agencies in Baltimore. Social Network theory implies that actors (e.g., HIV agencies) establish linkages amongst themselves in order to access resources (e.g., information). Relational Coordination theory suggests that high quality coordination among agencies or teams relies on the seven dimensions of frequency, timeliness and accuracy of communication, problem-solving communication, knowledge of agencies' work, mutual respect and shared goals. We collected data on frequency of contact from 57 agencies using a roster method. Response options were ordinal ranging from 'not at all' to 'daily'. We analyzed data using social network measures. Next, we selected agencies with which at least one-third of the sample reported monthly or more frequent interaction. This yielded 11 agencies whom we surveyed on seven relational coordination dimensions with questions scored on a Likert scale of 1-5. Network density, defined as the proportion of existing connections to all possible connections, was 20% when considering monthly or higher interaction. Relational coordination scores from individual agencies to others ranged between 1.17 and 5.00 (maximum possible score 5). The average scores for different dimensions across all agencies ranged between 3.30 and 4.00. Shared goals (4.00) and mutual respect (3.91) scores were highest, while scores such as knowledge of each other's work and problem-solving communication were relatively lower. Combining theoretically driven analyses in this manner offers an innovative way to provide a comprehensive picture of inter-agency coordination and the quality of exchange that underlies

  6. Complex Dynamics in Information Sharing Networks

    NASA Astrophysics Data System (ADS)

    Cronin, Bruce

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

  7. Applications of schema theory in information security teaching

    NASA Astrophysics Data System (ADS)

    Qiu, Chunyan; Zhao, Wei; Han, Jialing; Jiang, Jianhua

    2012-04-01

    Information security knowledge is more and more important to students in universities of finance and economics. However, mastering the skill of information security is not easy to them. Schema theory is applied into information security teaching to help students improve their skills. The teaching result shows that there is a significant difference in final exam and practice exam between the proposed model and a regular teaching model.

  8. Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.

    2005-01-01

    A long-running difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality of all red-world players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. This paper shows that the same information theoretic mathematical structure, known as Product Distribution (PD) theory, addresses both issues. In this, PD theory not only provides a principle formulation of bounded rationality and a set of new types of mean field theory in statistical physics; it also shows that those topics are fundamentally one and the same.

  9. Mining Heterogeneous Social Networks for Egocentric Information Abstraction

    NASA Astrophysics Data System (ADS)

    Li, Cheng-Te; Lin, Shou-De

    Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify combination of relations as features and compute statistical dependencies as feature values. These features, either linear or eyelie, intend to capture the semantic information in the surrounding environment of the ego. Then we design three abstraction measures to distill representative and important information to construct the abstracted graphs for visual presentation. The evaluations conducted on a real world movie datasct and an artificial crime dataset demonstrate that the abstractions can indeed retain significant information and facilitate more accurate and efficient human analysis.

  10. Application of four-layer neural network on information extraction.

    PubMed

    Han, Min; Cheng, Lei; Meng, Hua

    2003-01-01

    This paper applies neural network to extract marsh information. An adaptive back-propagation algorithm based on a robust error function is introduced to build a four-layer neural network, and it is used to classify Thematic Mapper (TM) image of Zhalong Wetland in China and then extract marsh information. Comparing marsh information extraction results of the four-layer neural network with three-layer neural network and the maximum likelihood classifier, conclusion can be drawn as follows: the structure of the four-layer neural network and the adaptive back-propagation algorithm based on the robust error function is effective to extract marsh information. The four-layer neural network adopted in this paper succeeded in building the complex model of TM image, and it avoided the problem of great storage of remotely sensed data, and the adaptive back-propagation algorithm speeded up the descending of error. Above all, the four-layer neural network is superior to the three-layer neural network and the maximum likelihood classifier in the accuracy of the total classification and marsh information extraction. PMID:12850006

  11. The Network Information Management System (NIMS) in the Deep Space Network

    NASA Technical Reports Server (NTRS)

    Wales, K. J.

    1983-01-01

    In an effort to better manage enormous amounts of administrative, engineering, and management data that is distributed worldwide, a study was conducted which identified the need for a network support system. The Network Information Management System (NIMS) will provide the Deep Space Network with the tools to provide an easily accessible source of valid information to support management activities and provide a more cost-effective method of acquiring, maintaining, and retrieval data.

  12. Tensegrity II. How structural networks influence cellular information processing networks

    NASA Technical Reports Server (NTRS)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  13. A worldwide population information network: status and goals.

    PubMed

    Kolbe, H K

    1978-07-01

    The rapid growth of world population and changes in government policies and programs have brought many changes to the area of population information. These include an increase in the amount of population information and funds devoted to research as well as an increase in awareness of the need to improve access to population information. Population information resources are located mainly in the developed countries, and no adequate information flow has yet been established to and from the developing nations. In response to this need, emerging regional population information networks are already identifiable. Focus is on components of an international population information network; North America and Europe; Latin America; Asia; Africa; and POPINS (worldwide population information system) Proposal, the model, and a counterproposal. It is evident that a strong North American European network is coalescing rapidly. The Latin American Population Documentation Systems (DOCPAL) offers the promise of bringing order to population information in Latin America. In Asia and Africa the situation in regard to population networks looks encouraging. During the next 2-year period the POPINS proposal will be carefully scrutinized. On the basis of these efforts, it seems reasonable to predict that within a 7-10 year period a de facto worldwide population information network will be a reality. PMID:10308566

  14. Social contagion theory: examining dynamic social networks and human behavior.

    PubMed

    Christakis, Nicholas A; Fowler, James H

    2013-02-20

    Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a 'three degrees of influence' property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations. PMID:22711416

  15. Social contagion theory: examining dynamic social networks and human behavior

    PubMed Central

    Christakis, Nicholas A.; Fowler, James H.

    2013-01-01

    Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a ‘three degrees of influence’ property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations. PMID:22711416

  16. Information Security Analysis Using Game Theory and Simulation

    SciTech Connect

    Schlicher, Bob G; Abercrombie, Robert K

    2012-01-01

    Information security analysis can be performed using game theory implemented in dynamic simulations of Agent Based Models (ABMs). Such simulations can be verified with the results from game theory analysis and further used to explore larger scale, real world scenarios involving multiple attackers, defenders, and information assets. Our approach addresses imperfect information and scalability that allows us to also address previous limitations of current stochastic game models. Such models only consider perfect information assuming that the defender is always able to detect attacks; assuming that the state transition probabilities are fixed before the game assuming that the players actions are always synchronous; and that most models are not scalable with the size and complexity of systems under consideration. Our use of ABMs yields results of selected experiments that demonstrate our proposed approach and provides a quantitative measure for realistic information systems and their related security scenarios.

  17. Incorporating profile information in community detection for online social networks

    NASA Astrophysics Data System (ADS)

    Fan, W.; Yeung, K. H.

    2014-07-01

    Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

  18. Advanced information processing system: Input/output network management software

    NASA Technical Reports Server (NTRS)

    Nagle, Gail; Alger, Linda; Kemp, Alexander

    1988-01-01

    The purpose of this document is to provide the software requirements and specifications for the Input/Output Network Management Services for the Advanced Information Processing System. This introduction and overview section is provided to briefly outline the overall architecture and software requirements of the AIPS system before discussing the details of the design requirements and specifications of the AIPS I/O Network Management software. A brief overview of the AIPS architecture followed by a more detailed description of the network architecture.

  19. 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…

  20. Information Systems and Networks for Technology Transfer. Final Report.

    ERIC Educational Resources Information Center

    Page, John; Szentivanyi, Tibor

    Results of a survey of the information resources available in industrialized countries which might be used in a United Nations technology transfer program for developing countries are presented. Information systems and networks, organized information collections of a scientific and technical character, and the machinery used to disseminate this…

  1. Education, Nation States and the Globalization of Information Networks.

    ERIC Educational Resources Information Center

    Selwyn, Neil; Brown, Philip

    2000-01-01

    Noting the emerging global policyscape toward creating global information networks, this article analyzes policymaking across Europe, the United States, and East Asia. Employing a political-economy perspective, it compares seven countries' networks, highlighting the state's role and varying relationships among education, economy, and society, and…

  2. Are Social Networking Websites Educational? Information Capsule. Volume 0909

    ERIC Educational Resources Information Center

    Blazer, Christie

    2009-01-01

    More and more school districts across the country are joining social networking sites, such as Facebook and MySpace. This Information Capsule discusses the frequency with which school districts are using social networking sites, how districts are using the sites, and potential drawbacks associated with their use. Issues for districts to consider…

  3. Libraries in the Global, National, and Local Networked Information Infrastructure.

    ERIC Educational Resources Information Center

    McClure, Charles R.

    This paper explores the challenges and opportunities facing libraries as they evolve into the electronic networked environment, and looks at options for libraries in the year 2000 and beyond. The internationally networked environment has fundamentally changed the way in which people acquire and use information resources and services. The paper…

  4. Information Networks: A Probablistic Model for Hierarchical Message Transfer.

    ERIC Educational Resources Information Center

    Bhat, U. Narayan; And Others

    A strictly hierarchical message transfer scheme requires that a message follow a specified referral path unless finally it is either rejected or filled at any one of the information centers of the network. Thus at each node in the network three decisions can be made: satisfy, reject or refer the message to the succeeding node in the hierarchy.…

  5. Telecommunication Networks for Libraries and Information Systems: Approaches to Development.

    ERIC Educational Resources Information Center

    Bystrom, John W.

    The focus of this paper is on the development of telecommunication networking by library and information networks. The word "development" here includes all the processes necessary to broad application and encompasses the necessary social, political, industrial and professional responses to technological advancement as well as technological…

  6. Reconstruction of transcriptional network from microarray data using combined mutual information and network-assisted regression.

    PubMed

    Wang, X-D; Qi, Y-X; Jiang, Z-L

    2011-03-01

    Many methods had been developed on inferring transcriptional network from gene expression. However, it is still necessary to design new method that discloses more detailed and exact network information. Using network-assisted regression, the authors combined the averaged three-way mutual information (AMI3) and non-linear ordinary differential equation (ODE) model to infer the transcriptional network, and to obtain both the topological structure and the regulatory dynamics. Synthetic and experimental data were used to evaluate the performance of the above approach. In comparison with the previous methods based on mutual information, AMI3 obtained higher precision with the same sensitivity. To describe the regulatory dynamics between transcription factors and target genes, network-assisted regression and regression without network, respectively, were applied in the steady-state and time series microarray data. The results revealed that comparing with regression without network, network-assisted regression increased the precision, but decreased the fitting goodness. Then, the authors reconstructed the transcriptional network of Escherichia coli and simulated the regulatory dynamics of genes. Furthermore, the authors' approach identified potential transcription factors regulating yeast cell cycle. In conclusion, network-assisted regression, combined AMI3 and ODE model, was a more precisely to infer the topological structure and the regulatory dynamics of transcriptional network from microarray data. [Includes supplementary material]. PMID:21405197

  7. Architectural Design for the Global Legal Information Network

    NASA Technical Reports Server (NTRS)

    Kalpakis, Konstantinos

    1999-01-01

    In this report, we provide a summary of our activities regarding the goals, requirements analysis, design, and prototype implementation for the Global Legal Information Network, a joint effort between the Law Library of Congress and NASA.

  8. Developing Visualization Techniques for Semantics-based Information Networks

    NASA Technical Reports Server (NTRS)

    Keller, Richard M.; Hall, David R.

    2003-01-01

    Information systems incorporating complex network structured information spaces with a semantic underpinning - such as hypermedia networks, semantic networks, topic maps, and concept maps - are being deployed to solve some of NASA s critical information management problems. This paper describes some of the human interaction and navigation problems associated with complex semantic information spaces and describes a set of new visual interface approaches to address these problems. A key strategy is to leverage semantic knowledge represented within these information spaces to construct abstractions and views that will be meaningful to the human user. Human-computer interaction methodologies will guide the development and evaluation of these approaches, which will benefit deployed NASA systems and also apply to information systems based on the emerging Semantic Web.

  9. Sender-receiver systems and applying information theory for quantitative synthetic biology.

    PubMed

    Barcena Menendez, Diego; Senthivel, Vivek Raj; Isalan, Mark

    2015-02-01

    Sender-receiver (S-R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S-R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning. PMID:25282688

  10. Sender–receiver systems and applying information theory for quantitative synthetic biology

    PubMed Central

    Barcena Menendez, Diego; Senthivel, Vivek Raj; Isalan, Mark

    2015-01-01

    Sender–receiver (S–R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S–R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning. PMID:25282688

  11. Feasibility Evaluation of an On-site Generator Network by the Cooperative Game Theory

    NASA Astrophysics Data System (ADS)

    Komiyama, Ryoichi; Hayashi, Taketo; Fujii, Yasumasa; Yamaji, Kenji

    On-site generator, such as CGS (cogeneration system), is allegedly considered to be an effective end-use energy system in order to accomplish primary energy conservation, CO2 emission mitigation and system cost reduction, which characteristics eventually improve the whole performance of an existing energy system for the future. Considering the drawback of installing an end-use CGS into the customer with small or middle scale floor space, however, it is difficult to achieve those distinctive features because the thermal-electricity ratio of CGS does not always be in agreement with that of customer energy demand. In order to overcome that matching deficiency, it is hence better to organize an on-site generator network based on mutual electricity and heating transmission. But focusing on some cogenerators underlying their behaviors on maximizing their own profits, this on-site network, which situation corresponds to a grand coalition, is not necessarily established because of each cogenerator’s motivation to form a partial coalition and acquire its own profit as much as possible. In this paper, we attempt to analyze the optimal operation of an on-site generator network and identify by applying the nucleolus of the cooperative game theory the optimal benefit allocation strategy in order for the cogenerators to construct the network. Regarding the installation site of this network, the center of Tokyo area is assumed, which locational information includes floor space and so forth through a GIS (geographic information system) database. The results from the nucleolus suggest that all districts should impartially obtain the benefit from organizing network for the purpose of jointly attaining the system total cost reduction.

  12. Turisbook: Social Network of Tourism with Geographical Information

    NASA Astrophysics Data System (ADS)

    García, Eva; García, Antonio; de-Marcos, Luis; Hilera, José-Ramón

    This article presents the social network of tourism Turisbook. This social network allows sharing information and opinions about worldwide travel destinations. It incorporates a geo-location system of points on a map, so that user can search quickly and easily tourist points in a given area. The system allows users to upload their own tourist points with associated information and photos. In this manner, the system feeds with user contributions, allowing discovering places that usually do not appear in traditional guidebooks.

  13. 75 FR 57521 - Networking and Information Technology Research and Development (NITRD) Program: Draft NITRD 2010...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-09-21

    ... FOUNDATION Networking and Information Technology Research and Development (NITRD) Program: Draft NITRD 2010... Coordination Office for Networking and Information Technology Research and Development (NITRD) requests... National Coordination Office for the Networking and Information Technology Research and Development...

  14. On a theory of stability for nonlinear stochastic chemical reaction networks

    NASA Astrophysics Data System (ADS)

    Smadbeck, Patrick; Kaznessis, Yiannis N.

    2015-05-01

    We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms.

  15. On a theory of stability for nonlinear stochastic chemical reaction networks

    SciTech Connect

    Smadbeck, Patrick; Kaznessis, Yiannis N.

    2015-05-14

    We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms.

  16. Order from noise: Toward a social theory of geographic information

    USGS Publications Warehouse

    Poore, B.S.; Chrisman, N.R.

    2006-01-01

    In the so-called Information Age, it is surprising that the concept of information is imprecisely defined and almost taken for granted. Historic and recent geographic information science (GIScience) literature relies on two conflicting metaphors, often espoused by the same author in adjacent paragraphs. The metaphor of invariance, derived from telecommunications engineering, defines information as a thing to be transported without loss through a conduit. Another metaphor, originating in the utopian movements of the 19th century, locates information within a hierarchy of refinement-a stopping place on the path to convert mere data into higher forms of knowledge and perhaps to wisdom. Both metaphors rely on long-forgotten debates outside geography and preclude us from seeing that there are important social and ethical concerns in the relationship between geographic information technologies and society. We examine the conflicts between competing metaphors and propose a social theory of geographic information. ?? 2006 by Association of American Geographers.

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

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  18. Information routing driven by background chatter in a signaling network.

    PubMed

    Domedel-Puig, Núria; Rué, Pau; Pons, Antonio J; García-Ojalvo, Jordi

    2011-12-01

    Living systems are capable of processing multiple sources of information simultaneously. This is true even at the cellular level, where not only coexisting signals stimulate the cell, but also the presence of fluctuating conditions is significant. When information is received by a cell signaling network via one specific input, the existence of other stimuli can provide a background activity -or chatter- that may affect signal transmission through the network and, therefore, the response of the cell. Here we study the modulation of information processing by chatter in the signaling network of a human cell, specifically, in a Boolean model of the signal transduction network of a fibroblast. We observe that the level of external chatter shapes the response of the system to information carrying signals in a nontrivial manner, modulates the activity levels of the network outputs, and effectively determines the paths of information flow. Our results show that the interactions and node dynamics, far from being random, confer versatility to the signaling network and allow transitions between different information-processing scenarios. PMID:22174668

  19. EDITORIAL: Focus on Quantum Information and Many-Body Theory

    NASA Astrophysics Data System (ADS)

    Eisert, Jens; Plenio, Martin B.

    2010-02-01

    Quantum many-body models describing natural systems or materials and physical systems assembled piece by piece in the laboratory for the purpose of realizing quantum information processing share an important feature: intricate correlations that originate from the coherent interaction between a large number of constituents. In recent years it has become manifest that the cross-fertilization between research devoted to quantum information science and to quantum many-body physics leads to new ideas, methods, tools, and insights in both fields. Issues of criticality, quantum phase transitions, quantum order and magnetism that play a role in one field find relations to the classical simulation of quantum systems, to error correction and fault tolerance thresholds, to channel capacities and to topological quantum computation, to name but a few. The structural similarities of typical problems in both fields and the potential for pooling of ideas then become manifest. Notably, methods and ideas from quantum information have provided fresh approaches to long-standing problems in strongly correlated systems in the condensed matter context, including both numerical methods and conceptual insights. Focus on quantum information and many-body theory Contents TENSOR NETWORKS Homogeneous multiscale entanglement renormalization ansatz tensor networks for quantum critical systems M Rizzi, S Montangero, P Silvi, V Giovannetti and Rosario Fazio Concatenated tensor network states R Hübener, V Nebendahl and W Dür Entanglement renormalization in free bosonic systems: real-space versus momentum-space renormalization group transforms G Evenbly and G Vidal Finite-size geometric entanglement from tensor network algorithms Qian-Qian Shi, Román Orús, John Ove Fjærestad and Huan-Qiang Zhou Characterizing symmetries in a projected entangled pair state D Pérez-García, M Sanz, C E González-Guillén, M M Wolf and J I Cirac Matrix product operator representations B Pirvu, V Murg, J I Cirac

  20. EDITORIAL: Focus on Quantum Information and Many-Body Theory

    NASA Astrophysics Data System (ADS)

    Eisert, Jens; Plenio, Martin B.

    2010-02-01

    Quantum many-body models describing natural systems or materials and physical systems assembled piece by piece in the laboratory for the purpose of realizing quantum information processing share an important feature: intricate correlations that originate from the coherent interaction between a large number of constituents. In recent years it has become manifest that the cross-fertilization between research devoted to quantum information science and to quantum many-body physics leads to new ideas, methods, tools, and insights in both fields. Issues of criticality, quantum phase transitions, quantum order and magnetism that play a role in one field find relations to the classical simulation of quantum systems, to error correction and fault tolerance thresholds, to channel capacities and to topological quantum computation, to name but a few. The structural similarities of typical problems in both fields and the potential for pooling of ideas then become manifest. Notably, methods and ideas from quantum information have provided fresh approaches to long-standing problems in strongly correlated systems in the condensed matter context, including both numerical methods and conceptual insights. Focus on quantum information and many-body theory Contents TENSOR NETWORKS Homogeneous multiscale entanglement renormalization ansatz tensor networks for quantum critical systems M Rizzi, S Montangero, P Silvi, V Giovannetti and Rosario Fazio Concatenated tensor network states R Hübener, V Nebendahl and W Dür Entanglement renormalization in free bosonic systems: real-space versus momentum-space renormalization group transforms G Evenbly and G Vidal Finite-size geometric entanglement from tensor network algorithms Qian-Qian Shi, Román Orús, John Ove Fjærestad and Huan-Qiang Zhou Characterizing symmetries in a projected entangled pair state D Pérez-García, M Sanz, C E González-Guillén, M M Wolf and J I Cirac Matrix product operator representations B Pirvu, V Murg, J I Cirac

  1. Network algorithms for information analysis using the Titan Toolkit.

    SciTech Connect

    McLendon, William Clarence, III; Baumes, Jeffrey; Wilson, Andrew T.; Wylie, Brian Neil; Shead, Timothy M.

    2010-07-01

    The analysis of networked activities is dramatically more challenging than many traditional kinds of analysis. A network is defined by a set of entities (people, organizations, banks, computers, etc.) linked by various types of relationships. These entities and relationships are often uninteresting alone, and only become significant in aggregate. The analysis and visualization of these networks is one of the driving factors behind the creation of the Titan Toolkit. Given the broad set of problem domains and the wide ranging databases in use by the information analysis community, the Titan Toolkit's flexible, component based pipeline provides an excellent platform for constructing specific combinations of network algorithms and visualizations.

  2. The analysis of network transmission method for welding robot information

    NASA Astrophysics Data System (ADS)

    Cheng, Weide; Zhang, Hua; Liu, Donghua; Wang, Hongbo

    2012-01-01

    On the asis of User Datagram Protocol (UserDatagram Protocol, UDP), to do some improvement and design a welding robot network communication protocol (welding robot network communicate protocol: WRNCP), working on the fields of the transport layer and application layer of TCP / IP protocol. According to the characteristics of video data, to design the radio push-type (Broadcast Push Model, BPM) transmission method, improving the efficiency and stability of video transmission.and to designed the network information transmission system, used for real-time control of welding robot network.

  3. The analysis of network transmission method for welding robot information

    NASA Astrophysics Data System (ADS)

    Cheng, Weide; Zhang, Hua; Liu, Donghua; Wang, Hongbo

    2011-12-01

    On the asis of User Datagram Protocol (UserDatagram Protocol, UDP), to do some improvement and design a welding robot network communication protocol (welding robot network communicate protocol: WRNCP), working on the fields of the transport layer and application layer of TCP / IP protocol. According to the characteristics of video data, to design the radio push-type (Broadcast Push Model, BPM) transmission method, improving the efficiency and stability of video transmission.and to designed the network information transmission system, used for real-time control of welding robot network.

  4. The Philosophy of Information as an Underlying and Unifying Theory of Information Science

    ERIC Educational Resources Information Center

    Tomic, Taeda

    2010-01-01

    Introduction: Philosophical analyses of theoretical principles underlying these sub-domains reveal philosophy of information as underlying meta-theory of information science. Method: Conceptual research on the knowledge sub-domains in information science and philosophy and analysis of their mutual connection. Analysis: Similarities between…

  5. Activity Theory in Information Systems Research and Practice: Theoretical Underpinnings for an Information Systems Development Model

    ERIC Educational Resources Information Center

    Mursu, Anja; Luukkonen, Irmeli; Toivanen, Marika; Korpela, Mikko

    2007-01-01

    Introduction: The purpose of information systems is to facilitate work activities: here we consider how Activity Theory can be applied in information systems development. Method. The requirements for an analytical model for emancipatory, work-oriented information systems research and practice are specified. Previous research work in Activity…

  6. Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?

    PubMed Central

    Antonopoulos, Chris G.; Srivastava, Shambhavi; Pinto, Sandro E. de S.; Baptista, Murilo S.

    2015-01-01

    We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization. PMID:26317592

  7. Automated Physico-Chemical Cell Model Development through Information Theory

    SciTech Connect

    Peter J. Ortoleva

    2005-11-29

    The objective of this project was to develop predictive models of the chemical responses of microbial cells to variations in their surroundings. The application of these models is optimization of environmental remediation and energy-producing biotechnical processes.The principles on which our project is based are as follows: chemical thermodynamics and kinetics; automation of calibration through information theory; integration of multiplex data (e.g. cDNA microarrays, NMR, proteomics), cell modeling, and bifurcation theory to overcome cellular complexity; and the use of multiplex data and information theory to calibrate and run an incomplete model. In this report we review four papers summarizing key findings and a web-enabled, multiple module workflow we have implemented that consists of a set of interoperable systems biology computational modules.

  8. Social networks predict selective observation and information spread in ravens

    PubMed Central

    Rubenstein, Daniel I.; Bugnyar, Thomas; Hoppitt, William; Mikus, Nace; Schwab, Christine

    2016-01-01

    Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission. PMID:27493780

  9. Social networks predict selective observation and information spread in ravens.

    PubMed

    Kulahci, Ipek G; Rubenstein, Daniel I; Bugnyar, Thomas; Hoppitt, William; Mikus, Nace; Schwab, Christine

    2016-07-01

    Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission. PMID:27493780

  10. Competition between Homophily and Information Entropy Maximization in Social Networks

    PubMed Central

    Zhao, Jichang; Liang, Xiao; Xu, Ke

    2015-01-01

    In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective. PMID:26334994

  11. Competition between Homophily and Information Entropy Maximization in Social Networks.

    PubMed

    Zhao, Jichang; Liang, Xiao; Xu, Ke

    2015-01-01

    In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective. PMID:26334994

  12. Information Architecture without Internal Theory: An Inductive Design Process.

    ERIC Educational Resources Information Center

    Haverty, Marsha

    2002-01-01

    Suggests that information architecture design is primarily an inductive process, partly because it lacks internal theory and partly because it is an activity that supports emergent phenomena (user experiences) from basic design components. Suggests a resemblance to Constructive Induction, a design process that locates the best representational…

  13. Attachment-dissociation network: some thoughts about a modern complex theory.

    PubMed

    Bovensiepen, Gustav

    2006-06-01

    The paper revises the complex theory in the light of modern infant research, neurosciences and object relation theory. The author takes up Jean Knox's idea to understand complexes as analogies to the internal working models of attachment theory. The author proposes to understand complexes as dissociated sub-networks out of the network structure of the psyche; these sub-networks contain the internal working models, the characteristic affects and unconscious expectation phantasies. With this network model one can try to understand severe defensive organizations in some patients as a pathological organization of different complexes. This is illustrated by a clinical example. PMID:16712687

  14. Dynamics of rumor-like information dissemination in complex networks

    NASA Astrophysics Data System (ADS)

    Nekovee, Maziar; Moreno, Yamir; Bianconi, Ginestra

    2005-03-01

    An important dynamic process that takes place in complex networks is the spreading of information via rumor-like mechanisms. In addition to their relevance to propagation of rumors and fads in human society, such mechanism are also the basis of an important class of collective communication protocols in complex computer networks, such as the Internet and the peer-to-peer systems. In this talk we present results of our analytical, numerical and large-scale Monte Carlo simulation studies of this process on several classes of complex networks, including random graphs, scale-free networks, and random and small-world topological graphs. Our studies point out to important differences between the dynamics of rumor spreading and that of virus spreading in such networks, and provide new insights into the complex interplay between the spreading phenomena and network topology.

  15. How multiple social networks affect user awareness: The information diffusion process in multiplex networks

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Tang, Shaoting; Fang, Wenyi; Guo, Quantong; Zhang, Xiao; Zheng, Zhiming

    2015-10-01

    The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ . Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.

  16. Reassembling the Social - An Introduction to Actor-Network-Theory

    NASA Astrophysics Data System (ADS)

    Latour, Bruno

    2005-09-01

    Reassembling the Social is a fundamental challenge from one of the world's leading social theorists to how we understand society and the 'social'. Bruno Latour's contention is that the world 'social' as used by Social Scientists has become laden with assumptions to the point where it has become misnomer. When the adjective is applied to a phenomenon, it is used to indicate a stabilized state of affairs, a bundle of ties that in due course may be used to account for another phenomenon. But Latour also finds the word used as if it described a type of material, in a comparable way to an adjective such as 'wooden' or 'steely'. Rather than simply indicating what is already assembled together, it is now used in a way that makes assumptions about the nature of what is assembled. It has become a word that designates two distinct things: a process of assembling: and a type of material, distinct from others. Latour shows why 'the social' cannot be thought of as a kind of material or domain, and disputes attempts to provide a 'social explanation' of other states of affairs. While these attempts have been productive (and probably necessary) in the past, the very success of the social sciences mean that they are largely no longer so. At the present stage it is no longer possible to inspect the precise constituents entering the social domain. Latour returns to the original meaning of 'the social' to redefine the notion and allow it to trace connections again. It will then be possible to resume the traditional goal of the social sciences, but using more refined tools. Drawing on his extensive work examining the 'assemblages' of nature, Latour finds it necessary to scrutinize thoroughly the exact content of what is assembled under the umbrella of Society. This approach, a 'sociology of associations' has become known as Actor-Network-Theory, and this book is an essential introduction both for those seeking to understand Actor-Network-Theory, or the ideas of one of its most

  17. Unified-theory-of-reinforcement neural networks do not simulate the blocking effect.

    PubMed

    Calvin, Nicholas T; J McDowell, J

    2015-11-01

    For the last 20 years the unified theory of reinforcement (Donahoe et al., 1993) has been used to develop computer simulations to evaluate its plausibility as an account for behavior. The unified theory of reinforcement states that operant and respondent learning occurs via the same neural mechanisms. As part of a larger project to evaluate the operant behavior predicted by the theory, this project was the first replication of neural network models based on the unified theory of reinforcement. In the process of replicating these neural network models it became apparent that a previously published finding, namely, that the networks simulate the blocking phenomenon (Donahoe et al., 1993), was a misinterpretation of the data. We show that the apparent blocking produced by these networks is an artifact of the inability of these networks to generate the same conditioned response to multiple stimuli. The piecemeal approach to evaluate the unified theory of reinforcement via simulation is critiqued and alternatives are discussed. PMID:26319369

  18. Network application and framework for quality of information processing

    NASA Astrophysics Data System (ADS)

    Marcus, Kelvin; Cook, Trevor; Scott, Lisa; Toth, Andrew

    2012-06-01

    To improve the effectiveness of network-centric decision making, we present a distributed network application and framework that provides users with actionable intelligence reports to support counter insurgency operations. ARL's Quality of Information (QoI) Intelligence Report Application uses QoI metrics like timeliness, accuracy, and precision combined with associated network performance data, such as throughput and latency, and mission-specific information requirements to deliver high quality data to users; that is data delivered in a manner which best supports the ability to make more informed decisions as it relates to the current mission. This application serves as a testing platform for integrated experimentation and validation of QoI processing techniques and methodologies. In this paper, we present the software-system framework and architecture, and show an example scenario that highlights how the framework aids in network integration and enables better data-to-decision.

  19. Nambu-Goldstone effective theory of information at quantum criticality

    NASA Astrophysics Data System (ADS)

    Dvali, Gia; Franca, Andre; Gomez, Cesar; Wintergerst, Nico

    2015-12-01

    We establish a fundamental connection between quantum criticality of a many-body system, such as Bose-Einstein condensates, and its capacity of information-storage and processing. For deriving the effective theory of modes in the vicinity of the quantum critical point, we develop a new method by mapping a Bose-Einstein condensate of N -particles onto a sigma model with a continuous global (pseudo)symmetry that mixes bosons of different momenta. The Bogolyubov modes of the condensate are mapped onto the Goldstone modes of the sigma model, which become gapless at the critical point. These gapless Goldstone modes are the quantum carriers of information and entropy. Analyzing their effective theory, we observe information-processing properties strikingly similar to the ones predicted by the black hole portrait. The energy cost per qubit of information-storage vanishes in the large-N limit and the total information-storage capacity increases with N either exponentially or as a power law. The longevity of information-storage also increases with N , whereas the scrambling time in the over-critical regime is controlled by the Lyapunov exponent and scales logarithmically with N . This connection reveals that the origin of black hole information storage lies in the quantum criticality of the graviton Bose-gas, and that much simpler systems that can be manufactured in table-top experiments can exhibit very similar information-processing dynamics.

  20. Inferring influenza global transmission networks without complete phylogenetic information

    PubMed Central

    Aris-Brosou, Stéphane

    2014-01-01

    Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25 years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence. PMID:24665342

  1. Temporal variability of the optimal monitoring setup assessed using information theory

    NASA Astrophysics Data System (ADS)

    Fahle, Marcus; Hohenbrink, Tobias L.; Dietrich, Ottfried; Lischeid, Gunnar

    2015-09-01

    Hydrology is rich in methods that use information theory to evaluate monitoring networks. Yet in most existing studies, only the available data set as a whole is used, which neglects the intraannual variability of the hydrological system. In this paper, we demonstrate how this variability can be considered by extending monitoring evaluation to subsets of the available data. Therefore, we separately evaluated time windows of fixed length, which were shifted through the data set, and successively extended time windows. We used basic information theory measures and a greedy ranking algorithm based on the criterion of maximum information/minimum redundancy. The network investigated monitored surface and groundwater levels at quarter-hourly intervals and was located at an artificially drained lowland site in the Spreewald region in north-east Germany. The results revealed that some of the monitoring stations were of value permanently while others were needed only temporally. The prevailing meteorological conditions, particularly the amount of precipitation, affected the degree of similarity between the water levels measured. The hydrological system tended to act more individually during periods of no or little rainfall. The optimal monitoring setup, its stability, and the monitoring effort necessary were influenced by the meteorological forcing. Altogether, the methodology presented can help achieve a monitoring network design that has a more even performance or covers the conditions of interest (e.g., floods or droughts) best.

  2. ("Un")Doing Standards in Education with Actor-Network Theory

    ERIC Educational Resources Information Center

    Fenwick, Tara J.

    2010-01-01

    Recent critiques have drawn important attention to the depoliticized consensus and empty promises embedded in network discourses of educational policy. While acceding this critique, this discussion argues that some forms of network analysis--specifically those adopting actor-network theory (ANT) approaches--actually offer useful theoretical…

  3. Scalable Hierarchical Network Management System for Displaying Network Information in Three Dimensions

    NASA Technical Reports Server (NTRS)

    George, Jude (Inventor); Schlecht, Leslie (Inventor); McCabe, James D. (Inventor); LeKashman, John Jr. (Inventor)

    1998-01-01

    A network management system has SNMP agents distributed at one or more sites, an input output module at each site, and a server module located at a selected site for communicating with input output modules, each of which is configured for both SNMP and HNMP communications. The server module is configured exclusively for HNMP communications, and it communicates with each input output module according to the HNMP. Non-iconified, informationally complete views are provided of network elements to aid in network management.

  4. Symmetric polynomials in information theory: Entropy and subentropy

    SciTech Connect

    Jozsa, Richard; Mitchison, Graeme

    2015-06-15

    Entropy and other fundamental quantities of information theory are customarily expressed and manipulated as functions of probabilities. Here we study the entropy H and subentropy Q as functions of the elementary symmetric polynomials in the probabilities and reveal a series of remarkable properties. Derivatives of all orders are shown to satisfy a complete monotonicity property. H and Q themselves become multivariate Bernstein functions and we derive the density functions of their Levy-Khintchine representations. We also show that H and Q are Pick functions in each symmetric polynomial variable separately. Furthermore, we see that H and the intrinsically quantum informational quantity Q become surprisingly closely related in functional form, suggesting a special significance for the symmetric polynomials in quantum information theory. Using the symmetric polynomials, we also derive a series of further properties of H and Q.

  5. Stimulus information stored in lasting active and hidden network states is destroyed by network bursts.

    PubMed

    Dranias, Mark R; Westover, M Brandon; Cash, Sidney; VanDongen, Antonius M J

    2015-01-01

    In both humans and animals brief synchronizing bursts of epileptiform activity known as interictal epileptiform discharges (IEDs) can, even in the absence of overt seizures, cause transient cognitive impairments (TCI) that include problems with perception or short-term memory. While no evidence from single units is available, it has been assumed that IEDs destroy information represented in neuronal networks. Cultured neuronal networks are a model for generic cortical microcircuits, and their spontaneous activity is characterized by the presence of synchronized network bursts (SNBs), which share a number of properties with IEDs, including the high degree of synchronization and their spontaneous occurrence in the absence of an external stimulus. As a model approach to understanding the processes underlying IEDs, optogenetic stimulation and multielectrode array (MEA) recordings of cultured neuronal networks were used to study whether stimulus information represented in these networks survives SNBs. When such networks are optically stimulated they encode and maintain stimulus information for as long as one second. Experiments involved recording the network response to a single stimulus and trials where two different stimuli were presented sequentially, akin to a paired pulse trial. We broke the sequential stimulus trials into encoding, delay and readout phases and found that regardless of which phase the SNB occurs, stimulus-specific information was impaired. SNBs were observed to increase the mean network firing rate, but this did not translate monotonically into increases in network entropy. It was found that the more excitable a network, the more stereotyped its response was during a network burst. These measurements speak to whether SNBs are capable of transmitting information in addition to blocking it. These results are consistent with previous reports and provide baseline predictions concerning the neural mechanisms by which IEDs might cause TCI. PMID:25755638

  6. Stimulus information stored in lasting active and hidden network states is destroyed by network bursts

    PubMed Central

    Dranias, Mark R.; Westover, M. Brandon; Cash, Sidney; VanDongen, Antonius M. J.

    2015-01-01

    In both humans and animals brief synchronizing bursts of epileptiform activity known as interictal epileptiform discharges (IEDs) can, even in the absence of overt seizures, cause transient cognitive impairments (TCI) that include problems with perception or short-term memory. While no evidence from single units is available, it has been assumed that IEDs destroy information represented in neuronal networks. Cultured neuronal networks are a model for generic cortical microcircuits, and their spontaneous activity is characterized by the presence of synchronized network bursts (SNBs), which share a number of properties with IEDs, including the high degree of synchronization and their spontaneous occurrence in the absence of an external stimulus. As a model approach to understanding the processes underlying IEDs, optogenetic stimulation and multielectrode array (MEA) recordings of cultured neuronal networks were used to study whether stimulus information represented in these networks survives SNBs. When such networks are optically stimulated they encode and maintain stimulus information for as long as one second. Experiments involved recording the network response to a single stimulus and trials where two different stimuli were presented sequentially, akin to a paired pulse trial. We broke the sequential stimulus trials into encoding, delay and readout phases and found that regardless of which phase the SNB occurs, stimulus-specific information was impaired. SNBs were observed to increase the mean network firing rate, but this did not translate monotonically into increases in network entropy. It was found that the more excitable a network, the more stereotyped its response was during a network burst. These measurements speak to whether SNBs are capable of transmitting information in addition to blocking it. These results are consistent with previous reports and provide baseline predictions concerning the neural mechanisms by which IEDs might cause TCI. PMID:25755638

  7. Creating Possible Selves: Information Disclosure Behaviour on Social Networks

    ERIC Educational Resources Information Center

    Bronstein, Jenny

    2014-01-01

    Introduction: This study investigates the creation of alternative identities or possible selves on social networks by examining self-presentation and self-disclosure as elements of the information disclosure behaviour of Facebook users. Method. An online questionnaire was distributed amongst library and information science students at Bar-Ilan…

  8. Museum Information Networks. Museum Data Bank Research Report No. 5.

    ERIC Educational Resources Information Center

    Chenhall, Robert G.

    Several museum information processing systems are discussed in relation to their underlying assumptions regarding museum information and networking needs that have resulted in inadequate service to some kinds of museums, particularly historical museums. Rather than any of these systems, a modular approach is proposed that would allow each museum…

  9. Child Rights Information Network Newsletter, 2000-2002.

    ERIC Educational Resources Information Center

    Khan, Andrea, Ed.; Greenwood, Laura, Ed.

    These five newsletter issues communicate activities of the Child Rights Information Network (CRIN) and report on information resources and world-wide activities concerning children and child rights. The March 2000 issue focuses on children's right to education, assessing the matter form a range of differing perspectives, at international and…

  10. Accessibility and Integrity of Networked Information Collections. Background Paper.

    ERIC Educational Resources Information Center

    Lynch, Clifford A.

    This paper considers questions related to the integrity and accessibility of new electronic information resources. It begins with a review of recent developments in networked information resources and the tools to identify, navigate, and use such resources. An overview is then given of the issues involved in access and integrity questions. Links…

  11. Gender, Information and Communication Networks: Implications for Extension.

    ERIC Educational Resources Information Center

    Subedi, A.; Garforth, Chr.

    1996-01-01

    In Nepal a study of 243 female and 179 male farmers showed that males demonstrated horizontal communication patterns among themselves; females actively sought information in both horizontal and heterogeneous patterns. Extension strategies that take advantage of gender-based networks were suggested to increase the flow of information. (SK)

  12. Towards Data Abstraction in Networked Information Retrieval Systems.

    ERIC Educational Resources Information Center

    Fuhr, Norbert

    1999-01-01

    Discussion of networked information retrieval and the interoperability of heterogeneous information-retrieval systems shows how differences concerning search operators and database schemas can be handled by applying data-abstraction concepts in combination with uncertain inference. Highlights include data types, data independence, schema…

  13. University medical information network--past, present, and future.

    PubMed

    Kiuchi, T; Sakurai, T; Ohe, K; Ohashi, Y; Kaihara, S

    1998-01-01

    The University Medical Information Network (UMIN), established in 1989, is a network service organization for national university hospitals in Japan. It has provided various medical information services to medical professionals, including database, electronic mail, and news services. Although its initial network was constructed as a closed network using N1 protocol, it now adopts TCP/IP protocol and is open to other medical professionals via the Internet. The next UMIN network system is planned to be constructed as a secure virtual closed network on the Internet, using cipher technology, and to provide secure information services to national university hospitals via the closed network, and to other medical professional via the Internet. User friendly interface and flexible system development were made possible by adopting TCP/IP, and the number of users dramatically increased accordingly. However, the database, software design, and human organizations developed in the N1 era have now proven to be of great value, and contribute to todayís flourishing state of the UMIN. PMID:10384491

  14. Entropy and information causality in general probabilistic theories

    NASA Astrophysics Data System (ADS)

    Barnum, Howard; Barrett, Jonathan; Orloff Clark, Lisa; Leifer, Matthew; Spekkens, Robert; Stepanik, Nicholas; Wilce, Alex; Wilke, Robin

    2010-03-01

    We investigate the concept of entropy in probabilistic theories more general than quantum mechanics, with particular reference to the notion of information causality (IC) recently proposed by Pawlowski et al (2009 arXiv:0905.2292). We consider two entropic quantities, which we term measurement and mixing entropy. In the context of classical and quantum theory, these coincide, being given by the Shannon and von Neumann entropies, respectively; in general, however, they are very different. In particular, while measurement entropy is easily seen to be concave, mixing entropy need not be. In fact, as we show, mixing entropy is not concave whenever the state space is a non-simplicial polytope. Thus, the condition that measurement and mixing entropies coincide is a strong constraint on possible theories. We call theories with this property monoentropic. Measurement entropy is subadditive, but not in general strongly subadditive. Equivalently, if we define the mutual information between two systems A and B by the usual formula I(A: B)=H(A)+H(B)-H(AB), where H denotes the measurement entropy and AB is a non-signaling composite of A and B, then it can happen that I(A:BC)theory in which measurement entropy is strongly subadditive, and also satisfies a version of the Holevo bound, is informationally causal, and on the other hand we observe that Popescu-Rohrlich boxes, which violate IC, also violate strong subadditivity. We also explore the interplay between measurement and mixing entropy and various natural conditions on theories that arise in quantum axiomatics.

  15. Mutual information-based LPI optimisation for radar network

    NASA Astrophysics Data System (ADS)

    Shi, Chenguang; Zhou, Jianjiang; Wang, Fei; Chen, Jun

    2015-07-01

    Radar network can offer significant performance improvement for target detection and information extraction employing spatial diversity. For a fixed number of radars, the achievable mutual information (MI) for estimating the target parameters may extend beyond a predefined threshold with full power transmission. In this paper, an effective low probability of intercept (LPI) optimisation algorithm is presented to improve LPI performance for radar network. Based on radar network system model, we first provide Schleher intercept factor for radar network as an optimisation metric for LPI performance. Then, a novel LPI optimisation algorithm is presented, where for a predefined MI threshold, Schleher intercept factor for radar network is minimised by optimising the transmission power allocation among radars in the network such that the enhanced LPI performance for radar network can be achieved. The genetic algorithm based on nonlinear programming (GA-NP) is employed to solve the resulting nonconvex and nonlinear optimisation problem. Some simulations demonstrate that the proposed algorithm is valuable and effective to improve the LPI performance for radar network.

  16. Enhancing topology adaptation in information-sharing social networks

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

  17. Design of surface-water data networks for regional information

    USGS Publications Warehouse

    Moss, Marshall E.; Gilroy, E.J.; Tasker, Gary D.; Karlinger, M.R.

    1982-01-01

    This report describes a technique, Network Analysis of Regional Information (NARI), and the existing computer procedures that have been developed for the specification of the regional information-cost relation for several statistical parameters of streamflow. The measure of information used is the true standard error of estimate of a regional logarithmic regression. The cost is a function of the number of stations at which hydrologic data are collected and the number of years for which the data are collected. The technique can be used to obtain either (1) a minimum cost network that will attain a prespecified accuracy and reliability or (2) a network that maximizes information given a set of budgetary and time constraints.

  18. Diffusion processes of fragmentary information on scale-free networks

    NASA Astrophysics Data System (ADS)

    Li, Xun; Cao, Lang

    2016-05-01

    Compartmental models of diffusion over contact networks have proven representative of real-life propagation phenomena among interacting individuals. However, there is a broad class of collective spreading mechanisms departing from compartmental representations, including those for diffusive objects capable of fragmentation and transmission unnecessarily as a whole. Here, we consider a continuous-state susceptible-infected-susceptible (SIS) model as an ideal limit-case of diffusion processes of fragmentary information on networks, where individuals possess fractions of the information content and update them by selectively exchanging messages with partners in the vicinity. Specifically, we incorporate local information, such as neighbors' node degrees and carried contents, into the individual partner choice, and examine the roles of a variety of such strategies in the information diffusion process, both qualitatively and quantitatively. Our method provides an effective and flexible route of modulating continuous-state diffusion dynamics on networks and has potential in a wide array of practical applications.

  19. Using Bayesian networks to support decision-focused information retrieval

    SciTech Connect

    Lehner, P.; Elsaesser, C.; Seligman, L.

    1996-12-31

    This paper has described an approach to controlling the process of pulling data/information from distributed data bases in a way that is specific to a persons specific decision making context. Our prototype implementation of this approach uses a knowledge-based planner to generate a plan, an automatically constructed Bayesian network to evaluate the plan, specialized processing of the network to derive key information items that would substantially impact the evaluation of the plan (e.g., determine that replanning is needed), automated construction of Standing Requests for Information (SRIs) which are automated functions that monitor changes and trends in distributed data base that are relevant to the key information items. This emphasis of this paper is on how Bayesian networks are used.

  20. What Information Theory Says about Bounded Rational Best Response

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.

    2005-01-01

    Probability Collectives (PC) provides the information-theoretic extension of conventional full-rationality game theory to bounded rational games. Here an explicit solution to the equations giving the bounded rationality equilibrium of a game is presented. Then PC is used to investigate games in which the players use bounded rational best-response strategies. Next it is shown that in the continuum-time limit, bounded rational best response games result in a variant of the replicator dynamics of evolutionary game theory. It is then shown that for team (shared-payoff) games, this variant of replicator dynamics is identical to Newton-Raphson iterative optimization of the shared utility function.

  1. Using complex networks towards information retrieval and diagnostics in multidimensional imaging

    PubMed Central

    Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

    2015-01-01

    We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers. PMID:26626047

  2. Using complex networks towards information retrieval and diagnostics in multidimensional imaging

    NASA Astrophysics Data System (ADS)

    Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

    2015-12-01

    We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.

  3. Ranking streamflow model performance based on Information theory metrics

    NASA Astrophysics Data System (ADS)

    Martinez, Gonzalo; Pachepsky, Yakov; Pan, Feng; Wagener, Thorsten; Nicholson, Thomas

    2016-04-01

    The accuracy-based model performance metrics not necessarily reflect the qualitative correspondence between simulated and measured streamflow time series. The objective of this work was to use the information theory-based metrics to see whether they can be used as complementary tool for hydrologic model evaluation and selection. We simulated 10-year streamflow time series in five watersheds located in Texas, North Carolina, Mississippi, and West Virginia. Eight model of different complexity were applied. The information-theory based metrics were obtained after representing the time series as strings of symbols where different symbols corresponded to different quantiles of the probability distribution of streamflow. The symbol alphabet was used. Three metrics were computed for those strings - mean information gain that measures the randomness of the signal, effective measure complexity that characterizes predictability and fluctuation complexity that characterizes the presence of a pattern in the signal. The observed streamflow time series has smaller information content and larger complexity metrics than the precipitation time series. Watersheds served as information filters and and streamflow time series were less random and more complex than the ones of precipitation. This is reflected the fact that the watershed acts as the information filter in the hydrologic conversion process from precipitation to streamflow. The Nash Sutcliffe efficiency metric increased as the complexity of models increased, but in many cases several model had this efficiency values not statistically significant from each other. In such cases, ranking models by the closeness of the information-theory based parameters in simulated and measured streamflow time series can provide an additional criterion for the evaluation of hydrologic model performance.

  4. DEFENSE ENVIRONMENTAL NETWORK AND INFORMATION EXCHANGE (DENIX)

    EPA Science Inventory

    DENIX serves as a central platform for the dissemination of environment, safety and occupational health (ESOH) news, policy, and guidance within Department of Defense (DoD) activities worldwide, in support of the national defense mission. DENIX informs ESOH professionals of salie...

  5. A DNA network as an information processing system.

    PubMed

    Santini, Cristina Costa; Bath, Jonathan; Turberfield, Andrew J; Tyrrell, Andy M

    2012-01-01

    Biomolecular systems that can process information are sought for computational applications, because of their potential for parallelism and miniaturization and because their biocompatibility also makes them suitable for future biomedical applications. DNA has been used to design machines, motors, finite automata, logic gates, reaction networks and logic programs, amongst many other structures and dynamic behaviours. Here we design and program a synthetic DNA network to implement computational paradigms abstracted from cellular regulatory networks. These show information processing properties that are desirable in artificial, engineered molecular systems, including robustness of the output in relation to different sources of variation. We show the results of numerical simulations of the dynamic behaviour of the network and preliminary experimental analysis of its main components. PMID:22606034

  6. Evolutionary Optimization of Network Structures using Informative Genotype Tag

    NASA Astrophysics Data System (ADS)

    Ando, Shin; Iba, Hitoshi

    Evolutionary computation has been applied to numerous design tasks, including design of electric circuits, neural networks, and genetic circuits. Though it is a very effective solution for optimizing network structures, genetic algorithm faces many difficulties, often referred to as the permutation problems, when both topologies and the weights of the network are the target of optimization. We propose a new crossover method used in conjunction with a genotype with information tags. The information tags allow GA to recognize and preserve the common structure of parent chromosomes during genetic crossover. The method is implemented along with subpopulating strategies to make the parallel evolution of network topology and weights feasible and efficient. The proposed method is evaluated on a few typical and practical problems, and shows improvement from conventional methodologies and genotypes.

  7. Models, Entropy and Information of Temporal Social Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Kun; Karsai, Márton; Bianconi, Ginestra

    Temporal social networks are characterized by heterogeneous duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.

  8. Efficiency of informational transfer in regular and complex networks

    NASA Astrophysics Data System (ADS)

    Vragović, I.; Louis, E.; Díaz-Guilera, A.

    2005-03-01

    We analyze the process of informational exchange through complex networks by measuring network efficiencies. Aiming to study nonclustered systems, we propose a modification of this measure on the local level. We apply this method to an extension of the class of small worlds that includes declustered networks and show that they are locally quite efficient, although their clustering coefficient is practically zero. Unweighted systems with small-world and scale-free topologies are shown to be both globally and locally efficient. Our method is also applied to characterize weighted networks. In particular we examine the properties of underground transportation systems of Madrid and Barcelona and reinterpret the results obtained for the Boston subway network.

  9. Information processing in neural networks with the complex dynamic thresholds

    NASA Astrophysics Data System (ADS)

    Kirillov, S. Yu.; Nekorkin, V. I.

    2016-06-01

    A control mechanism of the information processing in neural networks is investigated, based on the complex dynamic threshold of the neural excitation. The threshold properties are controlled by the slowly varying synaptic current. The dynamic threshold shows high sensitivity to the rate of the synaptic current variation. It allows both to realize flexible selective tuning of the network elements and to provide nontrivial regimes of neural coding.

  10. Transition modes in Ising networks: an approximate theory for macromolecular recognition.

    PubMed Central

    Keating, S; Di Cera, E

    1993-01-01

    For a statistical lattice, or Ising network, composed of N identical units existing in two possible states, 0 and 1, and interacting according to a given geometry, a set of values can be found for the mean free energy of the 0-->1 transition of a single unit. Each value defines a transition mode in an ensemble of nu N = 3N - 2N possible values and reflects the role played by intermediate states in shaping the energetics of the system as a whole. The distribution of transition modes has a number of intriguing properties. Some of them apply quite generally to any Ising network, regardless of its dimension, while others are specific for each interaction geometry and dimensional embedding and bear on fundamental aspects of analytical number theory. The landscape of transition modes encapsulates all of the important thermodynamic properties of the network. The free energy terms defining the partition function of the system can be derived from the modes by simple transformations. Classical mean-field expressions can be obtained from consideration of the properties of transition modes in a rather straightforward way. The results obtained in the analysis of the transition mode distributions have been used to develop an approximate treatment of the problem of macromolecular recognition. This phenomenon is modeled as a cooperative process that involves a number of recognition subsites across an interface generated by the binding of two macromolecular components. The distribution of allowed binding free energies for the system is shown to be a superposition of Gaussian terms with mean and variance determined a priori by the theory. Application to the analysis of the biologically interaction of thrombin with hirudin has provided some useful information on basic aspects of the interaction, such as the number of recognition subsites involved and the energy balance for binding and cooperative coupling among them. Our results agree quite well with information derived independently

  11. Networking for Teacher Learning: Toward a Theory of Effective Design.

    ERIC Educational Resources Information Center

    McDonald, Joseph P.; Klein, Emily J.

    2003-01-01

    Examines how teacher networks design for teacher learning, describing several dynamic tensions inherent in the designs of a sample of teacher networks and assessing the relationships of these tensions to teacher learning. The paper illustrates these design concepts with reference to the work of seven networks that aim to revamp teacher' knowledge…

  12. Scalable Networked Information Processing Environment (SNIPE)

    SciTech Connect

    Fagg, G.E.; Moore, K.; Dongarra, J.J. |; Geist, A.

    1997-11-01

    SNIPE is a metacomputing system that aims to provide a reliable, secure, fault tolerant environment for long term distributed computing applications and data stores across the global Internet. This system combines global naming and replication of both processing and data to support large scale information processing applications leading to better availability and reliability than currently available with typical cluster computing and/or distributed computer environments.

  13. Gravity effects on information filtering and network evolving.

    PubMed

    Liu, Jin-Hu; Zhang, Zi-Ke; Chen, Lingjiao; Liu, Chuang; Yang, Chengcheng; Wang, Xueqi

    2014-01-01

    In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model. PMID:24622162

  14. Gravity Effects on Information Filtering and Network Evolving

    PubMed Central

    Liu, Jin-Hu; Zhang, Zi-Ke; Chen, Lingjiao; Liu, Chuang; Yang, Chengcheng; Wang, Xueqi

    2014-01-01

    In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model. PMID:24622162

  15. Game Theory Based Security in Wireless Body Area Network with Stackelberg Security Equilibrium.

    PubMed

    Somasundaram, M; Sivakumar, R

    2015-01-01

    Wireless Body Area Network (WBAN) is effectively used in healthcare to increase the value of the patient's life and also the value of healthcare services. The biosensor based approach in medical care system makes it difficult to respond to the patients with minimal response time. The medical care unit does not deploy the accessing of ubiquitous broadband connections full time and hence the level of security will not be high always. The security issue also arises in monitoring the user body function records. Most of the systems on the Wireless Body Area Network are not effective in facing the security deployment issues. To access the patient's information with higher security on WBAN, Game Theory with Stackelberg Security Equilibrium (GTSSE) is proposed in this paper. GTSSE mechanism takes all the players into account. The patients are monitored by placing the power position authority initially. The position authority in GTSSE is the organizer and all the other players react to the organizer decision. Based on our proposed approach, experiment has been conducted on factors such as security ratio based on patient's health information, system flexibility level, energy consumption rate, and information loss rate. Stackelberg Security considerably improves the strength of solution with higher security. PMID:26759829

  16. Game Theory Based Security in Wireless Body Area Network with Stackelberg Security Equilibrium

    PubMed Central

    Somasundaram, M.; Sivakumar, R.

    2015-01-01

    Wireless Body Area Network (WBAN) is effectively used in healthcare to increase the value of the patient's life and also the value of healthcare services. The biosensor based approach in medical care system makes it difficult to respond to the patients with minimal response time. The medical care unit does not deploy the accessing of ubiquitous broadband connections full time and hence the level of security will not be high always. The security issue also arises in monitoring the user body function records. Most of the systems on the Wireless Body Area Network are not effective in facing the security deployment issues. To access the patient's information with higher security on WBAN, Game Theory with Stackelberg Security Equilibrium (GTSSE) is proposed in this paper. GTSSE mechanism takes all the players into account. The patients are monitored by placing the power position authority initially. The position authority in GTSSE is the organizer and all the other players react to the organizer decision. Based on our proposed approach, experiment has been conducted on factors such as security ratio based on patient's health information, system flexibility level, energy consumption rate, and information loss rate. Stackelberg Security considerably improves the strength of solution with higher security. PMID:26759829

  17. Information theory, animal communication, and the search for extraterrestrial intelligence

    NASA Astrophysics Data System (ADS)

    Doyle, Laurance R.; McCowan, Brenda; Johnston, Simon; Hanser, Sean F.

    2011-02-01

    We present ongoing research in the application of information theory to animal communication systems with the goal of developing additional detectors and estimators for possible extraterrestrial intelligent signals. Regardless of the species, for intelligence (i.e., complex knowledge) to be transmitted certain rules of information theory must still be obeyed. We demonstrate some preliminary results of applying information theory to socially complex marine mammal species (bottlenose dolphins and humpback whales) as well as arboreal squirrel monkeys, because they almost exclusively rely on vocal signals for their communications, producing signals which can be readily characterized by signal analysis. Metrics such as Zipf's Law and higher-order information-entropic structure are emerging as indicators of the communicative complexity characteristic of an "intelligent message" content within these animals' signals, perhaps not surprising given these species' social complexity. In addition to human languages, for comparison we also apply these metrics to pulsar signals—perhaps (arguably) the most "organized" of stellar systems—as an example of astrophysical systems that would have to be distinguished from an extraterrestrial intelligence message by such information theoretic filters. We also look at a message transmitted from Earth (Arecibo Observatory) that contains a lot of meaning but little information in the mathematical sense we define it here. We conclude that the study of non-human communication systems on our own planet can make a valuable contribution to the detection of extraterrestrial intelligence by providing quantitative general measures of communicative complexity. Studying the complex communication systems of other intelligent species on our own planet may also be one of the best ways to deprovincialize our thinking about extraterrestrial communication systems in general.

  18. Generalised squeezing and information theory approach to quantum entanglement

    NASA Technical Reports Server (NTRS)

    Vourdas, A.

    1993-01-01

    It is shown that the usual one- and two-mode squeezing are based on reducible representations of the SU(1,1) group. Generalized squeezing is introduced with the use of different SU(1,1) rotations on each irreducible sector. Two-mode squeezing entangles the modes and information theory methods are used to study this entanglement. The entanglement of three modes is also studied with the use of the strong subadditivity property of the entropy.

  19. Algorithmic information theory and the hidden variable question

    NASA Astrophysics Data System (ADS)

    Fuchs, Christopher

    1992-02-01

    The admissibility of certain nonlocal hidden-variable theories are explained via information theory. Consider a pair of Stern-Gerlach devices with fixed nonparallel orientations that periodically perform spin measurements on identically prepared pairs of electrons in the singlet spin state. Suppose the outcomes are recorded as binary strings l and r (with l sub n and r sub n denoting their n-length prefixes). The hidden-variable theories considered here require that there exists a recursive function which may be used to transform l sub n into r sub n for any n. This note demonstrates that such a theory cannot reproduce all the statistical predictions of quantum mechanics. Specifically, consider an ensemble of outcome pairs (l,r). From the associated probability measure, the Shannon entropies H sub n and H bar sub n for strings l sub n and pairs (l sub n, r sub n) may be formed. It is shown that such a theory requires that the absolute value of H bar sub n - H sub n be bounded - contrasting the quantum mechanical prediction that it grow with n.

  20. Algorithmic information theory and the hidden variable question

    NASA Technical Reports Server (NTRS)

    Fuchs, Christopher

    1992-01-01

    The admissibility of certain nonlocal hidden-variable theories are explained via information theory. Consider a pair of Stern-Gerlach devices with fixed nonparallel orientations that periodically perform spin measurements on identically prepared pairs of electrons in the singlet spin state. Suppose the outcomes are recorded as binary strings l and r (with l sub n and r sub n denoting their n-length prefixes). The hidden-variable theories considered here require that there exists a recursive function which may be used to transform l sub n into r sub n for any n. This note demonstrates that such a theory cannot reproduce all the statistical predictions of quantum mechanics. Specifically, consider an ensemble of outcome pairs (l,r). From the associated probability measure, the Shannon entropies H sub n and H bar sub n for strings l sub n and pairs (l sub n, r sub n) may be formed. It is shown that such a theory requires that the absolute value of H bar sub n - H sub n be bounded - contrasting the quantum mechanical prediction that it grow with n.

  1. [Research on Zhejiang blood information network and management system].

    PubMed

    Yan, Li-Xing; Xu, Yan; Meng, Zhong-Hua; Kong, Chang-Hong; Wang, Jian-Min; Jin, Zhen-Liang; Wu, Shi-Ding; Chen, Chang-Shui; Luo, Ling-Fei

    2007-02-01

    This research was aimed to develop the first level blood information centralized database and real time communication network at a province area in China. Multiple technology like local area network database separate operation, real time data concentration and distribution mechanism, allopatric backup, and optical fiber virtual private network (VPN) were used. As a result, the blood information centralized database and management system were successfully constructed, which covers all the Zhejiang province, and the real time exchange of blood data was realised. In conclusion, its implementation promote volunteer blood donation and ensure the blood safety in Zhejiang, especially strengthen the quick response to public health emergency. This project lays the first stone of centralized test and allotment among blood banks in Zhejiang, and can serve as a reference of contemporary blood bank information systems in China. PMID:17490550

  2. Interspecific social networks promote information transmission in wild songbirds.

    PubMed

    Farine, Damien R; Aplin, Lucy M; Sheldon, Ben C; Hoppitt, William

    2015-03-22

    Understanding the functional links between social structure and population processes is a central aim of evolutionary ecology. Multiple types of interactions can be represented by networks drawn for the same population, such as kinship, dominance or affiliative networks, but the relative importance of alternative networks in modulating population processes may not be clear. We illustrate this problem, and a solution, by developing a framework for testing the importance of different types of association in facilitating the transmission of information. We apply this framework to experimental data from wild songbirds that form mixed-species flocks, recording the arrival (patch discovery) of individuals to novel foraging sites. We tested whether intraspecific and interspecific social networks predicted the spread of information about novel food sites, and found that both contributed to transmission. The likelihood of acquiring information per unit of connection to knowledgeable individuals increased 22-fold for conspecifics, and 12-fold for heterospecifics. We also found that species varied in how much information they produced, suggesting that some species play a keystone role in winter foraging flocks. More generally, these analyses demonstrate that this method provides a powerful approach, using social networks to quantify the relative transmission rates across different social relationships. PMID:25673683

  3. Interspecific social networks promote information transmission in wild songbirds

    PubMed Central

    Farine, Damien R.; Aplin, Lucy M.; Sheldon, Ben C.; Hoppitt, William

    2015-01-01

    Understanding the functional links between social structure and population processes is a central aim of evolutionary ecology. Multiple types of interactions can be represented by networks drawn for the same population, such as kinship, dominance or affiliative networks, but the relative importance of alternative networks in modulating population processes may not be clear. We illustrate this problem, and a solution, by developing a framework for testing the importance of different types of association in facilitating the transmission of information. We apply this framework to experimental data from wild songbirds that form mixed-species flocks, recording the arrival (patch discovery) of individuals to novel foraging sites. We tested whether intraspecific and interspecific social networks predicted the spread of information about novel food sites, and found that both contributed to transmission. The likelihood of acquiring information per unit of connection to knowledgeable individuals increased 22-fold for conspecifics, and 12-fold for heterospecifics. We also found that species varied in how much information they produced, suggesting that some species play a keystone role in winter foraging flocks. More generally, these analyses demonstrate that this method provides a powerful approach, using social networks to quantify the relative transmission rates across different social relationships. PMID:25673683

  4. Reliability theory for diffusion processes on interconnected networks

    NASA Astrophysics Data System (ADS)

    Khorramzadeh, Yasamin; Youssef, Mina; Eubank, Stephen

    2014-03-01

    We present the concept of network reliability as a framework to study diffusion dynamics in interdependent networks. We illustrate how different outcomes of diffusion processes, such as cascading failure, can be studied by estimating the reliability polynomial under different reliability rules. As an example, we investigate the effect of structural properties on diffusion dynamics for a few different topologies of two coupled networks. We evaluate the effect of varying the probability of failure propagating along the edges, both within a single network as well as between the networks. We exhibit the sensitivity of interdependent network reliability and connectivity to edge failures in each topology. Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia 24061, USA.

  5. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

    PubMed

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency. PMID:26609303

  6. Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

    PubMed Central

    Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

    2015-01-01

    Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency. PMID:26609303

  7. CytoITMprobe: a network information flow plugin for Cytoscape

    PubMed Central

    2012-01-01

    Background Cytoscape is a well-developed flexible platform for visualization, integration and analysis of network data. Apart from the sophisticated graph layout and visualization routines, it hosts numerous user-developed plugins that significantly extend its core functionality. Earlier, we developed a network information flow framework and implemented it as a web application, called ITM Probe. Given a context consisting of one or more user-selected nodes, ITM Probe retrieves other network nodes most related to that context. It requires neither user restriction to subnetwork of interest nor additional and possibly noisy information. However, plugins for Cytoscape with these features do not yet exist. To provide the Cytoscape users the possibility of integrating ITM Probe into their workflows, we developed CytoITMprobe, a new Cytoscape plugin. Findings CytoITMprobe maintains all the desirable features of ITM Probe and adds additional flexibility not achievable through its web service version. It provides access to ITM Probe either through a web server or locally. The input, consisting of a Cytoscape network, together with the desired origins and/or destinations of information and a dissipation coefficient, is specified through a query form. The results are shown as a subnetwork of significant nodes and several summary tables. Users can control the composition and appearance of the subnetwork and interchange their ITM Probe results with other software tools through tab-delimited files. Conclusions The main strength of CytoITMprobe is its flexibility. It allows the user to specify as input any Cytoscape network, rather than being restricted to the pre-compiled protein-protein interaction networks available through the ITM Probe web service. Users may supply their own edge weights and directionalities. Consequently, as opposed to ITM Probe web service, CytoITMprobe can be applied to many other domains of network-based research beyond protein-networks. It also enables

  8. A game theory approach to target tracking in sensor networks.

    PubMed

    Gu, Dongbing

    2011-02-01

    In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result. PMID:20194057

  9. Extraction of business relationships in supply networks using statistical learning theory.

    PubMed

    Zuo, Yi; Kajikawa, Yuya; Mori, Junichiro

    2016-06-01

    Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer-supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer-supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer-supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities. PMID:27441294

  10. Telecommunications Information Network: A Model for On-Demand Transfer of Medical Information. Annual Report.

    ERIC Educational Resources Information Center

    Lorenzi, Nancy M.; And Others

    This report describes and evaluates the first year of a demonstration project to develop an on-demand telecommunications network linking four remote hospitals in southwestern Ohio to the University of Cincinnati Medical Center. The Telecommunications Information Network (TIN) is designed to allow health care professionals at those hospitals to…

  11. Measuring information flow in cellular networks by the systems biology method through microarray data

    PubMed Central

    Chen, Bor-Sen; Li, Cheng-Wei

    2015-01-01

    In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells. PMID:26082788

  12. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

    PubMed Central

    Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

    2016-01-01

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect. PMID:27403421

  13. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression.

    PubMed

    Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

    2016-01-01

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect. PMID:27403421

  14. Enabling information management systems in tactical network environments

    NASA Astrophysics Data System (ADS)

    Carvalho, Marco; Uszok, Andrzej; Suri, Niranjan; Bradshaw, Jeffrey M.; Ceccio, Philip J.; Hanna, James P.; Sinclair, Asher

    2009-05-01

    Net-Centric Information Management (IM) and sharing in tactical environments promises to revolutionize forward command and control capabilities by providing ubiquitous shared situational awareness to the warfighter. This vision can be realized by leveraging the tactical and Mobile Ad hoc Networks (MANET) which provide the underlying communications infrastructure, but, significant technical challenges remain. Enabling information management in these highly dynamic environments will require multiple support services and protocols which are affected by, and highly dependent on, the underlying capabilities and dynamics of the tactical network infrastructure. In this paper we investigate, discuss, and evaluate the effects of realistic tactical and mobile communications network environments on mission-critical information management systems. We motivate our discussion by introducing the Advanced Information Management System (AIMS) which is targeted for deployment in tactical sensor systems. We present some operational requirements for AIMS and highlight how critical IM support services such as discovery, transport, federation, and Quality of Service (QoS) management are necessary to meet these requirements. Our goal is to provide a qualitative analysis of the impact of underlying assumptions of availability and performance of some of the critical services supporting tactical information management. We will also propose and describe a number of technologies and capabilities that have been developed to address these challenges, providing alternative approaches for transport, service discovery, and federation services for tactical networks.

  15. Information dynamics of brain-heart physiological networks during sleep

    NASA Astrophysics Data System (ADS)

    Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

    2014-10-01

    This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

  16. Self-Organized Information Processing in Neuronal Networks: Replacing Layers in Deep Networks by Dynamics

    NASA Astrophysics Data System (ADS)

    Kirst, Christoph

    It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.

  17. Suppressing disease spreading by using information diffusion on multiplex networks

    NASA Astrophysics Data System (ADS)

    Wang, Wei; Liu, Quan-Hui; Cai, Shi-Min; Tang, Ming; Braunstein, Lidia A.; Stanley, H. Eugene

    2016-07-01

    Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate.

  18. Suppressing disease spreading by using information diffusion on multiplex networks.

    PubMed

    Wang, Wei; Liu, Quan-Hui; Cai, Shi-Min; Tang, Ming; Braunstein, Lidia A; Stanley, H Eugene

    2016-01-01

    Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate. PMID:27380881

  19. Suppressing disease spreading by using information diffusion on multiplex networks

    PubMed Central

    Wang, Wei; Liu, Quan-Hui; Cai, Shi-Min; Tang, Ming; Braunstein, Lidia A.; Stanley, H. Eugene

    2016-01-01

    Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate. PMID:27380881

  20. Running a network on a shoestring: the Global Invasive Species Information Network

    USGS Publications Warehouse

    Jarnevich, Catherine S.; Simpson, Annie; Graham, James J; Newman, Gregory J.; Bargeron, Chuck T.

    2015-01-01

    The Global Invasive Species Information Network (GISIN) was conceptualized in 2004 to aggregate and disseminate invasive species data in a standardized way. A decade later the GISIN community has implemented a data portal and three of six GISIN data aggregation models in the GISIN data exchange Protocol, including invasive species status information, resource URLs, and occurrence data. The portal is based on a protocol developed by representatives from 15 countries and 27 organizations of the global invasive species information management community. The GISIN has 19 data providers sharing 34,343 species status records, 1,693,073 occurrences, and 15,601 resource URLs. While the GISIN's goal is to be global, much of its data and funding are provided by the United States. Several initiatives use the GISIN as their information backbone, such as the Great Lakes Early Detection Network (GLEDN) and the North American Invasive Species Network (NAISN). Here we share several success stories and organizational challenges that remain.

  1. Fragmentation network of doubly charged methionine: Interpretation using graph theory.

    PubMed

    Ha, D T; Yamazaki, K; Wang, Y; Alcamí, M; Maeda, S; Kono, H; Martín, F; Kukk, E

    2016-09-01

    The fragmentation of doubly charged gas-phase methionine (HO2CCH(NH2)CH2CH2SCH3) is systematically studied using the self-consistent charge density functional tight-binding molecular dynamics (MD) simulation method. We applied graph theory to analyze the large number of the calculated MD trajectories, which appears to be a highly effective and convenient means of extracting versatile information from the large data. The present theoretical results strongly concur with the earlier studied experimental ones. Essentially, the dication dissociates into acidic group CO2H and basic group C4NSH10. The former may carry a single or no charge and stays intact in most cases, whereas the latter may hold either a single or a double charge and tends to dissociate into smaller fragments. The decay of the basic group is observed to follow the Arrhenius law. The dissociation pathways to CO2H and C4NSH10 and subsequent fragmentations are also supported by ab initio calculations. PMID:27608997

  2. Information Spread of Emergency Events: Path Searching on Social Networks

    PubMed Central

    Hu, Hongzhi; Wu, Tunan

    2014-01-01

    Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

  3. Analysis of co-occurrence networks with clique occurrence information

    NASA Astrophysics Data System (ADS)

    Shen, Bin; Li, Yixiao

    2014-12-01

    Most of co-occurrence networks only record co-occurrence relationships between two entities, and ignore the weights of co-occurrence cliques whose size is bigger than two. However, this ignored information may help us to gain insight into the co-occurrence phenomena of systems. In this paper, we analyze co-occurrence networks with clique occurrence information (CNCI) thoroughly. First, we describe the components of CNCIs and discuss the generation of clique occurrence information. And then, to illustrate the importance and usefulness of clique occurrence information, several metrics, i.e. single occurrence rate, average size of maximal co-occurrence cliques and four types of co-occurrence coefficients etc., are given. Moreover, some applications, such as combining co-occurrence frequency with structure-oriented centrality measures, are also discussed.

  4. Chaotic, informational and synchronous behaviour of multiplex networks.

    PubMed

    Baptista, M S; Szmoski, R M; Pereira, R F; Pinto, S E de Souza

    2016-01-01

    The understanding of the relationship between topology and behaviour in interconnected networks would allow to charac- terise and predict behaviour in many real complex networks since both are usually not simultaneously known. Most previous studies have focused on the relationship between topology and synchronisation. In this work, we provide analytical formulas that shows how topology drives complex behaviour: chaos, information, and weak or strong synchronisation; in multiplex net- works with constant Jacobian. We also study this relationship numerically in multiplex networks of Hindmarsh-Rose neurons. Whereas behaviour in the analytically tractable network is a direct but not trivial consequence of the spectra of eigenvalues of the Laplacian matrix, where behaviour may strongly depend on the break of symmetry in the topology of interconnections, in Hindmarsh-Rose neural networks the nonlinear nature of the chemical synapses breaks the elegant mathematical connec- tion between the spectra of eigenvalues of the Laplacian matrix and the behaviour of the network, creating networks whose behaviour strongly depends on the nature (chemical or electrical) of the inter synapses. PMID:26939580

  5. Chaotic, informational and synchronous behaviour of multiplex networks

    NASA Astrophysics Data System (ADS)

    Baptista, M. S.; Szmoski, R. M.; Pereira, R. F.; Pinto, S. E. De Souza

    2016-03-01

    The understanding of the relationship between topology and behaviour in interconnected networks would allow to charac- terise and predict behaviour in many real complex networks since both are usually not simultaneously known. Most previous studies have focused on the relationship between topology and synchronisation. In this work, we provide analytical formulas that shows how topology drives complex behaviour: chaos, information, and weak or strong synchronisation; in multiplex net- works with constant Jacobian. We also study this relationship numerically in multiplex networks of Hindmarsh-Rose neurons. Whereas behaviour in the analytically tractable network is a direct but not trivial consequence of the spectra of eigenvalues of the Laplacian matrix, where behaviour may strongly depend on the break of symmetry in the topology of interconnections, in Hindmarsh-Rose neural networks the nonlinear nature of the chemical synapses breaks the elegant mathematical connec- tion between the spectra of eigenvalues of the Laplacian matrix and the behaviour of the network, creating networks whose behaviour strongly depends on the nature (chemical or electrical) of the inter synapses.

  6. Chaotic, informational and synchronous behaviour of multiplex networks

    PubMed Central

    Baptista, M. S.; Szmoski, R. M.; Pereira, R. F.; Pinto, S. E. de Souza

    2016-01-01

    The understanding of the relationship between topology and behaviour in interconnected networks would allow to charac- terise and predict behaviour in many real complex networks since both are usually not simultaneously known. Most previous studies have focused on the relationship between topology and synchronisation. In this work, we provide analytical formulas that shows how topology drives complex behaviour: chaos, information, and weak or strong synchronisation; in multiplex net- works with constant Jacobian. We also study this relationship numerically in multiplex networks of Hindmarsh-Rose neurons. Whereas behaviour in the analytically tractable network is a direct but not trivial consequence of the spectra of eigenvalues of the Laplacian matrix, where behaviour may strongly depend on the break of symmetry in the topology of interconnections, in Hindmarsh-Rose neural networks the nonlinear nature of the chemical synapses breaks the elegant mathematical connec- tion between the spectra of eigenvalues of the Laplacian matrix and the behaviour of the network, creating networks whose behaviour strongly depends on the nature (chemical or electrical) of the inter synapses. PMID:26939580

  7. Role of information theoretic uncertainty relations in quantum theory

    NASA Astrophysics Data System (ADS)

    Jizba, Petr; Dunningham, Jacob A.; Joo, Jaewoo

    2015-04-01

    Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson-Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again, improvement over both the Robertson-Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.

  8. Role of information theoretic uncertainty relations in quantum theory

    SciTech Connect

    Jizba, Petr; Dunningham, Jacob A.; Joo, Jaewoo

    2015-04-15

    Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson–Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again, improvement over both the Robertson–Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.

  9. Integrated information theory of consciousness: an updated account.

    PubMed

    Tononi, G

    2012-12-01

    This article presents an updated account of integrated information theory of consciousness (liT) and some of its implications. /IT stems from thought experiments that lead to phenomenological axioms (existence, compositionality, information, integration, exclusion) and corresponding ontological postulates. The information axiom asserts that every experience is spec~fic - it is what it is by differing in its particular way from a large repertoire of alternatives. The integration axiom asserts that each experience is unified- it cannot be reduced to independent components. The exclusion axiom asserts that every experience is definite - it is limited to particular things and not others and flows at a particular speed and resolution. /IT formalizes these intuitions with postulates. The information postulate states that only "differences that make a difference" from the intrinsic perpective of a system matter: a mechanism generates cause-effect information if its present state has selective past causes and selective future effects within a system. The integration postulate states that only information that is irreducible matters: mechanisms generate integrated information only to the extent that the information they generate cannot be partitioned into that generated within independent components. The exclusion postulate states that only maxima of integrated information matter: a mechanism specifies only one maximally irreducible set of past causes and future effects - a concept. A complex is a set of elements specifying a maximally irreducible constellation of concepts, where the maximum is evaluated over elements and at the optimal spatiatemporal scale. Its concepts specify a maximally integrated conceptual information structure or quale, which is identical with an experience. Finally, changes in information integration upon exposure to the environment reflect a system's ability to match the causal structure of the world. After introducing an updated definition of

  10. Application of geographic information system in distribution power network automation

    NASA Astrophysics Data System (ADS)

    Wei, Xianmin

    2011-02-01

    Geographic information system (GIS) is the computer system in support of computer software with collection, storage, management, retrieval and comprehensive analysis of a variety of geospatial information, with various forms output data and graphics products. This paper introduced GIS data organization and its main applications in distribution power network automation, including both offline and online, and proposed component-based system development model and the need to establish WEBGIS and reliability.

  11. A social network model for the development of a 'Theory of Mind'

    NASA Astrophysics Data System (ADS)

    Harré, Michael S.

    2013-02-01

    A "Theory of Mind" is one of the most important skills we as humans have developed; It enables us to infer the mental states and intentions of others, build stable networks of relationships and it plays a central role in our psychological make-up and development. Findings published earlier this year have also shown that we as a species as well as each of us individually benefit from the enlargement of the underlying neuro-anatomical regions that support our social networks, mediated by our Theory of Mind that stabilises these networks. On the basis of such progress and that of earlier work, this paper draws together several different strands from psychology, behavioural economics and network theory in order to generate a novel theoretical representation of the development of our social-cognition and how subsequent larger social networks enables much of our cultural development but at the increased risk of mental disorders.

  12. Intellectual property and networked health information: issues and principles.

    PubMed Central

    Cate, F H

    1996-01-01

    Information networks offer enormous potential for improving the delivery of health care services, facilitating health-related decision-making, and contributing to better health. In addition, advanced information technologies offer important opportunities for new markets, targeted information products and services, greater accessibility, lower costs and prices, and more rapid and efficient distribution. Realizing the full potential of those information resources requires the resolution of significant intellectual property issues, some of which may be affected by special features of health information. For example, the government is a significant funder and originator of health-related information. In addition, much of that information is of great importance to the population and benefits not only individual users, but also employers, insurance companies, the government, and society as a whole. The government must therefore continue to provide particularly important health information to the public, and facilitate that information's accessibility and reliability, while avoiding unnecessary competition with private information providers. Congress and courts must modify or interpret current copyright law as necessary to guarantee that it does not interfere with innovation in tailored health information or exceed its constitutional boundaries and restrict access to information, as opposed to expression. Both producers and users of information must work with the government to educate the public about the availability of health information and the rights of and limitations upon users under copyright law. PMID:8826629

  13. Specificity, promiscuity, and the structure of complex information processing networks

    NASA Astrophysics Data System (ADS)

    Myers, Christopher

    2006-03-01

    Both the top-down designs of engineered systems and the bottom-up serendipities of biological evolution must negotiate tradeoffs between specificity and control: overly specific interactions between components can make systems brittle and unevolvable, while more generic interactions can require elaborate control in order to aggregate specificity from distributed pieces. Complex information processing systems reveal network organizations that navigate this landscape of constraints: regulatory and signaling networks in cells involve the coordination of molecular interactions that are surprisingly promiscuous, and object-oriented design in software systems emphasizes the polymorphic composition of objects of minimal necessary specificity [C.R. Myers, Phys Rev E 68, 046116 (2003)]. Models of information processing arising both in systems biology and engineered computation are explored to better understand how particular network organizations can coordinate the activity of promiscuous components to achieve robust and evolvable function.

  14. Topology for efficient information dissemination in ad-hoc networking

    NASA Technical Reports Server (NTRS)

    Jennings, E.; Okino, C. M.

    2002-01-01

    In this paper, we explore the information dissemination problem in ad-hoc wirless networks. First, we analyze the probability of successful broadcast, assuming: the nodes are uniformly distributed, the available area has a lower bould relative to the total number of nodes, and there is zero knowledge of the overall topology of the network. By showing that the probability of such events is small, we are motivated to extract good graph topologies to minimize the overall transmissions. Three algorithms are used to generate topologies of the network with guaranteed connectivity. These are the minimum radius graph, the relative neighborhood graph and the minimum spanning tree. Our simulation shows that the relative neighborhood graph has certain good graph properties, which makes it suitable for efficient information dissemination.

  15. Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach

    PubMed Central

    Alaerts, Kaat; Geerlings, Franca; Herremans, Lynn; Swinnen, Stephan P.; Verhoeven, Judith; Sunaert, Stefan; Wenderoth, Nicole

    2015-01-01

    Background The ability to recognize, understand and interpret other’s actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network. Method Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC). Results Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength). Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD. Conclusion While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON. PMID:26317222

  16. Tufts academic health information network: concept and scenario.

    PubMed

    Stearns, N S

    1986-04-01

    Tufts University School of Medicine's new health sciences education building, the Arthur M. Sackler Center for Health Communications, will house a modern medical library and computer center, classrooms, auditoria, and media facilities. The building will also serve as the center for an information and communication network linking the medical school and adjacent New England Medical Center, Tufts' primary teaching hospital, with Tufts Associated Teaching Hospitals throughout New England. Ultimately, the Tufts network will join other gateway networks, information resource facilities, health care institutions, and medical schools throughout the world. The center and the network are intended to facilitate and improve the education of health professionals, the delivery of health care to patients, the conduct of research, and the implementation of administrative management approaches that should provide more efficient utilization of resources and save dollars. A model and scenario show how health care delivery and health care education are integrated through better use of information transfer technologies by health information specialists, practitioners, and educators. PMID:3708191

  17. ODIN. Online Database Information Network: ODIN Policy & Procedure Manual.

    ERIC Educational Resources Information Center

    Townley, Charles T.; And Others

    Policies and procedures are outlined for the Online Database Information Network (ODIN), a cooperative of libraries in south-central Pennsylvania, which was organized to improve library services through technology. The first section covers organization and goals, members, and responsibilities of the administrative council and libraries. Patrons…

  18. Information Networks for On-Line Bibliographic Retrieval.

    ERIC Educational Resources Information Center

    Karlander, Bo; Sem-Sandberg, Sverre

    This study evaluates the economic and financial aspects of the application of teleprocessing and telecommunications to the transfer of scientific and technological information, especially in the context of developing countries. It was intended to facilitate comparison of the relative value of a teleprocessing network with that of other modes of…

  19. Regional Industry Workforce Development: The Gulf Coast Petrochemical Information Network

    ERIC Educational Resources Information Center

    Hodgin, Johnette; Muha, Susan

    2008-01-01

    The Gulf Coast Petrochemical Information Network (GC-PIN) is a workforce development partnership among industry businesses and area institutions of higher education in the four-county Gulf Coast region. GC-PIN partners develop new industry-specific curricula, foster industry career awareness, and retrain existing employees in new technologies.

  20. Maximizing Information Diffusion in the Cyber-physical Integrated Network.

    PubMed

    Lu, Hongliang; Lv, Shaohe; Jiao, Xianlong; Wang, Xiaodong; Liu, Juan

    2015-01-01

    Nowadays, our living environment has been embedded with smart objects, such as smart sensors, smart watches and smart phones. They make cyberspace and physical space integrated by their abundant abilities of sensing, communication and computation, forming a cyber-physical integrated network. In order to maximize information diffusion in such a network, a group of objects are selected as the forwarding points. To optimize the selection, a minimum connected dominating set (CDS) strategy is adopted. However, existing approaches focus on minimizing the size of the CDS, neglecting an important factor: the weight of links. In this paper, we propose a distributed maximizing the probability of information diffusion (DMPID) algorithm in the cyber-physical integrated network. Unlike previous approaches that only consider the size of CDS selection, DMPID also considers the information spread probability that depends on the weight of links. To weaken the effects of excessively-weighted links, we also present an optimization strategy that can properly balance the two factors. The results of extensive simulation show that DMPID can nearly double the information diffusion probability, while keeping a reasonable size of selection with low overhead in different distributed networks. PMID:26569254

  1. 78 FR 7797 - Homeland Security Information Network Advisory Committee (HSINAC)

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-02-04

    ... SECURITY Homeland Security Information Network Advisory Committee (HSINAC) AGENCY: OPS/OCIO, DHS. ACTION: Committee Management; Notice of Federal Advisory Committee Meeting. SUMMARY: The Homeland Security... Steigman, Department of Homeland Security, OPS CIO-D Stop 0426, 245 Murray Lane, SW., BLDG 410,...

  2. Copyright Aspects of CATV as Utilized in Information Networking.

    ERIC Educational Resources Information Center

    Bachrach, Morton W.

    It can be expected that Cable Antenna Television (CATV) systems will serve as conduits for tomorrow's information networks. CATV holds promise for fulfilling this need because of its broad-band multi-channel capability. CATV can be thought of as having two basic functions. i.e., retransmitting TV programs, and initiating its own programs and…

  3. A Network Client Using the Gopher Information Discovery Protocol

    Energy Science and Technology Software Center (ESTSC)

    1993-10-05

    WSGOPHER uses the protocol known as Gopher, which is described in Internet RFC 1436. Specifically Gopher is a client/server protocol. Gopher servers provide information across the network to Gopher clients. WSGOPHER is an implementation of a Gopher client for Microsoft Windows 3.1 and Windows Sockets version 1.1.

  4. Installing an Integrated Information System in a Centralized Network.

    ERIC Educational Resources Information Center

    Mendelson, Andrew D.

    1992-01-01

    Many schools are looking at ways to centralize the distribution and retrieval of video, voice, and data transmissions in an integrate information system (IIS). A centralized system offers greater control of hardware and software. Describes media network planning to retrofit an Illinois' high school with a fiber optic-based IIS. (MLF)

  5. WSGOPHER. A Network Client Using the Gopher Information Discovery Protocol

    SciTech Connect

    Brooks, D.L.

    1993-10-05

    WSGOPHER uses the protocol known as Gopher, which is described in Internet RFC 1436. Specifically Gopher is a client/server protocol. Gopher servers provide information across the network to Gopher clients. WSGOPHER is an implementation of a Gopher client for Microsoft Windows 3.1 and Windows Sockets version 1.1.

  6. Audit Trail Management System in Community Health Care Information Network.

    PubMed

    Nakamura, Naoki; Nakayama, Masaharu; Nakaya, Jun; Tominaga, Teiji; Suganuma, Takuo; Shiratori, Norio

    2015-01-01

    After the Great East Japan Earthquake we constructed a community health care information network system. Focusing on the authentication server and portal server capable of SAML&ID-WSF, we proposed an audit trail management system to look over audit events in a comprehensive manner. Through implementation and experimentation, we verified the effectiveness of our proposed audit trail management system. PMID:26262379

  7. Identifiers and Their Role in Networked Information Applications.

    ERIC Educational Resources Information Center

    Lynch, Clifford

    1998-01-01

    Discussion of assigning identifiers to books and bibliographic citations focuses on identifiers in the networked information environment, including URLs (Uniform Resource Locators); URNs (Uniform Resource Names); the OCLC Persistent URL (PURL); the SICI (Serial Item and Contribution Identifier) code; and the Digital Object Identifier (DOI). (LRW)

  8. Networking the Land: Rural America in the Information Age.

    ERIC Educational Resources Information Center

    Conte, Christopher

    This report describes 10 projects funded by the federal Technology Opportunities Program, in which people in isolated regions are finding ways to connect to new information networks and are reaping social, economic, and educational benefits. In the sprawling Navajo Nation, where many families lack even basic telephone service, local tribal…

  9. The Implications of a Mixed Media Network for Information Interchange.

    ERIC Educational Resources Information Center

    Meaney, John W.

    A mixed media network for information interchange is what we are always likely to have. Amid the current permutations of the storage and distribution media we see the emergence of two trends -- toward the common denominators of electronic display on the TV system and of digital processing and control. The economic implications of a mixed network…

  10. A novel information cascade model in online social networks

    NASA Astrophysics Data System (ADS)

    Tong, Chao; He, Wenbo; Niu, Jianwei; Xie, Zhongyu

    2016-02-01

    The spread and diffusion of information has become one of the hot issues in today's social network analysis. To analyze the spread of online social network information and the attribute of cascade, in this paper, we discuss the spread of two kinds of users' decisions for city-wide activities, namely the "want to take part in the activity" and "be interested in the activity", based on the users' attention in "DouBan" and the data of the city-wide activities. We analyze the characteristics of the activity-decision's spread in these aspects: the scale and scope of the cascade subgraph, the structure characteristic of the cascade subgraph, the topological attribute of spread tree, and the occurrence frequency of cascade subgraph. On this basis, we propose a new information spread model. Based on the classical independent diffusion model, we introduce three mechanisms, equal probability, similarity of nodes, and popularity of nodes, which can generate and affect the spread of information. Besides, by conducting the experiments in six different kinds of network data set, we compare the effects of three mechanisms above mentioned, totally six specific factors, on the spread of information, and put forward that the node's popularity plays an important role in the information spread.

  11. Contextualized Network Analysis: Theory and Methods for Networks with Node Covariates

    NASA Astrophysics Data System (ADS)

    Binkiewicz, Norbert M.

    and rely on molecular markers or the anatomy of a single neuron. These methodologies do not scale. Hence, it is important to investigate the relationship between cell type, as it is currently defined, and other quantities that can be observed or derived at scale. Examining the conditional independence of neuronal cell type and network community structure given neuron location suggests network communities are informative for characterizing neuronal cell type.

  12. Why hydrological predictions should be evaluated using information theory

    NASA Astrophysics Data System (ADS)

    Weijs, S. V.; Schoups, G.; van de Giesen, N.

    2010-12-01

    Probabilistic predictions are becoming increasingly popular in hydrology. Equally important are methods to test such predictions, given the topical debate on uncertainty analysis in hydrology. Also in the special case of hydrological forecasting, there is still discussion about which scores to use for their evaluation. In this paper, we propose to use information theory as the central framework to evaluate predictions. From this perspective, we hope to shed some light on what verification scores measure and should measure. We start from the ''divergence score'', a relative entropy measure that was recently found to be an appropriate measure for forecast quality. An interpretation of a decomposition of this measure provides insight in additive relations between climatological uncertainty, correct information, wrong information and remaining uncertainty. When the score is applied to deterministic forecasts, it follows that these increase uncertainty to infinity. In practice, however, deterministic forecasts tend to be judged far more mildly and are widely used. We resolve this paradoxical result by proposing that deterministic forecasts either are implicitly probabilistic or are implicitly evaluated with an underlying decision problem or utility in mind. We further propose that calibration of models representing a hydrological system should be the based on information-theoretical scores, because this allows extracting all information from the observations and avoids learning from information that is not there. Calibration based on maximizing utility for society trains an implicit decision model rather than the forecasting system itself. This inevitably results in a loss or distortion of information in the data and more risk of overfitting, possibly leading to less valuable and informative forecasts. We also show this in an example. The final conclusion is that models should preferably be explicitly probabilistic and calibrated to maximize the information they provide.

  13. Integrated information theory of consciousness: an updated account.

    PubMed

    Tononi, G

    2012-01-01

    This article presents an updated account of integrated information theory of consciousness (IIT) and some of its implications. IIT stems from thought experiments that lead to phenomenological axioms and ontological postulates. The information axiom asserts that every experience is one out of many, i.e. specific - it is what it is by differing in its particular way from a large repertoire of alternatives. The integration axiom asserts that each experience is one, i.e. unified - it cannot be reduced to independent components. The exclusion axiom asserts that every experience is definite - it is limited to particular things and not others and flows at a particular speed and resolution. IIT formalizes these intuitions with three postulates. The information postulate states that only "differences that make a difference" from the intrinsic perspective of a system matter: a mechanism generates cause-effect information if its present state has specific past causes and specific future effects within a system. The integration postulate states that only information that is irreducible matters: mechanisms generate integrated information only to the extent that the information they generate cannot be partitioned into that generated within independent components. The exclusion postulate states that only maxima of integrated information matter: a mechanism specifies only one maximally irreducible set of past causes and future effects - a concept. A complex is a set of elements specifying a maximally irreducible constellation of concepts, where the maximum is evaluated at the optimal spatio-temporal scale. Its concepts specify a maximally integrated conceptual information structure or quale, which is identical with an experience. Finally, changes in information integration upon exposure to the environment reflect a system's ability to match the causal structure of the world. After introducing an updated definition of information integration and related quantities, the article

  14. Evaluating reliability and resolution of ensemble forecasts using information theory

    NASA Astrophysics Data System (ADS)

    Weijs, Steven; van de Giesen, Nick

    2010-05-01

    Ensemble forecasts are increasingly popular for the communication of uncertainty towards the public and decision makers. Ideally, an ensemble forecast reflects both the uncertainty and the information in a forecast, which means that the spread in the ensemble should accurately represent the true uncertainty. For ensembles to be useful, they should be probabilistic, as probability is the language to precisely describe an incomplete state of knowledge, that is typical for forecasts. Information theory provides the ideal tools to deal with uncertainty and information in forecasts. Essential to the use and development of models and forecasts are ways to evaluate their quality. Without a proper definition of what is good, it is impossible to improve forecasts. In contrast to forecast value, which is user dependent, forecast quality, which is defined as the correspondence between forecasts and observations, can be objectively defined, given the question that is asked. The evaluation of forecast quality is known as forecast verification. Numerous techniques for forecast verification have been developed over the past decades. The Brier score (BS) and the derived Ranked Probability Score (RPS) are among the most widely used scores for measuring forecast quality. Both of these scores can be split into three additive components: uncertainty, reliability and resolution. While the first component, uncertainty, just depends on the inherent variability in the forecasted event, the latter two measure different aspects of the quality of forecasts themselves. Resolution measures the difference between the conditional probabilities and the marginal probabilities of occurrence. The third component, reliability, measures the conditional bias in the probability estimates, hence unreliability would be a better name. In this work, we argue that information theory should be adopted as the correct framework for measuring quality of probabilistic ensemble forecasts. We use the information

  15. 75 FR 55360 - Networking and Information Technology Research and Development (NITRD) Program: Draft NITRD 2010...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-09-10

    ... Networking and Information Technology Research and Development (NITRD) Program: Draft NITRD 2010 Strategic Plan AGENCY: The National Coordination Office (NCO) for Networking and Information Technology Research... and Information Technology Research and Development (NITRD) requests comments from the...

  16. The Weight-control Information Network (WIN) | NIH MedlinePlus the Magazine

    MedlinePlus

    ... Javascript on. Feature: Reducing Childhood Obesity The Weight-control Information Network (WIN) Past Issues / Spring - Summer 2010 ... overweight children, here are tips from the Weight-control Information Network (WIN), an information service of the ...

  17. Modeling and analysis of systems using the Least Action Principle in information theory

    NASA Astrophysics Data System (ADS)

    Guldal, Serkan

    The Least Action Principle (LAP) is widely applied to search for solutions to problems of classical mechanics. It claims that if an infinitesimal change does not affect the action of a system, that action would be the least action. In classical mechanics, if a particle moves from point A to point B, it will search for the path that requires the least energy consumption. Modeling physical phenomena utilizing LAP helps to predict an equation of motion and this principle is applicable for all known systems. In this dissertation, we use the maximum independent set from graph theory as a guiding principle to model for least action. It is shown that the maximum independence number will correspond to the least action. We demonstrate the use of the LAP in information theory and its use in the modeling and the analysis of systems. Thus, we will have a general model which is applicable to any known system. We have applied the LAP model to the Conant network decomposition technique. We demonstrated the applicability of our model in our case study that the Conant network decomposition technique computes the least action of the given network of the given data set.

  18. A brief historical introduction to Euler's formula for polyhedra, topology, graph theory and networks

    NASA Astrophysics Data System (ADS)

    Debnath, Lokenath

    2010-09-01

    This article is essentially devoted to a brief historical introduction to Euler's formula for polyhedra, topology, theory of graphs and networks with many examples from the real-world. Celebrated Königsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real physical systems are included. We also mention some important and modern applications of graph theory or network problems from transportation to telecommunications. Graphs or networks are effectively used as powerful tools in industrial, electrical and civil engineering, communication networks in the planning of business and industry. Graph theory and combinatorics can be used to understand the changes that occur in many large and complex scientific, technical and medical systems. With the advent of fast large computers and the ubiquitous Internet consisting of a very large network of computers, large-scale complex optimization problems can be modelled in terms of graphs or networks and then solved by algorithms available in graph theory. Many large and more complex combinatorial problems dealing with the possible arrangements of situations of various kinds, and computing the number and properties of such arrangements can be formulated in terms of networks. The Knight's tour problem, Hamilton's tour problem, problem of magic squares, the Euler Graeco-Latin squares problem and their modern developments in the twentieth century are also included.

  19. Untangling Word Webs: Graph Theory and the Notion of Density in Second Language Word Association Networks.

    ERIC Educational Resources Information Center

    Wilks, Clarissa; Meara, Paul

    2002-01-01

    Examines the implications of the metaphor of the vocabulary network. Takes a formal approach to the exploration of this metaphor by applying the principles of graph theory to word association data to compare the relative densities of the first language and second language lexical networks. (Author/VWL)

  20. A Social Network Perspective on Teacher Collaboration in Schools: Theory, Methodology, and Applications

    ERIC Educational Resources Information Center

    Moolenaar, Nienke M.

    2012-01-01

    An emerging trend in educational research is the use of social network theory and methodology to understand how teacher collaboration can support or constrain teaching, learning, and educational change. This article provides a critical synthesis of educational literature on school social networks among educators to advance our understanding of the…

  1. A Brief Historical Introduction to Euler's Formula for Polyhedra, Topology, Graph Theory and Networks

    ERIC Educational Resources Information Center

    Debnath, Lokenath

    2010-01-01

    This article is essentially devoted to a brief historical introduction to Euler's formula for polyhedra, topology, theory of graphs and networks with many examples from the real-world. Celebrated Konigsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real…

  2. Estimating topological properties of weighted networks from limited information.

    PubMed

    Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

    2015-10-01

    A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems. PMID:26565153

  3. Estimating topological properties of weighted networks from limited information

    NASA Astrophysics Data System (ADS)

    Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

    2015-10-01

    A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.

  4. Estimating topological properties of weighted networks from limited information

    NASA Astrophysics Data System (ADS)

    Gabrielli, Andrea; Cimini, Giulio; Garlaschelli, Diego; Squartini, Angelo

    A typical problem met when studying complex systems is the limited information available on their topology, which hinders our understanding of their structural and dynamical properties. A paramount example is provided by financial networks, whose data are privacy protected. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here we develop a reconstruction method, based on statistical mechanics concepts, that exploits the empirical link density in a highly non-trivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems. Acknoweledgement to ``Growthcom'' ICT - EC project (Grant No: 611272) and ``Crisislab'' Italian Project.

  5. Small-World Network Spectra in Mean-Field Theory

    NASA Astrophysics Data System (ADS)

    Grabow, Carsten; Grosskinsky, Stefan; Timme, Marc

    2012-05-01

    Collective dynamics on small-world networks emerge in a broad range of systems with their spectra characterizing fundamental asymptotic features. Here we derive analytic mean-field predictions for the spectra of small-world models that systematically interpolate between regular and random topologies by varying their randomness. These theoretical predictions agree well with the actual spectra (obtained by numerical diagonalization) for undirected and directed networks and from fully regular to strongly random topologies. These results may provide analytical insights to empirically found features of dynamics on small-world networks from various research fields, including biology, physics, engineering, and social science.

  6. Intrusion-Tolerant Location Information Services in Intelligent Vehicular Networks

    NASA Astrophysics Data System (ADS)

    Yan, Gongjun; Yang, Weiming; Shaner, Earl F.; Rawat, Danda B.

    Intelligent Vehicular Networks, known as Vehicle-to-Vehicle and Vehicle-to-Roadside wireless communications (also called Vehicular Ad hoc Networks), are revolutionizing our daily driving with better safety and more infortainment. Most, if not all, applications will depend on accurate location information. Thus, it is of importance to provide intrusion-tolerant location information services. In this paper, we describe an adaptive algorithm that detects and filters the false location information injected by intruders. Given a noisy environment of mobile vehicles, the algorithm estimates the high resolution location of a vehicle by refining low resolution location input. We also investigate results of simulations and evaluate the quality of the intrusion-tolerant location service.

  7. Using social network analysis to inform disease control interventions.

    PubMed

    Marquetoux, Nelly; Stevenson, Mark A; Wilson, Peter; Ridler, Anne; Heuer, Cord

    2016-04-01

    Contact patterns between individuals are an important determinant for the spread of infectious diseases in populations. Social network analysis (SNA) describes contact patterns and thus indicates how infectious pathogens may be transmitted. Here we explore network characteristics that may inform the development of disease control programes. This study applies SNA methods to describe a livestock movement network of 180 farms in New Zealand from 2006 to 2010. We found that the number of contacts was overall consistent from year to year, while the choice of trading partners tended to vary. This livestock movement network illustrated how a small number of farms central to the network could play a potentially dominant role for the spread of infection in this population. However, fragmentation of the network could easily be achieved by "removing" a small proportion of farms serving as bridges between otherwise isolated clusters, thus decreasing the probability of large epidemics. This is the first example of a comprehensive analysis of pastoral livestock movements in New Zealand. We conclude that, for our system, recording and exploiting livestock movements can contribute towards risk-based control strategies to prevent and monitor the introduction and the spread of infectious diseases in animal populations. PMID:26883965

  8. Part mutual information for quantifying direct associations in networks.

    PubMed

    Zhao, Juan; Zhou, Yiwei; Zhang, Xiujun; Chen, Luonan

    2016-05-01

    Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks. PMID:27092000

  9. Rich club neurons dominate Information Transfer in local cortical networks

    NASA Astrophysics Data System (ADS)

    Nigam, Sunny; Shimono, Masanori; Sporns, Olaf; Beggs, John

    2015-03-01

    The performance of complex networks depends on how they route their traffic. It is unknown how information is transferred in local cortical networks of hundreds of closely-spaced neurons. To address this, it is necessary to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512 electrode array (60 μm spacing) to record spontaneous activity at 20 kHz, simultaneously from up to 700 neurons in slice cultures of mouse somatosensory cortex for 1 hr at a time. We used transfer entropy to quantify directed information transfer (IT) between pairs of neurons. We found an approximately lognormal distribution of firing rates as reported in in-vivo. Pairwise information transfer strengths also were nearly lognormally distributed, similar to synaptic strengths. 20% of the neurons accounted for 70% of the total IT coming into, and going out of the network and were defined as rich nodes. These rich nodes were more densely and strongly connected to each other expected by chance, forming a rich club. This highly uneven distribution of IT has implications for the efficiency and robustness of local cortical networks, and gives clues to the plastic processes that shape them. JSPS.

  10. Impact of Repeated Exposures on Information Spreading in Social Networks.

    PubMed

    Zhou, Cangqi; Zhao, Qianchuan; Lu, Wenbo

    2015-01-01

    Clustered structure of social networks provides the chances of repeated exposures to carriers with similar information. It is commonly believed that the impact of repeated exposures on the spreading of information is nontrivial. Does this effect increase the probability that an individual forwards a message in social networks? If so, to what extent does this effect influence people's decisions on whether or not to spread information? Based on a large-scale microblogging data set, which logs the message spreading processes and users' forwarding activities, we conduct a data-driven analysis to explore the answer to the above questions. The results show that an overwhelming majority of message samples are more probable to be forwarded under repeated exposures, compared to those under only a single exposure. For those message samples that cover various topics, we observe a relatively fixed, topic-independent multiplier of the willingness of spreading when repeated exposures occur, regardless of the differences in network structure. We believe that this finding reflects average people's intrinsic psychological gain under repeated stimuli. Hence, it makes sense that the gain is associated with personal response behavior, rather than network structure. Moreover, we find that the gain is robust against the change of message popularity. This finding supports that there exists a relatively fixed gain brought by repeated exposures. Based on the above findings, we propose a parsimonious model to predict the saturated numbers of forwarding activities of messages. Our work could contribute to better understandings of behavioral psychology and social media analytics. PMID:26465749

  11. Guest editorial. Body sensor networks: from theory to emerging applications.

    PubMed

    Jovanov, Emil; Poon, Carmen C Y; Yang, Guang-Zhong; Zhang, Y T

    2009-11-01

    The use of sensor networks for healthcare, well-being, and working in extreme environments has long roots in the engineering sector in medicine and biology community. With the maturity of wireless sensor networks, body area networks (BANs), and wireless BANs (WBANs), recent efforts in promoting the concept of body sensor networks (BSNs) aim to move beyond sensor connectivity to adopt a system-level approach to address issues related to biosensor design, interfacing, and embodiment, as well as ultralow-power processing/communication, power scavenging, autonomic sensing, data mining, inferencing, and integrated wireless sensor microsystems. As a result, the system architecture based on WBAN and BSN is becoming a widely accepted method of organization for ambulatory and ubiquitous monitoring systems. This editorial paper presents a snapshot of the current research and emerging applications and addresses some of the challenges and implementation issues. PMID:19887307

  12. The Deep Space Network information system in the year 2000

    NASA Technical Reports Server (NTRS)

    Markley, R. W.; Beswick, C. A.

    1992-01-01

    The Deep Space Network (DSN), the largest, most sensitive scientific communications and radio navigation network in the world, is considered. Focus is made on the telemetry processing, monitor and control, and ground data transport architectures of the DSN ground information system envisioned for the year 2000. The telemetry architecture will be unified from the front-end area to the end user. It will provide highly automated monitor and control of the DSN, automated configuration of support activities, and a vastly improved human interface. Automated decision support systems will be in place for DSN resource management, performance analysis, fault diagnosis, and contingency management.

  13. Viral information propagation in the Digg online social network

    NASA Astrophysics Data System (ADS)

    Freeman, Mark; McVittie, James; Sivak, Iryna; Wu, Jianhong

    2014-12-01

    We propose the use of a variant of the epidemiological SIR model to accurately describe the diffusion of online content over the online social network Digg.com. We examine the qualitative properties of our viral information propagation model, demonstrate the model’s applications to social media spread in online social networks with particular focus on accurately predicting user voting behavior over a period of 50 h. The model allows us to characterize the peak time, turning point, viral period and final size (total number of votes), and gives much improved prediction of user voting behaviors than other established models.

  14. Conceptual Framework for Developing a Diabetes Information Network

    PubMed Central

    Riazi, Hossein; Langarizadeh, Mostafa; Larijani, Bagher; Shahmoradi, Leila

    2016-01-01

    Objective: To provide a conceptual framework for managing diabetic patient care, and creating an information network for clinical research. Background: A wide range of information technology (IT) based interventions such as distance learning, diabetes registries, personal or electronic health record systems, clinical information systems, and clinical decision support systems have so far been used in supporting diabetic care. Previous studies demonstrated that IT could improve diabetes care at its different aspects. There is however no comprehensive conceptual framework that defines how different IT applications can support diverse aspects of this care. Therefore, a conceptual framework that combines different IT solutions into a wide information network for improving care processes and for research purposes is widely lacking. In this study we describe the theoretical underpin of a big project aiming at building a wide diabetic information network namely DIANET. Research design and methods: A literature review and a survey of national programs and existing regulations for diabetes management was conducted in order to define different aspects of diabetic care that should be supported by IT solutions. Both qualitative and quantitative research methods were used in this study. In addition to the results of a previous systematic literature review, two brainstorming and three expert panel sessions were conducted to identify requirements of a comprehensive information technology solution. Based on these inputs, the requirements for creating a diabetes information network were identified and used to create a questionnaire based on 9-point Likert scale. The questionnaire was finalized after removing some items based on calculated content validity ratio and content validity index coefficients. Cronbach’s alpha reliability coefficient was also calculated (αTotal= 0.98, P<0.05, CI=0.95). The final questionnaire was containing 45 items. It was sent to 13 clinicians at two

  15. Information Exchange and Information Disclosure in Social Networking Web Sites: Mediating Role of Trust

    ERIC Educational Resources Information Center

    Mital, Monika; Israel, D.; Agarwal, Shailja

    2010-01-01

    Purpose: The purpose of this paper is to examine the mediating effect of trust on the relationship between the type of information exchange (IE) and information disclosure (ID) on social networking web sites (SNWs). Design/methodology/approach: Constructs were developed for type of IE and trust. To understand the mediating role of trust a…

  16. DELIVERING TIMELY ENVIRONMENTAL INFORMATION TO YOUR COMMUNITY: THE BOULDER AREA SUSTAINABILITY INFORMATION NETWORK: OTHER

    EPA Science Inventory

    NRMRL-CIN-1577 Petersen*, D., Barber, L., Dilworth, G, Fiebelkorn, T., McCaffrey, M., Murphy, S., Rudkin, C., Scott, D., and Waterman, J. Delivering Timely Environmental Information to your Community: The Boulder Area Sustainability Information Network. EPA/625/C-01/010. The Te...

  17. The network and the remodeling theories of aging: historical background and new perspectives.

    PubMed

    Franceschi, C; Valensin, S; Bonafè, M; Paolisso, G; Yashin, A I; Monti, D; De Benedictis, G

    2000-09-01

    Two general theories, i.e. "the network theory of aging" (1989) and "the remodeling theory of aging" (1995), as well as their implications, new developments, and perspectives are reviewed and discussed. Particular attention has been paid to illustrate: (i) how the network theory of aging fits with recent data on aging and longevity in unicellular organisms (yeast), multicellular organisms (worms), and mammals (mice and humans); (ii) the evolutionary and experimental basis of the remodeling theory of aging (immunological, genetic, and metabolic data in healthy centenarians, and studies on the evolution of the immune response, stress and inflammation) and its recent development (the concepts of "immunological space" and "inflamm-aging"); (iii) the profound relationship between these two theories and the data which suggest that aging and longevity are related, in a complex way, to the capability to cope with a variety of stressors. PMID:11053678

  18. Price-Based Information Routing in Complex Satellite Networks for

    NASA Astrophysics Data System (ADS)

    Hussein, I.; Su, J.; Wang, Y.; Wyglinski, A.

    Future space-based situational awareness and space surveillance systems are envisioned to include a large array of satellites that seek to cooperatively achieve full awareness over given space and terrestrial domains. Given the complexity of the communication network architecture of such a system, in this paper we build on the system architecture that was proposed by the presenting author in the 2008 AMOS conference and propose an efficient, adaptable and scalable price-based routing and bandwidth allocation algorithm for the generation, routing and delivery of surveillance information in distributed wireless satellite networks. Due to the potentially large deployments of these satellites, the access points employed in a centralized network control scheme would easily be overwhelmed due to lack of spectral bandwidth, synchronization issues, and multiple access coordination. Alternatively, decentralized schemes could facilitate the flow and transference of information between data gatherers and data collectors via mechanisms such as (multi-hop) routing, allocation of spectral bandwidths per relaying node, and coordination between adjacent nodes. Although there are numerous techniques and concepts focusing on the network operations, control, and management of sensor networks, existing solution approaches require the use of information for routing, allocation, and decision-making that may not be readily available to the satellites in a timely fashion. This is especially true in the literature on price-based routing, where the approach is almost always game theoretic or relies on optimization techniques. Instead of seeking such techniques, in this paper we present algorithms that will (1) be energy-aware, (2) be highly adaptable and responsive to demands and seek delivery of information to desired nodes despite the fact that the source and destination are not globally known, (3) be secure, (4) be efficient in allocating bandwidth, (5) be decentralized and allow for

  19. Leveraging percolation theory to single out influential spreaders in networks

    NASA Astrophysics Data System (ADS)

    Radicchi, Filippo; Castellano, Claudio

    2016-06-01

    Among the consequences of the disordered interaction topology underlying many social, technological, and biological systems, a particularly important one is that some nodes, just because of their position in the network, may have a disproportionate effect on dynamical processes mediated by the complex interaction pattern. For example, the early adoption of a commercial product by an opinion leader in a social network may change its fate or just a few superspreaders may determine the virality of a meme in social media. Despite many recent efforts, the formulation of an accurate method to optimally identify influential nodes in complex network topologies remains an unsolved challenge. Here, we present the exact solution of the problem for the specific, but highly relevant, case of the susceptible-infected-removed (SIR) model for epidemic spreading at criticality. By exploiting the mapping between bond percolation and the static properties of the SIR model, we prove that the recently introduced nonbacktracking centrality is the optimal criterion for the identification of influential spreaders in locally tree-like networks at criticality. By means of simulations on synthetic networks and on a very extensive set of real-world networks, we show that the nonbacktracking centrality is a highly reliable metric to identify top influential spreaders also in generic graphs not embedded in space and for noncritical spreading.

  20. Leveraging percolation theory to single out influential spreaders in networks.

    PubMed

    Radicchi, Filippo; Castellano, Claudio

    2016-06-01

    Among the consequences of the disordered interaction topology underlying many social, technological, and biological systems, a particularly important one is that some nodes, just because of their position in the network, may have a disproportionate effect on dynamical processes mediated by the complex interaction pattern. For example, the early adoption of a commercial product by an opinion leader in a social network may change its fate or just a few superspreaders may determine the virality of a meme in social media. Despite many recent efforts, the formulation of an accurate method to optimally identify influential nodes in complex network topologies remains an unsolved challenge. Here, we present the exact solution of the problem for the specific, but highly relevant, case of the susceptible-infected-removed (SIR) model for epidemic spreading at criticality. By exploiting the mapping between bond percolation and the static properties of the SIR model, we prove that the recently introduced nonbacktracking centrality is the optimal criterion for the identification of influential spreaders in locally tree-like networks at criticality. By means of simulations on synthetic networks and on a very extensive set of real-world networks, we show that the nonbacktracking centrality is a highly reliable metric to identify top influential spreaders also in generic graphs not embedded in space and for noncritical spreading. PMID:27415287